Three Essays: Conflict Risks and Investment,
Terrorism and Information, and Conservatism in
Accounting for Oil Price Changes
Ao Li
A Thesis Submitted for the degree of Doctor of Philosophy of
The Australian National University
February 2019
© Copyright by Ao Li 2019
All Rights Reserved
DECLARATION OF ORIGINALITY
This thesis is an original written work incorporating an account of research completed in the
Doctor of Philosophy (Commerce) program. It has not been submitted for any other degree or purposes. To my best knowledge, the thesis contains no material previously published or written by another person except where due reference is made in the text of the thesis.
______
Ao Li
Research School of Accounting
The Australian National University
ii ACKNOWLEDGEMENT
First and foremost, I would like to express my sincere gratitude to the chair of my supervisory panel Professor Neil Fargher for his continuous support, patience, encouragement and invaluable guidance. Neil has always been supportive and has given me freedom to pursue projects of interest to me. Without his supervision and persistent help, this thesis would not have been possible.
I would also like to thank my supervisory committee members Associate Professor Janet Lee and Dr Lily (Lijuan) Zhang for their precious comments and suggestions on the development of the thesis.
I would like to offer special thanks to Associate Professor Janet Lee and Professor Juliana Ng for their understanding and unconditional support when I decided to change research topic and supervisory committee at the end of my first year.
My sincere thanks goes to Professor Greg Shailer, Associate Professor Mark Wilson, and
Associate Professor Louise Lu, Dr Tejshree Kala, Penny Zhang and members of Research
School of Accounting for their help and encouragement throughout my PhD journey.
I am deeply grateful to my family for their unwavering love at all times.
This thesis was edited by Elite Editing, and editorial intervention was restricted to Standards D and E of the Australian Standards for Editing Practice.
iii ABSTRACT
This thesis consists of three stand-alone studies across a diverse spectrum of financial accounting. The first study examines the influence of conflict risk on investment efficiency for multinational enterprises. This study argues that firms with exposure to higher conflict risks are likely to experience higher information uncertainties and weaker monitoring, which consequently leads to suboptimal investment. Employing a sample of U.S.-listed firms with subsidiaries in conflict-affected countries, this study provides evidence that the higher the conflict risk firms are exposed to, the more likely they will experience underinvestment. Results shows that greater exposure to conflict risk reduces multinational enterprises’ (MNEs) overall financial reporting quality. Results of this study also support the finding in prior literature that better financial reporting quality helps reduce suboptimal investment.
The second study explores foreign exchange market reactions to a sample of 15 recent ISIS terrorist attacks in Europe. Prior studies suggest that capital markets react to terrorist attacks and this expectation is picked up by the press. However, much of this evidence arises from the
9/11 attacks, which may not generalize to more recent terrorist attacks. Using a sample of ISIS attacks in Europe, this study finds that these events led to decreases in returns within five minutes of some attacks. Negative returns are most pronounced around the announcement of confirmed casualties for the first attack in Belgium in 2014 and the high profile Paris attack and
London Bridge attack. These responses do not persist. The effects are short-lived and foreign exchange markets recover within the day of the attack. There is also an increase in volatility around some attacks, however, any reaction is short-lived and markets recover within a day.
This study shows that the economic importance of terrorist attacks in Western countries is declining. While terrorist attacks attract much attention in the media, there is relatively little impact on financial markets and any reaction is short-lived.
iv The third study explores how earnings incorporate information reflected in product market prices. Banker, Basu, and Byzalov (2017) suggest that earnings exhibit asymmetric timeliness in the recognition of gains and losses arising from multiple factors, including stock returns, changes in operating cash flow and changes in sales. Changes in sales and cash flows can arise from changes in the volume of sales and changes in product market prices. This study extends this prior research to explore whether earnings exhibit an asymmetric pattern in timeliness of gain and loss recognition in regards to information reflected in product market price changes.
Employing a sample of U.S. firms from the oil and gas sector, this study shows that earnings respond asymmetrically to lagged changes in oil prices by recognizing bad news (negative oil price changes) more fully and in a more timely fashion than good news (positive oil price changes). This study shows that product market prices provide timely information for estimating future cash flows and that product market prices are able to signal potential changes in future cash flow ahead of accounting-based performance measures.
v TABLE OF CONTENTS
Declaration of Originality ...... ii Acknowledgement ...... iii Abstract ...... iv Table of Contents ...... vi List of Figures ...... ix List of Tables ...... x CHAPTER 1 : Introduction ...... 1 Introduction ...... 1 Study 1: The Influence of Conflict Risk on Investment Efficiency for Multinational Enterprises ...... 3 Study 2: The Impact of Terrorism on Financial Markets: Intra-day Evidence from Foreign Exchange Market Reactions to ISIS Attacks ...... 6 Study 3: The Role of Product Markets in Asymmetrically Timely Gain and Loss Recognition: Evidence from the U.S. Oil and Gas Industry ...... 9 Thesis Structure ...... 12 CHAPTER 2 : The Influence of Conflict Risk on Investment Efficiency for Multinational Enterprises (Study 1) ...... 13 Introduction ...... 13 Literature Review ...... 17 2.2.1 Investment Environment in Conflict Zones ...... 17 2.2.2 Political Uncertainty and Financial Markets ...... 19 2.2.3 Financial Reporting Quality and Investment Efficiency ...... 21 Hypotheses Development ...... 23 2.3.1 Conflict Risk and Underinvestment ...... 23 2.3.2 Conflict Risk and Financial Reporting Quality...... 25 2.3.3 Financial Reporting Quality and Investment Efficiency ...... 25 Methodology ...... 26 2.4.1 Research Design ...... 26 2.4.2 Variable Measurement ...... 29 2.4.2.1 Measurement of conflict risk ...... 29 2.4.2.2 Proxy for investment efficiency ...... 30 2.4.2.3 Proxy for financial reporting quality ...... 31 Data and Sample ...... 36 2.5.1 Sample Selection ...... 36 2.5.2 Descriptive Statistics ...... 42 Empirical Results ...... 46 2.6.1 Conflict Risk and Underinvestment ...... 46 2.6.2 Conflict Risk, Financial Reporting Quality and Investment Efficiency ...... 54 Additional Tests ...... 57 2.7.1 Ranked Measure of Suboptimal Investment ...... 57 2.7.2 Conflict Risk and Overinvestment ...... 58 2.7.3 Alternative Measures of Suboptimal Investment ...... 59 2.7.4 Materiality of Investment in Conflict Regions ...... 60 Discussion and Conclusion ...... 63 CHAPTER 3 : The Impact of Terrorism on Financial Markets: Intra-Day Evidence from Foreign Exchange Market Reactions to ISIS Attacks (Study 2) ...... 66 Introduction ...... 66 Literature Review ...... 69
vi 3.2.1 Terrorism and the Macroeconomy ...... 70 3.2.2 Terrorism and Stock Markets ...... 72 3.2.3 Terrorism and Foreign Exchange Markets ...... 76 Hypotheses Development ...... 78 3.3.1 Foreign Exchange Market Reactions to Terrorist Attacks ...... 78 3.3.2 Increase In Volatility Around Terrorist Attacks ...... 79 Methodology ...... 80 3.4.1 Data and Sample ...... 80 3.4.1.1 Sample of ISIS Terrorist Attacks ...... 80 3.4.1.2 Identification of Event Timing ...... 85 3.4.1.3 Speed of Information Dissemination ...... 87 3.4.2 Research Design ...... 88 3.4.2.1 Measurement of Foreign Exchange returns ...... 88 3.4.2.2 Modelling the Response of Exchange Rate Returns to News ...... 90 Empirical Results ...... 91 3.5.1 Descriptive Statistics ...... 91 3.5.2 Response to Terrorist Attack Reports Using an Event Study Approach ...... 94 3.5.3 Response to Terrorist Attack Reports Using a GARCH Approach ...... 99 3.5.4 Persistence of News Effects from Terrorist Attacks on Foreign Exchange Returns ...... 102 3.5.5 Influence of Terrorist Attacks on Foreign Exchange Rate Volatility ...... 105 Discussion and Conclusion ...... 107 CHAPTER 4 : The Role of Product Markets in Asymmetrically Timely Gain and Loss Recognition: Evidence from the U.S. Oil and Gas Industry (Study 3) ...... 109 Introduction ...... 109 Literature Review ...... 114 4.2.1 Macroeconomic Impacts of Oil Price Shocks ...... 114 4.2.2 Oil Prices and the Stock Market in General ...... 115 4.2.3 Oil Prices and Stocks for the O&G Industry...... 117 4.2.4 Asymmetrical Timeliness of Earnings Recognition ...... 118 Hypothesis Development ...... 121 Research Design ...... 126 Data and Sample ...... 131 4.5.1 Sample Selection ...... 131 4.5.2 Descriptive Statistics ...... 133 Empirical Results ...... 137 Additional Analysis ...... 142 Sensitivity Analysis ...... 151 4.8.1 Extension of the Original Basu (1997) Model ...... 151 4.8.2 Timing of Oil Price Changes ...... 153 4.8.3 O&G Firm Sensitivity to Oil Price Changes ...... 155 4.8.4 Full-Cost Method vs. Successful-Effort Method ...... 157 4.8.5 Additional Controls for Economic Factors and Reporting Factors ...... 159 Discussion and Conclusion ...... 161 CHAPTER 5 : Conclusion ...... 163 Introduction ...... 163 Overview of the Three Studies ...... 164 5.2.1 Study 1: The Influence of Conflict Risk on Investment Efficiency for Multinational Enterprises ...... 164
vii 5.2.2 Study 2: The Impact of Terrorism on Financial Markets: Intra-day Evidence from Foreign Exchange Market Reactions to ISIS Attacks ...... 164 5.2.3 Study 3: The Role of Product Markets in Asymmetrically Timely Gain and Loss Recognition: Evidence from the U.S. Oil and Gas Industry ...... 165 Contributions ...... 166 References ...... 169 Appendix 2.1 Relation Between Conflict Intensity and Underinvestment (Overinvestment) with Control for Firm-fixed Effect ...... 185 Appendix 2.2 Regression Analysis for Conflict Intensity and Financial Reporting Quality with Control for Firm-fixed Effect ...... 187 Appendix 3.1 Event Exchange Rate Returns (GBP/USD) ...... 188 Appendix 3.2 Terrorist Attacks’ Influence on GBP/USD Exchange Rate Return Volatilities ...... 189 Appendix 3.3 Contemporaneous Effects of Terrorist Attacks on GBP/USD Exchange Rate Returns ...... 190 Appendix 4.1 Replication of Banker et al. (2017) ...... 191
viii LIST OF FIGURES
Figure 3.1 Event Timeline for Study 2 ...... 86 Figure 3.2 Event Windows and Benchmark Comparison Windows ...... 94 Figure 4.1 Hypothesis Development for Study 3 ...... 126 Figure 4.2 West Texas Intermediate (WTI) Oil Price, January 2012–January 2017 .... 133
ix LIST OF TABLES
Table 2.1 Variable Definitions for Study 1 ...... 34 Table 2.2 Sample Selection ...... 38 Table 2.3 Sample Description: Top 30 Investment Destination Conflict Countries (By Number of Subsidiaries), 2015 ...... 40 Table 2.4 Sample Distribution by Industry ...... 41 Table 2.5 Descriptive Statistics for Full Sample ...... 43 Table 2.6 Correlation Matrix for Full Sample ...... 45 Table 2.7 Regression Analysis for Conflict Risk and Investment Efficiency ...... 47 Table 2.8 Relation between Conflict Intensity and Underinvestment (Overinvestment) ...... 49 Table 2.9 Regression Analysis – Quintile Analysis for Subsamples of Underinvestment and Overinvestment ...... 52 Table 2.10 Regression Analysis for Conflict Intensity and Financial Reporting Quality ...... 55 Table 2.11 Regression Analysis for Underinvestment and Overinvestment with Alternative Measures for Conflict Intensity ...... 62 Table 3.1 Sample of ISIS Terrorist Attacks for Study 2 ...... 82 Table 3.2 Summary Statistics Exchange Rate Returns for Day – 1 to Day +1 Around the Date of First Media Reports of a Terrorist Attack ...... 93 Table 3.3 Cumulative Exchange Rate Returns 10 minutes around when a Terrorist Attack is First Reported ...... 96 Table 3.4 Cumulative Exchange Rate Returns 10 minutes around First Reports of Confirmed Casualties ...... 98 Table 3.5 Contemporaneous Effects of Terrorist Attacks on EURO/USD Exchange Rate Returns ...... 101 Table 3.6 Contemporaneous and Lagged Effects of Terrorist Attacks on EURO/USD Exchange Rate Returns ...... 104 Table 3.7 Terrorist Attacks’ Influence on EURO/USD Exchange Rate Return Volatility ...... 106 Table 4.1 Variable Definitions for Study 3 ...... 130 Table 4.2 Sample Selection ...... 132 Table 4.3 Descriptive Statistics for Full Sample ...... 135 Table 4.4 Correlation Matrix ...... 136 Table 4.5 Asymmetric Timeliness Estimates for Multiple Indicators Based on Banker et al. (2017) ...... 138 Table 4.6 Asymmetric Timeliness Estimates for Multiple Indicators Based on Ball and Shivakumar (2006) ...... 141 Table 4.7 Estimates for Tangible Asset Write-Down and Goodwill Impairment ...... 149 Table 4.8 Estimates for Tangible Asset Write-Down ...... 150 Table 4.9 Asymmetric Timeliness Estimates for Multiple Indicators Extension of Basu (1997) Model with Indicator for lagged Oil Price Returns ...... 152 Table 4.10 Asymmetric Timeliness Estimates for Multiple Indicators With Concurrent and Lagged Oil and Price Returns ...... 154 Table 4.11 Estimates for Earnings within Subsamples: Crude Petroleum and Natural Gas Firms vs. Oil and Gas Field Service Firms ...... 156 Table 4.12 Estimates for Earnings within Subsamples: Full-Cost Firms vs Successful- Effort Firms ...... 158
x Table 4.13 Asymmetric Timeliness Estimates for Multiple Indicators with Additional Controls ...... 160
xi CHAPTER 1: INTRODUCTION
Introduction
This thesis comprises three studies addressing issues in financial accounting. The studies encompass conflict risk and investment efficiency, impact of terrorism on financial markets, and conservatism in accounting for oil price changes.
My research focuses on the relation between international security, business and accounting.
Specifically, I am interested in exploring how managers and investors respond to uncertainties and risks raised by conflict events. This interest in international security issues and business motivated the first two studies in this thesis assessing conflict risks and terrorism attacks respectively. The third topic is related in the sense of considering the effects of uncertainty and volatility from changes in oil prices; however, the study evolved to examine a more traditional accounting issue in considering how the changes in oil prices influence accounting conservatism.
Study one initiated from a premise of the increasing role of private sector investment in peace- building in conflict locations, as repeatedly addressed by the UN Security Council (UN, 2013).
Private investments are found to positively contribute to sustaining peace and conflict prevention in conflict-affected states (Peschka, 2011). However, few studies examine how managers of multinational enterprises (MNEs) respond to the risks of operations in conflict regions. With widely distributed international operations, MNEs are frequently exposed to a variety of location-specific hazards, particularly when subsidiaries are set up in conflict-prone environments (Dai, Eden, & Beamish, 2013). In addition to exploiting the benefits from diversified geographic locations, the essence of strategy for MNEs involves managing and avoiding locational hazards (Piscitello, 2011). This study is therefore motivated to explore the question: How do managers of MNEs adjust their investment strategies in response to conflict
1 risks? This led to the study examining investment efficiency for MNEs with operations in conflict-affected regions.
Study two develops from the study of conflict in general to a more specific focus on terrorism.
With the increasing frequency of violent terrorist attacks occurring globally from 2003 1
(Institute of Economics and Peace [IEP], 2016), there is growing interest in empirical research examining the effects of terrorist attacks on the economy. Prior research largely focuses on the influence of terrorist attacks on macroeconomic factors and stock markets, particularly on the influence of 9/11 and its aftermath. This study extends prior studies to explore the impact of a recent series of ISIS attacks on foreign exchange returns and volatilities.
Study three is a more traditional financial accounting research topic examining how earnings respond to the information reflected in product market price changes. A report from Duff &
Phelps (2017) observes a significant decline of total goodwill impairment recorded by U.S. public companies from 2015 to 2016 along with the recovery of oil prices during the period, suggesting a potential association between product market prices and financial reporting.
Extensive literature has explored the impact of oil price shocks on the macroeconomy and financial markets and found a positive (negative) association between oil price and stock returns for the O&G (other industries) sector. A recent study (Banker, Basu, & Byzalov, 2017) shows that the timeliness of earnings recognition is asymmetrically associated with change in sales after controlling for stock returns and changes in operating cash flow. This study extends
Banker et al. (2017) to examine the role of the product market in firms’ asymmetrically timely gain and loss recognition.
1 The number of terrorist attacks and deaths from terrorism started to increase dramatically after the U.S. invaded Iraq in 2003 (IEP, 2016).
2 To this end, this thesis presents three studies with the aim of building understanding of the impact of conflicts and terrorism on business, and also of learning how earnings incorporate information reflected in product market prices. The remainder of this chapter is organized as follows. A brief description, including research question, motivations and contributions, methodology, and major findings, for each of the three studies is provided in Sections 1.2, 1.3 and 1.4 respectively. Section 1.5 outlines the remaining structure of this thesis.
Study 1: The Influence of Conflict Risk on Investment Efficiency for
Multinational Enterprises
Study one examines the influence of conflict risk on investment efficiency for MNEs. Conflict risk has been identified as a key risk factor for MNEs (e.g., Fluor, 2018; KBR, 2018)2. Business operations in conflict regions may suffer from challenges including security threats, macroeconomic instability, obstacles to utilities, insufficient labor, and weak regulatory enforcement (Mills & Fan, 2006). Prior studies suggest that conflicts change the investment environment and create additional risks for firms operating in conflict zones (Mills & Fan,
2006). These findings raise the question of whether conflict risk also affects firms’ investment decisions and efficiency.3 This study aims to extend prior research by examining the impact of
2 Many of the annual reports for the top 30 U.S. government contractors in Iraq and Afghanistan (based on data available from the General Service Administration’s Federal Procurement Data System) indicate that they are exposed to a high level of security risk caused by the instabilities in these conflict-intense locations. KBR, an American engineering, procurement, and construction company, disclosed that their work in high-risk locations including Iraq, Afghanistan, certain parts of Africa and the Middle East, could result in employee and contractor harm and incurred substantial costs to maintain the safety of their employees and properties (KBR, 2018). Fluor Corporation, a multinational engineering and construction firm that has operations in countries with high political and security risks including Afghanistan, Iraq, Kazakhstan, Russia, Argentina and Mozambique, mentioned in their annual report that the high security risks in these countries affect the supply and price of oil, disrupt their operations, and, as a result, lead to high security costs (Fluor, 2018). 3 Optimal investment efficiency refers to the situation where firms undertake projects with positive net present value (NPV) under the scenario of no market friction, such as adverse selection or agency costs (Biddle et al., 2009). Underinvestment occurs when a firm passes up investment opportunities that would have positive NPV in the absence of adverse selection. Overinvestment is defined as investing in projects with negative NPV.
3 conflict risk on investment efficiency for MNEs. To understand the effect of conflict risk on firms’ investment efficiency, the research question for this study is stated as:
RQ: How does conflict risk affect MNEs’ investment decisions and investment efficiency?
This study argues that geographic exposure to conflicts constitutes a significant problem for
MNEs’ investment efficiency. First, I hypothesize that the higher the conflict risk that MNEs are exposed to, the more likely they will experience underinvestment. With exposure to high conflict risk, MNEs are expected to bypass investment opportunities because of the high uncertainties in the conflict-affect regions and lack of sufficient information on profitability.
Bypassing projects with a positive NPV can lead to underinvestment at the corporate level.
Second, this study argues that firms with investment in conflict zones have relatively poor financial reporting quality. Conflict-affected environments typically expose companies to weak property rights protection, regulatory enforcement, and administration (Hernandez-Crespo,
2011; Mills & Fan, 2006). To operate in these regions, businesses may move to the informal sector and rely on local networks. Businesses in the informal sector tend not to keep timely and accurate financial reporting because of a lack of monitoring. Operating in such a way gives managers greater discretion and consequently reduces MNE financial reporting quality.
The investment efficiency literature (e.g., Biddle, Hilary, & Verdi, 2009; F. Chen et al. 2011) suggests that better financial reporting quality helps to reduce suboptimal investment. To examine whether the negative association between financial reporting quality and investment efficiency holds in situations where underinvestment is more likely, this study also tests an existing hypothesis that the poorer the overall financial reporting quality of MNEs’ financial reports, the more likely they will experience investment inefficiency.
4 This study extends F. Chen et al.’s (2011) model, which was initially developed to examine the association between financial reporting quality and investment efficiency, to test the predicted association between conflict risk and investment efficiency. I use national-level measures of conflict intensity provided by the Heidelberg Institute for International Conflict Research (HIIK) to identify the conflict risk that MNEs are exposed to. The sample for this study is U.S. MNEs with investment in conflict zones.
Results indicate that when MNEs are exposed to relatively high conflict risk, they are more likely to underinvest. Facing high information uncertainty on the projects’ profitability because of conflict risk, firms tend to delay or bypass investment opportunities, to wait for more information. In this situation, underinvestment is likely to be more pronounced. This study finds evidence that firms facing higher conflict risk are more likely to have lower financial reporting quality. Consistent with prior literature, this study shows that financial reporting quality is negatively associated with suboptimal investment, indicating that better financial reporting quality helps to reduce investment inefficiency.
This study contributes to three streams of literature. First, it advances understanding on the link between conflict risk and firm-level investments. Prior research suggests that conflict risk reduces the likelihood of survival for foreign subsidiaries in conflict regions (Dai et al., 2013).
This study takes a step further to assess the impact of conflict risk on corporate investment performance. Second, this study adds to the literature in terms of the relation between uncertainties and economic outcomes. Most research has focused on political uncertainties (e.g.,
Białkowski, Gottschalk, & Wisniewski, 2008; Cao, Li, & Liu, 2017; Jens, 2017; Kesten &
Mungan, 2015; Pástor & Veronesi, 2013). This study extends the current understanding on the impact of violent conflicts within the broad category of the impact of political uncertainties on economic outcomes. Third, this study contributes to the literature examining the association
5 between reporting quality and investment efficiency. Prior studies in this field primarily focus on the argument of information asymmetry and agency costs via examining the impact of factors such as financial constraints (e.g., Biddle et al., 2009; Lara, Osma, & Penalva, 2015) and ownership structure (e.g., Chen et al., 2011; Chen et al., 2011). This study adds to the literature by examining the impact of geographic risks, rarely studied, on firms’ investment decision and performance.
Study 2: The Impact of Terrorism on Financial Markets: Intra-day
Evidence from Foreign Exchange Market Reactions to ISIS Attacks
Study one shows that uncertainties arising from conflicts induce underinvestment. Study two examines the influence of a specific type of conflict, terrorist attacks, on foreign exchange markets. Terrorist attacks have attracted increasing attention as a growing business risk to be considered by investors in decision-making (Jain & Grosse, 2009; Luo, 2009). Prior studies show that terrorist attacks increase financial instabilities and cost, and weaken investors’ confidence (Abadie & Gardeazabel, 2003; Johnston & Nedelescu, 2005; Lenain, Bonturi, &
Koen, 2002). The growing frequency of terrorist attacks highlights the need to further explore the influence of terrorist attacks on financial markets and how markets respond to such information. In particular, this study addresses the research question:
RQ: How do foreign exchange markets react to information uncertainties raised by terrorist attacks?
The original aspects of this study include a study of the recent ISIS attacks, which are argued to vary in nature from prior attacks such as 9/11, and the use of intra-day foreign exchange data to examine real-time reactions to news announcement in a very liquid market rather than the more typical analysis of changes in end-of-day equity prices. Prior studies find a negative
6 reaction to major terrorist attacks on equities markets. In particular, the negative impact of the
September 11 2001 terrorist attacks has been extensively documented (e.g., Charles & Darne,
2006; Karolyi & Martell, 2010). Results from these studies may not however be applicable to smaller or more recent terrorist attacks. The 9/11 attacks were unusual because 1) of the magnitude of the traumatic damage that the 9/11 terrorist attacks caused and 2) the direct impact on the World Trade Center complex and other buildings in Wall Street led to a trading halt.
This study extends to re-examine the proposition of the negative impact of terrorist attacks on financial markets using a sample of a recent series of ISIS terrorist attacks, with variations in damage and social impact, to draw a conclusion that can be generalized to a broader class of information events.
Foreign exchange markets have several advantages as a setting to examine the market reaction to terrorist attacks. First, the foreign exchange market is one of the largest and most liquid financial markets and is likely to reflect the influence of a significant exogenous shock on a timely basis. The foreign exchange market in particular should reveal any significant capital flight to safer countries (Goel, Cagle, & Shawky, 2017), such as the U.S., following terrorist incidents in Europe. Second, foreign exchange markets are primarily over-the-counter and the major traders are banks, markets, and foreign exchange traders (Cornett, Schwarz, & Szakmary,
1995), sophisticated investors expected to be more sensitive to changes of conditions in the markets. Third, longer trading hours in foreign exchange markets allows us to observe intra- day market reactions to terrorist attacks that occur during local non-trading hours for equity markets.
Using a sample of 15 of the higher profile ISIS attacks, and measuring the impact on the
EURO/USD exchange rate, this study finds that few events affected foreign exchange rates significantly; most notably, the first attack in Belgium in 2014 and the high profile London
7 Bridge attack. These events led to decreases in returns within five minutes of the attack being reported and of the official announcement of casualties. There is an increase in volatility around other attacks; however, any reaction is short-lived and markets recover within a day.
This study contributes to the literature by examining the immediate reaction of foreign exchange markets to information on terrorist attacks. First, this study extends prior research focusing on the economic costs of 9/11 (e.g., Carter & Simkins, 2004; Charles & Darne, 2006;
Coleman, 2012; Karolyi & Martell, 2010), to examine a recent series of ISIS attacks in Europe.
This study shows that in the post-9/11 period, the economic importance of terrorist attacks in
Western countries is declining. While terrorist attacks attract much attention in the media, their impacts on financial markets are limited. Second, this study adds to the literature examining the impact of unexpected exogenous shocks on financial markets. Terrorist attacks have a clearly identifiable event time, attract wide and immediate media coverage and are free from privileged information (e.g., Coleman, 2012; Kollias, Papadamou, & Siriopoulos, 2012), which enables more precise testing and understanding of market reactions to new information.
Third, this study contributes to the literature employing intra-day foreign exchange return data to explore the immediate influence of terrorism shocks on markets. Many of the prior studies employing daily data to examine the impact of terrorist attacks on financial markets are problematic as using closing prices of the day to measure returns and volatility is unlikely to capture the instant movement in stock prices and returns immediately after attacks occur and may lead to inaccurate conclusions of the movement in the market. This study shows that for the recent attacks, the impact on foreign exchange markets is short-lived and can be recovered within one day.
8 Study 3: The Role of Product Markets in Asymmetrically Timely Gain and
Loss Recognition: Evidence from the U.S. Oil and Gas Industry
The third study explores the link between product market price changes and asymmetrical timeliness of earnings recognition, which fits within the broader area of studies of accounting conservatism. This study combines two strands of the literature. The first investigates conditional accounting conservatism. These studies typically follow Basu’s (1997) model and use stock returns as a proxy for news about future cash flows. The piecewise-linear effect of stock returns on earnings is interpreted as a measure of more timely loss recognition by firms
(e.g., Ball, Kothari, & Robin, 2000; Black, Chen, & Cussatt, 2018; Holthausen & Watts, 2001;
Nikolaev, 2010). The second strand of literature explores the link between oil price changes and economic activities. Extensive research has examined the association between oil price shocks, macroeconomic variables and stock returns (e.g., Cuñado & de Gracia, 2003, 2014;
Dhaoui, Goutte, & Guesmi, 2018; Filis, Degiannakis, & Floros, 2011; Gisser & Goodwin, 1986;
Hamilton, 1983; Kilian & Park, 2009).
The intersection of these strands of the literature is the focus of this paper. Very little research has directly examined the impact of oil prices on accounting outcomes. Prior studies have documented asymmetric associations between earnings and stock returns (e.g., Basu, 1997), cash flows (e.g., Ball & Shivakumar, 2006; Collins, Hribar, & Tian, 2014; Hsu, O’Hanlon, &
Peasnell, 2012) and changes in sales (e.g., Banker et al., 2016, 2017). This study extends Banker et al. (2017) to examine the role of product market prices in the asymmetric timeliness in gain and loss recognition for the U.S. O&G industry. The research question for this study is stated as:
RQ: Do earnings respond asymmetrically to changes in product market prices?
9 Specifically, this study tests whether earnings exhibit an asymmetric pattern in timeliness of gain and loss recognition in regards to information reflected in oil price changes in the O&G sector. The O&G industry offers a unique context for studying the effects of changes in product market prices on accounting outcomes. First, the business outlook for this industry is closely associated with commodity prices (Deloitte, 2016; Halliburton, 2018). O&G companies are typically considered price takers in the global market for crude oil and changes in oil prices are beyond O&G firm control. Reductions in commodity prices can have a material adverse effect on business and consolidated financial conditions, including potential asset impairments and severance costs. Second, oil prices at the market level are not likely to be affected by firm-level characteristics, such as competition, firms’ business model and opportunistic managerial behaviors.
Third, the O&G industry provides a setting to re-evaluate the role of change in sales in asymmetric timeliness earnings recognition, as proposed by Banker et al. (2017). Murray,
Agard, and Barajas (2018) suggest that predicting future sales based on past sales patterns is only appropriate if demand patterns can be detected and accurately modelled. Time series methods of sales forecasting are only suitable for firms with sufficient historical data and steady demand. Companies in the O&G industry satisfy these criteria given that 1) there is sufficient historical data for oil prices, production and trading, and 2) crude oil is the most widely used source of fuel, supplying around one-third of the world’s energy needs, which guarantees a steady demand (Dunn & Holloway, 2012). Therefore, the O&G industry provides an ideal setting for studying the relations between market-level non-accounting risk factors and accounting outcomes.
This study argues that product market prices are likely to be a leading indicator of sales changes, providing complementary information about future cash flows. Price changes reflect changes
10 in the balance of supply and demand, which can be indicative for both future prices reflected in revenue and selling quantities. Therefore, product market prices are expected to add information for future cash flows. Prior studies (e.g., Banker et al., 2017) show that earnings exhibit asymmetrical associations with news about future cash flows (i.e., recognizing unfavorable information in a timelier and more full fashion than favorable information). If product market prices, which are useful for future cash flow estimation, have a similar effect as earnings, then changes in market prices are expected to have an asymmetric effect on earnings. This study therefore hypothesizes that oil price changes have an asymmetric effect on earnings conditional on stock returns, cash flow changes and sale change asymmetries.
Employing a sample of 1,224 firm-year observations for 219 firms in the U.S. O&G industry between 2002 and 2016, this study finds that earnings respond asymmetrically to changes in oil prices by recognizing bad news (negative oil price changes) in a fuller and timelier fashion than good news (positive oil price changes). The results suggest that product market prices are an important indicator for conditional conservatism and provide useful and incremental information (relative to stock returns and changes in operating cash flows) on asymmetrically timely gain and loss recognition.
This study’s contributions to the literature are twofold. First, few studies have examined the linkage between product market prices and accounting reporting decisions. This study adds to the literature to show that accounting-based earnings respond asymmetrically to changes in product market prices (i.e., negative price shocks are recognized in a fuller and timelier fashion than positive price shocks in product markets). Second, this study shows that accounting reflects changes in product market prices in a delayed fashion. Results suggest that one-year lagged oil price returns have an asymmetric impact on O&G firms’ net income, indicating that product market prices are able to signal changes in future cash flows ahead of accounting-based
11 performance measures. It appears to take time before information about product market price changes becomes fully reflected in financial reporting.
This study is also of value to stakeholders who demand accounting conservatism, such as lenders and investors. Negative product market price shocks are able to signal potential sacrifices of earnings quicker than other accounting-based performance measures, providing lenders with an opportunity to reduce their downside risk and take early protective action.
Thesis Structure
The remainder of this thesis is structured as follows. Chapter 2 presents the first study, which examines the influence of conflict risk on MNEs’ investment efficiency. Chapter 3 presents the second study, which explores foreign exchange market reactions to ISIS terrorist attacks in
Europe. The third study, presented in Chapter 4, examines the role of the product market in asymmetrically timely gain and loss recognition with evidence from the U.S. O&G sector.
Chapter 5 concludes this thesis.
12 CHAPTER 2: THE INFLUENCE OF CONFLICT RISK ON INVESTMENT EFFICIENCY FOR MULTINATIONAL ENTERPRISES (STUDY 1)
Introduction
MNEs are often exposed to conflict risk because their operations are in unstable host countries
(Dai et al., 2013). The literature suggests that businesses in conflict-prone environments suffer from high security costs, weak regulatory protection, and unpredictable policy changes
(Hernandez-Crespo, 2011; Mills & Fan, 2006). MNEs, especially those with operations in conflict regions, identify conflict risk as one of their key risk factors. Many of the annual reports for the top 30 U.S. government contractors in Iraq and Afghanistan4 indicate that they are exposed to a high level of conflict risk caused by instabilities in these conflict-intense locations5
(i.e., Fluor, 2018; KBR, 2018; Tutor Perini Corporation, 2015). For example, operations in conflict-affected regions are exposed to risks including high political instability, unpredictable attacks and civil disturbances, unstable economic, financial, and market conditions, inconsistent policy and legislation, and changes in labor conditions (Tutor Perini Corporation 2015).6
The UN and World Bank have repeatedly addressed the important role of foreign investment in peace-building and conflict prevention, but few studies have explored how MNEs manage their investments in response to different levels of conflict intensity. Most relevant to this research, Dai et al. (2013) found that conflict can reduce the likelihood of MNE survival in
4 The data for U.S. government contractors in Iraq and Afghanistan are obtained from the General Service Administration’s Federal Procurement Data System. 5 KBR, an American engineering, procurement, and construction company, disclosed that their work in high-risk locations including Iraq, Afghanistan, certain parts of Africa and the Middle East could result in employee and contractor harm and incur substantial costs to maintain the safety of their employees and properties (KBR, 2018). Fluor Corporation, a multinational engineering and construction firm that has operations in countries with high political and security risks including Afghanistan, Iraq, Kazakhstan, Russia, Argentina, and Mozambique, mentioned in their annual report that the high security risks in these countries affect the supply and price of oil and disrupt their operations, resulting in high security costs (Fluor, 2018). 6 Tutor Perini Corporations is one of the largest general contractors in the U.S.
13 conflict zones. The question arises as to how managers of MNEs effectively manage benefits and risks related to doing business in conflict zones. This study is therefore motivated to examine the impact of conflict risk on investment efficiency for MNEs. To understand the effect of conflict risk on firms’ investment efficiency, the research question for this study is stated as:
RQ: How does conflict risk affect MNEs’ investment decisions and investment efficiency?
Prior studies suggest that firms tend to delay investments when faced with political uncertainties.
Cao et al.(2017) and Kesten and Mungan (2015) suggest that risks and uncertainties raised by political instability discourage market activities such as cross-border acquisitions and IPOs.
During periods of political instability, firms tend to delay their investment projects because of high information uncertainty. Mills and Fan (2006) and Hernandez-Crespo (2011) indicate that business operations in conflict zones are exposed to high risks. Businesses suffer from high security costs, weak regulatory protection, and sometimes, unpredictable policy changes.
Facing high conflict risk, firms may wait and make investment decisions when there is more information about projects’ profitability (Cao et al., 2017). Therefore, this study argues that when an investment project is associated with high conflict risk and uncertainty, MNEs are more likely to bypass the project because of lack of sufficient information about its profitability, even though the projects may have positive NPVs, leading to underinvestment.
Conflict-affected environments typically expose companies to weak property rights protection, regulatory enforcement, and administration (Hermandez-Crespo, 2011; Mills & Fan, 2006). To operate in these regions, businesses may move to the informal sector and rely on local networks.
Businesses in the informal sector may not keep timely and accurate financial reporting because of a lack of monitoring. Operating in such a way gives managers greater discretion and consequently reduces MNE financial reporting quality. I therefore anticipate that firms with investment in conflict zones have relatively poor financial reporting quality. This leads to my
14 second hypothesis: the higher the conflict risk that MNEs are exposed to, the poorer the overall quality of their financial reports.
Prior studies suggest that higher-quality financial reporting increases investment efficiency
(e.g., Bushman & Smith, 2001; Healy & Palepu, 2001; Lambert, Leuz, & Verrecchia, 2007).
The investment efficiency literature (e.g., Biddle et al., 2009; Chen et al., 2011) suggest that better financial reporting quality helps to reduce suboptimal investment. With the exception of
Biddle et al. (2009), much prior research ignores whether firms are more prone to overinvestment or underinvestment. This study investigates whether higher financial reporting quality reduces the likelihood that a firm deviates from the expected investment level in a setting where firms would be expected to be prone to underinvestment. This study therefore also tests the hypothesis that the poorer the overall financial reporting quality of MNEs’ financial reports, the more likely they will experience investment inefficiency after controlling for conflict risk.
This study follows the method developed by F. Chen et al. (2011), which was designed to examine the relation between financial reporting quality and investment efficiency. This study extends the model to consider the predicted association between conflict risk and investment efficiency. I use national-level measures of conflict intensity provided by HIIK to identify the conflict risk that MNEs are exposed to. The sample for this study is U.S. MNEs with investments in conflict zones.
Results show that when MNEs are exposed to relatively high conflict risk, they are more likely to defer or bypass investment opportunities. Facing high information uncertainty on the projects’ profitability caused by conflict risk, firms are likely to delay investment to wait for more information. In this situation, underinvestment is likely to be more pronounced. Results are consistent when using the full sample or a subsample of firms exhibiting underinvestment. This
15 study also finds evidence that firms exposed to higher levels of conflict risk have lower financial reporting quality. Consistent with prior literature, this study shows that financial reporting quality is negatively associated with suboptimal investment, indicating that better financial reporting quality helps to reduce investment inefficiency.
This study contributes to both practice and the literature. Risk disclosure has received attention because of apparent increased uncertainty in the business environment including terrorist attacks (Brown, Tian, & Tucker, 2018). While the SEC (2010) has warned companies to avoid generic risk factor disclosure, the concern is that many corporate risk disclosures do not reveal, worse, exaggerate the risks faced. In considering the materiality of risks to be disclosed, a key issue is understanding the underlying level of risk. The SEC Report on Review of Disclosure
Requirements in Regulation S-K (2013) also notes the need for an alternative and more appropriate approach to risk disclosures. This SEC report highlighted the importance of disclosure relating to non-U.S. operations. By examining the association between conflict risks and investment efficiency, I provide evidence that sufficient disclosure on overseas operations, especially those in conflict zones, is of importance to assessing investment efficiency.
This study contributes to three streams of literature. First, it advances understanding on the link between conflict risk and firm-level investments. Driffield, Jones, and Crotty (2013) studied the prevalence of firms investing in conflict-affected countries and found that countries with weaker institutions and fewer concerns about corporate social responsibility are more likely to invest in conflict regions. Dai et al. (2013) suggest that conflict risks reduce the likelihood of survival for foreign subsidiaries in conflict regions. This study takes a step further, to explore
MNE investment performance in conflict-affected environments.
Second, this study adds to the literature in terms of the relation between uncertainties and economic outcomes. Most research has focused on political uncertainties (e.g., Bialkowski et
16 al., 2008; Cao et al., 2017; Jens, 2017; Kesten & Mungan, 2015; Pástor & Veronesi, 2013). This study extends the current understanding on the impact of violent conflicts, within the broad category of political uncertainties, on economic outcomes.
Third, this study contributes to the literature examining the association between reporting quality and investment efficiency. Prior studies in this field primarily focus on the argument of information asymmetry and agency costs via examining the impact of factors such as financial constraints (e.g., Biddle et al., 2009; Lara et al., 2015) and ownership structure (e.g., Chen et al., 2011; Chen et al., 2011). This study adds to the literature by examining the impact of geographically defined risks, rarely studied, on firms’ investment decision and performance.
The remainder of this chapter is constructed as follows. Section 2.2 provides a review of the literature in the areas of investment environment in conflict zones, political uncertainties and financial markets, and financial reporting quality and investment efficiency. Section 2.3 develops the hypotheses of this study. Section 2.4 describes the methodology, including research design and variable measurement. Section 2.5 describes the data and sample. Section
2.6 presents the results of the influence of conflict on investment efficiency and financial reporting quality. Section 2.9 concludes the chapter.
Literature Review
2.2.1 Investment Environment in Conflict Zones
Conflicts cause riots and instabilities, which significantly affect the business environment in conflict-affected regions. Firms with investments in conflict zones are exposed to risks from a various of sources, including physical security, contract enforcement and judicial redress, access to finance, supply of skilled labor and tax administration (Mills & Fan, 2006). These
17 risks not only impose additional costs on foreign firms trying to secure their properties and assets in the high-risk region, but can constrain their ability to generate returns.
Mills and Fan (2006) and Hernandez-Crespo (2011) highlight weak regulatory enforcement in conflict regions. They point out that judicial redress for asset and property rights protection in conflict zones is normally poor. Foreign investors may suffer from disruptions caused by conflicts such as ‘informal’ expropriation of property, which forces investors to obtain investment opportunities and maintain operations through bribery or relying on the local social network (Mills & Fan, 2006). When the normal rules of investment and operation, such as registering business, obtaining licenses and enforcing contractual obligations, cannot be legally enforced, firms may strategically choose to lower the profile of the business in the unsecured and destabilized environment, and move from the formal to the informal sector. Operating in the informal sector without sufficient regulatory protection increases risks for the business and leaves foreign participants in a vulnerable position, which leads to additional costs for the
MNEs’ investment in conflict zones.
Prior studies provide evidence that conflict risks negatively affect the investment environment and endanger the survival of a business. Dai et al. (2013) show that conflict risk reduces the survival of foreign subsidiaries in conflict zones. Their sample consists of 670 Japanese MNE subsidiaries in 25 conflict-afflicted host countries between 1987 and 2006. Employing the concepts of place (the conflict zone) and space (geographic concentration and dispersion of other home-country firms), they found subsidiaries are less likely to survive when exposed to
18 greater conflict threats, in both a static7 and a dynamic8 sense. Their results indicate a negative association between conflict threat exposure and MNE subsidiary survival.
Driffield et al. (2013) investigated the determinants of a firm’s strategy to invest in conflict regions. Their sample comprises 16,900 observations for 2,509 MNEs that have investments in regions with a low level of human development from nine countries for the sample period 1997–
2009. Results suggest that firms from weaker institutional backgrounds and firms with concentrated ownership are more likely to invest in conflict zones. These firms face less scrutiny from stakeholders and are more likely to undertake investment projects that may otherwise attract criticism and objections from shareholders. This study shows that when there is strong governance and shareholder monitoring, firms are less likely to take investment projects with high conflict risk.
Overall, these studies indicate that conflicts create additional risk for firms operating in conflict- affected environments, which not only challenges profitability, but endangers survival.
2.2.2 Political Uncertainty and Financial Markets
Conflict regions are largely analogous to areas where political risk is high. I therefore briefly consider prior research on how political uncertainty affects economic outcomes. A recent stream of literature examines the correlation between political uncertainty and economic outcomes (e.g., Cao et al., 2017; Kelly, Pástor, & Veronesi, 2016; Pástor & Veronesi, 2013).
These studies use national elections as a proxy for political uncertainty, and, based on the “bad news principle”,9 argue that markets react negatively during a national election because of the
7 Static exposure indicates whether a subsidiary is located inside a conflict-affected environment. 8 Dynamic exposure describes the combined impact of the focal subsidiary’s location relative to the center of each conflict zone, the number of conflict zones and the size of zones. 9 The “bad news principle” refers to the phenomenon that only the expected severity of future bad news will determine whether to invest or not today (Bernanke, 1983). Bernanke suggests when the investment project is
19 high information risk. When elections occur, information risk in the market increases because of policy inconsistency and market fear of policy changes (Cao et al., 2017). They find that markets react negatively to national election periods, including via higher capital costs (Kesten
& Mungan 2015), fewer IPOs (Bialkowski et al., 2008; Kesten & Mungan, 2015), fewer merges and acquisitions (M&As) (Cao et al., 2017) and fewer investment projects (Jens, 2017).
Pástor and Veronesi (2013) argue that political uncertainty increases market uncertainties and risks in two ways: 1) prior to the policy change, investors are concerned with whether there will be change and if yes, how this would affect their returns; and 2) after the change, investors will have to trade under an unfamiliar policy environment, which can increase the cost of learning the new policy. Employing data from the U.S. market, their results show that greater political uncertainty increases the equity risk premium and consequently increases the cost of equity financing. Kesten and Mungan (2015) concur, finding evidence that political uncertainty increases the cost of capital for IPO firms and frequency of IPOs reduces during election periods as political uncertainty increases. Bialkowski et al. (2008) presents similar results, finding that national elections led to greater market volatility in 27 OECD countries.
Facing high information risk during elections, firms tend to be more prudent in their investment decisions. Cao et al. (2017) assessed the relation between political uncertainty and cross-border
M&As. They found that elections in the acquirer country encouraged firms to acquire targets abroad to diversify increasing risks in the domestic market induced by political uncertainty.
Conversely, the number of inbound cross-border acquisitions declined significantly prior to the elections in target countries. Jens (2017), using U.S. gubernatorial elections as a proxy for uncertainty, examined the association between political uncertainty and U.S. firm investment.
exposed to great uncertainty and is costly to reverse, investors have an incentive to delay the option until they have more information about the prospects and profitability of the projects as the value of the option will increase along with the waiting, especially when the source of uncertainty renews itself from time to time.
20 Jens documented a 5% decline in investment before all elections and up to a 15% investment decrease for those firms particularly susceptible to political uncertainty. This result shows that firms delay their investment prior to elections to avoid the excessive risks raised by political uncertainty.
Overall, these studies show that when political uncertainty is high, firms are more likely to defer their investment projects due to high information uncertainty. Information from conflict regions with such circumstances is largely analogous to areas where political risk is high, as both conflict risk and political uncertainty leave firms facing unstable government administration and inconsistent policies. Therefore, it is expected that when conflict risk is high, firms will exhibit similar investment behavior as when they are exposed to high political risk.
2.2.3 Financial Reporting Quality and Investment Efficiency
Information asymmetry and agency costs are the two main frictions discussed in prior investment efficiency literature (e.g., Biddle et al., 2009; Chen et al., 2011; Cheng, Dhaliwal,
& Zhang, 2013; Lara et al., 2015). Information asymmetry refers to situations where managers possess more private information than outsider investors. It facilitates adverse selection where managers overvalue the securities they intend to offer because they are better informed than investors on the true value of firms’ assets and future growth opportunities. Assuming investors understand managers’ intentions and behaviors, they would discount the newly issued securities accordingly. Consequently, managers from firms with profitable projects will be disinclined to issue new securities, which results in underinvestment. Agency costs rise when managers’ and shareholders’ interests are not well aligned (Jensen & Meckling, 1976), which leads to a moral hazard problem. In this situation, managers have the incentive to choose investment projects to maximize their own benefits instead of acting in the best interest for shareholders, which can cause underinvestment and/or overinvestment.
21 Biddle et al. (2009) suggest that investment inefficiency raised by adverse selection and moral hazard can be reduced by better financial reporting quality.10 They argue that better financial reporting quality helps to make projects with positive NPVs more visible to investors, and thus, reduces adverse selection cost. Also, high-quality reporting reduces information asymmetry, which improves investors’ monitoring of managers’ self-interest-seeking behavior. Their results show that better financial reporting quality helps reduce both overinvestment and underinvestment. Examining the impact of financial reporting quality on investment efficiency for private firms in emerging markets and finding that financial reporting quality positively affects investment efficiency, F. Chen et al. (2011) concur. The association between reporting quality and investment efficiency increases when bank financing is used and decreases when there are incentives to minimize earnings for tax purposes.
Later studies extend Biddle et al. (2009) to examine aspects of reporting quality and their impact on investment efficiency, including additional disclosure of internal controls and financial report weakness (Cheng et al., 2013), accounting conservatism (Lara et al., 2015) and financial reporting comparability (Chen et al., 2011). Cheng et al. (2013) compared investment performance in pre- and post-SOX11 periods and found that additional disclosure on firms’ material internal control weakness improves investment efficiency. Lara et al. (2015) argued that conservatism encourages timely reporting of losses and enables stakeholder to monitor managers’ self-interest behavior. Moreover, conservatism assists managers’ investment decisions, such as early abandonment of loss-making projects to increase future returns. Their
10 Financial reporting quality in Biddle et al. (2009) is measured by accrual quality derived from the Dechow and Dichev model, modified accrual quality derived from the Wyscoki (2009) model and the FOG index introduced by Li (2008). 11 Prior to SOX, firms were only required to disclose their internal control effectiveness if they changed auditor. SOX Section 302 mandates that a firms’ CEO and CFO certify in periodic SEC filings that they have “evaluated and presented in the report their conclusions about the effectiveness of their internal controls based on their evaluation.” SOX Section 404 requires that an internal control report, which details the assessment of the effectiveness of the firms’ internal control structure and procedures with respect to financial reporting, need to be included in the annual report.
22 results show that conservatism produces better quality reports, which leads to reductions in both underinvestment and overinvestment. Chen, Young, and Zhuang (2012) argue that better financial reporting comparability helps to reduce information asymmetry because it reduces managers’ uncertainties about market conditions and improves their ability to select better investment projects. Their results show that there is a positive association between financial comparability and investment efficiency.
This section reviewed relevant literature for this study. Studies of investment efficiency suggest that investment can deviate from its optimal level because of information asymmetry and agency costs. Overall, these studies show that better financial reporting quality helps to improve investment efficiency via reducing information asymmetry between managers and investors and limiting managers’ self-interest. Studies of political uncertainty show that high political risk raises the cost of capital and discourages investment activities. Conflict is a common trigger for political instability. Better financial reporting quality can help to mitigate the issues of moral hazard and adverse selection and therefore ameliorate investment inefficiency. Few studies have explored the effect of conflict risk on investment. They find that the complexity of conflicts and weak property rights protection in conflict regions reduce the likelihood of survival for foreign operations in conflict regions. I extend this group of studies to investigate how conflict risk affects financial reporting quality and investment efficiency.
Hypotheses Development
2.3.1 Conflict Risk and Underinvestment
When political risk is high, firms are less inclined to invest (e.g., Cao et al., 2017; Jens, 2017;
Kelly et al., 2016; Kesten & Mungan, 2015). The negative association between uncertainty and firms’ willingness to invest is determined by the irreversibility of the firm’s investment
(Bernanke, 1983). When information about projects’ profitability is uncertain and it is costly to
23 reverse the investment, firms are likely to bypass such investment projects regardless of the projects’ returns.
Investment projects in conflict zones can be costly to reverse or withdraw once started, which reduces the reversibility of such investments. Many projects are in the infrastructure, energy and telecommunication sectors, which require substantial investment of capital in the early stage (Mills & Fan, 2006). Information about investment profitability in conflict zones can be highly uncertainty and outcomes difficult to predict. Given that profitability is uncertain and the high cost to reverse investment when conflict risk is high, MNEs may choose to bypass or defer projects, which leads to underinvestment, regardless of the projected return of the investment (Cao et al., 2017; Jens, 2017). Therefore, it is expected that there is a positive association between conflict risk and underinvestment.
Hence, I anticipate that
Hypothesis 2.1: The higher the conflict risk that MNEs are exposed to, the more likely
they will experience underinvestment.
This hypothesis needs to be tested given that conflict-risk-related increases to operational and security costs can create internal financial constraints for corporations. Hovakimian (2011) shows that internal financial constraints improve investment efficiency by limiting the amount of capital under manager discretion. With limited capital, managers are likely to allocate scarce resources more efficiently and defer investment projects motivated by self-interest. Therefore, it is possible that conflict risks can improve investment efficiency.
24 2.3.2 Conflict Risk and Financial Reporting Quality
Conflict risk is likely to compromise financial reporting quality because of the lack of effective regulatory enforcement in conflict zones. In conflict-intense regions, government are likely to have poor administration power and suffer from corruption (Driffield et al., 2013). Corruption makes obtaining local licenses and permits more costly for foreign investors (Javorcik & Wei,
2009). MNEs may need to rely on bribery and local social networks to obtain business opportunities (Mills & Fan, 2006; O’Toole & Tarp, 2014). Using local social networks raises the issue of the increased use of off-the-book transactions (O’Toole & Tarp, 2014) which can lead to spending that is hard to track and monitor. Further, this abnormal spending associated with conflict risk creates opportunities for fraud, which consequently reduces overall financial reporting quality. Increased use of incentive payments, agent fees or bribes to attract business further reduces reporting quality as transactions are reported so as not to draw attention to payments that might be considered illegal under the International Anti-Bribery and Fair
Competition Act 1998. Further, companies operating in high-conflict zones may not wish to draw attention to profits earned from conflict zones, creating further incentive for management to be less than fully transparent in reporting results from these segments. Overall, high conflict risk is expected to be reflected in low-reporting quality because of potentially incomplete and inaccurate reporting. Therefore, I predict that:
Hypothesis 2.2: The higher the conflict risk that MNEs are exposed to, the poorer the
overall quality of their financial reports.
2.3.3 Financial Reporting Quality and Investment Efficiency
Biddle et al. (2009) and F. Chen et al. (2011) suggest that financial reporting quality is positively associated with investment efficiency in both U.S. and emerging markets. To
25 examine whether the relation between investment efficiency and financial reporting found by prior studies holds in situations where underinvestment is likely, I test the following hypothesis:12
Hypothesis 2.3: The poorer the overall quality of MNEs’ financial reports, the more
likely they will experience underinvestment after controlling for conflict risk.
Methodology
2.4.1 Research Design
This study follows the model developed by F. Chen et al. (2011) to examine the influence of conflict risk on MNE investment efficiency. F. Chen et al. (2011) test the relation between financial reporting quality and investment efficiency in emerging markets. Their independent variable is financial reporting quality, measured as the aggregation of discretionary accruals, discretionary revenues and discretionary current accruals. The dependent variable is suboptimal investment, which is the deviation from optimal investment.
Based on F. Chen et al. (2011), the following model is developed for this study to test H2.1 and
H2.3:
퐼푛푣퐸푓푓푖푡+1 = 0 + 1퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 + 2퐹푅푄푖푡 (2.1)
+ 3퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 ∗ 퐹푅푄푖푡
+ 훴훾푗퐶표푛푡푟표푙푗푖푡 + 훴훾푗퐼푛푑푢푠푡푟푦푗푖푡
+ 훴훾푗푌푒푎푟푗푖푡 + 휀푖푡
Where
12 I also considered the mediating effect of financial reporting quality on the relation between conflict risk and underinvestment and tested it following the four-step approach proposed by Baron and Kenny (1986). The results do not indicate any mediating effect of financial reporting quality.
26 InvEff = Excess investment measured by the residuals from investment model (2.3) Conflict_Intensity = Average conflict intensity level for the conflict regions that a firm has subsidiaries in. 13 If a firm has no subsidiaries in identified conflict regions, Conflict_Intensity will be zero. FRQ = Aggregated financial reporting quality proxy calculated as the average of standardized DisAccr, the absolute residual of the Kothari et al. (2005) performance- adjusted discretionary accruals model, multiplied by −1; DisRev, the absolute residual of the McNichols and Stubben (2008) discretionary revenue model, multiplied by −1; and DD, the absolute residual of modified Dechow and Dichev (2002) model, multiplied by −1.
I include industry and year fixed effects using Fama and French’s 48-industry classification and report the estimated coefficients using standard errors clustered by firm and year. The dependent variable InvEff is the deviation from optimal investment, the proxy for suboptimal investment. Details on the estimation process for InvEff (Model 2.3) are included in next section.
The independent variable of interest for H2.1 is 퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖,푡 for firm i in year t.
Conflict_Intensity measures the average conflict risk that a firm’s subsidiaries are exposed to.
It is calculated as the average conflict level for all the conflict regions in which firms have subsidiaries. If a firm has no subsidiaries in an identified conflict region, Conflict_Intensity will be zero. 1 captures the direct relation between suboptimal investment and conflict intensity.
H2.1 predicts that there is a positive association between conflict risk and underinvestment; therefore, 1 is expected to be negative.
FRQ is the aggregated financial reporting quality proxy calculated as the average of standardized discretionary accruals (DisAccr), which is the absolute residual of the Kothari et al. (2005) performance-adjusted discretionary accruals model, multiplied by −1; discretionary
13 As a nation can experience multiple conflicts in the same period, I take the average intensity level for all conflicts for one nation.
27 revenue (DisRev), which is the absolute residual of the McNichols and Stubben (2008) discretionary revenue model, multiplied by −1; and abnormal accruals (DD), which is the absolute residual of modified Dechow and Dichev (2002) model, multiplied by −1. Estimation processes for DisRev, DisAccr and DD are discussed in detail in the following section.
H2.3 predicts that investment efficiency is negatively associated with FRQ. Coefficient for
FRQ (2) examines the direct effect of FRQ on firms’ investment level when firms face conflict risk; therefore, 2 is expected to be negative. An interaction term is created for
퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦 with 퐹푅푄. The effect of poor FRQ for firms exposed to high conflict risk is captured by 2 + 3.
Following prior studies (e.g., Biddle et al., 2009; Biddle et al., 2016; Chen et al., 2011;
Goodman, Neamtiu, Shroff, & White, 2014), I include control variables that influence firms’ investment efficiency, including firm size, market-to-book ratio, return on assets, loss, leverage, asset tangibility, financial slack, cash flow-to-sales ratio, operating cycle, Altman Z-score, cash flow volatility, sales volatility and investment volatility. The control variables are defined in
Table 2.1.
To test H2.2, that conflict risk is negatively associated with financial reporting quality, I use the following regression model with FRQ as the dependent variable and Conflict_Intensity as the independent variable:
퐹푅푄푖푡+1 = 훽0 + 훽1퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 + 훴훾푗퐶표푛푡푟표푙푗푖푡 (2.2)
+ 훴훾푗퐼푛푑푢푠푡푟푦푗푖푡 + 훴훾푗푌푒푎푟푗푖푡 + 휀푖푡
Hypothesis 2.2 predicts that conflict intensity is negatively associated with financial reporting quality; therefore, 훽1 is expected to be negative. Variables that can affect financial reporting
28 qualities, including firm size, market-to-book ratio, return on assets, loss, leverage, asset tangibility, financial slack, cash flow-to-sales ratio, operating cycle, Altman Z-score, cash flow volatility, sales volatility and investment volatility, are included in the model.
In estimating the Ordinary Least Squares regressions, I adjust the standard errors for heteroscedasticity using a two-dimensional cluster at the firm and year level. Controlling for industry-specific shocks to investment, I also include industry fixed effects using Fama and
French’s (1997) 48-industry classification.
2.4.2 Variable Measurement
2.4.2.1 Measurement of conflict risk
The conflict risk that each firm is exposed to in a given year is measured by the variable 퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡. The conflict data used to calculate the variable were obtained from HIIK, which publishes annual conflict barometers 14 that provide conflict intensity assessments for each identified conflict using a 5-point Likert scale. The conflict intensity score ranges from 1–5, which stand for dispute, non-violent crisis, violent crisis, limited war and war respectively. Dispute and non-violent crisis are non-violent conflicts, whereas the other three are violent conflicts. For each identified conflict, HIIK details the countries involved, conflict parties, conflict issues, starting year of the conflict, and changes in conflict intensity level from the last reporting period. A total of 17315 conflict countries were identified from the conflict barometers for the sample period.
14 Barometers for 1992–1996 and 1998–2001 are available in German language only. The English version of Barometer 1997 is available. However, in consideration of data continuity, this study employs data for the sample period 2002–2015. 15 China, Japan, Singapore, France, England, Australia, Germany and Canada are not considered conflict countries in this study, given that conflicts in these countries are mainly non-violent, such as system and ideology conflicts.
29 퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 is calculated in two steps. As a country can experience multiple conflicts in a given year, this study first estimates a country-level conflict intensity score for the year for each of the identified conflict countries, taking the average intensity score for all conflicts in the nation for that year. The firm-year conflict intensity score, 퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡, is then estimated as the average conflict intensity for all conflict countries that one firm has subsidiaries in for the year. 퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 ranges from 0–5, where the higher the value, the greater the conflict risk. If a firm has no subsidiaries in identified conflict regions,
퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 will be zero.
2.4.2.2 Proxy for investment efficiency
As indicated by Biddle et al. (2009), optimal investment efficiency refers to the situation where firms undertake projects with positive NPV under a scenario of no market friction. Optimal investment efficiency is of course unobservable. Following prior research (Biddle et al., 2009;
Biddle et al., 2016; Chen et al., 2011; Goodman et al., 2014), investment efficiency is measured as the deviation from optimal investment using a model where investment is predicted by the firm’s growth opportunity.
Recent studies (Biddle et al., 2016; Goodman et al., 2014) measure deviations from optimal investment based on marginal Q theory where Tobin’s Q16 represents investment opportunities
(Tobin, 1969). Deviations from optimal investments are measured as the residuals from the function where capital investment is regressed against sales growth, cash flow and Tobin’s Q.
Following Goodman et al. (2014), I employ the following model to predict investment:
16 Tobin’s Q is the ratio of the market value of an additional unit of capital to its replacement cost, which is proxied by market-to-book ratio (MTB).
30 퐼푛푣푒푠푡푚푒푛푡푖푡 = 훼0 + 훼1푆푎푙푒푠_퐺푟표푤푡ℎ푖푡−1 + 훼2푀푇퐵푖푡−1 + 훼3퐶퐹푂푖푡−1 (2.3)
+ 훼4푅푂퐴푖푡−1 + 훼5훥퐴푆푆퐸푇푖푡
+ 훼6퐼푛푣푒푠푡푚푒푛푡푖푡−1 + 휀3푖푡 Where
Investment = new investment,17 calculated as total capital expenditure minus expenses for depreciation and amortization, scaled by five-year moving average of total assets from year t-5 to t-1 Sales_Growth = the percentage change in sales from year t-1 to t MTB = market-to-book ratio proxy for Tobin’s Q CFO = cash flow from operations scaled by five-year moving average of total assets from year t-5 to t-1 ROA = return on asset 훥퐴푆푆퐸푇 = change in total asset from year t-1 to t 휀3 = suboptimal investment
I estimated the investment model for each industry-year group using Fama and French’s 48- industry classification, with at least 20 observations in each industry.
2.4.2.3 Proxy for financial reporting quality
Following F. Chen et al. (2011), financial reporting quality is measured using discretionary accruals (DisAccr), discretionary revenues (DisRev) and discretionary current accruals (DD).
Discretionary accruals are estimated following Kothari et al. (2005) for each industry18–year group having at least 20 observations in each industry, shown as Model 2.4 below.
푇퐴푖푡 = 훼0 + 훼1(1⁄퐴푇푖.푡−1) + 훼2훥푆퐴퐿퐸푖푡 + 훼3푃푃퐸푁푇푖푡 + 훼4푅푂퐴푖푡 (2.4)
+ 휀푖푡
17 Richardson (2006) suggests that capital expenditure can be divided into two parts: maintaining existing assets and financing new investment projects with positive NPV. A majority of the sample for this study are from the manufacturing and equipment industries, which are subject to large amounts of depreciation. Including depreciation expenses in capital expenditure is likely to create noise for the test as depreciation and amortization may not closely map firms’ investment performance. Therefore, I use new investment in this study. 18 Fama and French 48-industry classification.
31 Where TA is total accruals, calculated as change in non-cash current assets minus change in current non-interest-bearing liabilities, minus depreciation and amortization expenses; ΔSALE is annual change in revenues scaled by lagged total assets; PPENT is the net property, plant, and equipment, scaled by lagged total assets; and ROA is return on assets. The residuals produced by the regression model represent discretionary accruals. Following F. Chen et al.
(2011), I use the absolute values of discretionary accruals as the proxy for FRQ. The absolute values of discretionary accruals (DisAccr) are multiplied by −1; therefore, higher DisAccr represents higher FRQ.
The second proxy for FRQ is discretionary revenues. Following McNichols and Stubben (2008) and Stubben (2010) for each industry19–year group, discretionary revenues are estimated using
Model 2.5 below.
훥퐴푅푖푡 = 훼0 + 훼1훥푆퐴퐿퐸푖푡 + 휀푖푡 (2.5)
Where ΔAR is the annual change in accounts receivables from year t-1 to t and ΔSALE is the annual change in revenue scaled by lagged total assets. Discretionary revenues are residuals from the model and I take the absolute values of discretionary revenues to proxy for FRQ.
Absolute values of discretionary revenues are multiplied by −1 (DisRev); thus, higher DisRev means better FRQ.
The final proxy for FRQ is discretionary current accruals predicted by Dechow and Dichev’s
(2002) model as modified by McNichols (2002) and Francis, LaFond, Olsson, and Schipper
(2005) (Model 2.6) for each industry20–year group.
19 Fama and French’s 48-industry classification. 20 Fama and French’s 48-industry classification.
32 푇퐶퐴푖푡 = 훼0 + 훼1푂퐶퐹푖푡−1 + 훼2푂퐶퐹푖푡 + 훼3푂퐶퐹푖푡+1 + 훼4훥푆퐴퐿퐸푖푡 (2.6)
+ 훼5푃푃퐸푁푇푖푡 + 휀푖푡
Where TCA is total current accruals, measured as the change in non-cash current assets minus the change in current non-interesting-bearing liabilities, scaled by lagged total asset, and OCF is the cash flow from operations, measured as the sum of net income, depreciation and amortization, and changes in current liabilities, minus changes in current assets, scaled by lagged total assets. Residuals from the regression model are the discretionary current accruals.
I take the absolute values of discretionary current accruals as the proxy for FRQ. The absolute values of discretionary current accruals are multiplied by −1 (DD), so that higher DD means higher FRQ.
Variable definitions are displayed in Table 2.1.
33 Table 2.1 Variable Definitions for Study 1
Variable Definition and Data Item Variables Used to Measure Suboptimal Investments: 퐼푛푣푒푠푡푚푒푛푡푖푡 = new capital investment of a firm. It equals total capital investment (Compustat item ‘CAPX’) minus amortization and depreciation (Compustat item ‘DP’), scaled by the five-year moving average of total assets from year t-5 to t-1. 푆푎푙푒푠_퐺푟표푤푡ℎ푖푡 = percentage change in sales (Compustat item 'SALE') from year t-1 to t. 푀푇퐵푖푡 = market-to-book ratio, calculated by ('AT'+'CSHO'*'PRCC_F'- 'CEQ'-'TXDB')/'AT'. 퐶푎푠ℎ_퐹푙표푤푖푡 = cash flows from operations, calculated by Compustat item 'IB'+'DP', scaled by the five-year moving average of total assets from year t-5 to t-1. 푅푂퐴푖푡 = return on assets, computed by Compustat item 'PI' divided by 'AT'). 훥퐴푠푠푒푡푖푡 = changes in total assets (Compustat item 'AT') from year t-1 to t. Dependent Variables: 퐼푛푣퐸푓푓푖푡 = suboptimal investments. It is residuals estimated from Model 2.3. 푀퐼퐸푖푡 = a category variable equal to 0 if a firm invests efficiently, -1 if firms underinvest, and 1 if firms overinvest. To generate this variable, we sort residuals obtained from Model 2.1 within each year and divide them into quartiles. We then use these quartiles to create the category variable 'MIE': if a firm-year observation falls in the bottom quartile, then 'MIE' is set to -1 (representing underinvestment). If it falls in the top quartile, then 'MIE' is set to 1 (representing overinvestment). Finally, 'MIE' is equal to 0 if a firm-year observation falls in the middle two quartiles (representing efficient investment). 푈푁퐷퐸푅푖푡 = absolute values of negative residuals estimated from Model 2.3 to represent underinvestment. Therefore, higher values for UNDER stand for more pronounced underinvestment. 푂푉퐸푅푖푡 = positive residuals estimated from Model 2.3 represent overinvestment. Independent Variables: 퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 = average conflict intensity level for the conflict regions that a firm has subsidiaries in. If a firm has no subsidiaries in identified conflict regions, Conflict_Intensity will be zero. 푃푒푟_푐표푛푖푡 = 퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦 푛푢푚푏푒푟 표푓 푠푢푏푠𝑖푑푎푟𝑖푒푠 𝑖푛 푐표푛푓푙𝑖푐푡 푟푒푔𝑖표푛푠 × ( ) 푡표푡푎푙 푛푢푚푏푒푟 표푓 푠푢푏푠𝑖푑푎푟𝑖푒푠 퐹푅푄푖푡 = aggregated financial reporting quality proxy calculated as the average of standardized DisRev, DisAccr and DD. 퐷𝑖푠퐴푐푐푟푖푡 = residuals from Kothari et al.’s (2005) model, which represents the estimation errors total accruals. I multiply the absolute values of residuals by -1. Therefore, higher kFRQ represents higher financial reporting quality.
34 퐷𝑖푠푅푒푣푖푡 = residuals from equation which represent the discretionary revenues. I multiply the absolute values of discretionary revenues by -1. Thus, the higher the values of mFRQ, the higher financial reporting quality. 퐷퐷푖푡 = residuals from equation represent the estimation errors in the current accruals not associated with operating cash flows and that cannot be explained by the change in revenue and the level of PPE. I multiply the absolute values of residuals by -1. Thus, higher values of ddFRQ represent higher financial reporting quality. Control Variables: 푆𝑖푧푒푖푡 = log value of market values expressed in U.S. dollars. (missing much MV from Compustat) 푀푇퐵푖푡 = market-to-book ratio, calculated by ('AT'+'CSHO'*'PRCC_F'- 'CEQ'-'TXDB')/'AT'. 푅푂퐴푖푡 = return on assets, computed by Compustat item 'PI' divided by 'AT'). 퐿푂푆푆푖푡 = an indicator variable that takes the value of 1 if net income before extraordinary items ('IB') is negative and 0 otherwise. 퐿푒푣푒푟푎푔푒푖푡 = ratio of long-term debt ('DLTT') to the sum of long-term debt and the market value of equity ('DLTT'+'CSHO'*'PRCC_F'). 푇푎푛푔𝑖푏𝑖푙𝑖푡푦푖푡 = ratio of property, plant and equipment (Compustat item 'PPENT') to total assets (Conpustat item 'AT'). 푆푙푎푐푘푖푡 = ratio of cash (Compustat item 'CHE') to property, plant and equipment (Compustat item 'PPENT'). 퐶퐹푂 _푆푎푙푒푖푡 = ratio of cash flow from operations (Compustat item 'IB'+'DP') to sales (Compustat item 'SALE'). 푂푝푒푟푎푡𝑖푛푔_퐶푦푐푙푒푖푡 = receivables to sales (Compustat item 'RECT'/'SALE') plus inventory to COGS (Compustat item 'INVT'/'COGS') multiplied by 360. 푍_푆푐표푟푒푖푡 = Altman Z-score, calculated by 'PI/'AT'*3.3+'SALE/'AT'+0.25*'RE/'AT'+0.5*(('ACT''LCT')/'AT' ). 𝜎(퐶퐹푂)푖푡 = standard deviation of the cash flow from operations (calculated by item 'IB'+'DP') deflated by average total asset from years t-5 to t- 1. 𝜎(푆푎푙푒푠)푖푡 = standard deviation of sales (Compustat item 'SALE') deflated by average total asset from years t-5 to t-1. 𝜎(퐼푛푣푒푠푡)푖푡 = standard deviation of investment (item 'CAPX')) deflated by average total asset from years t-5 to t-1. 푆푎푙푒푠_퐺푟표푤푡ℎ푖푡 = the percentage change in sales (Compustat item 'SALE') from year t-1 to t. 퐼푛푑푢푠푡푟푦푖푡 = Fama and French 48 industry classification.
Predicting Abnormal Accruals Using Kothari et al. (2005) 푇퐴푖푡 = total accruals measured by the change in non-cash current assets minus the change in current non-interest-bearing liabilities, minus depreciation and amortization expense for firm i at year t, scaled by lagged total assets (Assetsi,t-1). 훥푆퐴퐿퐸푖푡 = annual change in revenues (Compustat item 'SALE') scaled by lagged total assets (Compustat item ‘AT’).
35 푃푃퐸푁푇푖푡 = property, plant, and equipment (Compustat item ‘PPENT’) for firm i at year t, scaled by lagged total assets (Compustat item 'AT'). 푅푂퐴푖푡 = return on assets, computed by Compustat item 'PI' divided by 'AT'). Predicting Discretionary Revenues Using McNichols and Stubben (2008) and Stubben (2010) 훥퐴푅푖푡 = annual change in accounts receivable (Compustat item ‘RECT’). 훥푆퐴퐿퐸푖푡 = annual change in revenues (Compustat item 'SALE') scaled by lagged total assets (Compustat item ‘AT’). Predicting Abnormal Accruals Dechow and Dichev (2002) as Modified by McNichols (2002) and Francis et al. (2005) 푇퐶퐴푖푡 = total current accruals, measured as the change in non-cash current assets (Δ‘ACT’-Δ‘CHE’) minus the change in current non- interesting-bearing liabilities (Δ‘LCT’+Δ‘DLC’), scaled by lagged total assets ((Compustat item ‘AT’). 푂퐶퐹푖푡 = cash flow from operations, measured as the sum of net income, depreciation and amortization, and changes in current liabilities, minus changes in current assets, scaled by lagged total assets [(‘SALE’+‘DP’+Δ‘LCT’-Δ‘ACT’)/lagged‘AT’].
Data and Sample
2.5.1 Sample Selection
This study examines the impact of conflict risk on MNEs’ investment efficiency. Ideally, the population of interest comprises MNEs with investments in conflict zones. However, because of data availability, this study focuses on a sample of 6,103 U.S. corporations listed on major
U.S. exchanges. Information on subsidiaries are obtained from the Exhibit 21 of the 10-K filings for each firm. I obtain these data from Scott Dyreng’s website.21 Conflict data are obtained from
HIIK for the period 2002–2015.22
The sample selection process is detailed in Table 2.2. The sample period for this study is restricted to 2002–2015 because of data availability for conflict intensity. To generate variables
21 https://sites.google.com/site/scottdyreng/Home/data-and-code/EX21-Dataset. Scott Dyreng’s subsidiaries data cover the period to March 2015. I obtain subsidiaries information for the 2015 financial year from the Orbis database. 22 Barometers for 1992–1996 and 1998–2001 are available in German language only. The English version of Barometer 1997 is available. However, in consideration of data continuity, this study employs data for the sample period 2002–2015.
36 for 2002–2015, 219,151 U.S. firm-year observations for the period 1996–2016 to calculate a 5- year moving average of total asset for the sample period are obtained from Compustat. Some
23,359 duplicated observations, caused by changes in financial reporting dates, were removed from the sample; in addition, 30,234 observations with no information for the financial statement section were removed from the sample. There are 87,421 observations missing one or more variables to calculate proxies for investment. New investment is calculated as total investment minus depreciation and amortization expenses. Consist with prior research (e.g.,
Biddle et al., 2009), financial firms (i.e., SIC codes in the 6000–6999 range) were excluded considering the different nature of investment for these firms. Investment is typically only disclosed by industries subject to capital expenditures. Those firms in industries that do not necessarily have capital expenditure, such as financial service and banking, were excluded in this step, leaving 44,911 23 firm-year observations with available investment efficiency variables for the sample period 2002–2015. After deducting 3,056 observations with missing data for any of the three FRQ measures or for control variables to generate the aggregated FRQ, the final sample size comprises 41,855 firm-year observations for 6,103 MNEs with available data to test the effect of conflict risk on investment efficiency.
23 Using Goodman et al.’s (2014) model with total investment as the dependent variable to calculate abnormal investment.
37 Table 2.2 Sample Selection Firm-year Firm All observations in the Compustat database with listing status information between 1997–2016 (North America, USD) 219,151 22,793 Less duplicates (change of financial date) (23,359) (0) Less observations without information in the financial statement section (30,234) (3,320)
Less observations in finance industry (SIC 6000–6999) (33,226) (3,671) Less observations with missing data on investment for 2002– 2015 (87,421) (9,326) Observations with the investment efficiency variable for 2002–2015 44,911 6,476
Observations with missing Compustat data for any of the three financial reporting quality measures (DisRev, DisAccr, DD) or for control variables (3,056) (373)
Sample size for the main tests 41,855 6,103 Subsamples Firm-year Observations with the investment efficiency variable for 2002–2014 44,911 (1) Missing data on discretionary revenue (DisRev) and control variables (2,729) Sample size for the main tests with DisRev as a proxy for financial reporting quality 42,182 (2) Missing data on discretionary accruals (DisAccr) and control variables (2,732) Sample size for the main tests with DisAccr as a proxy for financial reporting quality 42,179 (3) Missing data on the modified Dechow–Dichev measure (DD) and control variables (3,025) Sample size for the main tests with DD as a proxy for financial reporting quality 41,886
38 Table 2.3 provides a summary of sample characteristics. The sample includes 173 conflict countries. The 30 conflict zones that accommodate most U.S. subsidiaries are listed in Panel A.
The countries with the most conflict activities include Argentina, Brazil, South Korea, Mexico,
India and Spain. Table 2.4 shows the sample distribution according to the Fama and French 48- industry classification. Among the sample, manufacturing- and business-equipment-related industries are more heavily weighted and the banking and finance industries are the least weighted. As this study uses capital expenditure to measure investment, observations in industries that do not rely heavily on non-current assets such as buildings, structures, machinery and equipment, were eliminated in sample selection process because of lack of data on capital expenditures.
39 Table 2.3 Sample Description: Top 30 Investment Destination Conflict Countries (By Number of Subsidiaries), 2015 Number of U.S. Number of Sample- Average Firms having Firm-Owned Conflict Region Conflict Subsidiaries in the Subsidiaries in the Intensity Region Region Mexico 3.43 196 868 India 2.64 187 637 Brazil 2.25 150 418 Spain 1.33 101 292 Korea, Republic Of 2.00 97 297 Malaysia 2.00 67 152 Israel 2.50 61 236 South Africa 1.67 59 155 Taiwan, Republic Of China 3.00 57 135 Thailand 1.75 56 134 Argentina 2.00 53 188 Chile 1.71 51 124 Poland 1.00 51 95 Philippines 2.86 46 93 Russian Federation 1.92 36 85 Turkey 2.67 36 65 Norway 1.00 34 73 Indonesia 2.11 33 52 Hungary 1.67 27 62 United Arab Emirates 1.00 26 44 Colombia 2.67 24 47 Venezuela 2.00 23 47 Romania 1.00 22 60 Peru 2.33 21 43 Greece 2.00 20 32 Cyprus 2.00 15 46 Nigeria 2.67 15 33 Slovakia 1.00 13 42 Costa Rica 1.00 11 15 Morocco 2.67 9 14 This table presents the top 30 conflict countries, by number of subsidiaries, that U.S. companies have subsidiaries in for 2015. A total of 173 conflict countries/regions were identified from HIIK barometers. China, Japan, Singapore, France, England, Australia, Germany and Canada are not considered conflict countries in this study given that conflicts in these countries are mainly non-violent, such as system and ideology conflicts. Conflict intensity is the average conflict intensity for all conflicts in a given country in 2015. Conflict intensity rating ranges from 1–5, which stands for dispute, non-violent crisis, violent crisis, limited war and war respectively. Other identified conflict regions include Italy, Denmark, Norway, Uruguay, New Zealand, Pakistan, Saudi Arabia, Kenya, Panama, Croatia, Ecuador, Finland, Guatemala, Sri Lanka, Lebanon, Nicaragua, Slovenia, Uganda, Ukraine, Belarus, Indonesia, Puerto Rico, Tanzania, Uganda, Zimbabwe, Angola, Bangladesh, Bolivia, Bolivia, Bulgaria, Dominican Republic, El Salvador, Estonia, Georgia, Honduras, Liberia, Saudi Arabia, Serbia, Algeria, Armenia, Bosnia and Herzegovina, Botswana, Kazakhstan, Macedonia, Papua New Guinea, Cambodia, Cameroon, Congo (Brazzaville), Equatorial Guinea, Fiji, Gabon, Ghana, Haiti, Iran, North Korea, Kuwait, Lesotho, Moldova, Montenegro, Paraguay, Senegal, Togo, Tunisia, Uzbekistan.
40 Table 2.4 Sample Distribution by Industry Firm-year Firms Observations Fama and French 12-industry classification No. % No. %
Consumer Non-Durables – Food, Tobacco, Textiles, Apparel, Leather, Toys 344 5.64% 2,567 6.13% Consumer Durables – Cars, TV's, Furniture, Household Appliances 173 2.83% 1,224 2.92% Manufacturing – Machinery, Trucks, Planes, Off Furn, Paper, Com Printing 685 11.22% 4,994 11.93% Oil, Gas, and Coal Extraction and Products 317 5.19% 2,123 5.07% Chemicals and Allied Products 168 2.75% 1,268 3.03% Business Equipment – Computers, Software, and Electronic Equipment 1,566 25.66% 10,423 24.90% Telephone and Television Transmission 224 3.67% 1,437 3.43% Utilities 122 2.00% 1,276 3.05% Wholesale, Retail, and Some Services 599 9.81% 4,242 10.13% Healthcare, Medical Equipment, and Drugs 905 14.83% 5,954 14.23% Other – Mines, Constr, BldMt, Trans, Hotels, Bus Serv, Entertainment 1,000 16.39% 6,347 15.16%
Total 6,103 100% 41,855 100%
41 2.5.2 Descriptive Statistics
Table 2.5 presents descriptive statistics for the measures of suboptimal investment, FRQ and control variables. All continuous variables are winsorized at the 1% and 99% levels to mitigate the possible influence of outliers. InvEff, the signed residuals estimated from Model 2.3, proxy for suboptimal investment. The mean of InvEff is −0.0003 and median is −0.0020, which indicates that suboptimal investment estimated from Model 2.3 is approximately normally distributed. To consider the possible impact of skewness, I include an alternative measure of investment efficiency using ranked suboptimal investment as the dependent variable; results are shown in the section on Additional Tests. Distributions of the control variables are similar to prior studies (e.g., Biddle et al., 2009). Mean (−0.0462) and median (0.0004) values for FRQ indicate negative skewedness. The FRQ mean is influenced by large negative values.
Conflict_Intensity is coded 0 for those with no subsidiaries in conflict regions. Mean for
Conflict_Intensity is 0.6587 and standard deviation is 0.8923. In the full sample of 41,855 firm- year observations, 15,945 have subsidiaries in conflict regions. The distribution of
Conflict_Intensity is therefore negatively skewed with p50 at 0 showing that around half of the sample has no investment in conflict-affected regions. P95 with Conflict_Intensity at 2 indicates that around 5% of the full sample is exposed to medium and/or high conflict risk (involving violent crises).
42 Table 2.5 Descriptive Statistics for Full Sample
Variables N mean sd min p1 p5 p25 p50 p75 p95 p99 max InvEff 41855 -0.0003 0.0517 -0.1327 -0.1327 -0.0628 -0.0166 -0.0020 0.0110 0.0670 0.1935 0.1935 Conflict_Intensity 41855 0.6587 0.8923 0.0000 0.0000 0.0000 0.0000 0.0000 1.6173 2.0000 3.0000 3.1667 FRQ 41855 -0.0462 0.4528 -6.1854 -1.0802 -0.0300 0.0000 0.0004 0.0019 0.0165 0.0557 0.0725 Size 41855 5.5543 2.4688 -0.2860 -0.2860 1.4028 3.7873 5.6442 7.2919 9.5944 11.1631 11.1631 MTB 41855 2.3413 2.8947 0.5326 0.5326 0.7605 1.0993 1.5068 2.3680 6.2197 22.3802 22.3802 ROA 41855 -0.1085 0.5780 -4.1767 -4.1767 -0.8413 -0.0773 0.0375 0.0996 0.2237 0.3890 0.3890 LOSS 41855 0.3767 0.4846 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 1.0000 1.0000 1.0000 Leverage 41855 0.1695 0.2184 -0.0579 0.0000 0.0000 0.0000 0.0783 0.2657 0.6507 0.8933 1.0000 Tangibility 41855 0.2581 0.2384 0.0048 0.0048 0.0166 0.0696 0.1719 0.3857 0.7736 0.8986 0.8986 Slack 41855 5.0136 13.8879 0.0006 0.0006 0.0114 0.1350 0.6609 3.2025 23.0483 100.6410 100.6410 CFO_SALE 41855 -0.5250 3.3802 -28.2086 -28.2086 -1.8687 -0.0022 0.0724 0.1510 0.3526 0.6503 0.6503 OPERATING_CYCLE 41855 0.0010 0.0009 0.0000 0.0000 0.0001 0.0005 0.0008 0.0013 0.0024 0.0061 0.0061 Z_SCORE 41855 0.2511 4.2690 -30.3856 -30.3856 -4.6751 0.3323 1.0744 1.7809 3.1479 4.5045 4.5045 𝜎CFO 41855 0.1656 0.2569 0.0138 0.0138 0.0233 0.0475 0.0833 0.1645 0.5846 1.7755 1.7755 𝜎SALE 41855 0.6241 0.9608 0.0207 0.0207 0.0543 0.1670 0.3201 0.6560 2.1531 6.6977 6.6977 𝜎Invest 41855 0.0583 0.1122 0.0018 0.0018 0.0039 0.0111 0.0234 0.0541 0.2233 0.8105 0.8105 InvEff stands for abnormal investment, which is the residuals estimated from: 퐼푛푣푒푠푡푚푒푛푡푖푡 = 훼0 + 훼1푆푎푙푒푠_퐺푟표푤푡ℎ푖푡−1 + 훼2푀푇퐵푖푡−1 + 훼3퐶퐹푂푖푡−1 + 훼4푅푂퐴푖푡−1 + 훼5훥퐴푆푆퐸푇푖푡 + 훼6퐼푛푣푒푠푡푚푒푛푡푖푡−1 + 푎푏푛표푟푚푎푙 𝑖푛푣푒푠푡푚푒푛푡푖푡 InvEff is estimated by industry-year using the Fama and French 48-industry classification. All other variables are defined in Table 2.1.
43 Table 2.6 reports Pearson correlations among variables. Consistent with H2.1,
Conflict_intensity is negatively correlated with InvEff and significantly related at the 0.05 level.
Conflict_intensity is positively associated with ROA, indicating that investment in conflict- affected regions is likely to generate higher returns. However, as correlation results do not control for differences in firm, industry and year, the next section presents multivariate results.
44 Table 2.6 Correlation Matrix for Full Sample
(12) (2) (9) (11) OPERAT (13) (1) Conflict_ (3) (4) (5) (6) (7) (8) Tangibili (10) CFO_SA ING_CY Z_SCOR (14) (15) (16) InvEff Intensity FRQ Size MTB ROA LOSS Leverage ty Slack LE CLE E 𝜎CFO 𝜎SALE 𝜎Invest InvEff 1 Conflict_In tensity -0.0246* 1 FRQ 0.0012 0.0627* 1 Size -0.0107 0.2598* 0.2018* 1 MTB -0.0028 -0.0357* -0.5252* -0.1145* 1 ROA 0.0050 0.0979* 0.4298* 0.2922* -0.6766* 1 LOSS -0.0245* -0.1083* -0.1534* -0.4191* 0.1559* -0.4131* 1 Leverage -0.0392* -0.0451* 0.0703* 0.0041 -0.1605* 0.0033 0.1290* 1 Tangibility 0.0146 -0.1117* 0.0768* 0.1655* -0.1077* 0.0633* -0.0818* 0.3537* 1 Slack 0.0075 -0.0673* -0.1133* -0.1809* 0.1177* -0.1051* 0.1367* -0.1838* -0.2803* 1 CFO_SAL E -0.0178* 0.0770* 0.2272* 0.1909* -0.3710* 0.4807* -0.2612* 0.0816* 0.1106* -0.2787* 1 OPERATI NG_CYCL E 0.0152* -0.0145 -0.0776* -0.0918* 0.0774* -0.0824* 0.0897* -0.0992* -0.1626* 0.0930* -0.2620* 1 Z_SCORE 0.0069 0.0832* 0.4844* 0.2214* -0.7534* 0.8885* -0.3049* 0.0086 0.0219* -0.1043* 0.4244* -0.0843* 1 * * * * * * * * * * * * 𝜎CFO 0.0519 -0.0645 -0.4491 -0.2450 0.4249 -0.3598 0.1634 -0.1616 -0.0810* 0.1641 -0.2891 0.0763 -0.3536 1 𝜎SALE 0.0324* -0.0141 -0.2288* -0.1622* 0.1532* -0.0641* 0.0064 -0.1286* -0.1121* 0.0667* -0.0406* -0.0227* -0.0204* 0.6318* 1 𝜎Invest 0.1562* -0.0800* -0.1237* -0.0869* 0.1088* -0.0553* 0.0184* -0.0229* 0.3195* -0.0378* -0.0467* 0.0031 -0.0765* 0.5401* 0.4314* 1 Variables defined in Table 2.1. * p < 0.05
45 Empirical Results
2.6.1 Conflict Risk and Underinvestment
H2.1 predicts that the higher the conflict risk that firms are exposed to, the more likely they will experience underinvestment. Table 2.7 reports regression estimates for Model 2.1, which tests the relations between conflict risk and suboptimal investment. When testing the full sample, the adjusted R2 is 5.0%, lower than that of F. Chen et al. (2011). The differences in goodness of fit can be attributed to differences in sample and measurement for suboptimal investment used in these two studies. 24 Coefficients are estimated using standard errors adjusted for two- dimensional clustering at the firm and firm level. H2.1 predicts that conflict risk stimulates underinvestment. The negative coefficient 훽1 (−0.0009) supports H2.1, suggesting that when conflict risk increases by one, suboptimal investment decreases by 0.9%. The result is significant at the 0.01 level (two-tailed). As for the control variables, consistent with prior studies, loss-making firms are more likely to underinvest.
24 F. Chen et al. (2011) employ a sample of private firms from emerging countries that exhibits different sample characteristics from this study. Also, F. Chen et al. (20111) test underinvestment and overinvestment separately by dividing signed InvEff into two subsamples, where the underinvestment subsample has an adjusted R2 around 45% and the overinvestment subsample has an adjusted R2 around 14%.
46 Table 2.7 Regression Analysis for Conflict Risk and Investment Efficiency
Predicted VARIABLES InvEff MIE Signs Conflict_Intensity - -0.0009*** -0.0189*** (-3.4107) (-2.6249) FRQ -0.0003 0.0058 (-0.2102) (0.2307) Conflict_Intensity×FRQ -0.0142 -0.5049** (-1.3349) (-1.9916) Size 0.0000 0.0061** (0.2925) (2.2350) MTB -0.0002** -0.0035 (-2.0818) (-1.5014) ROA -0.0020 -0.0668*** (-1.6074) (-2.8356) LOSS - -0.0022*** -0.0543*** (-2.8156) (-3.5506) Leverage - -0.0061*** -0.0645** (-4.1780) (-2.0393) Tangibility - -0.0041* -0.0775* (-1.9124) (-1.7251) *** Slack 0.0000 0.0020 (1.2302) (3.2350) CFO_SALE -0.0005 -0.0008 (-1.2896) (-0.1247) OPERATING_CYCLE 0.0676 0.6340 (0.2334) (0.1697) Z_SCORE 0.0001 0.0019 (1.0443) (0.7165) 𝜎CFO -0.0080*** -0.0715 (-2.6154) (-1.4901) 𝜎SALE -0.0019*** -0.0318*** (-3.0546) (-2.7916) 𝜎Invest 0.1003*** 1.0913*** (10.9289) (9.9575) Constant 0.0036 0.0866* (1.3191) (1.6742)
Industry-fix effect Yes Yes Year-fix effect Yes Yes
Observations 41,855 41,855 Adj R-squared 0.050 0.022 This table presents the results for model: 퐼푛푣퐸푓푓(푀퐼퐸)푖푡+1 = 훼0 + 훼1퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 + 훼2퐹푅푄푖푡 + 훼3퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 ∗ 퐹푅푄푖푡 + 훴훾푗퐶표푛푡푟표푙푗푖푡 + 훴훾푗퐼푛푑푢푠푡푟푦푗푖푡 + 훴훾푗푌푒푎푟푗푖푡 + 휀푖,푡 InvEff stands for abnormal investment, which is the residuals estimated from: 퐼푛푣푒푠푡푚푒푛푡푖푡 = 훼0 + 훼1푆푎푙푒푠_퐺푟표푤푡ℎ푖푡−1 + 훼2푀푇퐵푖푡−1 + 훼3퐶퐹푂푖푡−1 + 훼4푅푂퐴푖푡−1 + 훼5ΔASSET푖푡 + 훼6퐼푛푣푒푠푡푚푒푛푡푖푡−1 + 푎푏푛표푟푚푎푙 𝑖푛푣푒푠푡푚푒푛푡푖푡 Variables are defined in Table 2.1 *** p < 0.01, ** p < 0.05, * p < 0.1
47 To further explore the effects that conflict risk has on underinvestment, I separate InvEff into two subsamples of underinvestment and overinvestment based on the residuals estimated from
Model 2.3. Observations with negative residuals are classified into the group UNDER
(comprising 55% (22,902) of the full sample), whereas those with positive suboptimal investments are classified into OVER. UNDER takes the absolute values of negative residuals estimated from Model 2.3; hence, higher values for UNDER suggest more severe underinvestment. The coefficient for Conflict_Intensity is therefore expected to be positive, to support the positive association between conflict risk and underinvestment proposed by H2.1.
The first four columns of Table 2.8 exhibit the regression results for the test of the four financial reporting quality proxies with UNDER as the dependent variable25. The models have adjusted
R2 24.8–27.2%, slightly lower compared with F. Chen et al. (2011) (45%) when using underinvestment as the dependent variable. Results in Table 2.8 show that there are no significant relations between conflict risk and underinvestment. The insignificant coefficient for Conflict_Intensity×FRQ suggests no moderating effect of financial reporting quality on the association between conflict risk and investment efficiency.
25 I acknowledge that the choice to operate in a conflict zone can be endogenous. To address the potential self- selection bias for firms that have subsidiaries in conflict zones and for those that do not invest in conflict zones, I re-performed regression model (2.1) for the two subsamples of underinvestment and overinvestment with controls for firm-fixed effects. The results are presented in Appendix 2.1. The results are consistent with those reported in Table 2.8 where there are no significant relationships between conflict risk and underinvestment. Across the four FRQ proxies, conflict intensity is significantly negatively associated with overinvestment, which indicates that the greater the conflict risk that a firm is exposed to, the less likely the firm is to take excessive risk and overinvest beyond the optimal investment level.
48 Table 2.8 Relation between Conflict Intensity and Underinvestment (Overinvestment)
Dependent Variable = UNDER Dependent Variable = OVER VARIABLES FRQ DisAccr DisRev DD FRQ DisAccr DisRev DD
Conflict_Intensity -0.0003 -0.0000 -0.0002 -0.0001 -0.0010** -0.0009** -0.0010* -0.0009** (-1.3851) (-0.1234) (-0.6860) (-0.1771) (-2.3444) (-2.0463) (-1.9024) (-2.1403) FRQ -0.0025* -0.0043 (-1.8801) (-1.4859) Conflict_Intensity×FRQ -0.0007 0.0016 (-0.8705) (0.9063) *** DisAccr -0.0030 -0.0057* (-3.9530) (-1.6670) Conflict_Intensity×DisAccr 0.0000 0.0007 (0.0781) (0.3645) *** DisRev -0.0373 -0.0303* (-5.7628) (-1.9508) Conflict_Intensity×DisRev -0.0042 0.0003 (-0.7869) (0.0277) *** DD -0.0042 -0.0086*** (-4.9509) (-2.8681) Conflict_Intensity×DD 0.0000 0.0004 (0.0563) (0.2648) Size -0.0012*** -0.0011*** -0.0010*** -0.0011*** -0.0017*** -0.0017*** -0.0016*** -0.0016*** (-10.9166) (-10.1503) (-8.6523) (-9.9340) (-8.6597) (-8.4006) (-7.7091) (-8.0988) MTB 0.0000 0.0000 0.0000 0.0000 0.0002** 0.0002* 0.0002** 0.0002** (0.6323) (1.0384) (1.0901) (0.8653) (1.9704) (1.9233) (2.1487) (2.0478) ROA 0.0012** 0.0013** 0.0011** 0.0012** -0.0002 -0.0000 -0.0001 0.0001 (2.2090) (2.3369) (2.0718) (2.3216) (-0.1694) (-0.0324) (-0.0959) (0.0605) LOSS 0.0021*** 0.0022*** 0.0019*** 0.0020*** -0.0024** -0.0023** -0.0024** -0.0025** (3.8416) (3.9714) (3.5540) (3.7071) (-2.4052) (-2.3238) (-2.4301) (-2.5586) Leverage -0.0070*** -0.0079*** -0.0077*** -0.0075*** -0.0138*** -0.0136*** -0.0134*** -0.0135*** (-5.5473) (-6.3363) (-6.1657) (-6.0104) (-6.5548) (-6.5173) (-6.3950) (-6.4332) Tangibility 0.0234*** 0.0227*** 0.0237*** 0.0224*** 0.0171*** 0.0168*** 0.0178*** 0.0164***
49 (12.3910) (12.1750) (12.6775) (11.8784) (5.0032) (4.9258) (5.1951) (4.8219) Slack -0.0000*** -0.0000*** -0.0000*** -0.0000*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** (-3.6042) (-3.7441) (-3.8079) (-3.6606) (-5.6764) (-5.8483) (-5.6154) (-5.9171) CFO_SALE -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 (-0.7607) (-0.9901) (-1.3223) (-0.8700) (-0.7294) (-0.7159) (-0.9537) (-0.5121) OPERATING_CYCLE 0.1017 0.0666 0.0092 0.0909 0.2344 0.2104 0.1479 0.2174 (0.8062) (0.5259) (0.0731) (0.7211) (0.7681) (0.6885) (0.4976) (0.7118) Z_SCORE 0.0001 0.0001** 0.0001 0.0001* 0.0002* 0.0003** 0.0002 0.0003* (1.3444) (1.9662) (1.0089) (1.7727) (1.6870) (1.9860) (1.4508) (1.9451) 𝜎CFO 0.0051*** 0.0051*** 0.0053*** 0.0051*** -0.0070** -0.0073*** -0.0065** -0.0075** (3.1567) (3.2274) (3.3120) (3.1810) (-2.4338) (-2.6126) (-2.2224) (-2.5083) 𝜎SALE 0.0009** 0.0009** 0.0005 0.0008** -0.0007 -0.0008 -0.0011 -0.0008 (2.2319) (2.1984) (1.2987) (2.0377) (-0.9242) (-1.0571) (-1.4111) (-1.0125) 𝜎Invest 0.0610*** 0.0612*** 0.0614*** 0.0610*** 0.1295*** 0.1287*** 0.1291*** 0.1288*** (9.2792) (9.4338) (9.4395) (9.2450) (14.6843) (14.6624) (14.7008) (14.6492) Constant 0.0097*** 0.0133*** 0.0113*** 0.0129*** 0.0366* 0.0360* 0.0348 0.0359* (2.6534) (3.9377) (3.2021) (3.7609) (1.7028) (1.6798) (1.6228) (1.6745)
Industry-fix effect Yes Yes Yes Yes Yes Yes Yes Yes Year-fix effect Yes Yes Yes Yes Yes Yes Yes Yes
Observations 22,902 23,073 23,066 22,920 18,928 19,084 19,088 18,941 Adj R-squared 0.251 0.249 0.249 0.248 0.273 0.271 0.270 0.272 FRQ is the aggregated financial reporting quality proxy calculated as the average of standardized DisAccr, DisRev and DD. DisAccr is discretionary accruals estimated following Kothari et al. (2005). DisRev is discretionary revenue estimated following McNichols and Stubben (2008) and Stubben (2010). DD is discretionary current accruals predicted by Dechow and Dichev’s (2002) model as modified by McNichols (2002) and Francis et al. (2005). Other variables are defined in Table 2.1. Coefficients are estimated using standard errors adjusted for two-dimensional clustering at the firm and year level. *** p < 0.01, ** p < 0.05, * p < 0.1
50 To test further whether the lack of significant association between conflict risk and underinvestment is driven by suboptimal investment distributions, I employ quintile regression analyses to examine how the association between these two variables varies among quintiles.
For quintile analyses, I ranked firm-year observations in each subsample based on the magnitude of suboptimal investment within each year and divided these into quintiles where 1 stands for the lowest quintile and 5 stands for the highest. Results of the quintile analyses are exhibited in Table 2.9; the first five columns show the results using UNDER as the dependent variable.
The quintile analyses results show positive associations between Conflict_Intensity and underinvestment for the middle three quintiles, indicating that when conflict risk is high, firms are likely to experience underinvestment. No significant results for the top quintile were observed; reasons for these firms to underinvest could include the significant negative association between LOSS and UNDER. Loss creates financial constraints for firms, limiting their ability to invest.
Overall, the negative coefficients for Conflict_Intensity in Model 2.2 support H2.1 that the higher the conflict risk firms are exposed to, the more likely they will experience underinvestment. The result is consistent with the “bad news principle” that markets react negatively to high information uncertainty by forgoing or delaying investment projects, even those with positive NPVs. Consequently, underinvestment is more severe when conflict-related information risk is high.
51 Table 2.9 Regression Analysis – Quintile Analysis for Subsamples of Underinvestment and Overinvestment
Dependent variable = UNDER Dependent Variable = OVER VARIABLES Quintile=1 Quintile=2 Quintile=3 Quintile=4 Quintile=5 Quintile=1 Quintile=2 Quintile=3 Quintile=4 Quintile=5
Conflict_Intensity 0.0000 0.0001* 0.0001** 0.0005*** 0.0005 0.0000 -0.0000 -0.0001* -0.0003* -0.0044*** (1.0154) (1.9033) (2.0275) (3.2911) (0.4949) (0.0644) (-0.6310) (-1.6751) (-1.7873) (-3.1718) FRQ -0.0004*** 0.0003* 0.0001 0.0008** 0.0084*** -0.0001 0.0004** -0.0002 0.0002 -0.0027 (-4.3121) (1.7388) (0.6585) (1.9863) (3.4261) (-0.9511) (2.0524) (-0.7610) (0.5077) (-0.5498) Conflict_Intensity×FRQ -0.0001 0.0005 0.0027 -0.0136 0.0035 -0.0015* 0.0011** 0.0008 -0.0229*** -0.0119 (-0.0562) (0.2594) (1.0365) (-1.0099) (0.1984) (-1.6931) (2.1737) (0.4769) (-2.5962) (-0.4563) Size 0.0000 -0.0000 -0.0000** -0.0001* -0.0014*** -0.0000** 0.0000 -0.0000 0.0000 -0.0028*** (0.3846) (-0.8344) (-2.1853) (-1.8495) (-4.2787) (-2.4835) (0.2500) (-0.7452) (0.0370) (-5.4030) MTB -0.0000 0.0000 -0.0000 0.0001 0.0004 -0.0000 0.0000 -0.0000 -0.0000 0.0000 (-1.5126) (1.5116) (-0.3218) (1.1543) (1.4131) (-0.6911) (1.0590) (-0.1203) (-0.3316) (0.0652) ROA 0.0000 0.0004* 0.0000 0.0000 -0.0011 0.0001 -0.0001 0.0002 0.0001 0.0042 (0.1940) (1.7628) (0.0960) (0.0116) (-0.3551) (1.0090) (-0.7287) (0.6897) (0.1668) (0.6977) LOSS -0.0000 0.0001 -0.0001 0.0005** -0.0034 -0.0001 0.0002* -0.0001 -0.0001 -0.0018 (-0.2519) (0.8686) (-0.6483) (2.0318) (-1.6092) (-0.9398) (1.8855) (-0.8276) (-0.2277) (-0.5105) Leverage 0.0002 0.0002 -0.0002 -0.0011** 0.0026 -0.0001 0.0002 -0.0000 -0.0006 -0.0072 (1.4658) (0.8585) (-0.7561) (-2.1426) (0.6190) (-1.2323) (0.8493) (-0.0091) (-0.9225) (-1.0633) Tangibility 0.0000 0.0004 0.0004 0.0008 0.0181*** -0.0004*** 0.0000 0.0004 0.0003 0.0033 (0.0950) (1.4339) (1.1330) (1.1525) (4.1608) (-2.6542) (0.0943) (0.9762) (0.3897) (0.3916) Slack 0.0000 0.0000 -0.0000 -0.0000 0.0001 -0.0000*** 0.0000* 0.0000** -0.0000 -0.0002* (0.2170) (0.1266) (-1.2242) (-0.2776) (1.2518) (-2.9488) (1.7583) (2.0781) (-0.9796) (-1.8989) CFO_SALE 0.0000 0.0000 -0.0000 0.0002** 0.0007 -0.0000 0.0000 0.0000 -0.0001 -0.0013 (0.7958) (0.2928) (-0.4063) (2.2026) (1.3146) (-0.5922) (1.2313) (0.3373) (-0.7345) (-1.1317) OPERATING_CYCLE 0.0061 -0.0209 0.0582** 0.0827 0.2398 -0.0038 0.0196 0.0201 0.0995 0.7510 (0.2026) (-1.3347) (1.9868) (1.4992) (1.0097) (-0.4117) (1.1691) (0.8128) (1.0883) (0.5269) Z_SCORE 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 (0.6899) (-1.6002) (-0.0894) (-0.1398) (0.1568) (-0.2797) (1.1367) (-0.4777) (0.0696) (0.0042) 𝜎CFO -0.0001 -0.0002 0.0002 0.0001 0.0087** 0.0000 -0.0005* 0.0002 0.0016* -0.0133 (-0.5497) (-0.6485) (0.5269) (0.1092) (2.1096) (0.0594) (-1.7656) (0.4583) (1.7054) (-1.4648) 𝜎SALE -0.0000 0.0000 0.0000 0.0003* -0.0012 -0.0001* 0.0001 0.0002 0.0002 -0.0004 (-0.6575) (0.2859) (0.1576) (1.9309) (-1.0010) (-1.7753) (0.9484) (1.6164) (1.0464) (-0.1855) 𝜎Invest 0.0006 0.0003 0.0001 -0.0028 0.0662*** -0.0001 0.0016* -0.0007 0.0010 0.1271*** (1.3023) (0.4230) (0.1221) (-1.5317) (4.8996) (-0.1474) (1.7012) (-0.6314) (0.4179) (7.2313) Constant 0.0004* 0.0073*** 0.0116*** 0.0236*** 0.0722*** 0.0010*** 0.0046*** 0.0129*** 0.0219*** 0.0702***
52 (1.8872) (20.1887) (50.5820) (35.6732) (25.5410) (6.0203) (19.8401) (47.5746) (27.0926) (19.1527)
Industry-fix effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year-fix effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 4,585 4,581 4,580 4,581 4,575 3,789 3,786 3,787 3,786 3,780 Adj R-squared 0.284 0.279 0.431 0.371 0.268 0.327 0.316 0.394 0.423 0.260 Coefficients are estimated using standard errors adjusted for two-dimensional clustering at the firm and year level. *** p < 0.01, ** p < 0.05, * p < 0.1
53 2.6.2 Conflict Risk, Financial Reporting Quality and Investment Efficiency
H2.2 predicts that the higher the conflict risk that MNEs are exposed to, the poorer the overall quality of their financial reports. Table 2.10 exhibits the regression results for Model 2.3, with the four measures of FRQ as the dependent variables and Conflict_Intensity as the independent variable. Conflict_Intensity is positively associated with FRQ, with coefficient −0.0045 significant at the 0.05 level (two-tailed). This negative association indicates that when conflict risk is high, financial reporting quality is likely to decrease, supporting H2.2. However, no significant associations between Conflict_Intensity and the three individual financial reporting quality proxies were found. As I measure financial reporting quality using corporate-level data, the influence of conflict risk on financial reporting must arise from overall corporate results; therefore, the association between conflict risk and financial reporting quality may be moderated by other corporate factors.
54 Table 2.10 Regression Analysis for Conflict Intensity and Financial Reporting Quality
VARIABLES FRQ DisAccr DisRev DD
Conflict_Intensity -0.0045** -0.0008 0.0003 -0.0025 (-2.2185) (-0.4340) (0.8846) (-1.3497) Size 0.0131*** 0.0162*** 0.0052*** 0.0162*** (9.6590) (14.5795) (34.3022) (16.4106) MTB -0.0115*** -0.0065*** -0.0004*** -0.0044*** (-5.5349) (-4.0277) (-3.8592) (-3.3856) ROA 0.0587** 0.0623*** 0.0029** 0.0281* (2.4895) (3.7865) (2.3347) (1.8493) LOSS 0.0332*** 0.0317*** -0.0019*** -0.0034 (5.0432) (5.7849) (-2.5841) (-0.6736) Leverage -0.0376*** -0.0011 0.0069*** 0.0217** (-3.3968) (-0.1036) (4.5868) (2.2366) Tangibility 0.0280 -0.0025 0.0261*** -0.0378** (1.3050) (-0.1293) (13.6142) (-2.3418) Slack 0.0006*** -0.0000 0.0000 -0.0001 (2.5878) (-0.2237) (0.1593) (-0.6900) CFO_SALE -0.0028* -0.0015 -0.0011*** 0.0040*** (-1.7748) (-1.2201) (-12.7349) (3.5715) OPERATING_CYCLE -2.4048 -2.7486 -2.1465*** -0.9460 (-1.0311) (-1.3907) (-7.6179) (-0.5952) Z_SCORE 0.0037 0.0100*** -0.0002 0.0058*** (1.2320) (4.6748) (-1.3793) (3.0241) 𝜎CFO -0.5491*** -0.2403*** -0.0171*** -0.2301*** (-13.4540) (-7.9302) (-6.7458) (-9.7453) 𝜎SALE 0.0227*** -0.0162** -0.0111*** -0.0053 (2.7750) (-2.4116) (-15.4945) (-1.0330) 𝜎Invest 0.1611*** 0.0416 0.0166*** 0.0200 (2.7181) (0.8933) (3.2736) (0.5074) Constant 0.0203 -0.1329*** -0.0614*** -0.0760*** (1.2966) (-6.6818) (-15.4604) (-4.1464)
Industry-fix effect Yes Yes Yes Yes Year-fix effect Yes Yes Yes Yes
Observations 42,190 45,890 45,903 42,249 Adj R-squared 0.373 0.375 0.207 0.313 This table presents the results from the regression model: 퐹푅푄푖푡+1=훽0 + 훽1퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 + Σ훾푗퐶표푛푡푟표푙푗푖푡 + 훴훾푗퐼푛푑푢푠푡푟푦푗푖푡 + 훴훾푗푌푒푎푟푗푖푡 + 휀푖푡 Financial reporting quality is measured using four proxies. FRQ is the aggregated financial reporting quality proxy calculated as the average of standardized DisAccr, DisRev and DD. DisAccr is discretionary accruals estimated following Kothari et al. (2005). DisRev is discretionary revenue estimated following McNichols and Stubben (2008) and Stubben (2010). DD is discretionary current accruals predicted by Dechow and Dichev’s (2002) model as modified by McNichols (2002) and Francis et al. (2005). Variables are defined in Table 2.1. Coefficients are estimated using standard errors adjusted for two-dimensional clustering at the firm-year level. *** p < 0.01, ** p < 0.05, * p < 0.1
55 H2.3 predicts that financial reporting quality is negatively associated with suboptimal investment. As shown in Table 2.7, no supporting evidence of H2.3 was found when testing the full sample. When testing the subsample of underinvestment, aggregated FRQ, DisAccr, DisRev and DD are significantly negatively associated with UNDER, with coefficients of −0.0025,
−0.0030, −0.0373 and −0.0042 respectively, indicating that higher financial reporting quality helps to reduce underinvestment. Consistent with prior research, better financial reporting quality is also found to reduce overinvestment. For the subsample of overinvestment, coefficients for aggregated FRQ, DisAccr, DisRev and DD are −0.0043, −0.0057, −0.0303 and
−0.0086 respectively. Except for the aggregated FRQ, coefficients for the other three individual
FRQ proxies are all statistically significant. The results provide evidence to support H2.3, which predicts that financial reporting quality helps to reduce both underinvestment and overinvestment, supporting findings from previous studies (e.g., Biddle et al., 2009; Chen et al.,
2011).
In summary, this section uses regression analysis to test the predictions that: 1) there is a positive association between conflict risk and underinvestment (H2.1), 2) conflict risk is negatively correlated with financial reporting quality (H2.2) and financial reporting quality is negatively associated with suboptimal investment (H2.3). Test results support H2.1. The negative association between InvEff and conflict risk indicates that when conflict risk is high, firms are more likely to underinvest. In this situation, firms are likely to experience more pronounced underinvestment relative firms that have no investment in conflict-affected regions.
Conflict_Intensity is found to significantly reduce the aggregated financial reporting quality, which supports H2.2. Evidence is found to support H2.3 for subsamples of both underinvestment and overinvestment across all four proxies for financial reporting quality.
56 Additional Tests
To examine the robustness of the main results, I perform additional analyses on the relationship between conflict risk and investment efficiency. First, to mitigate potential measurement errors in measuring investment inefficiency, I rank suboptimal investment estimated from Model 2.3 into quartiles to create an alternative measure of investment inefficiency, and re-estimate Model
2.1. Second, I use alternative models to measure suboptimal investments and re-perform the main tests. Third, I test the relations between conflict risk and overinvestment.
2.7.1 Ranked Measure of Suboptimal Investment
To mitigate measurement error, I categorize firms based on the magnitude of residuals estimated from Model 2.3. Following Biddle et al. (2009), I ranked all firm-year observations based on the magnitude of the residuals within each year and divided them into quantiles and then created a category variable “MIE”. If a firm falls into the bottom quartile, “MIE” equals
“−1”, representing underinvestment. If a firm falls into the top quartile, “MIE” equals “1”, representing overinvestment. MIE equals “0” if it falls into the middle two quartiles, which represent efficient investment.
푀퐼퐸푖푡+1 = 훽0 + 훽1퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 + 훽2퐹푅푄푖,푡 (2.7)
+ 훽3퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 × 퐹푅푄푖푡
+ 훴훾푗퐶표푛푡푟표푙푗푖푡 + 훴훾푗퐼푛푑푢푠푡푟푦푗푖푡
+ 훴훾푗푌푒푎푟푗푖푡 + 휀푖,푡
Table 2.7 presents the regression estimates using the rank data. The results support H2.1 and are consistent with the main test results when using InvEff as the dependent variable. The coefficient for Conflict_Intensity (훽1) is negatively significant at the 0.01 level with the value of −0.0189, which supports H2.1. This coefficient for Conflict_Intensity is more economically significant when using MIE as the dependent variable than regressing against InvEff (−0.0189),
57 which indicates that conflict risk has a more pronounced influence on firms in the top and bottom quartiles.
2.7.2 Conflict Risk and Overinvestment
Conflict risk increases firms’ operational and security costs, which can create internal financial constraints for corporations. Hovakimian (2011) shows that internal financial constraints improve investment efficiency by limiting the amount of capital under manager discretion. With limited capital, managers are likely to allocate scarce resources more efficiently and deter investment projects for their self-interest, which improves investment efficiency. Biddle et al.
(2009) found that financial constraints help to reduce overinvestment as managers have the incentive to improve investment efficiency to reduce costs of capital. Therefore, it is possible that conflict risk is negatively correlated with overinvestment. To test whether conflict risk reduces overinvestment, I performed the main test (Model 2.2) with OVER as the dependent variable.
The last four columns of Table 2.8 present regression results for the test of four financial reporting quality proxies with OVER as the dependent variable26. When regressing against
OVER, model have greater explanatory power, with adjusted R2 around 27%, compared with regressing against UNDER. Coefficients are estimated using standard errors adjusted for clustering at the firm-year and firm level.
Consistent across the four FRQ proxies, conflict intensity is significantly negatively associated with overinvestment, which indicates that the greater the conflict risk that a firm is exposed to, the less likely the firm is to take excessive risk and overinvest beyond the optimal investment
58 level. The results hold when firm-fix effect is controlled for (as exhibited in Appendix 2.1). As for the control variables, market capitalization is significantly negatively associated with both underinvestment and overinvestment, which is consistent with prior research.
The significant association between conflict risk and overinvestment is driven by those observations with high overinvestment, as indicated by the quintile analyses. In the quintile analysis (as shown in Table 2.9), I only found significant positive associations between
Conflict_Intensity and overinvestment for the top three quintiles, but not for the other two quintiles.
2.7.3 Alternative Measures of Suboptimal Investment
To test the robustness of the results, I also employ two alternative measures of suboptimal investment, following Biddle et al. (2009) and F. Chen et al. (2011). Biddle et al. (2009) develop a model where investment is a function of growth opportunities, which is proxied by sales growth (Model 2.8). Gao and Sidhu (2018) adjust Biddle et al.’s model by regressing investment against three-period-lagged sales growth (Model 2.9). The models are as follow:
퐼푛푣푒푠푡푚푒푛푡푖푡 = 0 + 1푆푎푙푒푠_퐺푟표푤푡ℎ푖푡 (2.8)
퐼푛푣푒푠푡푚푒푛푡푖푡 = 0 + 1푆푎푙푒푠_퐺푟표푤푡ℎ푖푡−1 + 훼2푆푎푙푒푠_퐺푟표푤푡ℎ푖푡−2 (2.9)
+ 훼3푆푎푙푒푠_퐺푟표푤푡ℎ푖푡−3 + 휀푖푡
Similar to Biddle et al. (2009), F. Chen et al. (2011) estimate expected investment as a function of revenue growth. As the relationship between revenue growth and investment can be different between positive growth and negative growth in the previous period, F. Chen et al. also include an indicator variable NEG in the model. NEG equals 1 if there was negative growth in period t-1 and 0 if there was positive growth. Chen et al.’s model is as follows:
59 퐼푛푣푒푠푡푚푒푛푡푖푡 = 훼0 + 훼1푁퐸퐺푖푡−1 + 훼2푆푎푙푒푠_퐺푟표푤푡ℎ푖푡−1 (2.10)
+ 훼3푁퐸퐺푖,푡−1 × 푆퐴퐿퐸_퐺푅푂푊푇퐻푖푡−1
+ 휀푖푡
Using the residuals estimated from Gao and Sidhu’s (2018) and Chen et al.’s (2011) model, I re-perform the main test. Results are consistent with the earlier test that conflict risk decreases overinvestment but no significant association between Conflict_Intensity and UNDER is documented. Results are not tabulated.
2.7.4 Materiality of Investment in Conflict Regions
Taking the materiality of investment in conflict regions into consideration, I constructed an alternative measure of conflict risk, Per_Con, which is calculated as Conflict_Intensity multiplied by (number of subsidiaries in conflict region/total number of subsidiaries of a firm).
푈푁퐷퐸푅푖푡+1 = 훽0 + 훽1푃푒푟_퐶표푛푖푡 + 훽2퐹푅푄푖푡 + 훽3푃푒푟_퐶표푛푖푡 × 퐹푅푄푖푡 (2.11)
+ 훴훾푗퐶표푛푡푟표푙푗푖푡 + 훴훾푗퐼푛푑푢푠푡푟푦푗푖푡
+ 훴훾푗푌푒푎푟푗푖푡 + 휀푖푡
푂푉퐸푅푖푡+1 = 훽0 + 훽1푃푒푟_퐶표푛푖푡 + 훽2퐹푅푄푖푡 + 훽3푃푒푟_퐶표푛푖푡 × 퐹푅푄푖푡 (2.12)
+ 훴훾푗퐶표푛푡푟표푙푗푖푡 + 훴훾푗퐼푛푑푢푠푡푟푦푗푖푡
+ 훴훾푗푌푒푎푟푗푖푡 + 휀푖,푡 Where Per_con = 퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦 푛푢푚푏푒푟 표푓 푠푢푏푠𝑖푑푎푟𝑖푒푠 𝑖푛 푐표푛푓푙𝑖푐푡 푟푒푔𝑖표푛푠 × ( ) 푡표푡푎푙 푛푢푚푏푒푟 표푓 푠푢푏푠𝑖푑푎푟𝑖푒푠
Table 2.11 presents the regression when using Per_Con as the independent variable. Results support H2.1, which predicts that conflict risk stimulates underinvestment. The coefficient for
Per_Con is not stastistically significant for either of the regressions with UNDER or OVER as the dependent variable, indicating that firms’ investment efficiency is sensitive to the aggregated level of conflict risk they are exposed to but not to the number of subsidiaries they
60 own in conflict-affected environments. I acknowledge the limitation that Per_con is not the best measure of the materiality of investment in conflict regions, as the number of subsidiaries does not capture the size of investments or operations in those regions. Subsidiary-level financial data, such as assets and capital expenditure, would be a better measure to estimate the importance of investment in conflict regions to firms. However, due to data availability, this study was unable to construct such a measure.
61 Table 2.11 Regression Analysis for Underinvestment and Overinvestment with Alternative Measures for Conflict Intensity
VARIABLES UNDER OVER Per_Con -0.0002 -0.0003 (-0.5701) (-0.4837) FRQ -0.0050*** -0.0046 (-2.6635) (-1.6222) Per_Con×FRQ 0.0024** 0.0020 (2.3476) (1.1039) Size -0.0012*** -0.0019*** (-11.0830) (-9.4299) MTB 0.0001 0.0002** (1.4422) (2.0153) ROA 0.0010 -0.0002 (1.5954) (-0.1586) LOSS 0.0026*** -0.0024** (4.5757) (-2.4022) Leverage -0.0065*** -0.0140*** (-4.8543) (-6.6189) Tangibility 0.0227*** 0.0175*** (11.8059) (5.1244) Slack -0.0001*** -0.0001*** (-3.9992) (-5.6923) CFO_SALE -0.0000 -0.0001 (-0.5541) (-0.7265) OPERATING_CYCLE 0.1295 0.2203 (0.9976) (0.7219) Z_SCORE 0.0001 0.0002* (1.5151) (1.7299) 𝜎CFO 0.0053*** -0.0070** (3.4293) (-2.4329) 𝜎SALE 0.0007* -0.0007 (1.7926) (-0.9124) 𝜎Invest 0.0517*** 0.1293*** (6.8316) (14.6647) Constant 0.0013 0.0143*** (0.5858) (3.8543)
Industry-fix effect Yes Yes Year-fix effect Yes Yes
Observations 22.902 18,928 Adj R-squared 0.250 0.259 This table reports the results for the regression model: 푈푁퐷퐸푅 (푂푉퐸푅)푖푡+1 = 훽0 + 훽1푃푒푟_퐶표푛푖푡 + 훽2퐹푅푄푖푡 + 훽3푃푒푟_퐶표푛푖푡 × 퐹푅푄푖푡 + 훴훾푗퐶표푛푡푟표푙푗푖푡 + 훴훾푗퐼푛푑푢푠푡푟푦푗푖푡 + 훴훾푗푌푒푎푟푗푖푡 + 휀푖,푡 Per_con is calculated as Conflict_Intensity multiplied by (number of subsidiaries in conflict regions/total number of subsidiaries). Other variables are defined in Table 2.1. Coefficients are estimated using standard errors adjusted for two-dimensional clustering at the firm-year level. *** p < 0.01, ** p < 0.05, * p < 0.1
62 Overall, results from the additional tests are consistent with the main test, which provides supportive evidence to H2.1 that the higher the conflict risk that MNEs are exposed to, the more likely they will experience underinvestment. The results are consistent when employing alternative measures of conflict risk and suboptimal investment. The test for the relationship between conflict risk and overinvestment shows that conflict risk decreases overinvestment; however, this association is limited to the top quintile group of overinvestment.
Discussion and Conclusion
This study explores the influence of conflict risk on MNEs’ investment efficiency; specifically, this study tests whether conflict risk stimulates suboptimal investment. The investment efficiency literature (e.g., Biddle et al., 2009; Chen et al., 2011) focuses on the link between financial reporting quality and investment efficiency based on the theory of information asymmetry and agency cost. This study extends prior studies to examine the impact that conflict risk has on firms’ investment efficiency with a sample of U.S. listed MNEs with subsidiaries in conflict-affected regions.
Results support H2.1 that when MNEs are exposed to relatively high conflict risk, they are more likely to underinvest. Because of high information uncertainty on project profitability caused by conflict risk, firms postpone investment decisions to wait for more information. In this situation, underinvestment is likely to be more severe. Results are consistent when using the full sample and subsample of underinvestment. Quintile analyses for the underinvestment subsample shows that the association between conflict risk and suboptimal investment changes with location of the suboptimal investment distribution. The influence of conflict risk on underinvestment is more pronounced in the middle three quintiles. Observations in the top quintile (most severe underinvestment) are large loss-making firms, which creates financial constraints restricting their ability to invest.
63 H2.2 predicts that the higher the conflict risk that MNEs are exposed to, the poorer the overall quality of their financial reports. To operate in conflict-intense regions, firms may need to rely on local social networks, which raises off-the-book transactions. These transactions can be hard to track and monitor, creating opportunities for manipulation. This study found supporting evidence for a significant negative association between conflict intensity and financial reporting quality; specifically, when using the aggregated proxy for overall financial reporting quality.
This study also tests the relationship between financial reporting quality and investment efficiency in H2.3. Consistent with prior research (e.g., Biddle et al., 2009; Chen et al., 2011), financial reporting quality is found to be negatively associated with both underinvestment and overinvestment, showing that better financial quality helps to mitigate both underinvestment and overinvestment. The results are robust when suboptimal investments were regressed against all four financial reporting quality proxies.
To examine the robustness of the results, this study performed additional tests to further explore the relation between conflict risk and investment efficiency. Additional tests provided supportive and consistent results to the main tests. The positive association between conflict risk and underinvestment predicted by H2.1 is supported when employing alternative measures of suboptimal investment. The test for the relationship between conflict risk and overinvestment provided evidence that conflict risk decreases overinvestment for the top three quintile groups.
The results of this study need to be interpreted with caution, as there are several limitations. For study one, to examine the direct influence that conflict risk has on investment efficiency, the ideal population of interest is U.S.-owned subsidiaries in conflict regions. However, because of availability of subsidiary data, this study focuses on corporate-level investment efficiency. As such, the influence of conflict risk on investment efficiency must be based on overall corporate results. Second, as suggested by prior literature, the volume of trade and deals between two
64 countries can be affected by bilateral relations. This factor was not incorporated into the model of investment. Further research could consider the influence of diplomatic factors on firms’ investment decisions.
65 CHAPTER 3: THE IMPACT OF TERRORISM ON FINANCIAL MARKETS: INTRA-DAY EVIDENCE FROM FOREIGN EXCHANGE MARKET REACTIONS TO ISIS ATTACKS (STUDY 2)
Introduction
Between 2014 and 2017, ISIS became the most prominent international terrorist organization in Europe by number of attacks and casualties (IEP, 2016); that is, terrorist actions attributed to followers of the Islamic State of Iraq and al-Sham, also known as Islamic State of Iraq and
Levant (ISIL), were considered the highest profile terrorist threat to Europe during this period because ISIS had mounted a campaign against Western civilization to promote its paramount goal of securing and expanding the Islamic state (Al-Tamimi, 2013; Shane & Hubbard, 2014).
The expected negative impact on capital markets is frequently discussed in the press following any new attack (e.g., Board & Botter, 2016; Cox, 2017). This expectation is consistent with prior studies that find that capital markets react negatively to terrorist attacks (e.g., Chesney,
Reshetar, & Karaman, 2011; Karolyi & Martell, 2010). However, even while reporting on the expected effects of particular ISIS terror attacks, some press reports documented little actual reaction in equity markets and reported that financial markets were not ‘freaked out’ (e.g., Egan,
2015).
Prior findings on the impact of terrorist attacks on financial markets are mixed. Prior studies have found a negative reaction to major terrorist attacks on equity markets; in particular, the negative impact of the September 11 2001 terrorist attacks has been extensively documented
(e.g., Charles & Darne, 2006; Karolyi & Martell, 2010).27 Results from these studies may not
27 Karolyi and Martell (2010) examine 75 events and find an average decline of 2.24% in equity value but this response is reduced to 0.83% when 9/11 is excluded. For a comprehensive review of the impact of terrorism events on stock, bond and commodity markets, refer to Chesney et al. (2011).
66 however be applicable to smaller or more recent terrorist attacks.28 Chen and Siems (2004) argue that U.S. capital markets are more resilient than in the past and recover sooner from terrorist attacks. Other recent studies suggest that attacks do not have a significant effect on stock or bond market returns (Eldor & Melnick, 2018; Goel et al., 2017) or the effects are transient (e.g., Kollias et al., 2011a, 2011b). This study is therefore motivated to examine the vulnerability of financial markets to the recent ISIS terrorist attacks.
Second, this study is motivated to examine the impact of terrorist attacks on foreign exchange markets, which is underresearched in the literature. Foreign exchange markets have several advantages as a setting to examine the market reaction to terrorist attacks. First, the foreign exchange market is one of the largest and most liquid financial markets (Treepongkaruna &
Gray, 2009) and is likely to reflect the influence of a significant exogenous shock in a timely manner. The foreign exchange market in particular should reveal any significant capital flight to safer countries (Goel et al., 2017), such as the U.S., following terrorist incidents in Europe.
Second, foreign exchange markets are primarily over-the-counter and the major traders are banks, markets and foreign exchange traders, who are sophisticated investors and expected to be more sensitive to changes in conditions in the markets. Third, longer trading hours in foreign exchange markets allow us to observe intra-day market reactions to terrorist attacks that occur during local non-trading hours for equity markets.
Using a sample of 15 of the higher profile ISIS attacks in Europe, and measuring the impact on the EURO/USD exchange rate, I find a currency depreciation in response to announcement of many of the ISIS attacks. For example, the first major ISIS attack in the region at the Jewish
Museum in Belgium, the high profile London Bridge attacks, and to a lesser extent, the
28 The 9/11 attacks were unusual because of 1) the magnitude of the traumatic damage that the 9/11 terrorist attacks caused and 2) the direct impact on the World Trade Center complex and other buildings on Wall Street, which led to a trading halt.
67 November Paris Attack were associated with a depreciation in exchange rates. The depreciation is most pronounced around the announcement of the confirmation of casualties. The effects are mostly short-lived and foreign exchange markets are typically able to recover within a day of the attack. There was an increase in volatility around seven of the ISIS attacks; however, any reaction is short-lived. The evidence from the foreign exchange markets suggests that capital market responses to major attacks such as the events of 9/11 should not be generalized to smaller, more recent terrorist attacks.
This study contributes to the literature by providing a better understanding of the dissemination of information in financial markets. First, this study adds to the literature that examines the costs of terrorism. This study extends prior research focusing on the aftermath of 9/11 (e.g.,
Carter & Simkins, 2004; Charles & Darne, 2006; Coleman, 2012; Karolyi & Martell, 2010), to examine ISIS attacks that are much smaller than 9/11 but with wider terror networks distributed in the targeted countries (Benmelech & Klor, 2018). This study shows that in post-9/11 period, the economic importance of terrorist attacks in Western countries is declining. While terrorist attacks attract much attention in the media, impacts on financial markets are limited.
Second, this study adds to the literature examining the impact of unexpected exogenous shocks on financial markets. Terrorist attacks exhibit advantages over other forms of exogenous events examined by prior studies.29 Terrorist attacks have clearly identifiable event times, attract wide and immediate media coverage and are free from privileged information (e.g., Coleman 2012,
Kollias et al., 2012), which enables more precise testing and understanding of market reactions to new information. Results from this study can be valuable to cross-border investors and MNEs,
29 Some studies use catastrophic events as a sample to test market reactions to exogenous shocks (e.g., Humphrey & Simkins, 2016); however, catastrophic events, such as the Gulf oil spill, normally last for a longer time period and can have long-lasting effects on the environment, which makes it hard to identify the specific event time and examine the immediate impact on the market.
68 central banks, and foreign exchange participants on the magnitude and timing of terrorist attacks on markets.
Third, this study contributes to the literature employing intra-day foreign exchange return data to explore the immediate influence of terrorism shocks on markets. Prior studies employing daily data to examine the impact of terrorist attacks on financial markets can be problematic.
Using daily data is unlikely to capture the instant movement in stock prices and returns immediately after attacks. Financial markets are able to recover more quickly from terrorist attacks after 9/11 (Chen & Siems, 2004). Kollias et al. (2011b) found that the London Stock
Exchange recovered within one trading day after the 2005 London attacks. Moreover, daily data using closing prices to measures returns and volatility may lead to inaccurate conclusions on the movement in the market.30 This study shows that for the recent attacks, the impact on foreign exchange markets is short-lived and can be recovered within one day.
This chapter is constructed as follows. Section 3.2 briefly reviews prior research related to the impact of terrorist attacks on the economy and financial markets. Hypotheses are developed in
Section 3.3. Section 3.4 details the methodology including sample selection and empirical approach. Section 3.5 discusses the results and Section 3.6 concludes.
Literature Review
Terrorist attacks involve intentionally indiscriminate violence against public civilian targets as a means to create terror or fear, to influence a wider audience and achieve certain political, religious, or ideological aims (Fortna, 2015). Terrorist attacks are difficult to predict and are
30 Cornett et al. (1995) suggest that currency return patterns markedly differ from other asset groups and that when focusing only on closing prices, incomplete and incorrect conclusions can be drawn.
69 hard to control (Beck, 2002) and can lead to large numbers of causalities and fatalities.31
Terrorist attacks are associated with direct costs, such as loss of life and the destruction of infrastructure, and indirect costs that can have a broader impact across the economy. This study extends to the literature examining the economic costs of terrorist attacks; specifically, this literature review covers three strands of literature: 1) the impact of terrorism on the macroeconomy, 2) the influence of terrorist attacks on stock markets, and 3) the link between attacks and foreign exchange markets.
3.2.1 Terrorism and the Macroeconomy
The first subcomponent of the literature is that examining the macroeconomic consequences of terrorist attacks. Terrorism can jeopardize economic activity directly by destroying an economy’s human and physical capital stock, and indirectly, via government and market participant reactions (Meierrieks & Gries, 2012). Enders and Sandler (1996) suggest that terrorism-imposed economic costs may stem from multiple channels, including depressing tourist revenue, reducing foreign direct investment (FDI), destroying infrastructure and causing economic disruption. For example, Enders et al. (1992) find that terrorism significantly affected tourism in Greece, Italy and Austria. Similar negative impacts of terrorism on tourism are also documented in Greece, Israel and Turkey (Drakos & Kutan, 2003; Feridun, 2011). Enders and
Sandler (1996) find that terrorism reduced FDI by an average of 13.5% and 11.9% per annum between 1975 and 1991 in Spain and Greece respectively. Greenbaum, Dugan, and LaFree
(2007) show that terrorist attacks reduced business expansion and employment numbers in the years following an attack in Italy (1985–1997).
31 According to the Global Terrorism Index (GTI) issued by IEP (2015), in 2014, 32,685 people were killed in terrorist attacks, an increase of 80% from 2013. In 2014, 67 countries experienced at least one terrorist attack (IEP, 2015).
70 Eckstein and Tsiddon (2004) argue that terrorist attacks increase the risk of death and therefore inflate households’ subjective discount rate. As lives are endangered by terrorist attacks, the value of the future relative to the present reduces. Investment in the targeted countries is therefore likely to decrease because of the increased risk of death. In the long run, income and consumption are expected to fall as well. Higher discount rates lead to less saving such that in the steady state, investment, consumption and output would be lower than in a world without terror. They document that in Israel, terrorist attacks negatively affect consumption and continued terror would decrease annual consumption by 5% per capita. The negative impact of terrorism on growth is also documented in Asia, with Gaibulloev and Sandler (2008) finding that an additional terrorist incident per million persons reduced GDP per capita growth by about
1.5% for 1970–2004.
Blomberg, Hess, & Orphanides (2004) explored the macroeconomic consequences of international terrorism. Using a sample of 177 countries over 1968–2000, they documented that, on average, terrorist attacks have an economically significant negative effect on growth, but the reduction was insignificant for OECD countries. They also found that terror is associated with redirection of economic activity by crowding out investment while crowding in government spending. Nitsch and Schumacher (2004) assessed the impact of terrorism on international trade between more than 200 countries for the period 1968–1979 and found that terrorism reduced the volume of trade and the impact was economically large. When the number of terrorist incidences doubled (increased by 100%), bilateral trade flows fell by about 4%. Blomberg and
Hess (2006) confirmed the negative impact of terrorist attacks on trade and showed that a country targeted by terrorism experiences a average 5% decrease in bilateral trade. Other than trade, terrorism is also found to have significant negative impact on employment. Procasky and
Ujah (2016) showed that terrorist attacks have had a long-term impact on the sovereign credit
71 ratings of 102 countries. They found that growing terrorist activities lead to a reduction in outlook of a sovereign’s credit rating and the impact is greater in developing markets.
Abadie and Gardeazabal (2008) suggest that terrorist attacks induce larger international capital flow movements when attacks change expected returns on investment. The connection between terrorist attacks and international capital flows relies on the result that investors with a low level of risk aversion will abruptly change their international investment plans. In response to a reassessment of expected returns on investment in terror-hit countries, investors with low levels of risk aversion will consequently change their investment plans, shifting from terror-targeted regions to non-terror-targeted regions. With a sample of 110 countries, Abadie and Gardeazabal
(2008) show that for a one standard deviation change in terrorism risk, the net stock of a country’s FDI decreased by approximately 5% of GDP. Similarly, Enders et al. (2006) find that terrorist attacks against U.S. interests had significant negative impacts on the stock of U.S. FDI in OECD countries.
3.2.2 Terrorism and Stock Markets
There is a growing interest in literature that explores the relations between terrorist attacks and stock markets, particularly after 9/11. Prior studies have shown that terrorist attacks increase financial instability and weaken investor confidence (Abadie & Gardeazabel, 2003; Johnston
& Nedelescu, 2005; Lenain et al., 2002), and have attracted attention as a business risk to be considered by investors in decision-making (Jain & Grosse, 2009; Luo, 2009). Asset pricing theory implies that when bad news arrives, expected future asset values decrease.
Consistent with asset pricing theory, the literature has shown that terrorist attacks lead to negative stock returns around the day of the attacks (e.g., Balcilar, Gupta, Pierdzioch, & Wohar,
2018; Drakos, 2010; Eldor & Melnick, 2004; Karolyi & Martell, 2010; Ramiah, Cam, Calabro,
72 Maher, & Ghafouri, 2010). Abadie and Gardeazabel (2003) show that when the Basque ETA declared a ceasefire between 1998 and 1999, a sample of Basque stocks generated abnormal returns vis-à-vis non-Basque stocks on 22 trading days characterized by good news (indicating that the truce was credible), whereas after the ceasefire, Basque stocks significantly underperformed their counterparts in 66 days of trading. Eldor and Melnick (2004) examine the impact of terrorist attacks on Israeli financial markets. Their results suggest that terrorist attacks depress stock indices and reduce firms’ expected profit. The Israeli stock market lost 30% of its value during 2000–2003 as a result of the intensification of the Israeli–Palestinian conflict.
Karolyi and Martell (2010) find that terrorist attacks pose significant negative shocks to stock prices around the day of the attacks and this effect is more pronounced when the attacks occur in more democratic and developed countries.
A stream of research explores the contagion effect that terrorism has in stock markets. Nikkinen,
Omran, Sahlström, and Äijö (2008) examines the impact of 9/11 on 53 equity markets. Their results indicate 9/11 significantly increased market volatility across regions and led to significant negative returns in the short run, which quickly recovered afterwards. The level of impact depends on the degree to which the region is integrated with the global market. Less integrated regions, such as the Middle East and North Africa, are less influenced by the shock.
Similarly, Bilson, Brailsford, Hallett, and Shi (2012) suggest that 9/11 induced substantial contagion consequences, in particular, for equity markets in developed countries. S. Narayan et al. (2018) examine whether exposure to terrorism risk influences stock market integration among eight OECD countries for the period 2001–2014. Their result suggest that in Australia,
U.K., Germany and Turkey, terrorism risk is positively associated with the level of market integration, indicating the contagion effects of terrorism. By contrast, higher terrorism risk reduces Canadian and U.S. stock market integration with other OECD nations, which is consistent with the flight-to-safety hypothesis.
73 The literature also suggests that the impact of terrorism can be more pronounced in specific industries. Carter and Simkins (2004) examine the stock price reaction of firms in the air transport industry in the aftermath of 9/11, in light of market concerns of heightened risk with respect to these stocks. They found that on the first trading day after the 9/11 attacks, airline stocks were “pummelled” and there were larger and more significant abnormal returns for U.S. airlines than international airlines and airfreight firms. Ramiah et al. (2010) document significant short-term negative abnormal returns in the Australian market around 9/11, the 2004
Madrid bombings and 2005 London Bombings. The impact was particular large in the utilities sector, where industry return fell by 37.3% on the day of 9/11. Chesney et al. (2011) explore the impact of 77 terrorist events on stocks, bonds and commodities and found that two-thirds of terrorist attacks impose significant negative impacts on at least one stock market globally, primarily on the event day. They found that the insurance, travel and airline industries were most affected for global bond markets,32 and reacted negatively both on the event day and in the short-term period examined thereafter.
Drakos (2010) examines the impact of terror activities on stock returns using terrorist attacks as mood indicators. Edmans, Garcia, and Norli (2007) suggest that markets can be influenced by sentiment variables if such variables 1) drive the mood in a substantial and unambiguous manner so that their effect is powerful enough to show up in asset prices, 2) affect the mood of a large proportion of the population so that investors are likely to be affected as well, and 3) have effects that are correlated across the majority of individuals within a country. Terrorist attacks satisfy these three criteria. Targeted countries were left in shock, fear and anger after the attacks. Terrorism incidents may be seen as signs of future attacks elsewhere, thus leading to responses that have immense psychological, socioeconomic and psychosocial impact. Using
32 Except for the U.S., which reacts positively on the event day because of the “flight-to-quality” effect.
74 a sample of stock market indices for 22 countries for 1994–2004, Drakos shows that terrorist activity leads to significantly lower returns on the day a terrorist attack occurs and the negative effect of terrorist attacks is substantially amplified when the attacks cause higher psychosocial impact.
Terrorist attacks have also been found to increase stock return volatility (e.g., Arin, Ciferri, &
Spagnolo, 2008; Balcilar et al., 2018; Essaddam & Karagianis, 2014; Kollias et al., 2012). Arin et al. (2008) suggest that terrorism threats lead to greater volatility in stock markets based on evidence from six countries. Kollias et al. (2012) show the 2005 London Bombing increased volatility of stock market returns on the London Stock Exchange and the effect was also transmitted to the Frankfurt and Paris stock exchanges. Essaddam and Karagianis (2014) found that stock volatility increases on the day of terrorist attack and can remain significantly high for at least 15 days following the day of the attack. Overall, these studies show that terrorist attacks create negative shocks to stock markets, which lead to negative stock returns and greater volatility. Balcilar et al. (2018) test the effects of terror attacks on stock market returns and volatility in G7 countries, and suggest that terror attacks often have significant effects on returns, and significantly increase market volatility for Japan and the U.K.
Prior research also suggests that the impact of terrorist attacks on equity prices is typically transitory (e.g., Kollias et al. 2011a, 2011b). It has been argued that financial markets are able to recover more quickly from terrorist attacks after 9/11 (Chen & Siems, 2004; Kolias et al.,
2011b; Ramiah et al., 2010) and markets as a whole are “fairly insensitive to the major terrorist attacks post September 2001” (Ramiah, 2010, p. 66). For example, Kollias et al. (2011b) found that the London Stock Exchange recovered within one trading day from the 2005 London attacks. Of particular interest to this study using intra-day data in a liquid market is the speed with which the effect of terrorist attacks dissipate following recent ISIS terrorist attacks.
75 3.2.3 Terrorism and Foreign Exchange Markets
Terrorist attacks can affect exchange rates through their effects on macroeconomic fundamentals and by changing market participants’ expectations. Eckstein and Tsiddon (2004) argue that terrorist attacks increase the risk of death and therefore inflate households’ subjective discount rate. Higher discount rate results in less savings such that in the steady state, investment, consumption and output would be lower than in a world without terror. Based on the monetary model of exchange-rate determination, when terror has an adverse effect on output and consumption, terrorist attacks should trigger a depreciation in value of the targeted countries’ local currencies. Transnational terrorism, such as that conducted by ISIS, is likely to reduce corporate investment and reduce expected economic growth (Gaibulloev & Sandler,
2008). Consequently, demand for the local currency is likely to decrease with a subsequent depreciation in the local currency.
Other research suggests that terrorist attacks trigger reassessments of risks, leading to changes in international capital flows (e.g. Abadie & Gardeazabal, 2008; Gerlach & Yook, 2010). When confronted with uncertainty caused by unusual and unexpected events like terrorist attacks, markets may exhibit a flight-to-safety pattern (Caballero & Krishnamurthy, 2008). Investors seek to sell local currencies of terror-hit countries and shift towards safe assets. Supply for the local currencies is therefore likely to increase, which leads to depreciation of local currencies.
Moreover, Balcilar et al. (2017) argue that terrorist attacks reveal country-specific news, such as media speculation about the stability of the political system, which can give rise to a terror- driven contagion and transmission of volatility in international financial markets. When terror attacks trigger contagion effects in international financial markets, exchange-rates are likely to experience sharp and abrupt movement.
76 Few studies have explored the immediate reaction to terrorist attacks on foreign exchange markets. Eldor and Melnick (2004) test the effect of Palestinian terrorist attacks on the Israeli currency market and find that terrorist attacks lead to devaluations of the Israeli Shekel against the USD. Their results also suggest that the new information is incorporated in the foreign currency market on the day of the attack. Coleman (2012) examined foreign exchange market efficiency around Al-Qaida attacks using the minute-to-minute EUR/USD spot rate. He plots the change in EUR/USD spot exchange rate on 9/11, showing that the USD started to fall sharply against the Euro within 18 minutes of the first hit on the North tower. The USD reached its low (about four times lower than the pre-attack level) in approximately 100 minutes after the first impact.33 However, 9/11 was an unusually significant event that caused traumatic damage. The impact of 9/11 on markets may not be applicable to other terrorist attacks. Baliclar et al. (2017) find that terror attacks as reflected in a country terror index increased dollar–pound exchange rate volatility, particularly for the lower quartile of the conditional distribution for the period 1968–2009. P. Narayan et al. (2018) examine the impact of terrorist attacks on foreign exchange rate returns in 21 countries with local currencies quoted in USDs for the period
January 1996–December 2014.34 They find that terrorist attacks have a significant influence on foreign exchange returns. However, some countries experience exchange rate appreciations following a terrorist attack, while others depreciate (against the USD) and the effect is persistent for two days after the attacks. Overall, these studies suggest that terrorist attacks significantly influence foreign exchange markets. Of interest is whether these previous results generalize to the recent ISIS terrorist attacks.
33 Coleman (2012) plotted the change in GPB/USD spot exchange rate after 9/11 but did not perform any multivariate tests to examine the impact of terrorist attacks on returns and volatilities. 34 Though P. Narayan et al. (2018) measured foreign exchange returns at a 10-minute frequency, their measurement for the terrorism effect is measured on a daily basis, which is unable to capture the instant influence of terrorist attacks on the markets within the day of attacks.
77 Hypotheses Development
3.3.1 Foreign Exchange Market Reactions to Terrorist Attacks
Evidence suggests that terrorist attacks create negative shocks on targeted countries’ financial markets, which can lead to depreciation of the local currency against foreign currencies. Eldor and Melnick (2004) find that terrorist attacks in Israel led to devaluation of the Israeli Shekel against the USD. Coleman (2012) shows that the USD started to fall sharply against the Euro after the first hit on the North tower. P. Narayan et al. (2018), however, found mixed results on foreign exchange return movements in response to terrorist attacks. Though terrorist attacks have a significant influence on foreign exchange returns, some countries experience exchange rate appreciation following a terrorist attack while some currencies depreciate (against the
USD). However, their results need to be interpreted with caution as they did not differentiate types of terrorist attacks or the organisation responsible for each attack. To examine whether the negative relation between terrorist attacks and foreign exchange returns documented by prior studies holds, this study tests the hypothesis that:
Hypothesis 3.1: Foreign exchange markets react negatively to ISIS terrorist attacks.
Prior research suggests that markets react within 20–40 minutes from the time a terrorist attack occurs (Coleman, 2012). However, this reaction time seems slow for current financial markets.
As a result of advances in trading techniques and increases in information arrival rates, markets can respond within seconds to new information becoming public (Brogaard, Hendershott, &
Riordan, 2014; Foucault, Hombert, & Roşu, 2016). Given terrorist attacks are hard to predict, there is typically no information leakage prior to completed attacks. With no privileged information, markets are only able to respond when the information on attacks become publicly available. Traders need time to absorb the information, then react. This study therefore initially
78 proposes that the market will react within 10 minutes of the time of the first news report
(announcement of confirmed casualties).35
Reaction in foreign exchange markets will be measured using a 30-second frequency of exchange rate returns following the first news report of a given terrorist attack. News reports may not be the first information source for some terrorist attacks. With the explosion of internet and social media, much information is available, for example, via Twitter, at a faster rate than via traditional news media (Petrovic et al., 2013). First news of a terrorist attack can be made publicly available immediately through witness accounts, and the information can spread quickly via sharing and re-sharing the message on social networks (Doggett & Cantarero, 2016).
This study therefore chooses the event window [−5mins, 5mins] around the time of the first news report to control for this issue.
3.3.2 Increase In Volatility Around Terrorist Attacks
Uncertainty in markets is reflected in volatility. Market participants are not able to hedge unexpected shocks to markets (Arin et al., 2008). Terrorist attacks are difficult to predict. They are therefore expected to raise information uncertainty. When there is a lack of information on the cause and outcomes of a particular attack, markets initially react to the “signal” sent from the incidents (Kasperson et al., 1988; Slovic & Weber, 2002). “Signal value” of an event appears to be systematically related to the characteristics of the event and the hazard it reflects.
Initial reaction to the “signal” can be determined by direct personal experience with the risk object or through the receipt of information about the risk object. Great public concern can be stimulated if a risk is not well understood, not controllable or not well managed, in which case, further mishaps are likely (Slovic & Weber, 2002). The “signal value” of risks raised by terrorist
35 This study tests the influence of two sets of terrorist attack information: first media report of a given attack and announcement of confirmed casualties. Details on identification of events are listed in Section 3.4.1.2.
79 attacks can be further amplified in the transfer of information about the risk and in the response mechanisms of society, particular when the information volume is high (Kasperson et al., 1988).
High volumes of information help to mobilize latent fears about the risk and enhance the recollection of previous incidents, and therefore, increase uncertainty in markets. Given that terrorist attacks induce greater volatility in equity markets (Arin et al., 2008; Johnston &
Nedelescu, 2006), they are expected to have a similar impact on foreign exchange markets.
Therefore, this study proposes that:
Hypothesis 3.2: Foreign exchange market volatility increases around the time of
terrorist attacks.
Methodology
3.4.1 Data and Sample
3.4.1.1 Sample of ISIS terrorist attacks
The sample for this study consists of terrorist attacks committed by ISIS between May 2014 and June 2017.36 These events include both ISIS-directed and ISIS-inspired lone actor attacks.37
Data for terrorist activities, including event date, location, victim and casualty information and damaged caused are collected from the Global Terrorism Database (GTD) until the end of 2016.
Terrorist attacks that occurred after 2016 are identified from news media reports. Terrorism can be motivated and driven by a variety of country-specific factors and individual characteristics.
To reduce the variation in the sample with respect to the foreign exchange markets studied, this
36 In February 2014, Al-Qa’ida formally broke ties with ISIS with the leader of al-Qa’ida stating ISIS disobeyed the directions from al-Qa’ida to kill fewer civilians (IEP, 2015). Since then, ISIS has replaced al-Qa’ida as the biggest threat for attacks in the West. The first ISIS terrorist attack in this study’s sampled countries was on May 24 2014 in Brussels, Belgium. Therefore, the sample period covers the period May 2014–June 2017. 37 ISIS-inspired lone actor attacks refer to those attacks influenced by ISIS but undertaken without direct involvement from ISIS to further the ideology of the group. Lone actor attacks are committed by individuals who act alone and without the support of a terrorist organization (IEP, 2015).
80 study restricts the sample to only include ISIS terrorist attacks in Europe38 with the most direct impact on the foreign exchange markets studied.
This results in a sample of 15 ISIS attacks in Europe, including the U.K., during the sample period. Table 3.1 lists the sample events, along with information for each attack, including date and time when the terrorist attacks occurred, date and time for the first media report, date and time for the confirmed casualty announcement, and damage caused by each attack.
38 European countries sampled in this study include Belgium, Denmark, France and, Germany, reported to have experienced ISIS terrorist attacks during the sample period. Turkey is not included in this study because of ongoing conflict and political stability issues, which may affect reactions to specific attacks. Attacks in Russia are not included as Russia is not in the Euro zone.
81 Table 3.1 Sample of ISIS Terrorist Attacks for Study 2 No. Event City Date Day of the Time first confirmed Form of attack death injured week of report causality Attack (UTC) (UTC) (UTC) 1 Jewish Museum of Belgium Brussels, 24-May-14 Sunday 13:50 14:38 14:57 Armed Assault 4 0 shooting Belgium 2 Copenhagen Shooting Copenhagen, 14-Feb-15 Sunday 14:00 15:10 15:49 Armed Assault 2 5 Denmark 3 Saint-Quentin-Fallavier Saint-Quentin- 26-Jun-15 Saturday 7:30 8:51 9:07 Armed Assault 1 2 attack Fallavier, France 4 Thalys Train Attack On a train from 21-Aug-15 Saturday 15:45 17:32 17:47 Armed Assault 0 3 Paris to Amsterdam 5 November Paris Attack Paris, France 13-Nov-15 Saturday 20:14 20:49 00:23(+1d) Bombing/Explosion/ 130 368 Armed Assault/Hostage taking 6 Brussels bombings Brussels, 22-Mar-16 Thursday 6:58 7:13 8:02 Bombing/Explosion 32 340 Belgium 7 Nice Attack Nice, France 14-Jul-16 Friday 20:30 20:45 23:27 Vehicle-ramming 86 434 attack, Firearms 8 Wurzburg train attack Wurzburg, 18-Jul-16 Tuesday 19:00 20:23 20:41 Armed Assault 0 5 Germany 9 Ansbach bombing Ansbach, 24-Jul-16 Monday 20:12 20:53 21:16 Bombing/Explosion 1 5 Germany 10 Normandy church attack Normandy, 26-Jul-16 Wednesday 7:43 8:32 9:32 Armed Assault 1 3 France 11 Berlin Christmas Market Berlin, 19-Dec-16 Tuesday 19:02 19:20 00:49(+1d) Unarmed assault 12 56 Attack Germany
82 12 Westminster attack London, U.K. 22-Mar-17 Thursday 14:40 14:43 18:00 Armed Assault 6 49
13 Shooting of Paris Police Paris, France 20-Apr-17 Friday 18:47 19:03 19:16 Armed Assault 4 2 Officers 14 Manchester Arena Bombing Manchester, 23-May-17 Wednesday 21:31 21:45 06:36(+1d) Bombing/explosion/ 22 59 U.K. Armed assault 15 London Bridge and Borough London, U.K. 2-Jun-17 Saturday 21:24 21:24 3:00(+1d) Armed Assault 11 48 Market attack
83 Among the sample countries, many did not have a recent history of deadly terrorist attacks before the ISIS campaign started in mid-2014. Since then, many European countries have experienced deadly attacks. The brand of ISIS is relatively new to Europe and any attempted and/or completed attacks are therefore likely to take the region and foreign exchange markets by surprise, which provide us with a unique setting to test foreign exchange market efficiency around exogenous shocks. For example, the Copenhagen shooting on February 14 2015 that killed two people was the deadliest terrorist attack in the country’s history. Among the countries sampled, France has experienced the highest number of attacks (six), causing 222 deaths and
812 injuries. Germany and the U.K. each had three attacks during the sample period, with deaths
(injuries) of 13 (66) and 39 (156) respectively. Two attacks occurred in Brussels, Belgium.
In terms of severity, the Paris attack on November 13 2015 (local time) was the deadliest terrorist attack in Europe over the previous decade, with 130 deaths and 368 injuries. The second deadliest attack was in Nice on July 14 2016. A cargo truck was deliberately driven into the crowds who gathered for celebration of Bastille Day. This attack took 86 lives and 434 people were critically injured. The bombing attacks in Brussels Airport on December 2 2015 was the third most severe attack, with 130 people dead and 368 injured. These events with relatively severe damage share common characteristics. First, they often occurred on weekends or holidays, when there were celebrations and/or events scheduled so that more people were likely to get hurt. Terrorists can maximize their impact by creating a greater level of fear and terror, which can be quickly spread to the broader community. Second, these attacks are easily identified as terrorist activities because of the form of the attacks (bombings and explosions).39
Third, these attacks were able to attract quick media coverage.
39 In the process of searching for media coverage and Twitter posts for the attacks, I found people can normally quickly identify an attack as a terrorist activity if it targets a large number of civilians aimlessly (i.e., hurt as many people as possible, lack of care as to who they are hurting). For attacks targeting a specific group or target, for 84 3.4.1.2 Identification of event timing
The identification of the precise event time is crucial to testing the impact of news events. To identify the time of the first news report for a given terrorist attacks, this study uses the Factiva database, owned by Dow Jones & Company, to search news coverage on the sample attacks. In situations where no news reporting times were provided, Twitter was used to search the earliest repost for the attacks posted by media accounts. The first news report time is the time of the first news report on Factiva, or Twitter if the news report is not time-stamped on Factiva. For
Twitter, only Twitter posts from verified media accounts, for example, the BBC, were counted.
A flurry of news stories follows an attack. The first news report is important; however, it may not be sufficient for traders to identify the nature and scope of the attack. As terrorist attacks are hard to predict and often take targets by surprise, when markets receive a “signal” from a terrorist attack, stigma from previous attacks may serve as the reference point to guide their initial decisions. Therefore, in the absence of information on the actual damage that the attacks have caused, markets are likely to overreact (or underreact) to a terrorist attack when it is reported by news media for the first time.
When terrorist attacks are first reported, investors may panic because of the uncertainties raised by the attacks (Frey, Luechinger, & Stutzer, 2007), which can cloud their initial judgements.
When more information becomes available, markets will have better knowledge of the attacks’ impact and adjust their initial reaction accordingly. This study therefore also examines a second event: the announcements of casualties and injuries in each terrorist attack. The exact times for the confirmed casualty announcement for each event were obtained from Factiva in a similar
example, churches, police and guards, people normally need longer to evaluate whether the attack is related to terrorists. 85 manner as the first news reports for each attack. The relation between the attack, the first announcement and subsequent announcement is summarized in Figure 3.1.
Figure 3.1 Event Timeline for Study 2
Time (t)
Time Event 1: Event 2: of the First news Announcement attack media report of confirmed casualties
As summarized in Table 3.1, the time from event to first report varies from zero (London Bridge and Borough Market attack) to 81 minutes (Saint-Quentin-Fallavier attack). The mean (std. dev) time between the occurrence and first media report of the attacks is 36 (27) minutes. Specifically, for the severe attacks with number of deaths greater than 10,40 media produces more prompt coverage with a mean time for first media report of 16 minutes. These attacks normally involve bombs and targets on special dates and events, aiming to create mass damage in a short time, and can be quickly identified as terrorist attacks. For other attacks with fewer than four deaths, media reports emerged 40 minutes or longer after they occurred. With respect to the time of the first official report of casualties, the mean time between the attack (first media report) and first report of casualties was 161 (124) minutes. The time differences show that attacks with more
(less) severe damage attract quicker (slower) media coverage while taking longer (shorter) for officials to confirm and announce the damage. Attacks targeting events and venues with greater population density create mass damage in a short time and can be quickly identified as terrorist
40 November Paris attacks, Brussels bombings, Nice attack, Berlin Christmas Market attack, Manchester Arena Bombing and London Bridge and Borough Market attack. 86 attacks. Therefore, media is able to report them more quickly but a longer time is needed to evaluate the extent of the damage.
3.4.1.3 Speed of information dissemination
Much prior research examines daily returns. Nowadays, the speed of information dissemination in financial markets is within seconds (e.g., Brogaard, Hendershott, & Riordan, 2014; Foucault et al., 2016; Scholtus, van Dijk, & Frijns, 2014). News is not restricted to traditional press and corporate reports, but includes social media platforms such as Twitter and Facebook, further speeding the dissemination of information and expanding traders’ information set (Foucault et al., 2016). Rogers, Skinner, and Zechman (2016) find that trading volumes in securities markets starts to increase within 5 seconds of the information first becoming publicly available on media.
Advances in high frequency trading techniques such as HFT41 also helps accelerate market reactions to information. Hasbrouck and Saar (2013) suggest that the fastest responders can react within 2–3 microseconds.42
Reaction in foreign exchange markets are measured using exchange rate returns following the first news report of a given terrorist attack. News reports may not be the first information source for some terrorist attacks. With the explosion of internet and social media, more information is available, for example via Twitter, at a faster rate than traditional news media (Petrovic et al.,
2013). The first news message of a terrorist attack can be publicly available immediately through witnesses’ account and information can spread quickly via sharing and re-sharing the
41 HFT comprises the majority in financial markets. Estimates of HFT typically exceed 50% of total volume in U.S. list equities (SEC, 2014). SEC recognizes HFT as a dominant component of the current market structure and expects it to affect nearly all aspects of market performance. The introduction of hybrid market in 2006 with expanded automatic execution reduced the execution time from NYSE market orders from 10 seconds to under a second (Hendershott & Moulton, 2011). Using the NASDAQ database, Brogarard et al. (2014) found that HFTs predict price changes over horizons in less than 3–4 seconds. 42 A microsecond equals one millionth of a second. 87 message on social networks (Doggett & Cantarero, 2016). I therefore choose the event window to be [−5, + 5] minutes around the time of the first news report to control for this issue.
3.4.2 Research Design
3.4.2.1 Measurement of foreign exchange returns
To measure the reaction to terrorist attacks in Europe, I use the EURO/USD exchange rate. The available foreign exchange rate data consist of the available microsecond-time-stamped quotes for EURO/USD reported on the interbank Reuters network during May 1 2014–May 30 2017.
The foreign exchange rate data were obtained from the Thomson Reuters Tick History (TRTH) database provided by the Securities Industry Research Center of Australasia (SIRCA). Each quote contains a bid and ask price, accompanied by a time to the nearest millisecond.43 To capture the instant market reaction to terrorist attacks, 30-second returns were constructed from the EURO/USD exchange rate quotes.44 To capture the immediate market reaction to media reports (announcement of confirmed casualties) of terrorist attacks, a 30-second interval was chosen. Early studies (e.g., Andersen, Bollerslev, Diebold, & Vega, 2003; Danielsson & Payne,
2002) using indicative foreign exchange quotes suggest that at an aggregation of 5 minutes or above is a fairly good proxy for returns. Some recent studies of high frequency trading however use millisecond quote data to calculate mid-quote prices at a much shorter time period (e.g.,
Chordia, Green, & Kottimukkalur, 2016). As notice of terrorist attacks may require some
43 A millisecond is a thousandth of a second. 44 I acknowledge the shortcomings of using indicative foreign exchange quotes for intra-day foreign exchange studies as suggested by prior studies (e.g. Lyons 1995; Danielson & Payne 2002) that 1) these quote prices provide no measure of traded currency volume or transaction prices and 2) these quotes are indicative prices but not firm prices at which dealers can transact and 3) there is no information on the effective lifetime of the quotes as a timestamp is given for the entry of a quote pair but not for the exit of quote and 4) each quote is input by a single dealer (bank) and therefore the quotes are likely to reflect a dealer specific characteristics which may not represent the market. All these supposed limitations have no substantial bearing on the main results of this study as I use larger time frequencies than tick frequency. Danielsson and Payne (2002) shows that when frequencies longer than tick frequency (i.e. 5-minute foreign exchange constructed from quotes time-stamped to nearest second), the foreign exchange returns calculated based on quotes match closely to returns calculated from transactions prices. 88 clarification, millisecond quotes may be too fine. I calculate the mid-quote price series every
30 seconds.
Following Andersen and Bollerslev (1998), the average log prices were calculated at each 30- second mark by linearly interpolating the average of the log bid and log ask at the two closest ticks. The closest two quotes were weighted linearly by their inverse relative distance to the desired point of time and the log-price, log (푃푡), was defined as the midpoint of the logarithmic bid and ask prices. For example, if quote A and quote B are the two closest quotes to the desired point in time, and the bid-ask quote A has a time of 14:29:59.580 and is 1.1048–1.1053, whereas bid-ask pair for quote B has a time of 14:30:00.040 and is 1.1044–1.1052. The interpolated
2 [ln(1.1048)+ln(1.1053)] 1 [ln(1.1044)+ln(1.1052)] price at 14:30:00.000 would then be exp{ ∙ + ∙ }. 3 2 3 2
The nth 30-second return, 푅푡 , is defined as the difference between the midpoint of the logarithmic bid and ask at these appropriately spaced time intervals and divided by the log-
푃푡−푃푡−1 price at t-1 (푅푡 = ). All 2,880 30-second intervals during the 24-hour daily trading cycle 푃푡−1 with available data were used in calculating returns.
Weekends and holidays were not excluded. Prior studies (e.g., Andersen & Bollerslev, 1997a,
1997b; Andersen et al. 2003) suggest that activity in the foreign exchange market slows decidedly during weekends and certain holiday periods. These studies, therefore, excluded these thin trading periods from their samples. However, as terrorists strategically pick weekends and key holidays, when civilians are more likely to gather together for events or celebrations, to amplify the terror and impact of attacks, I retain all trading periods. When terrorist attacks occur during a non-trading period, returns on the first trading day following the attacks were
89 used, following Edmans et al. (2007).45 In examining the 30-second returns over the less active trading periods, such as the first and last couple of hours of each trading week, there can be
(short) periods of no apparent quote change in the TRTH database. This problem manifests itself as sequences of zero returns in places where missing quotes were interpolated. The problem primarily affects the estimation of ARCH effects over longer periods rather than the short event and benchmark comparison windows. To remedy this, hours containing fewer than
10 quotes were removed from each exchange rate series. For foreign exchange returns, each terrorist attack was tested for the period from day -1 to day +1 day around the events (i.e., first media report and announcement of confirmed casualties). If no returns were removed from the series because of a constant zero return, then each series would include a total of 5,761 30- second returns to estimate the linear regression and ARCH effects. The number of returns observations varies from 3,018 for the Paris attack (event 5) to 5,761 for the Manchester area bombings (events 12 and 14).
3.4.2.2 Modelling the response of exchange rate returns to news
I use an event study approach to evaluate the reaction in the foreign exchange markets to
46 terrorist attacks. We model the 30-second spot exchange rate return, 푅푡, as a linear function of lagged values of itself and an indicator variable for the event window (퐷_푒푣푒푛푡푡). 퐷_푒푣푒푛푡푡 is an indicator variable representing the 5-minute event window of the first news report for a
45 As Edmans et al. (2007) discussed, one drawback for this method is the potential asynchrony between events and returns can attenuate the results, as part of the reaction may have been incorporated in prices before the measurement period. The impact of a terrorist attack occurring during a non-trading period may be well-absorbed by the market and the market may reverse to pre-attack levels before the market opens again. However, returns on the first trading day after the attacks can reflect the impact of terrorist attacks on markets when the event and damage is known. 46 The setting appears to satisfy the assumptions for event study methodology as suggested by Kim and Klein (2017). The first assumption is that the market must be aware of the event dates. Second, the event must be unanticipated by the market. Third, the events should be relatively “clean”, that is, they should be self-contained and the direction of the market reaction should be relatively unambiguous. Fourth, the markets need a contextual base to evaluate effectiveness, considering the events are new to the markets. 90 given terrorist attack. I define t = 0 as the time when an attack is first reported. Consistent with prior research, we use a GARCH (1,1) with Student t distribution of error terms to model the exchange rate returns.
푅푡 = 훽0 + 훽1푅푡−1 + 훽2퐷_푒푣푒푛푡푡 + 휀푡 (3.1) 2 2 ℎ푡 ≡ 푉푎푟(푅푡|퐼푡−1) = 휔 + 훼휀푡−1 + 훽ℎ푡−1
Where
푅푡 is the 30-second spot exchange rate return beginning at time t. 퐷_푒푣푒푛푡푡 is equals 1 if t ≥ -5 minutes and t ≤ +5 minutes before and after an attack (official casualty) announcement is first reported by the media. 퐼푡−1 denotes all information available at time t-1.
The model is estimated within the eight hours around the events, where t ranges from −2,880 to +2,880 seconds. Terrorist attacks are expected to create a sense of fear and panic that quickly spread in the targeted countries and wider region, depressing investor risk assessments and expectations for future investment in the region. Local currencies in the targeted regions are therefore expected to depreciate against foreign currencies. Therefore, 훽2 is expected to be negative and significantly different from zero, indicating a negative market reaction.
Empirical Results
3.5.1 Descriptive Statistics
Table 3.2 provides a summary of the foreign exchanges rate returns for an initial evaluation period of Day -1 to Day +1 around the date of the first media report of the 15 terrorist attacks.
Column 1 shows the cumulative returns for the evaluation period. For 12 of 15 events, the foreign exchange return series there is a depreciation of the EURO against the USD. The initial indication is that there is a negative response to many of the terrorist attacks but in such a liquid
91 market it is necessary to consider the possible reaction over much shorter time intervals. The distribution of returns is consistent with that of high frequency exchange returns. A null hypothesis of normal distributions is strongly rejected for all event windows, indicating that the distributions of returns have a higher kurtosis than expected for a normal distribution, consistent with the distributions of foreign exchange returns in previous studies (e.g., P. Narayan et al.,
2018). Excessive kurtosis suggests fat tails in return distributions, which is a common feature in studies using high frequency foreign exchange returns (P. Narayan et al., 2018; Westerfield,
1977). The second last column of Table 3.2 shows the first-order autoregressive coefficients, which are significant for all 15 events. I therefore consider the significance of the short-window responses to the attacks using a GARCH (1,1) model.
92 Table 3.2 Summary Statistics Exchange Rate Returns for Day – 1 to Day +1 Around the Date of First Media Reports of a Terrorist Attack Cumulative Event Returns Std. Dev Skewness Kurtosis JB AR(1) N 1 Jewish Museum of Belgium shooting -0.0007 0.0612 0.1766 15.2132 <0.0000 0.6835*** 5715 2 Copenhagen Shooting -0.0046 0.1098 0.2167 13.0713 <0.0000 0.2634*** 5505 3 Saint-Quentin-Fallavier attack -0.0097 0.1742 -8.8269 457.6374 <0.0000 0.4088*** 4342 4 Thalys Train Attack 0.0162 0.1667 0.4103 11.2770 <0.0000 0.2374*** 3311 5 November Paris Attack -0.0025 0.1559 1.7626 44.2219 <0.0000 0.2012*** 3018 6 Brussels bombings -0.0067 0.1025 -0.3261 8.1956 <0.0000 0.4664*** 5756 7 Nice Attack -0.0055 0.1287 0.2515 18.2103 <0.0000 0.2990*** 4799 8 Wurzburg train attack -0.0029 0.1148 0.1683 6.9351 <0.0000 0.3028*** 5731 9 Ansbach bombing -0.0024 0.1040 0.0991 6.7195 <0.0000 0.2788*** 5587 10 Normandy church attack 0.0019 0.1117 0.2192 7.6136 <0.0000 0.3807*** 5759 11 Berlin Christmas Market Attack -0.0040 0.1110 0.0949 6.3079 <0.0000 0.2833*** 5745 12 Westminster attack -0.0025 0.0852 0.0616 6.1049 <0.0000 0.3142*** 5761 13 Shooting of Paris Police Officers -0.0019 0.0812 0.1209 9.2740 <0.0000 0.3917*** 5759 14 Manchester Arena Bombing -0.0023 0.0961 0.3408 14.5816 <0.0000 0.5523*** 5761 15 London Bridge and Borough Market attack 0.0037 0.0837 1.7562 39.4719 <0.0000 0.3967*** 5697 This table reports the cumulative exchange rate returns for the [-1day, 1day] period, standard deviation, skewness and kurtosis of the 30-second returns within the [-1day, 1day] period, skewness and kurtosis, a Jarque-Bera test of nonnormality of returns, and the AR(1) autocorrelation coefficient for returns around the events of first media reports of a given terrorist attack. The evaluation period covers the 30-second data from -1 day to +1 day around the events. Standard deviations are multiplied by 1,000 for ease of presentation. Returns are calculated every 30 seconds where means represents the average 30-second return within the time period -1 day to +1 day around the first media report of a terrorist attack. The autocorrelation coefficients are estimated with the conditional mean model of 푅푡 = 휕0 + 훽1푅−1 + 휀푡 and conditional variance model 2 2 ℎ푡 ≡ 푉푎푟(푅푡|퐼푡−1) = 휔 + 훼휀푡−1 + 훽ℎ푡−1 + 훾퐷_푒푣푒푛푡푡 with Student t distribution. * p < 0.10 **p < 0.05 ***p < 0.01 (two-tailed).
93 3.5.2 Response to Terrorist Attack Reports Using an Event Study Approach
I initially examine the reaction to the first news reports within 10 minutes of the time of the first news report and also from the announcement that casualties have been confirmed. An illustration of the event window and benchmark windows is shown in Figure 3.2.
Figure 3.2 Event Windows and Benchmark Comparison Windows
t (nth 30-sec -2885 -2865 -30 -10 0 +10 interval)
Benchmark comparison Benchmark Event window window 2 comparison [-5min, 5min] [-5min-1day, 5min-1day] window 1 [-15min, -5min]
Notes: Time, t, represents nth 30-second interval with a total of 2,880 30-second intervals in a 24- hour period.
Table 3.3 Column 1 reports the cumulative foreign exchange returns for the currency pair
EURO/USD47 between the 10-minute event window [−5 to +5 minutes]48. I start the window at
47 For the three attacks occurred in UK, I also tested their impact on GBP/USD exchange rate return. The results are exhibited in Appendix 3.1, 3.2 and 3.3 for 1) Cumulative Exchange Rate Returns for a 10 minute window around when a Terrorist Attack is Reported; 2) Contemporaneous Effects of Terrorist Attacks on Euro/USD Exchange Rate Returns; and 3) Terrorist Attacks’ Influence on GBP/USD Exchange Rate Return Volatilities respectively. The results in Appendix 3.1 indicate that for the Westminster attack and Manchester Arena Bombing, the cumulative event return in the [-5mins,5mins] is lower as compared to the benchmark window 1 and 2, indicating a negative impact of attacks on GBP/USD returns. 48 I also tested for the event window of [0, 5mins] for the fifteen sampled attacks, for both first media report and announcement of confirmed casualty, assuming there is no information leakage before the first media report using the GARCH approach. Results contemporaneous effects of terrorist attacks on EURO/USD exchange rates returns and terrorist attacks’ influence on EURO/USD exchange return volatility for the [0, +5mins] window are similar to the results for [-5min, 5min] window. The results for [0,5mins] window were not tabulated in this thesis.
94 −5 minutes of the official news service report to capture any more-timely social media reports.
The first benchmark for comparison is an interval of 10 minutes immediately before the event window [−15 to −5 minutes]. Terrorist attacks can be assumed to be unexpected, exogenous shocks, so a comparison period immediately before the event is appropriate. This first benchmark is reported as Column 2. Intra-day returns can however vary by time of day, so the second comparison period is the period for the same time of day as the event window but for the previous day (Column 4).
Table 3.3 shows that for eight of 15 events, the cumulative returns during the event window are lower than the benchmark (Column 3). The mean difference in returns is not however significantly different from zero across all events (t = 0.5313), nor is the proportion of events with negative returns different from fifty percent at a significance level of 10 percent. Events with the largest negative returns include the first few ISIS attacks in the region (i.e., Belgium shooting (event 1), Copenhagen Shooting (event 2), Saint-Quentin-Fallavier attack (event 3), and Thalys Train Attack (event 4)), and those events that caused relatively more severe damage, such as the November Paris attacks (event 5), and London Bridge and Borough attack (event
14).
95 Table 3.3 Cumulative Exchange Rate Returns 10 minutes around when a Terrorist Attack is First Reported (1) Cumulative (4) return (2) (3) Benchmark 2 (5) -5min to Benchmark 1 =(1)-(2) Previous Day =(1)-(4) † † Event Date 5min -15min to -5min Difference -5min to 5min Difference 1 Jewish Museum of Belgium shooting 23-May-14 -0.4698 0.2202 -0.6900 0.3888 -0.8586 2 Copenhagen Shooting 14-Feb-15 -0.6316 0.0419 -0.6735 0.2633 -0.8949 3 Saint-Quentin-Fallavier attack 26-Jun-15 -0.5435 -0.0057 -0.5379 -1.2911 0.7476 4 Thalys Train Attack 21-Aug-15 0.2043 0.3565 -0.1522 0.1191 0.0852 5 November Paris Attack 13-Nov-15 0.2547 0.3059 -0.0512 1.1075 -0.8528 6 Brussels bombings 22-Mar-16 0.3544 -1.3031 1.6575 -0.2439 0.5983 7 Nice Attack 14-Jul-16 0.2169 -0.0898 0.3067 0.0986 0.1183 8 Wurzburg train attack 18-Jul-16 -0.0308 0.0756 -0.1064 0.0804 -0.1112 9 Ansbach bombing 24-Jul-16 -0.1106 0.2279 -0.3385 0.0804 -0.1910 10 Normandy church attack 26-Jul-16 0.4190 -0.0822 0.5012 0.1368 0.2822 11 Berlin Christmas Market Attack 19-Dec-16 -0.0445 -0.8674 0.8229 -0.0291 -0.0154 12 Westminster attack 22-Mar-17 0.5944 -0.2918 0.8862 0.5528 0.0416 13 Shooting of Paris Police Officers 20-Apr-17 0.5040 0.0863 0.4177 -0.0933 0.5973 14 Manchester Arena Bombing 23-May-17 0.0000 -0.0058 0.0058 -0.0309 0.0309 15 London Bridge and Borough Market attack 2-Jun-17 -0.6629 -0.0062 -0.6567 0.2632 -0.9261 Mean difference 0.0928 -0.0899 T-statistics: Mean Difference < 0 0.5313 -0.6191 P(T 96 Table 3.4 compares the cumulative returns for the 10-minute window around the announcement of confirmed casualties. The mean difference in returns (Column 3) for the sampled 15 terrorist attacks is a depreciation of 36.5349 basis points and is significant at the 10% level one-tailed (t = −1.72, p < 0.0540). The announcement of confirmed casualties seems to have a greater impact on the market than the first media report. For 11 of 15 events, casualty announcements were followed by negative returns when comparing with benchmark 1, including the first attack of the series (Belgium shooting, event 1) and the attacks with higher number of deaths and injuries, such as Paris attack (event 5), Nice attack (event 7), Berlin Christmas Market attack (event 11), Westminster attack (event 12) Manchester Arena Bombing (event 14) and London Bridge attack (event 15). The biggest drop in returns is for the announcement of confirmed deaths for the November Paris attacks, with cumulative returns of −24.689 basis points. Similarly, the announcement returns are significantly lower than the returns for the second benchmark window, with a mean difference in returns of −27.19 basis points (Column 5), which is marginally smaller than zero at the 10% level one-tailed (t = −1.43, p < 0.0871). The results are indicative that announcement of confirmed casualties from terrorist attacks are associated with a depreciation of the EURO against the USD. The binomial probability test for the signs of differences in mean returns also suggest that announcements of confirmed casualties are likely to reduce returns around the event compared with the benchmark window 1 with probability of difference (column) being negative of 0.0592 (one-tailed). The results support H3.1 that foreign exchange markets react negatively to terrorist attacks; in particular, to announcements of confirmed casualties. 49 One basis point equals 0.0001 or 0.01%. 97 Table 3.4 Cumulative Exchange Rate Returns 10 minutes around First Reports of Confirmed Casualties (4) (1) (2) Cumulative Cumulative Cumulative (3) returns (5) Event Date return return =(1)-(2) -5min-1d to =(1)-(4) -5min to 5min -15mins to -5min Difference† 5mins -1d Difference† 1 Jewish Museum of Belgium shooting 23-May-14 -0.4698 0.2202 -0.6900 0.3888 -0.8586 2 Copenhagen Shooting 14-Feb-15 -0.6316 0.0419 -0.6735 0.2633 -0.8949 3 Saint-Quentin-Fallavier attack 26-Jun-15 0.4609 -0.6190 1.0799 0.2220 0.2389 4 Thalys Train Attack 21-Aug-15 -0.0469 -0.4853 0.4384 -1.0137 0.9668 5 November Paris Attack 14-Nov-15 -1.6517 0.8172 -2.4689 0.1880 -1.8397 6 Brussels bombings 22-Mar-16 -0.4001 -0.7113 0.3112 0.6343 -1.0344 7 Nice Attack 14-Jul-16 -0.5925 0.5404 -1.1329 -0.4669 -0.1256 8 Wurzburg train attack 18-Jul-16 0.0768 0.0809 -0.0041 -0.0394 0.1162 9 Ansbach bombing 24-Jul-16 -0.0215 -0.2974 0.2759 0.0783 -0.0998 10 Normandy church attack 26-Jul-16 -0.0454 0.0182 -0.0636 -0.6370 0.5916 11 Berlin Christmas Market Attack 20-Dec-16 0.2206 0.3055 -0.0849 -0.0037 0.2243 12 Westminster attack 22-Mar-17 -0.4885 0.4163 -0.9048 -0.7917 0.3032 13 Shooting of Paris Police Officers 20-Apr-17 -0.2123 0.2593 -0.4716 0.0178 -0.2301 14 Manchester Arena Bombing 24-May-17 0.0003 0.4340 -0.4337 0.5106 -0.5103 15 London Bridge and Borough Market attack 3-Jun-17 -0.6629 -0.0062 -0.6567 0.2632 -0.9261 Mean difference -0.3653 -0.2719 T-statistics: Mean Difference < 0 -1.7165* -1.4314* P(T 98 3.5.3 Response to Terrorist Attack Reports Using a GARCH Approach This section examines the significance of the contemporaneous effect of terrorist attacks on foreign exchange rate returns based on a GARCH (1,1) model of exchange rates. Model 3.1 is estimated for each attack over a two-day evaluation period [-1day, 1day] around the events. The results are reported in Table 3.5 for the first media report and the announcement of the confirmed casualties. Considering that information for terrorist attacks might be available on social media, such as Twitter and Facebook, before news media reports, the event window is set to be [-5mins, +5mins] around the time of the first media report, where t = 0 is the time of the first media report. D_event is the variable of interest, defined as an indicator variable that equals 1 if an observation is within the [-5mins, +5mins] event window.50 Prior literature suggests that terrorist attacks generate negative impacts on local currency values in targeted countries and reduce foreign exchange returns. Therefore, the coefficient on D_event is expected to be negative and significantly different from zero to support H3.1. Table 3.5 reports the coefficient on D_event for each event from the GARCH estimates of Model 3.1. The first three columns of Table 3.5 report the results for the first media report of terrorist attacks and the second three columns report the results for the announcement confirming the number of casualties. The coefficient on the indicator variable for the 10-minute event window (D_event) is significantly negative at the 5% level in only two of the 15 events.51 Consistent with the event study analysis, foreign exchange markets react strongly to the first ISIS attack in the sample, the shooting at the Jewish Museum of Belgium. As this was the first ISIS attack in the sample region, markets were arguably not familiar with the causes, intention 50 Also tested for the event window of [0, +5 mins] after the time for the first media report, assuming there is no information leakage before the first media report; results are similar to that with event window of [-5min, 5min]. 51 I also examined the event window [0, +5 minutes] after the time for the first media report assuming there was no information prior to the first media report. The results are consistent with those reported. 99 and outcomes of the attack; therefore, markets might have reacted to the “signal” of the attack based on their experience with 9/11 and other major terrorist attacks. The other attack to which foreign exchange markets reacted negatively (at the 5% level) was the first media report of the London Bridge and Borough Markets attack on June 2 2017. Table 3.5 also reports the results for the announcements of confirmed casualties for the 15 attacks. Similar to the first media report, foreign exchange markets reacted significantly to the first ISIS attack in our sample, the shooting t the Jewish Museum of Belgium and the major attack in London on the London Bridge and Borough Market. The results also indicate a significant negative reaction from the November Paris Attack. The November Paris Attack consisted of a series of attacks at multiple locations in Paris from 21:16 pm on November 13 2015 to 00:58 on November 14 2015 (local time) and was followed by a market closure period. The market reaction to the Paris attack may reflect the accumulated effect of information gathered during the market closure period. 100 Table 3.5 Contemporaneous Effects of Terrorist Attacks on EURO/USD Exchange Rate Returns First Media Report Announcement of Confirmed Casualties 퐷_푒푣푒푛푡푡 퐷_푒푣푒푛푡푡 Coef. z-stat n Coef. z-stat n 1. Jewish Museum of Belgium shooting -0.4266*** (-2.79) 5681 -0.4257*** (-2.79) 5715 2. Copenhagen Shooting -0.7954 (-1.62) 5387 -0.5950* (-1.89) 5505 3. Saint-Quentin-Fallavier attack -0.2728 (-0.86) 4340 0.2514 (0.66) 4310 4. Thalys Train Attack 0.0573 (0.16) 3307 -0.0986 (-0.27) 3281 5. November Paris Attack 0.1466 (0.43) 2996 -1.9790*** (-4.17) 5562 6. Brussels bombings 0.1904 (0.86) 5751 -0.2275 (-1.03) 5754 7. Nice Attack 0.1225 (0.44) 4797 -0.2885 (-1.03) 4513 8. Wurzburg train attack -0.0354 (-0.14) 5700 0.0147 (0.06) 5736 9. Ansbach bombing 0.0046 (0.02) 5507 -0.0030 (-0.01) 5547 10. Normandy church attack 0.2524 (1.09) 5757 -0.0310 (-0.13) 5759 11. Berlin Christmas Market Attack -0.0136 (-0.06) 5729 0.1406 (0.57) 5761 12. Westminster attack 0.3228* (1.75) 5761 -0.2656 (-1.45) 5761 13. Shooting of Paris Police Officers 0.2644 (1.50) 5757 -0.1114 (-0.63) 5759 14. Manchester Arena Bombing 0.0063 (0.03) 5761 -0.0006 (-0.00) 5760 15. London Bridge and Borough Market attack -0.4800*** (-3.34) 5649 -0.4110** (-2.09) 5697 The regression model has the following form 푅푡 = 휕0 + 훽1푅푡−1 + 훽1퐷_푒푣푒푛푡푡 + 휀푡, estimated using a GARCH (1, 1) model with Student t distribution. The coefficients for D_event are expressed in basis points (1 basis point equals 0.0001) for ease of presentation. D_event is an indicator variable that equals 1 if the observation is within the event window, 0 otherwise. The event window is -5 minutes to +5 minutes where t = 0 when a terrorist attack is first reported. The full sample period is -1 day to +1 day around the event. The length of each time period is 30 seconds and the foreign exchange return is calculated every 30 seconds. Therefore, the sample for each regression analysis consists of 5,761 time periods with 2,880 of these before t = 0 and 2,880 after t = 0. * p < 0.10 **p < 0.05 ***p < 0.01 (two-tailed). 101 Overall, while markets reacted to the first major ISIS attack in Europe, they evaluated the costs of other ISIS terrorist attacks and found these insufficient to affect the foreign exchange market on the day of the attack for most of the attacks that followed, with the possible exceptions of the London Bridge and Borough Market attack and the Paris attack. 3.5.4 Persistence of News Effects from Terrorist Attacks on Foreign Exchange Returns Some prior studies suggest that the negative impact from terrorist attacks on financial markets can persist for days (e.g., Brounen & Derwall, 2010; P. Narayan et al., 2018);52 other research suggests that financial markets are able to recover more quickly from terrorist attacks after 9/11 (e.g., Chen & Siems, 2004; Kollias et al., 2011a, 2011b). To examine the persistence of the influence of terrorist attacks on foreign exchange markets, I added an additional indicator to the GARCH (1,1) model: 푅푡 = 훾0 + 훾1푅푡−1 + 훾2퐷_푒푣푒푛푡푡 + 훾3퐷_푒푣푒푛푡푡−120 (3.2) + 훾3퐷_푒푣푒푛푡푡−240 + 휀푡 D_event is an indicator variable for the 10-minute window around the terrorist attack, and 퐷_푒푣푒푛푡푡−120 and 퐷_푒푣푒푛푡푡−240 are indicator variables for terrorist attacks occurring one hour and two hours prior to the return interval (Rt) respectively. The coefficients 훾3 and 훾4 estimate the lagged effect of past terrorist attacks on contemporary exchange rate returns, which suggest the predictive ability of information from past terrorist attacks. As the previous results show that the market only reacted significantly negatively to three events (the shooting at the Jewish Museum of Belgium, the London Bridge and Borough Market attacks and the 52 Brounen and Derwall (2010) suggest that the impact of terrorist attacks on stock markets can persist up to three days after the events. P. Narayan et al. (2018) show that foreign exchange markets do not completely reverse from the initial reaction to terrorist attacks in the two days following the attacks. 102 November Paris Attack), I only examine the lagged effect for these three attacks. Further, as the response to the first media report in the Paris attack was interrupted by a non-trading period, this event is excluded from the analysis. As reported in Table 3.6, the results indicate that for the Belgium shooting and London Bridge and Borough Market attack, the initial influence on foreign exchange market returns does not persist to the hour following the attacks. Only the Paris attack has a persistent impact on the markets in that the initial reaction has a weak persistence that is marginally significant for one hour following the attack but not beyond two hours. Further, as the response to the first media report in the Paris attack is interrupted by a non-trading period this persistence may relate to the broader context than just the news of the terrorist attack. Overall, the persistence in the 30 second foreign exchange returns is minimal even for the few large events with an initial negative reaction to the first news report. 103 Table 3.6 Contemporaneous and Lagged Effects of Terrorist Attacks on EURO/USD Exchange Rate Returns Contemporaneous Lagged Effect Lagged Effect Effect [t-60mins] [t-120mins] 퐷_푒푣푒푛푡푡 퐷_푒푣푒푛푡푡−120 퐷_푒푣푒푛푡푡−240 Coef (z-stat) Coef (z-stat) Coef (z-stat) n 1 -0.4273*** -0.7181 -0.7486 Jewish Museum of Belgium shooting (First media report) (-2.80) (-0.56) (-0.55) 5,715 2 -0.3130** -0.1103 -0.68903 Jewish Museum of Belgium shooting (Confirmed casualty) (-1.98) (-0.09) (-0.61) 5,680 3 -2.1100 *** -0.7884* 0.1962 November Paris Attack (Confirmed casualty) (-3.67) (-1.89) (0.30) 5,471 4 -0.5081*** -0.7907 -0.3645 London Bridge and Borough Market attack (First media report) (--3.22) (-0.63) (-0.10) 5,697 5 -0.4569*** -0.5990 0.3423 London Bridge and Borough Market attack (Confirmed casualty) (-2.88) (-0.34) (0.25) 5,648 The regression model has following form 푅푡 = 훾0 + 훾1푅푡−1 + 훾2퐷_푒푣푒푛푡푡 + 훾3퐷_푒푣푒푛푡푡−60 + 훾4퐷_푒푣푒푛푡푡−120 + 휀푡 estimated using a GARCH (1,1) model with Student t distribution. The coefficients for D_event are expressed in basis points (1 basis point equals 0.0001) for ease of presentation. D_event is an indicator variable that equals 1 if the observation is within the event window, 0 otherwise. The event window is -5 minutes to +5 minutes where t = 0 when a terrorist attack is first reported. The full sample period is -1 day to +1 day around the event. The length of each time period is 30 seconds and the foreign exchange return is calculated every 30 seconds and therefore the sample for each regression analysis consists of 5,761 time periods, with 2,880 of these before t = 0 and 2,880 after t = 0. * p < 0.10 ** p < 0.05 *** p < 0.01 (two-tailed). 104 3.5.5 Influence of Terrorist Attacks on Foreign Exchange Rate Volatility To test Hypothesis 3.2 that foreign exchange market volatility increases around the time of terrorist attacks, this study uses a GARCH (1, 1) model, with a variable 퐷_푒푣푒푛푡푡 in the conditional variance equation to test the influence of terrorist attacks as an exogenous shock to volatility in the two-day period [-1 day, +1 day] around the attacks. The conditional mean and conditional variance model are estimated as below: 푅푡 = 휕0 + 훽1푅푡−1 + 휀푡 (3.3) 2 2 ℎ푡 ≡ 푉푎푟(푅푡|퐼푡−1) = 휔 + 훼휀푡−1 + 훽ℎ푡−1 + 훾퐷_푒푣푒푛푡푡 where 퐷_푒푣푒푛푡푡 is included in the conditional variance equation to capture the exogenous shock to volatility. 훾 is therefore expected to be positively significant to support H3.2 (that return volatility increases around the time of terrorist attacks). The coefficients α and β reflect the dependence of the current foreign exchange return volatility on its past levels. The aggregation of 훼 + 훽 indicates volatility persistence, which measures how fast the foreign exchange rate dissipates.53 Large volatility persistence means the current shock will affect volatility in the long run. As suggested by Lamoureux and Lastrapes (1990) and Kalev, Liu, Pham, and Jarnecic (2004), the persistence of the conditional volatility of asset returns may be generated by serial correlation in the information arrival process. This implies that volatility persistence will be significantly reduced after the inclusion of 퐷_푒푣푒푛푡푡 in the conditional variance equation. 53 Student’s t distribution is assumed for error terms rather than the normal distribution, as the former can accommodate the excess kurtosis of the innovation (Bollerslev, 1987). 105 Table 3.7 Terrorist Attacks’ Influence on EURO/USD Exchange Rate Return Volatility First Media Report Confirmed Casualty 퐷_푒푣푒푛푡푡 퐷_푒푣푒푛푡푡 Coef. z-stat n Coef. z-stat n 1 Jewish Museum of Belgium shooting 1.3577** (2.30) 5715 1.2841** (1.97) 5715 2 Copenhagen Shooting 0.7649 (1.15) 5505 0.7066 (1.05) 5505 3 Saint-Quentin-Fallavier attack 0.3823 (0.63) 4342 0.2322 (0.44) 4310 4 Thalys Train Attack -0.4066 (-1.15) 3311 -0.9689* (-1.95) 3281 5 November Paris Attack -0.2976 (-0.68) 3018 1.5634** (2.54) 5562 6 Brussels bombings 0.9350 (1.52) 5756 1.9672*** (3.38) 5754 7 Nice Attack -0.2647 (-0.46) 4799 0.05607 (0.12) 4513 8 Wurzburg train attack 0.4471 (0.77) 5731 1.3587** (2.48) 5736 9 Ansbach bombing -0.5497 (-1.11) 5587 -2.0427*** (-3.84) 5547 10 Normandy church attack -0.6161 (-1.16) 5759 -0.1753 (-0.30) 5759 11 Berlin Christmas Market Attack 0.9443** (2.04) 5745 -0.3250 (-0.63) 5761 12 Westminster attack 1.2102** (2.10) 5761 0.1195 (0.32) 5761 13 Shooting of Paris Police Officers 0.9297* (1.70) 5759 0.9789* (1.92) 5759 14 Manchester Arena Bombing -1.2449** (-1.97) 5761 1.5145** (2.22) 5760 15 London Bridge and Borough Market attack 1.6312*** (3.36) 5697 1.7929*** (3.91) 5697 The coefficients (훾) are estimated using a GARCH (1, 1) model with the conditional mean model of 푅푡 = 휕0 + 훽1푅푡−1 + 휀푡 and conditional variance model ℎ푡 ≡ 2 2 푉푎푟(푅푡|퐼푡−1) = 휔 + 훼휀푡−1 + 훽ℎ푡−1 + 훾퐷_푒푣푒푛푡푡 with Student t distribution. D_event is an indicator variable that equals 1 if the observation is within the event window, 0 otherwise. The event window is -5 minutes to +5 minutes where t = 0 when a terrorist attack is first reported. The full sample period is -1 day to +1 day around the event. The length of each time period is 30 seconds and the foreign exchange return is calculated every 30 seconds and therefore the sample for each regression analysis consists of 5,761 time periods, with 2,880 of these before t = 0 and 2,880 after t = 0. * p < 0.10 ** p < 0.05 *** p < 0.01 (two-tailed). 106 Table 3.7 summarizes the estimated coefficients for the conditional variance equation for the GARCH (1,1) model54. The first columns show the outputs for the first media report for each terrorist attack. Consistent with previous analysis, the first ISIS attack in the sampled region created a higher-level shock on the foreign exchange market. The shooting at the Jewish Museum of Belgium resulted in a higher level of volatility in foreign exchange returns. For the Berlin Christmas Market attack, Westminster attacks and London Bridge attack, there was also a significant increase in foreign exchange market volatility when they were first reported. When the event is defined as the announcement of confirmed casualties for each attack, seven of 15 attacks are found to be significant in explaining the volatility of foreign exchange returns during the evaluation periods. Specifically, attacks resulting in relatively large numbers of deaths and injuries, such as the Paris attacks, Brussels bombings, Manchester Arena bombings and London Bridge attacks, induced a higher level of return volatility. Overall, the above results indicate that when terrorist attacks are first reported, there are high levels of uncertainty in the markets regarding the risk associated with the attacks. The effects are mostly short-lived and foreign exchange markets are able to recover within the day of the attack. Discussion and Conclusion This study examines the influence of ISIS terrorist attacks in the highly liquid foreign exchange market. Most announcements of ISIS terrorist events showed no systematic pattern of returns 54 Appendix 3.3 shows the estimated coefficients for the conditional variance equation for the GARCH (1,1) model for GBP/USD returns of the three attacks that occurred in the UK. The results shows that GBP/USD returns volatility increase significant when the three attacks were first reported by the media and when the confirmed casualties were announced. 107 in response to the attacks. Only a few ISIS attacks created a negative impact on foreign exchange returns. Specifically, the first major ISIS attack in the region at the Jewish Museum in Belgium, the London Bridge attacks, and to a lesser extent, the November Paris Attack were associated with a depreciation in exchange rates. The depreciation is most pronounced around the announcement of the confirmation of casualties. The effects are mostly short-lived and foreign exchange markets are typically able to recover within the day of the attack. This study shows that the economic importance of terrorist attacks in Western countries is declining. While terrorist attacks attract much attention in the media, there is relatively little impact on financial markets and any reaction is short-lived. This study is subject to a few limitations. First, this study focuses on ISIS attacks. The impact of terrorism could vary between domestic and transnational terrorist attacks. Second, this study focuses on the impact of ISIS attacks on foreign exchange markets—terrorism would be expected to affect companies in specific insurance and tourism-related industries. Third, this study focuses the impact of terrorist attacks on a single currency pair, EURO/USD. Future research could explore the influence of terror events on other targeted countries and their currencies. The results indicate that further research is needed to better understand the differences between attacks with a high shock value, such as 9/11 or the first major ISIS attack in Europe, and subsequent attacks with lesser impact. 108 CHAPTER 4: THE ROLE OF PRODUCT MARKETS IN ASYMMETRICALLY TIMELY GAIN AND LOSS RECOGNITION: EVIDENCE FROM THE U.S. OIL AND GAS INDUSTRY (STUDY 3) Introduction Conditional conservatism also known as asymmetrically timely loss recognition as income recognition in accounting is typically a choice between timely and postponed recognition of economic gains and losses (Ball. Kothari & Nikolaev, 2013). This study explores the role of the product market in firms’ asymmetrically timely recognition of gains and losses. Specifically, I examine whether changes in oil prices result in asymmetric timeliness in gain and loss recognition. Accounting conservatism refers to cases where a higher degree of verification is required to recognize gains in comparison to losses (Basu, 1997; Watts, 2003a). The literature on conservatism shows that firms are likely to recognize bad news about future cash flows in a more timely manner than good news (e.g., Basu, 1997; Ball & Shivakumar, 2006; Banker et al., 2017). Relying on the Basu (1997) model, a large body of research has examined conditional conservatism (e.g., Ball et al., 2000; Black et al., 2018; Holthausen & Watts, 2001; Nikolaev, 2010). This stream of research typically employs stock returns as a proxy for news about future cash flows and interprets the piecewise-linear effect of stock returns on earnings as a measure of more timely loss recognition by firms. In particular, Ball and Shivakumar (2006) show that earnings respond asymmetrically to changes in operating cash flows incremental to the asymmetric effect of stock returns. Banker et al. (2017) document that sales changes also have an asymmetric effect on earnings after controlling for the asymmetric effect of stock returns and operating cash flow. This research extends that study to examine the role of product market prices in asymmetric 109 timeliness in gain and loss recognition for the U.S. O&G industry. Banker et al. (2017) argue that, based on the assumption that sales forecasts is a major factor to be considered in the operating cash flow forecast, typically through projecting trends in recent sales (Chase, 2013), current sales changes are likely to be an vital predictor of future cash flow. This study argues product market prices are likely to be a leading indicator of sales changes, providing complementary information about future cash flows. Price changes in the product market can reflect changes in the balance of supply and demand, which can be indicative of future revenue. Therefore, product market prices are expected to add information on future cash flows. Prior studies (e.g., Banker et al., 2017) show that earnings exhibit asymmetrical association with news about future cash flows (i.e., recognizing unfavorable information in a more timely and fuller fashion than favorable information). If product market prices, which are useful for future cash flow estimation, have a similar effect as earnings, then changes in market prices are expected to have an asymmetric effect on earnings. This study predicts that oil price changes have an asymmetric effect on earnings conditional on the asymmetric effect of stock returns, changes in cash flows and changes in sales. This study focuses on the O&G industry because it offers a unique context for studying the effects of changes in product market prices on accounting outcomes. First, conservatism is an efficient contracting mechanism (Watts, 2003a) and is of great importance in debt contracting for O&G sector. According to the Ernst & Yang (2014) report, fund raising for O&G firms heavily relies (around eighty percent) on bank loans and bonds, which subject to various of covenants. Contracting parties therefore demand timely measures of performance and net asset values for debt contract purposes. Second, the business outlook for this industry is closely associated with commodity prices (Deloitte, 2016; Halliburton, 2017). Prices for oil and natural gas are sensitive to changes in market conditions and a variety of other economic factors. Relatively minor changes in the 110 supply of and demand for oil may result in large fluctuations in oil prices. O&G companies are typically considered price takers in the global market for crude oil. Changes in oil prices are beyond O&G firm control. Reductions in commodity prices can have a material adverse effect on business and consolidated financial conditions, including potential asset impairment and severance cost. Third, the O&G industry provides a setting to re-evaluate the role of changes in sales in asymmetric timeliness earnings recognition, as proposed by Banker et al. (2017). Murray et al. (2018) suggest that predicting future sales based on past sales is only appropriate if demand patterns can be detected and accurately modelled. Time series methods of sales forecasting are suitable for firms with sufficient historical data and steady demand. Companies in O&G industry satisfy these criteria given that 1) there is sufficient historical data for oil prices, production and trading, and 2) crude oil is the most widely used source of fuel, supplying about 30% of the world’s energy need, which guarantees a steady demand (Dunn & Holloway, 2012). Meanwhile, oil prices at the market level are not likely to be affected by firm-level characteristics, such as competition, firms’ business model and opportunistic managerial behaviors. Therefore, the O&G industry provides an ideal setting for studying the relations between market-level non-accounting risk factors and accounting outcomes. This study combines two strands of the literature. The first investigates conditional accounting conservatism. These studies typically follow Basu’s (1997) model and use stock returns to proxy news on future cash flows and interpret asymmetrical association between stock returns and earnings as a measure of more timely recognition of losses by companies. The second strand of literature explores the link between oil changes and economic activity. Extensive research has examined the association between oil price shocks, macroeconomic variables and stock 111 returns (e.g., Cuñado & de Gracia, 2003, 2014; Dhaoui et al., 2018; Filis et al., 2011; Gisser & Goodwin, 1986; Hamilton, 1983; Kilian & Park, 2009). The intersection of these strands is the focus of this study. Very little research has directly examined the impact of oil prices on accounting outcomes. This chapter argues that changes in oil prices are associated with future cash flows and can be transmitted to earnings through four pathways. First, changes in oil prices affect firms’ stock returns. Studies have shown that equity prices move systematically with oil prices (e.g., Cuñado & de Gracia, 2014; Filis et al., 2011; Jones & Kaul, 1996; Kilian & Park, 2009; Nandha & Faff, 2008). Empirical results indicate that oil prices play a predictive role in stock returns (Driesprong, Jacobsen, & Maat, 2008; Narayan & Gupta, 2015) and oil prices are positively associated with equity value in O&G sector (El-Sharif, Brown, Burton, Nixon, & Russell, 2005; Faff & Brailsford, 1999; Sadorsky, 2001). Given the linkage between oil prices and stock returns, this study expects oil prices to an indicator of future cash flows and thus have an asymmetric effect on earnings. Second, this study argues that oil prices influence firms’ operating cash flows. Lower oil prices may lead to operating costs of extracting the oil or natural gas exceeding the revenue generated, resulting in negative cash flows (Deloitte, 2016). Meanwhile, lower commodity price signals lower the present value of expected future cash flows, which can be bad news for earnings. The third way through which oil prices affect earnings is changes in sales. Sales revenue is determined by price and quantity. Change in oil prices will affect total revenues and future sales, and thus, future cash flows. A fourth impact of changes in prices of oil is via the use of fair values in accounting. Entities should consider any potential early warning signs of impairment and loss recognition raised by changes in oil prices. Lower expectations of cash flow lead to potential impairment, which reduces earnings. Overall, this study argues that oil price changes 112 provide incremental information for future cash flows and have an asymmetric effect on earnings conditional on stock returns, cash flow changes and sale change asymmetries. This study predicts that after controlling for the asymmetric effect of stock returns, operating cash flow changes and sales changes, earnings exhibit asymmetric association with oil price changes. Employing a sample of 1,224 firm-year observations for 219 firms in the U.S. O&G industry in 2002–2016, this study finds that earnings respond asymmetrically to changes in oil prices by recognising bad news (negative oil price changes) more fully and in a timelier fashion than good news (positive oil price changes). The results suggest that product market prices are an important indicator of conditional conservatism and provide useful and incremental information, for stock returns and changes in operating cash flows, for asymmetrically timely gain and loss recognition. This study makes two important contributions to the literature. First, few studies have examined the linkage between product market prices and accounting reporting decisions. Dayanandan and Donker (2011) document a positive significant relation between commodity prices of crude oil and accounting performance (i.e., ROE and ROA) for O&G firms in North America. This study extends prior research to show that accounting-based earnings respond asymmetrically to changes in product market prices, (i.e. negative price shocks are recognised more fully and in a timelier fashion than positive price shocks in product markets). Second, the results of this study show that accounting reflects changes in product market prices in a delayed fashion. Studies of conditional conservatism typically rely on firm-reported performance measures, such as cash flows and sales (e.g., Ball & Shivakumar, 2006; Banker et al., 2017) to examine their impact on concurrent earnings. This study shows that one-year lagged oil price returns have an asymmetric impact on O&G firms’ net income, indicating that product market prices are able to signal changes in future cash flows ahead of accounting-based performance measures. The 113 results suggest it takes time before information reflected in product market price changes is fully incorporated in financial reporting. The rest of the chapter is organized as follows. Section 4.2 provides a brief literature review. Section 4.3 develops the hypotheses. Section 4.4 describes research design and Section 4.5 discusses the data and the sample. Section 4.6 and 4.7 present main results and additional test results, respectively. Section 4.8 reports on the sensitivity analysis and Section 4.9 concludes the study. Literature Review This section reviews two lines of relevant research. The first group of studies are those examining the impact of oil price shocks on economic activities. There has been a continuing line of research examining the impact of oil prices on the macroeconomy and financial markets. The second group of studies include those investigating the factors explaining asymmetric timeliness of earnings recognition. 4.2.1 Macroeconomic Impacts of Oil Price Shocks The literature has empirically investigated the linkage between oil price shocks and macroeconomic factors, stock markets and equity values for firms in the O&G industry. The first stream of literature focuses on the macroeconomic impacts of oil prices. A group of studies suggest that positive oil price shocks precede economic recessions (e.g., Cuñado & de Gracia, 2003; Gisser & Goodwin, 1986; Hamilton, 1983). Rising oil prices signal increased scarcity of energy, which indicates potential reduction in availability of basic inputs to production and therefore a slowing of output growth and productivity (Brown & Yucel, 2002). Hamilton (1983) finds a negative correlation between oil prices and real outputs. Seven of eight recessions in the U.S. during the period 1947–1975 were found to be preceded by dramatic increases in crude 114 petroleum prices. Slower growth rates of output and money were documented following increases of oil prices, normally with a lag of three-quarters of a year. Rotemberg and Woodford (1996) documented an output loss of 2.5% in the five or six quarters following a positive oil price shock for a similar sample period to Hamilton (1983).55 Studies examining both oil price increase and decrease periods document asymmetric macroeconomic impacts of oil market boosts and collapses (e.g., Hamilton, 2011; Mork, 1989). In the U.S., Mork (1989) shows that GNP growth exhibits a statistically significant negative correlation with increases in the real price of oil, whereas it is not significantly correlated with decreases in the real price of oil. The negative impact of positive oil price shocks on aggregate U.S. economic activity is also supported by later empirical studies with an extended sample period (e.g., Gisser & Goodwin, 1986; Hamilton 1996, 2000, 2003; Hickman, Huntington, & Sweeney, 1987; Hooker, 1996; Raymond & Rich, 1997). Cuñado and de Gracia (2003, 2005) show similar negative macroeconomic impacts of positive oil price shocks in Europe and Asia respectively. Other than aggregated outputs, oil price changes and volatilities are found to have significant impacts on other economic variables, such as inflation (Cuñado & de Gracia, 2005; Hamilton, 2005) and currency exchange rates (e.g., Brahmasrene, Huang, & Sissoko, 2014). 4.2.2 Oil Prices and the Stock Market in General Extensive literature has examined the relations between oil prices and stock market returns. A risk–reward framework based on the capital asset pricing model (Fama & French, 2004; French, Schwert, & Stambaugh, 1987; Perold, 2004) is commonly used as the analytical framework for studies investigating the linkage between oil prices and stock market returns. Stock market 55 Rotemberg and Woodford (1996) covers a sample period of 1948–1980. 115 prices are estimated based on the expected cash flows discounted by the required rate of return, which can be sensitive to factors that affect expected cash flows or required rate of return (Filis et al., 2011). The economic literature suggests that oil price changes can affect macroeconomic variables such as GDP growth, inflation and currency exchange rates (Brahmasrene et al., 2014; Hamilton, 2005), which are expected to affect common stock returns (Fama & Schwert, 1977). Therefore, uncertainties and fluctuations in oil prices can contribute to an increase in equity risk premiums, which will consequently influence the discount rate applied to cash flows in stock assessment models and further affect stock prices. Meanwhile, upwards oil price changes can directly increase production costs and therefore decrease the value of cash flows used in stock assessment models (Jones et al., 2004). Jones and Kaul (1996), using a standard cash flow dividend valuation model, found that changes in oil prices have a detrimental effect on output and real stock returns in the U.S. during the post- war period. They conclude that the reaction of U.S. stock prices to oil prices can be completely accounted for by the impact of these shocks on real cash flows. Therefore, changes in oil prices should be able to predict stock market prices. Driesprong et al. (2008) find strong predictability of changes in oil prices in both developed and emerging markets. Narayan and Gupta (2015) concur, finding that both positive and negative oil price changes predict U.S. stock returns. Excluding the O&G industry, empirical evidence shows that an increase in oil prices has a negatively significant impact on stock market prices (e.g., Cuñado & de Gracia, 2014; Driesprong et al., 2008; Jones & Kaul, 1996; Narayan & Sharma, 2011; Sadorsky, 1999). For example, Driesprong et al. (2008) find that a rise in oil prices drastically lowers future stock returns using stock market data from 48 countries. This negative association is more pronounced between monthly stock returns and lagged monthly oil price changes, indicating underreactions in stock markets to information implied by changes in oil prices. Some studies 116 argue that oil price shocks receive asymmetric responses from stock markets depending on the nature of the shocks and whether the country is a net importer or net exporter of oil (e.g., Cuñado & de Gracia, 2014; Dhaoui et al., 2018; Filis et al., 2011; Kilian & Park, 2009). Kilian and Park (2009) show that the U.S. stock prices react negatively to oil price increases caused by oil- market-specific demand shocks, such as an increase in precautionary demand driven by concerns about future crude oil supply shortfalls. On the contrary, oil price increases caused by crude oil production disruptions exhibit no significant impact on stock returns. 4.2.3 Oil Prices and Stocks for the O&G Industry In contrast to the general stock market where returns are negatively sensitive to oil price shocks,56 empirical evidence shows that O&G industry stocks react positively to oil price increases (e.g., El-Sharif et al., 2005; Faff & Brailsford, 1999; Nandha & Faff, 2008; Sadorsky, 2001). In the O&G sector, when oil prices increase, given the selling quantity remains stable, revenues for O&G firms will grow. Future cash flows and stock prices are therefore likely to grow for firms in this sector. Sadorsky (2001) explores the relationship between oil prices and equity values in the Canadian O&G sector, and shows a positively significant association between crude oil price and O&G equity index. For the sample period from the final quarter of 1983 to the final quarter of 1999, a 1% change in oil prices is found to be associated with a change of 0.3% in the value of the index. El-Sharif et al. (2005) re-examined the link between crude oil prices and O&G equity values using a U.K. sample. Their evidence indicates that the association between oil prices and equity values is always positive and often highly significant, which suggests crude oil price 56 For sectors other than energy, oil is an input. Rises in oil prices increase costs and reduce profit, and therefore, are likely to jeopardize future cash flows and stock returns. 117 volatility has an direct impact on share values in the O&G sector. In contrast, the influence of oil price volatility on equity returns of non-O&G industries is minimal. Faff and Brailsford (1999) also document positive and significant oil price sensitivity in the O&G sector in Australia. Nandha and Faff (2008) find that oil price increases negatively affect market returns for all sectors excluding mining and O&G, by analysing 35 Data Stream global industry indices for the period 1983–2005. At the firm level, Al-Mudhaf and Goodwin (1993) analyze differences in market and oil price return associations for 29 New York Stock Exchange listed U.S. oil companies for a period around the 1973 oil shock. Using a multi-factor arbitrage pricing theory model, their results show that positive oil price shocks increase returns for oil firms. Overall, prior literature shows that oil price changes contain information for future cash flows. At the stock market level, oil prices are generally negatively associated with stock prices and returns. For the O&G industry, stock prices and returns respond positively to oil price shocks. 4.2.4 Asymmetrical Timeliness of Earnings Recognition An extensive body of literature has examined a variety of topics related to accounting conservatism.57 Beaver and Ryer (2005) distinguish between unconditional and conditional conservatism. Under unconditional conservatism, also known as ex ante or news-independent conservatism, book value of net assets is understated as a result of predetermined aspects of the accounting process. Conditional conservatism, also called ex post or news-dependent 57 For a more comprehensive review of this extensive literature, refer to Ruch and Taylor (2015) and Zhong and Li (2017). 118 conservatism, refers to the higher degree of verification to recognize gains (good news) than losses (bad news) (Basu, 1997; Watts, 2003a). Conditional conservatism is also known as asymmetrically timely loss recognition because income recognition in accounting is typically a choice between timely and postponed recognition of economic gains and losses (Ball et al., 2013). The incremental coefficient on negative returns is referred to as the asymmetric timeliness coefficient (Watts, 2003b). A stream of prior research beginning with Basu (1997) investigates the implications of conditional conservatism, focusing on why and how it implies that earnings are more positively associated with current share returns when they are negative than when they are positive. These studies suggest that earnings incorporate bad news about future cash flows more quickly than good news (e.g., Ball & Shivakumar, 2006; Ball et al., 2000; Banker et al., 2017; Basu, 1997; Black et al., 2018; Holthausen & Watts, 2001; Nikolaev, 2010). Basu (1997) predicts that stock returns and earnings tend to reflect losses in the same period, but stock returns reflect gains ahead of earnings. Basu develops his measure for asymmetric timeliness based on a regression of net income on stock returns with separate slopes for positive and negative returns in the same year. Positive and negative returns represent good and bad news respectively. Accounting income was chosen because it serves as a sensitive measure of financial reporting in general, for the reason that income statement variables are systematically associated with changes in balance sheet variables. Income statement timeliness therefore serves as an indicator for financial reporting timeliness (Ball et al., 2013). Basu found a higher coefficient of stock returns and a higher R2 from his regression for a sample of firms with negative stock returns than for a sample of firms with positive returns. These results suggest that bad news is incorporated into earnings on a timelier basis. This finding is consistent with the long-standing accounting principle grounded in the early research of Bliss (1924) that 119 accounting conservatism anticipates losses but not gains, and the asymmetric accounting rule that accountants should report the lowest value among the possible alternative values for assets and the highest alternative value for liabilities, as suggested by Watts and Zimmerman (1978). Ball and Shivakumar (2006) modified the Basu (1997) model to investigate the role of accruals accounting in the asymmetrically timely recognition of gains and losses. They suggest that asymmetrically timely recognition of gains and losses contribute to asymmetry in the relation between accruals and cash flows. Losses are more likely to be recognized in a timely manner because accrued non-cash charges decrease income, whereas recognition of gains are more likely to be deferred until they are realized in cash. They argue that economic gain and loss can be estimated as current-period cash flow plus any revision in the present value of expected future cash flows. Timely recognition of gains and losses is thus partly based on revisions of future cash flow expectations, which is made before accruals are realized. Therefore, timely recognition of gains and losses must be achieved via accruals. Consequently, accruals are expected to be positively associated with changes in current-period cash flows as current cash flows are likely to be positively correlated with expected future cash flows. Their results show that earnings incorporate information reflected in changes in operating cash flows asymmetrically by recognising bad news earlier than good news. Banker et al. (2017)58 further extend Ball and Shivakumar (2006) and argue changes in sales are a new major indicator for asymmetrical timeliness for earnings recognition. They suggest 58 Banker et al. (2016) argue that the piecewise-linear relation between earnings and stock returns can arise from cost stickiness rather than conditional conservatism. Cost stickiness refers to the asymmetric response of costs to sales increases versus decreases (e.g., Anderson et al., 2013; Banker et al., 2013; Banker & Byzalov, 2014; Banker et al., 2016; Weiss, 2010). Because sales changes and concurrent stock returns are positively correlated, cost stickiness leads to an asymmetric relation between earnings and stock returns, which is stronger for negative returns than for positive returns. Therefore, the standard estimates of asymmetric timeliness are likely biased upwards on average when sticky costs are prevalent. To control for this confounding effect, Banker et al. (2016) incorporate cost stickiness in standard conservatism models with additional variables of change in sales to the Basu (1997) model. 120 that changes in sale have an asymmetric effect on earnings conditional on both stock return and cash flow change asymmetries. Banker et al. (2017) argue changes in operating cash flow change accounts for part of the resource adjustment in response to the current shock and are a noisy indicator of the impact of these shocks on future cash flows. Operating cash flows are subject to transitory noise raised by normal variations in working capital (Ball & Shivakumar, 2006; Dechow, 1994), poor matching of capitalized expenditures including research and advertising (Dichev & Tang, 2009), and slow adjustment of resources in response to economic shocks (e.g., Horngren, Datar, & Rajan, 2014). Hence, changes in sales should provide significant incremental information for earnings recognition because sales changes are not affected by matching-related noise, and likely capture the fundamental drivers of future cash flows. Their results are consistent with the predictions that the inclusion of changes in sales add incremental information for asymmetric earnings recognition. This study focuses on conditional conservatism to examine whether accounting responds asymmetrically to the “good news” and “bad news” implied by changes in oil prices for O&G firms. Hypothesis Development Prior literature shows that cash flow changes and sales have an asymmetric effect on earnings incremental to that of stock returns (Ball & Shivakumar, 2006; Banker et al., 2017). This study extends the prior research to consider the role of product market prices in timely recognition of economic gains and losses in O&G sectors. There are four ways through which oil price shocks can be transmitted to earnings for these firms. First, changes in oil prices affect firms’ stock returns. The literature in energy economics provides extensive evidence that stock prices and returns move systematically with oil prices 121 (e.g., Cuñado & de Gracia, 2014; Filis et al., 2011; Jones & Kaul, 1996; Kilian & Park, 2009; Nandha & Faff, 2008). Empirical results show that oil prices play a predictive role in stock returns (Driesprong et al., 2008; Narayan & Gupta, 2015) and are positively associated with equity value in the O&G sector (El-Sharif et al., 2005; Faff & Brailsford, 1999; Sadorsky, 2001). Jones and Kaul (1996) suggest that responses from the U.S. stock market to oil shocks can be completely accounted for by the impact of these shocks on real cash flow. Therefore, changes in oil prices provide information for expectations of future cash flows. Given that changes in oil prices are positively correlated with stock prices for the O&G sector and stock returns are asymmetrically associated with earnings, changes in oil prices are expected to exhibit an asymmetric association with earnings. Second, oil price changes have a direct influence on O&G firms’ operating cash flows. Ball and Shivakumar (2006) argue that economic gain and loss can be measured of as current-period cash flow plus any revision in the present value of expected future cash flows. Lower oil prices may reduce the viability of drilling since the drilling and operating costs of extracting the oil or natural gas may exceed the revenue generated (Deloitte, 2016), which can result in a decrease in current-period cash flows. Lower commodity prices signal lower present values of expected future cash flows (reducing projects’ NPV). For example, given the long-term nature of many large-scale development projects, longer-term lower oil and natural gas prices can cause O&G firms to reduce or defer major expenditures. Reductions in oil prices can also affect the overall returns for existing projects, either lengthening the time until the expected returns are realized or impairing the value of assets. As the literature suggests that earnings are asymmetrically associated with changes in operating cash flows (e.g., Ball & Shivaakumar, 2006; Collins et al., 2014) and oil prices are positively related to cash flows for O&G firms (e.g., Jones & Kaul, 1996), changes in oil prices are expected to be associated with earnings, in a similar asymmetric pattern as changes in operating cash flows. 122 The third way through which oil prices affect earnings is impact on sales. Banker et al. (2017) suggest that changes in sales provide incremental information for conditional conservatism for its role in cash flow forecasting and reducing the noise in operating cash flow change. However, using changes in sales to project future sales can be noisy if demand patterns cannot be detected and accurately modelled (Murray et al., 2018). Time series methods of sales forecasting are only suitable for firms with sufficient historical data. Changes in oil prices are not affected by this noise. Oil prices are unified at the market level, which is not likely to be affected by factors influencing future sales, such as competition and individual firms’ business strategy. Oil is publicly traded in markets and the oil price is widely watched and followed, which make oil prices a more observable and objective indicator of projected future sales. Sales revenue is determined by price and quantity. Higher prices mean higher sales revenue given there is a steady demand for crude oil and refinery products in the market. For exploration companies, higher oil prices indicate higher future returns for their projects and higher value deals. Because the O&G industry is sensitive to changes in oil prices and their state of business is closely tied to oil price volatility, they are likely to use changes in oil prices as a major indicator of future sales and cash flow estimations. Given that oil prices are likely to be positively correlated with sales revenue and cash flow, which exhibit asymmetric association with earnings (Ball & Shivarkumar, 2006; Banker et al., 2016; Banker et al., 2017; Collins et al., 2014; Hsu et al., 2012), oil price changes are expected to be asymmetrically correlated with earnings recognition. A fourth impact of changes in oil prices is through the use of fair values in accounting. Lower expectations of cash flow lead to potential asset write-offs and impairment. Assets need to be assessed for write-down if carrying amount exceeds estimated fair value, but write ups are not permitted if oil prices increase subsequently. Increases in prices are reflected in the value of 123 revenues but not directly reflected in other asset values. Oil prices are not affected by matching- related noise in operating cash flow change or sales time series forecasting bias. Therefore, they are likely to capture the fundamental drivers of future cash flows in the O&G industry. This study argues that oil price changes provide significant incremental information for earnings recognition. Given that for O&G firms, higher oil prices mean higher stock returns, more operating cash flow and sales, positive (negative) oil price shocks are good (bad) news. Earnings are thus expected to exhibit asymmetric association with oil price changes. This study therefore hypotheses that: Hypothesis 4.1a: After controlling for the asymmetric effect of stock returns, operating cash flow changes and sales changes, earnings exhibit asymmetric association with oil price changes. As sales revenues is determined by selling prices and quantities, revenues can be closely correlated with prices if quantities remain stable. Thus, changes in prices can potentially provide similar information about future cash flows, which play a substituting role for sales changes. This study therefore includes an alternative hypothesis that: Hypothesis 4.1b: After controlling for the asymmetric effect of stock returns and operating cash flow changes, earnings exhibit asymmetric association with oil price changes. That is, the relation between changes in oil prices and earnings is not conditional on the asymmetric effect of changes in sales. An illustration for the development of the hypothesis is exhibited in Figure 4.1. The influence of changes in oil prices on economic gain and loss is transmitted through the channels of: 1) the predictive role of oil price to stock returns; 2) the impact of oil price changes on current 124 operating cash flow and revision of expected future cash flows; 3) the association between prices and sales revenues and 4) the use of fair value accounting. 125 Figure 4.1 Hypothesis Development for Study 3 Research Design This study examines the asymmetric responses of earnings to changes in product market prices. For expositional purposes, I start with the Basu (1997) asymmetric timeliness model:59 퐸퐴푅푁푖푡 = 휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 휀푖푡 (4.1) where 퐸퐴푅푁푖푡 is earnings in year t, scaled by beginning-of-year market value of equity, 푅퐸푇푖푡 is stock return for a 12-month period of fiscal year t, and 퐷푅푖푡 is a dummy variable equal to 1 if 푅퐸푇푖푡 is negative, and 0 otherwise. Conditional conservatism implies that the coefficient on 59 Replication of Banker et al. (2017) of the Basu (1997), Ball and Shivakumar (2006) and Banker et al. (2017) models is presented in Appendix 4.1. 126 퐷푅푖푡 × 푅퐸푇푖푡 is positive; that is, bad news (negative 푅퐸푇푖푡) is reflected in earnings to a greater extent than good news (positive 푅퐸푇푖푡). Ball and Shivakumar (2006) add the operating cash flow indicator to the Basu (1997) model: 퐸퐴푅푁푖푡 = 휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 (4.2) + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 휈푖푡 Where 훥퐶퐹푖푡 represents operating cash flow change in year t, scaled by beginning-of-year market value of equity, 퐷퐶푖푡 is a dummy variable equal to 1 if 퐷퐶푖푡 is negative, and 0 otherwise, and remaining variables are defined as above. Ball and Shivakumar (2006) predict positive coefficients on 퐷푅푖푡 × 푅퐸푇푖푡 and 퐷퐶푖푡 × 훥퐶퐹푖푡 , which represent conservatism for both indicators. Banker et al. (2017) further extend the conditional conservatism model by adding sales change as a third indicator: 퐸퐴푅푁푖푡 = 휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 (4.3) + 훽3퐷퐶푡 × 훥퐶퐹푖푡 + 훾1퐷푆푖푡 + 훾2훥푆퐴퐿퐸푆푖푡 + 훾3퐷푆푖푡 × 훥푆퐴퐿퐸푆푖푡 + 𝜍푖푡 where 훥푆퐴퐿퐸푆푖푡 is sales change in year t, scaled by beginning-of-year market equity, 퐷푆푖푡 is a dummy variable equal to 1 if 훥푆퐴퐿퐸푆푖푡 is negative, and 0 otherwise, and all other variables are defined as in Models 4.1 and 4.2. Banker et al. (2017) argue that the coefficient on 퐷푆푖푡 × 훥푆퐴퐿퐸푆푖푡 is positive; that is, conditional on both stock return and operating cash flow change asymmetries, earnings display asymmetric timeliness with respect to sales changes. Similar to Basu (1997) and Ball and Shivakumar (2006), positive asymmetric timeliness coefficients on 퐷퐶푡 × 훥퐶퐹푖푡 and 퐷푆푖푡 × 훥푆퐴퐿퐸푆푖푡 are expected, as they both likely contain useful information. 127 The coefficients on 푅퐸푇푖푡, 훥퐶퐹푖푡 and 훥푆퐴퐿퐸푆푖푡 capture recognition of good news (i.e., earnings response to favorable values of these variables). Banker et al. (2017) suggest that these coefficients can be either positive or negative. Roychowdhury and Watts (2007) argue that the coefficient on 푅퐸푇푖푡 can be negative in cases such as value-increasing (but capitalized) R&D expenditures reducing reported earnings, but increasing stock returns. Advertising expenditures may lead to a negative coefficient on 훥푆퐴퐿퐸푆푖푡. The coefficient on 훥퐶퐹푖푡 can be negative due to the noise-reduction role of operating accruals (Ball & Shivakumar, 2006; Dechow, 1994). The mechanical associations between earnings and concurrent cash flows and sales are controlled by 훥퐶퐹푖푡 and 훥푆퐴퐿퐸푆푖푡. In this study, I incorporate oil price changes as a new indicator of oil price returns to the Banker et al. (2017) three-indicator model: 퐸퐴푅푁푖푡 = 휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 (4.4) + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 훾1퐷푆푖푡 + 훾2훥푆퐴퐿퐸푆푖푡 + 훾3퐷푆푖푡 × 훥푆퐴퐿퐸푆푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 𝜍푖푡 where 푂푃푅푖푡−1 is the annualized oil price return in year t-1. There are three reasons for a lagged effect of oil prices on firm earnings. First, Hamilton (1983) and Hooker (1996) show that the economy normally responds for three–four quarters after the initial oil price shock. Second, Driesprong et al. (2008) suggest that investors seem to underreact to information in the price of oil and there is a lag between oil price shocks and stock market reactions. Third, Narayan and Sharma (2011), who document strong evidence of a lagged effect of oil prices on firm returns, suggest the mean reversion hypothesis as an alternative explanation for the lagged effect of oil prices on stock returns. Therefore, this study uses lagged oil price returns. 퐷푂푡−1 is an indicator variable equal to 1 if 푂푃푅푖푡−1 is negative, 0 otherwise. H4.1a predicts that changes in oil prices 128 exhibit asymmetric association with sales incremental to the stock return, operating cash flow and sales change. Therefore, the coefficient on 퐷푂푖푡−1× 푂푃푅푖푡−1 is expected to be positive (i.e., bad news (negative 푂푃푅푖푡−1) is reflected in earnings to a greater extent than good news (positive 푂푃푅푖푡−1)). All other variables are defined consistently with prior models. Positive asymmetric timeliness coefficients on 퐷푅푖푡 × 푅퐸푇푖푡, 퐷퐶푡 × 훥퐶퐹푖푡 and 퐷푆푖푡 × 훥푆퐴퐿퐸푆푖푡 are expected, in line with prior studies (Ball & Shivakumar, 2006; Banker et al., 2017; Basu, 1997). The coefficient for 푂푃푅푖푡−1 is expected to capture good news recognition that earnings respond to a positive oil price shock. As the literature (e.g., El-Sharif et al., 2005; Nandha & Faff, 2008; Sadorsky, 2011) suggests that increases in oil prices induce higher equity values and stock returns, I expect earnings to be positively associated with oil price returns. Thus, 훿3 is expected to be positive. However, 훿3 can be negative given that higher prices may negatively affect customers’ expenditure and reduce product demand in the market and consequently decrease reported earnings. The variable definitions are presented in Table 4.1. 129 Table 4.1 Variable Definitions for Study 3 Variable Definition and Data Item 퐸퐴푅푁푖푡 = net income (Compustat item NI) in year t, scaled by the market value of equity (PRCC_F×CSHO) at the beginning of the year; 푅퐸푇푖푡 = stock return for the 12-month period of fiscal year t (CRSP item RET); 퐷푅푖푡 = dummy variable that equals 1 if stock return 푅퐸푇푡 is negative, and 0 otherwise; 훥퐶퐹푖푡 = change in operating cash flow (Compustat item OANCF) from year t-1 to year t, scaled by market value of equity at the beginning of the year; 퐷퐶푖푡 = dummy variable that equals 1 if cash flow change 훥퐶퐹푡 is negative, and 0 otherwise; 훥푆퐴퐿퐸푆푖푡 = change in sales (Compustat item SALE) from year t-1 to year t, scaled by market value of equity at the beginning of the year; 퐷푆푖푡 = dummy variable that equals 1 if sales change 훥푆퐴퐿퐸푆푡 is negative, and 0 otherwise; 푂푃푅푖푡 = WTI oil price return for the 12-month period of fiscal year t; 퐷푂푖푡 = dummy variable that equals 1 if lagged WTI oil price return 푂푃푅푖푡 is negative, and 0 otherwise; 푊퐷푖푡 = long-lived asset write-down (Compustat item WDP) in year t, scaled by market value of equity at the beginning of the year; 퐺푊푖푡 = goodwill impairment (Compustat item GDWLIP) in year t, scaled by market value of equity at the beginning of the year. Additional Control Variables Used in Section 4.8 훥퐸푖푡 = change in pre-write-down earnings in year t (items PI-WDP- GDWLIP), scaled by market value of equity at the beginning of the year 퐷퐸푖푡 dummy variable that equals to 1 if 훥퐸푡 <0, and 0 otherwise; 훥퐺퐷푃푡 GDP growth in year t 퐵퐴푇퐻푖푡 훥퐸푡 if 훥퐸푡 below the median of the negative tail of 훥퐸푡, and 0 otherwise; 푆푀푂푂푇퐻푖푡 훥퐸푡 if 훥퐸푡 above the median of the positive tail of 훥퐸푡, and 0 otherwise; 퐷퐸퐵푇푖푡 A dummy variable that equals 1 if the firm’s debt if private (i.e. not publicly rated by Standard & Poor’s) and 0 otherwise. 130 Data and Sample 4.5.1 Sample Selection The sample selection process is detailed in Table 4.2. Crude oil prices are chosen as the proxy for product market prices for firms in O&G sectors.60 Annualized crude oil price returns are calculated using daily observations of West Texas Intermediate (WTI) crude oil prices.61 The WTI crude oil price data were retrieved from the U.S. Energy Information Administration (EIA) website. WTI represents the benchmark price of light, sweet group crude oil and is more liquid and actively traded than other benchmarks such as Brent in Europe, Dubai/Oman and Maya benchmarks (Bhar, Hammoudeh, & Thompson, 2008). Stock return data and financial report information are obtained from the CRSP/Compustat Merged database from 2002–2016. The sample period for this study starts from 2002 as data for asset write downs and goodwill impairment are only available in Compustat from then. I identify O&G firms as those with two-digit SIC equal to 13. Observations with missing or invalid data to calculate the variables for the regression analysis are discarded. Following Banker et al. (2017), observations with lagged stock price below $1 are removed from the sample. Following prior research (Banker et al., 2017; Hribar & Collins, 2002), observations with annual sales changes exceeding 50% were removed, to eliminate significant merges and large dispositions.62 The final sample comprises 1,224 observations for 219 U.S. O&G firms for the period 2002–2016. 60 During the sample period of this study, crude oil and natural gas prices generally moved in parallel, except for a slight divergence for the period September 2011–June 2012. 61 To minimize the impact of the fluctuation in crude oil prices in the empirical investigation, annualized oil price returns were used to measure the changes in oil prices. 62 Including these observations does not change the results. 131 Table 4.2 Sample Selection Firm-year Firm All observations in the CRSP/Compustat Merged (CCM database with listing status information between 2001– 67,984 8,551 2016) (North America, USD) Less non-reliable CCM links (2,326) (143) (440) (27) Less non-primary CCM links (2,151) (0) Less lagged stock price<$1 (12,635) (1,050) Less significant mergers and large dispositions Total observations with valid data for regression 50,432 7,331 variables for between 2001-2016 Total observations with valid data for regression 1,392 237 variables for the O&G sample between 2001 and 2016 Total observations with valid data for regression variables 1,224 219 for the sample between 2002-2016 for O&G firms Figure 4.2 shows the trends in oil prices as reflected in WTI crude oil prices (monthly). As is evident, oil prices were subject to large fluctuations during the sample period. Oil prices witnessed an upward trend during 2002–2008, reaching a historical high in July 2018 (USD $144.96/barrel) and then fell dramatically to USD $46.27 per barrel in December 2008. The price then gradually recovered to $108 per barrel in June 2014, before another dramatic fall to a low of less than $30 per barrel in February 2016. The sample period accounts for two rapid oil price declines, which provides variations in changes of oil prices to test whether earnings respond differently to good news and bad news arising from changes in product market conditions. 132 Figure 4.2 West Texas Intermediate (WTI) Oil Price, January 2012–January 2017 4.5.2 Descriptive Statistics The descriptive statistics are presented in Table 4.3. All continuous variables are winsorized at the top and bottom 1%. Following Banker et al. (2017), net income (퐸퐴푅푁푖푡), operating cash flow change (훥퐶퐹푖푡) and sales change (훥푆퐴퐿퐸푆푖푡) are scaled by the market value of equity at the beginning of the year. On average, net income is equal to −0.7% of lagged market value, and the median is 3%, which is at a similar level to prior studies (e.g., Banker et al. 2017). Consistent with the presence of conditional conservatism (Ball et al., 2000; Banker et al., 2017; Basu, 1997), scaled earnings are negatively skewed with a mean smaller than the median. Annual stock returns for O&G sector is 9.87% on average and the median is 5.32%. Some 45.18% of the sample has a negative stock return with 퐷푅푖푡 equal to 1, which is similar to the market- wide stock return performance documented in Banker et al. (2017). Average operating cash flow change is equal to −2.06% of the lagged market value and the median is 0.74%. Average sale change is equal to −2.16% of lagged market value with a median of 2.81%. Cash flow 133 decreases (퐷퐶푖푡) and sales decreases (퐷푆푖푡) account for 46.49% and 40.13% of the observations, respectively. In general, the distribution of net income, stock returns, operating cash flow change and sales change for this sample is largely consistent with prior studies (e.g., Banker et al., 2017). Annual crude oil price return is 7.12% for the period 2001–2015 and the median is 4.25%.63 The positively skewed oil price return indicates an overall increasing trend for oil prices during the sample period. Table 4.4 reports the Pearson correlations among variables. The pairwise correlation coefficients between stock return (푅퐸푇푖푡), scaled cash flow change (훥퐶퐹푖푡) and scaled sales change ( 훥푆퐴퐿퐸푆푖푡 ) are 0.1785 and 0.1071 respectively. The relatively low correlation coefficients indicate that these three indicators capture different aspects of firm performance and provide complementary information. The correlation coefficient between 훥퐶퐹푖푡 and 훥푆퐴퐿퐸푆푖푡 is 0.4974 (significant at the 0.05 level, two-tailed), suggesting a stronger association between cash flow and sales for the O&G sector compared with market-wide statistics (0.169) shown in Banker et al. (2017). These three indicators are significantly positively correlated with earnings. 푂푃푅푖푡−1 is positively correlated with earnings, operating cash flow changes and sales changes with coefficients of 0.2272, 0.2987 and 0.4021 respectively, consistent with the argument that oil price changes have a positive influence for O&G firms performance.64 63 Lagged oil price returns for the sample period 2002–2016 are actually the returns for 2001–2015. The mean and median for the oil price returns for 2002–2016 are 0.3481 and 0.1094 respectively. The differences in average returns for these two period is mainly attributed to the oil price recovery in 2016. 64 Though the correlation coefficient between 푂푃푅푖푡−1 and 푅퐸푇푖푡 is not statistically significant, 푅퐸푇푖푡 is positively correlated with concurrent oil price returns with a coefficient of 0.3843 (significant at the 0.05 level, two-tailed), indicating that stock prices react faster (than a one-year lag) to oil price changes. 134 Table 4.3 Descriptive Statistics for Full Sample Variable Mean S.D. Min Q1 Median Q3 Max 퐸퐴푅푁푖푡 -0.0726 0.3531 -2.0432 -0.0801 0.0305 0.0834 0.3972 퐷푅푖푡 0.4518 0.4979 0.0000 0.0000 0.0000 1.0000 1.0000 푅퐸푇푖푡 0.0987 0.5432 -0.8696 -0.2656 0.0532 0.4001 2.0955 퐷푅푖푡×푅퐸푇푖푡 -0.1586 0.2375 -0.8696 -0.2656 0.0000 0.0000 0.0000 퐷퐶푖푡 0.4649 0.499 0.0000 0.0000 0.0000 1.0000 1.0000 훥퐶퐹푖푡 -0.0206 0.1672 -0.7341 -0.0618 0.0074 0.0529 0.4635 퐷퐶푖푡×훥퐶퐹푖푡 -0.0614 0.1316 -0.7341 -0.0618 0.0000 0.0000 0.0000 퐷푆푖푡 0.3913 0.4882 0.0000 0.0000 0.0000 1.0000 1.0000 훥푆퐴퐿퐸푆푖푡 -0.0216 0.2833 -1.3574 -0.0646 0.0281 0.0984 0.6694 퐷푆푖푡×훥푆퐴퐿퐸푆푖푡 -0.0941 0.2302 -1.3574 -0.0646 0.0000 0.0000 0.0000 퐷푂푖푡−1 0.4281 0.4950 0.0000 0.0000 0.0000 1.0000 1.0000 푂푃푅푖푡−1 0.0712 0.3366 -0.5052 -0.2114 0.0425 0.2301 1.0096 퐷푂푖푡−1× 푂푃푅푖푡−1 -0.0952 0.1476 -0.5052 -0.2114 0.0000 0.0000 0.0000 푊퐷푖푡 -0.0035 0.0154 -0.1096 0.0000 0.0000 0.0000 0.0000 퐺푊푖푡 -0.0067 0.036 -0.2765 0.0000 0.0000 0.0000 0.0000 The table reports descriptive statistics for 1,224 firm-year observations from 2002–2016. All continuous variables are winsorized at the top and bottom 1%. The variable definitions are provided in Table 4.1. 135 Table 4.4 Correlation Matrix DO× DR× DC× DS× DO OPR EARN DR RET DC ΔCF DS ΔSALES OPR WD GW RET ΔCF ΔSALES (t-1) (t-1) (t-1) 퐸퐴푅푁푖푡 1 * 퐷푅푖푡 -0.2216 1 * * 푅퐸푇푖푡 0.2040 -0.7386 1 * * * 퐷푅푖푡×푅퐸푇푖푡 0.3961 -0.7372 0.7385 1 * * * * 퐷퐶푖푡 -0.2959 0.0952 -0.0935 -0.1141 1 * * * * * 훥퐶퐹푖푡 0.4213 -0.1115 0.1758 0.1925 -0.6246 1 * * * * * * 퐷퐶푖푡×훥퐶퐹푖푡 0.5263 -0.0864 0.0676 0.2078 -0.5209 0.8649 1 * * * * * * * 퐷푆푖푡 -0.3611 0.1197 -0.0986 -0.1286 0.4643 -0.3899 -0.3936 1 * * * * * * * * 훥푆퐴퐿퐸푆푖푡 0.4254 -0.0978 0.1071 0.1590 -0.3534 0.4974 0.5334 -0.5849 1 * * * * * * * 퐷푆푖푡×훥푆퐴퐿퐸푆푖푡 0.4600 -0.0458 0.022 0.1473 -0.2979 0.4418 0.5726 -0.4832 0.9020 1 * * * * * * * * * * 퐷푂푖푡−1 -0.2225 -0.0887 0.0665 0.0567 0.2695 -0.3063 -0.3138 0.4362 -0.3756 -0.3261 1 * * * * * * * * 푂푃푅푖푡−1 0.2272 0.0013 -0.0091 -0.0144 -0.2709 0.2987 0.3036 -0.4442 0.4021 0.3475 -0.7549 1 * * * * * * * * * 퐷푂푖푡−1×푂푃푅푖푡−1 0.3309 0.0091 -0.036 0.0488 -0.3768 0.3617 0.4013 -0.5602 0.5006 0.4738 -0.7457 0.7576 1 * * * * * * * * * * 푊퐷푖푡 0.2109 -0.0296 0.0257 0.0886 -0.1287 0.1167 0.1348 -0.1173 0.1810 0.1997* -0.0686 0.0586 0.1140 1 * * * * * * * * * * 퐺푊푖푡 0.2789 -0.1353 0.1519 0.2470 -0.0408 0.0474 0.0759 -0.0582 0.1353 0.1909 -0.0233 -0.0038 0.0909 0.1671 1 Pearson correlations are reported for the sample of 1,224 O&G firm-year observations from 2002 to 2016. Correlations with * are statistically significant at the 5 percent level. The variable definitions are provided in Table 4.1. 136 Empirical Results Table 4.5 reports regression estimates for Model 4.4. Columns 1 and 2 of Table 4.5 report the Banker et al. (2017) three-indicator model (Model 4.3), with (Column 1) and without (Column 2) controlling for firm fixed effects respectively, for comparison.65 Column 3 reports the regression results for the extended Banker et al. (2017) model (4.4) with the additional indicator of changes in oil prices. The regression is estimated with control for firm fixed effects, as suggested by Ball et al. (2013). As oil price return is calculated on an annual basis, which already accounts for change in economic conditions across years, year fixed effects are not included in the regressions with variables of oil price returns. Similar to Basu (1997), the asymmetric timeliness coefficient on 퐷푅푖푡×푅퐸푇푖푡is positive and significant (at the 0.01 level, two-tailed). The positive coefficient indicates that bad news (negative푅퐸푇푖푡) is recognized in concurrent earnings more fully than good news (positive 푅퐸푇푖푡 ), suggesting conditional conservatism for stock returns. Consistent with Ball and Shivakumar (2006), the coefficient for 퐷퐶푖푡 × 훥퐶퐹푖푡 is positive and significant (at the 0.01 level, two-tailed), indicating asymmetric timeliness with respect to concurrent operating cash flow change. The coefficient on 퐷푆푖푡 × 훥푆퐴퐿퐸푆푖푡 is consistent with Banker et al. (2017)—positive and significant—indicating asymmetric timeliness with respect to sales changes. 65 Patatoukas and Thomas (2011) report bias in firm-level cross-sectional asymmetry estimates of Basu’s (1997) indicators, which they attribute to scale effects. Ball et al. (2013) suggest that the issue can be addressed by including fixed-effects regressions. When firm-specific effects are taken into account, estimates do not exhibit the bias. I therefore estimate regressions in this study with controlling for firm fixed effects. 137 Table 4.5 Asymmetric Timeliness Estimates for Multiple Indicators Based on Banker et al. (2017) Banker et al. Banker et al. (2017) (2017) Four-Indicator Full Three- Full Three- Model Indicator Model Indicator Model predicted (1) (2) (3) VARIABLES sign EARN EARN EARN * * * 퐷푅푖푡 0.0392 0.0330 0.0381 (1.8618) (1.6498) (1.8007) 푅퐸푇푖푡 -0.0113 -0.0206 -0.0096 (-0.3680) (-0.7404) (-0.3104) *** *** *** 퐷푅푖푡×푅퐸푇푖푡 + 0.3488 0.2911 0.3438 (5.4533) (4.4574) (5.3546) 퐷퐶푖푡 -0.0022 0.0004 0.0009 (-0.1285) (0.0237) (0.0523) 훥퐶퐹푖푡 -0.0689 0.0320 -0.0587 (-0.4451) (0.2167) (-0.3769) *** ** *** 퐷퐶푖푡×훥퐶퐹푖푡 + 0.7158 0.4871 0.6966 (3.1213) (2.2604) (3.0179) *** * *** 퐷푆푖푡 -0.0747 -0.0385 -0.0659 (-4.0139) (-1.9390) (-3.1882) 훥푆퐴퐿퐸푆푖푡 -0.1070 -0.0744 -0.1076 (-1.5487) (-1.0934) (-1.5251) *** *** *** 퐷푆푖푡×훥푆퐴퐿퐸푆푖푡 + 0.3424 0.2656 0.3342 (3.8131) (3.0811) (3.6025) 퐷푂푖푡−1 -0.0004 (-0.0260) 푂푃푅푖푡−1 -0.0094 (-0.3437) 퐷푂푖푡−1× 푂푃푅푖푡−1 + 0.0872 (0.9824) Firm-fix effect - Yes Yes Year-fix effect Yes Yes - Observations 1,224 1,224 1,224 Adjusted R2 0.480 0.545 0.480 F-statistic for the full effect of *** *** *** 퐷푅푖푡, 푅퐸푇푖푡, 퐷푅푖푡×푅퐸푇푖푡 18.31 8.94 18.15 *** *** *** 퐷퐶푖푡, 훥퐶퐹푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 14.64 12.13 13.89 *** *** *** 퐷푆푖푡, 훥푆퐴퐿퐸푆푖푡, 퐷푆푖푡×훥푆퐴퐿퐸푆푖푡 26.43 10.57 15.97 퐷푂푖푡−1, 푂푃푅푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 0.57 F-statistics for the asymmetric effect of *** *** *** 퐷푅푖푡, 퐷푅푖푡×푅퐸푇푖푡 24.90 16.90 24.13 *** ** *** 퐷퐶푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 9.75 5.03 9.00 *** *** *** 퐷푆푖푡, 퐷푆푖푡×훥푆퐴퐿퐸푆푖푡 19.44 10.75 16.29 퐷푂푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 1.07 138 *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the results for model: Column 1 and Column 2: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푡 × 훥퐶퐹푖푡 + 훾1퐷푆푖푡 + 훾2훥푆퐴퐿퐸푆푖푡 + 훾3퐷푆푖푡 × 훥푆퐴퐿퐸푆푖푡 + 𝜍푖푡 Column 3: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 훾1퐷푆푖푡 + 훾2훥푆퐴퐿퐸푆푖푡 + 훾3퐷푆푖푡 × 훥푆퐴퐿퐸푆푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 𝜍푖푡 The variable definitions are provided in Table 4.1. H4.1a predicts that after controlling for the asymmetric effect of stock returns, operating cash flow changes and sales changes, earnings exhibit asymmetric association with oil price changes. The coefficient for 퐷푂푖푡−1× 푂푃푅푖푡−1 is therefore expected to be positive. Column 3 in Table 4.4 reports the regression for the full four-indicator model (Model 4.4), which examines the incremental effect of oil price changes on asymmetric timeliness of earnings recognition after controlling for stock returns, operating cash flow changes and sales changes. The results show that the coefficient for 퐷푂푖푡−1× 푂푃푅푖푡−1 is positive (0.0872) but not significant. Sales are determined by price and quantity. Given the relatively high correlation between 훥푆퐴퐿퐸푆푖푡 and 푂푃푅𝑖푡−1 (0.4021, significant at the 0.05 level) for my sample and high VIF for 훥푆퐴퐿퐸푆푖푡 (7.61) in regression Model 4.4, the coefficient for 퐷푂푖푡−1× 푂푃푅푖푡−1 could be biased 66 by collinearity issues between sales and price. I therefore replace 훥푆퐴퐿퐸푆푖푡 in the Banker et al. (2017) model with lagged oil price change (푂푃푅푖푡−1 ) to test whether earnings exhibit asymmetric association with oil price changes incremental to stock returns and operating cash flow changes to test for H4.1b. The model is specified as: 퐸퐴푅푁푖푡 = 휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 (4.5) + 훽3퐷퐶푡 × 훥퐶퐹푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 휀푖푡 66 The VIF for 훥푆퐴퐿퐸푆𝑖푡 in Model 4.4 (four-indicator model) is 7.61, which is the highest among all the variables in the model, indicating potential for multi-collinearity. The VIF for 푂푃푅푖푡−1 is 3.05, indicating a low likelihood of multi-collinearity issues between oil price change and other variables in the model. 139 where all variables are as defined previously. Column 1 of Table 4.6 reports the Ball and Shivakumar (2006) model before sales have been added. The results suggest that earnings respond asymmetrically to stock returns and changes in operating cash flows, with statistically significant coefficients for 퐷푅푖푡 × 푅퐸푇푖푡 (0.3644) and 퐷퐶푖푡 × 훥퐶퐹푖푡 (1.2140) (significant at the 0.01 level, two-tailed). When lagged oil price return is added to the model, the coefficient on 퐷퐶푖푡 × 훥퐶퐹푖푡 is reduced by 16.22%, from 1.2140 in the Ball and Shivakumar (2006) model (Column 1) to 1.0518 in the three-indicator model with lagged oil price return (Column 2). This suggest that oil price return is an important correlated omitted variable in the Ball and Shivakumar (2006) model for firms in the O&G industry. Column 2 in Table 4.6 reports the regression results for the three-indicator model, with sales 67 changes replaced by lagged oil price returns. The coefficient on 퐷푂푖푡−1× 푂푃푅푖푡−1 is 0.2992, significant at the 0.01 level (two-tailed), indicating that earnings are more sensitive to bad news (negative 퐷푂푖푡−1) than good news (positive 퐷푂푖푡−1), suggesting conditional conservatism for oil price returns. 67 The mean VIF for Model 4.5 is 3.29 and VIFs for variables in the model are all below 5, indicating low likelihood of multi-collinearity issues among the variables. 140 Table 4.6 Asymmetric Timeliness Estimates for Multiple Indicators Based on Ball and Shivakumar (2006) Three-Indicator Model: Ball and Shivakumar with lagged Oil Price (2006) Return predicted (1) (2) VARIABLES sign EARN EARN * * 퐷푅푖푡 0.0377 0.0372 (1.7293) (1.7173) 푅퐸푇푖푡 -0.0180 -0.0123 (-0.5642) (-0.3916) *** *** 퐷푅푖푡×푅퐸푇푖푡 + 0.3644 0.3495 (5.4376) (5.3065) ** 퐷퐶푖푡 -0.0353 -0.0107 (-1.9726) (-0.5811) 훥퐶퐹푖푡 -0.2465 -0.1835 (-1.5409) (-1.1414) *** *** 퐷퐶푖푡×훥퐶퐹푖푡 + 1.2140 1.0518 (5.4194) (4.5684) 퐷푂푖푡−1 0.0104 (0.6562) 푂푃푅푖푡−1 -0.0042 (-0.1541) *** 퐷푂푖푡−1× 푂푃푅푖푡−1 + 0.2992 (3.8476) Firm-fix effect Yes Yes Observations 1,224 1,224 Adjusted R2 0.432 0.447 F-statistic for the full effect of *** *** 퐷푅푖푡, 푅퐸푇푖푡, 퐷푅푖푡×푅퐸푇푖푡 18.11 18.15 *** *** 퐷퐶푖푡, 훥퐶퐹푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 45.49 28.86 *** 퐷푂푖푡−1, 푂푃푅푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 10.64 F-statistics for the asymmetric effect of *** *** 퐷푅푖푡, 퐷푅푖푡×푅퐸푇푖푡 25.40 24.11 *** *** 퐷퐶푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 30.94 20.95 *** 퐷푂푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 15.00 *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the results for model: Column 1 : 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 휈푖푡 Column 3: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂퐼푡 × 푂푃푅푖푡−1 + 휈푖푡 The variable definitions are presented in Table 4.1. The product market indicator (퐷푂푖푡−1) plays an incremental role in conditional conservatism. Based on the results, while an increase in oil price returns does not affect earnings significantly, 141 a $1 oil price return decrease (bad news) reduces earnings by 29.5 cents on average (=−0.0042 + 0.2992), indicating much quicker recognition of bad news than good news for oil price changes. The F-statistic, which tests whether positive and negative oil price returns have equal influence (퐷푂푖푡−1= 퐷푂푖푡−1×푂푃푅푖푡−1) is 15.00 (significant at the 0.01 level), indicating strong asymmetric response of earnings to oil price returns. Thus, this result supports the prediction that earnings exhibit asymmetric association with oil price changes after controlling for the asymmetric effect of stock return and operating cash flow changes. Overall, the results indicate that for firms in the O&G industry, earnings respond asymmetrically to good news and bad news from changes in oil prices. Bad news (negative oil price returns) are more fully recognized in concurrent earnings than good news (positive oil price returns). Oil price changes provide complementary information to stock returns and operating cash flow changes, which reflect information on future cash flows, supporting H4.1b. However, in the full four-indicator model (Model 4.4), neither positive nor negative lagged oil price returns exhibit significant association with earnings, because of the correlation between sales changes, oil price returns and earnings. The results suggest that lagged oil price changes and sales changes provide similar information regarding future cash flows.68 Additional Analysis Banker et al. (2017) suggest that asset write downs are the most fundamental manifestation of conservatism. Impairment tests are conducted for asset groups “at the lowest level for which identifiable cash flows are largely independent of cash flows of other assets and liabilities” 68 The results are consistent with firms as price takers with inelastic demand for their product across a reasonable price range. Under such assumptions, quantities produced and sold are expected to remain constant and the information provided by sales changes would primarily reflect the change in product market prices. 142 (SFAS, 144, para. 10). Therefore, indicators that help predict future cash flows for individual asset classes are relevant for assessing impairment. Banker et al. (2017) show that asset write off and impairment losses respond asymmetrically to stock returns, operating cash flow changes and sales changes. This study extends prior literature to test whether product market prices exhibit a similar asymmetric association with asset impairment. This study argues that oil price changes are expected to be an important factor considered by accountants in their impairment decisions in O&G firms because of the impact on inventory write-downs, long-lived asset write downs, impairment of intangible assets, and measuring and recognizing goodwill from acquisitions. For example, inventories are stated at the lower of cost and net realizable value.69 If oil prices decrease to a level lower than production cost, value of inventories needs to be impaired to net realizable value (PwC, 2017).70 For long-lived assets, SFAS 144 requires a two-step impairment test. The first step is to compare the estimated sum of undiscounted future cash flow associated with the asset (or asset group) with the asset’s carrying amount. If the undiscounted cash flows are less than the asset’s carrying amount, then in the second step, the asset is written down to its fair value. The fair value is calculated based on market prices if there is an active market for the assets or estimated as the total of discounted future cash flows when quoted market prices are absent. Indicators for decreases in expected future cash flows increase both the probability (in step 1) and the magnitude (in step 2) of the asset impairment. For O&G companies, impairment assessments for long-lived assets incorporate commodity market uncertainties including projected commodity pricing, supply and demand for goods and services, and future market conditions (Halliburton, 2017). If crude oil prices decline significantly and/or remain at low levels for a 69 Net realizable value is estimated as the selling price in the ordinary course of business, less reasonably predictable cost of completion, disposal and transportation. 70 Selling price of oil/oil products is estimated using the market price for oil at the balance sheet date. 143 sustained period of time, the long-lived assets’ carrying amounts are likely to be impaired because of the decrease in projected future cash flows. Given the long-term nature of many large-scale development projects, even perceptions of longer-term lower oil prices by oil firms can cause them to reduce or defer major expenditure (Halliburton, 2017).71 Decreases in oil prices can affect the overall returns for these projects by either extending the time until the expected returns are realized or recognising impairment expenses for the assets. Goodwill is recognized when the consideration paid is greater than the fair value of net asset acquired in M&A deals. Goodwill can represent access to new markets, community/government relationship, portfolio management, technology and expertise (PwC, 2017). O&G companies might be inclined to pay a premium to protect the value of existing O&G operations that they already own. As per ASU 2011-08, firms have the option of first performing a qualitative assessment to test goodwill for impairment on a reporting-unit-by- reporting-unit basis.72 If the qualitative assessment indicates that it is probable that carrying amount is greater than the fair value of a reporting unit, the entity will conduct the two-step goodwill impairment test as described in ASC 350; otherwise, the two-step goodwill impairment test is not required. When oil prices increase, firms pay premiums in acquisition deals for the high projected future cash flows that lead to goodwill recognition. When oil prices decrease significantly, estimated fair value based on projected future cash flows can be potentially below carrying amounts, leading to impairment of goodwill. 71 Halliburton is the eighth largest U.S. oil company based on market value (40.49 billion USD) in 2018 (www.statistc.com). Impairment for Halliburton in the 2016 fiscal year was 3.36 billion USD, whereas impairment for 2017 was 647 million USD, indicating a reverse relation between oil prices and corporate asset impairment recognitions. Halliburton suggests that the impairment in 2016 was primarily a result of the down turn in the energy market. The impairment consisted of fixed asset impairment and write offs, inventory write-downs, and impairment of intangible assets. 72 For example, in Halliburton, the test for goodwill impairment is carried out for each reporting unit, which is the same as their reportable segments, the Completion and Production division and the Drilling and Evaluation division, comparing the estimated fair value of each reporting unit with the reporting unit’s carrying value. 144 Overall, accounting standards for asset write downs and impairment suggest that firms will recognize unrealized losses reflected in unfavorable indicators of future cash flows in a timely manner. To test whether asset write downs and goodwill impairment exhibit an asymmetric association with oil price changes, following Banker et al. (2017), I replace the dependent variable, 퐸퐴푅푁푖푡, with long-lived asset write down (푊퐷푖푡) and goodwill impairment (퐺푊푖푡) in Model 4.5,73 to formulate the following two models: 푊퐷푖푡 = 휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 (4.6) + 훽 퐷퐶 + 훽 훥퐶퐹 + 훽 퐷퐶 × 훥퐶퐹 + 훿 퐷푂 1 푖푡 2 푖푡 3 푡 푖푡 1 푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 휀푖푡 퐺푊푖푡 = 휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 (4.7) + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푡 × 훥퐶퐹푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 휀푖푡 where 푊퐷푖푡 is the long-lived asset write down in year t, scaled by market value of equity at the beginning of the year, and 퐺푊푖푡 is the impairment in year t, scaled by market value of equity at the beginning of the year. The remaining variables were defined previously. Following Banker et al. (2017), missing values of 푊퐷푖푡 and 퐺푊푖푡 are replaced with 0. As write-downs are coded as negative numbers, the expected coefficient signs for 푊퐷푖푡 and 퐺푊푖푡 are positive (similar to the models with earnings as the dependent variable). The descriptive statistics and correlation coefficients for 푊퐷푖푡 and 퐺푊푖푡 are reported in Tables 4.3 and 4.4 respectively. Both tangible asset write downs and goodwill impairment are coded as negative given that they reduce earnings. Tangible asset write downs (푊퐷푖푡) and goodwill impairment (퐺푊푖푡) are scaled by the market value of equity at the beginning of the year. On 73 Given the relatively high likelihood of multi-collinearity issues in Model 4.4, the tests for asset write down and goodwill impairment are based on Model 4.5, which excludes change in sales but includes oil price return. 145 average, tangible asset write down is equal to −0.35% of lagged market value and average goodwill impairment is equal to −0.67% of lagged market value. Among firms reporting a non- zero tangible asset write down (12.91% of the sample), the mean is −3.90% of lagged market value and median is −1.20%. Some 6.9% of firms report a non-zero goodwill impairment, equivalent to −15.29% of lagged market value on average and the median is −4.95%. Table 4.7 reports the estimates for tangible asset write downs (Column 1) and goodwill impairment (Column 2). For the sample of O&G firms from 2002–2016, from Column 1, tangible asset write-downs do not present asymmetric responses to stock returns, operating cash flow changes or lagged oil price returns. The link between commodity prices and asset write- downs is likely to differ among the O&G activities that firms engaged in and their accounting policies (Alciatore, Easton, & Spear, 2000; Deloitte, 2015). For example, O&G entities engaging in exploration and production activities have the option to account for their operations using either the successful efforts or the full-cost method. The major difference between these methods is their treatment of costs related to exploration of new O&G reserves. Under successful efforts, firms only capitalize exploration costs relating to successful wells. All exploration costs for unsuccessful wells, or “dry holes”, are recorded as expenses. Alternatively, firms may choose the full-cost method, under which exploration costs relating to both dry and successful wells are capitalized. The SEC74 requires firms under the full-cost method to perform quarterly impairment tests, or ceiling tests, on their capitalized O&G assets, which requires firms to permanently write-down capitalized O&G assets to the extent that these assets’ net capitalized costs exceed the cost center ceiling. The choice of accounting method will directly 74 Regulation S-X, Rule 4-10. 146 affect the accounting for tangible asset write-downs and influence the reporting of net income and cash flows (Deloitte, 2015). According to SEC Regulation S-X, Rule 4-10, the “ceiling” is calculated as the sum of 1) the present value of estimated future net revenues computed by applying current prices of oil and gas reserves with a 10% discount rate for present value, 2) cost of any properties not being amortized, 3) lower of cost or the estimated fair value of unproved properties included in the amortized costs, and 4) (minus) any tax effects associated with differences between the book and tax basis of the excluded properties and the unproven properties being amortized. The ceiling test thus offers a “lower of cost or market” test. Given that the oil price is important in the calculation of the “ceiling”, changes in oil prices are expected to have a greater impact for full-cost firms’ asset write-down than for successful-effort firms. To test this proposition, I re- perform Model 4.6 with two subsamples of full-cost firms and successful-effort firms (see Table 4.8). Given that current oil prices are used in the calculation of the “ceiling”, an additional variable 푂푃푅푖푡 is included to control for the potential impact of current oil price returns on write-downs. Consistent with predictions, only write-downs for full-cost firms exhibit an asymmetrical association with both current and lagged oil price returns with coefficients of 0.0145 (퐷푂푖푡× 푂푃푅푖푡) and 0.0228 (퐷푂푖푡−1× 푂푃푅푖푡−1), significant at the 0.1 level (two-tailed). The results suggest full-cost firms are more sensitive to oil price changes and recognize asset write-downs triggered by oil price declines in a more timely and fuller fashion. From Column 2 in Table 4.7, goodwill impairment exhibits asymmetric association with stock returns and lagged oil price returns with positively significant coefficients for 퐷푅푖푡 ×푅퐸푇푖푡 (0.0635, significant at the 0.01 level) and 퐷푂푖푡−1× 푂푃푅푖푡−1 (0.0487, significant at the 0.1 level), indicating more timely recognition of goodwill impairment implied by bad news from stock 147 returns and oil price changes. However, no significant asymmetric association between changes in operating cash flows (퐷퐶푖푡 × 훥퐶퐹푖푡) and goodwill impairment were observed in this sample. Banker et al. (2017) suggest that as goodwill has an indefinite future life, goodwill impairment estimation is primarily based on long-term cash flow indicators. The value of goodwill incorporates cash flows that are not based on assets with a finite life in place. The long-term indicators, which reflect the change in total discounted cash flows over an infinite horizon, will therefore be able to provide more useful information for goodwill impairment relatively to short-term indicators. Low-price environment can be a sign of reduced long-term investment opportunities and expected future returns from existing projects. Potential buyers are therefore less likely to place a premium on O&G assets, which leads to less goodwill being recognized. Meanwhile, recorded goodwill will need to be assessed for impairment because of decreases in fair value. Results in this study are consistent with Banker et al. (2017) that the relative impact of long-term indicators (stock returns and oil price changes) are greater for goodwill impairment than short-term indicators (operating cash flow changes). 148 Table 4.7 Estimates for Tangible Asset Write-Down and Goodwill Impairment Three-Indicator Model: with lagged Oil Price Return Tangible Asset Goodwill Write-Down Impairment predicted (1) (2) VARIABLES sign WD GW 퐷푅푖푡 0.0001 0.0053 (0.0496) (0.7606) 푅퐸푇푖푡 -0.0003 -0.0007 (-0.1474) (-0.0720) *** 퐷푅푖푡×푅퐸푇푖푡 + 0.0054 0.0653 (0.8230) (2.9083) 퐷퐶푖푡 0.0012 0.0047 (0.5046) (0.5141) 훥퐶퐹푖푡 -0.0061 -0.0853 (-0.3793) (-1.2781) 퐷퐶푖푡×훥퐶퐹푖푡 + 0.0349 0.1645 (0.9927) (1.2334) 퐷푂푖푡−1 0.0018 0.0079 (0.6689) (1.4210) 푂푃푅푖푡−1 0.0026 0.0018 (0.6645) (0.1519) * 퐷푂푖푡−1× 푂푃푅푖푡−1 + 0.0177 0.0487 (1.6303) (1.8441) Firm-fix effect Yes Yes Year-fix effect - - Observations 1,224 1,224 Adjusted R2 0.174 0.069 *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the results for model: Column 1: 푊퐷푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 휈푖푡 Column 2: 퐺푊푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 휈푖푡 The variable definitions are presented in Table 4.1. 149 Table 4.8 Estimates for Tangible Asset Write-Down Full-Cost Successful- Full Sample Method Effort Method predicted (1) (2) (3) VARIABLES sign WD WD WD 퐷푅푖푡 -0.0005 -0.0044 -0.0038 (-0.1401) (-1.6430) (-1.0001) 푅퐸푇푖푡 -0.0028 -0.0030 0.0024 (-1.0109) (-0.9950) (0.7593) 퐷푅푖푡×푅퐸푇푖푡 + 0.0063 -0.0028 -0.0252 (0.4882) (-0.5302) (-1.4795) 퐷퐶푖푡 -0.0008 -0.0022 0.0010 (-0.2306) (-0.9199) (0.3077) 훥퐶퐹푖푡 -0.0107 0.0021 0.0120 (-0.5571) (0.3454) (0.3766) * 퐷퐶푖푡×훥퐶퐹푖푡 + 0.0215 -0.0148 0.0026 (0.3996) (-1.6797) (0.0678) 퐷푂푖푡 0.0013 0.0021 0.0016 (0.3365) (1.1056) (0.2844) 푂푃푅푖푡 0.0073 -0.0019 0.0094 (0.8314) (-0.5966) (1.4958) * 퐷푂푖푡× 푂푃푅푖푡 -0.0037 0.0145 0.0041 (-0.1783) (1.8936) (0.3337) 퐷푂푖푡−1 0.0045 -0.0009 -0.0036 (0.9204) (-0.3438) (-0.4472) 푂푃푅푖푡−1 0.0089 -0.0061 0.0049 (1.0683) (-1.4876) (0.6279) * 퐷푂푖푡−1× 푂푃푅푖푡−1 + 0.0311* 0.0228 -0.0270 (1.7053) (1.8853) (-1.0086) Firm-Fix effect Yes Yes Yes Observations 901 278 332 Adjusted R2 0.124 0.060 0.133 F-statistic for the full effect of 퐷푂푖푡, 푂푃푅푖푡, 퐷푂푖푡× 푂푃푅푖푡 0.49 1.45 1.35 * 퐷푂푖푡−1, 푂푃푅푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 0.06 2.79 0.03 F-statistics for the asymmetric effect of ** 퐷푂푖푡, 퐷푂푖푡× 푂푃푅푖푡 3.53 2.25 0.93 ** 퐷푂푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 2.35 4.48 1.13 *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the results for model: 푊퐷푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 휃1퐷푂푖푡 + 휃2푂푃푅퐼푡 + 휃3퐷푂푖푡 × 푂푃푅푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 휈푖푡 The variable definitions are presented in Table 4.1. 150 Sensitivity Analysis 4.8.1 Extension of the Original Basu (1997) Model To examine the robustness of the main results, I perform additional analyses on the relationship between oil price changes and earnings. First, to explore whether the asymmetric association between oil price changes and earning is conditional on changes in cash flow, I regressed accounting incomes on variables of stock returns and lag oil price returns (as an extension to the original Basu (1997) model). The regression results are presented in Table 4.9. Consistent with the main results, the coefficient for 퐷푂푖푡−1× 푂푃푅푖푡−1 is significantly positive, indicating an asymmetric pattern in timeliness of gain and loss recognition in regards to information reflected in oil price changes, after controlling for stock return (Column 2). When variables of oil price returns are added to the model, the coefficient of 퐷푅푖푡 × 푅퐸푇푖푡 reduces from 0.5262 (in Column 1) to 0.4589 (in Column 2), suggesting that product market prices provide complementary information to stock returns for cash flow estimations and the asymmetric association between change in product market prices is not conditional on changes in operating cash flow. The improved adjusted-R2 from 25.3% to 34% indicates that earnings are better explained with oil price changes taken into consideration. 151 Table 4.9 Asymmetric Timeliness Estimates for Multiple Indicators Extension of Basu (1997) Model with Indicator for lagged Oil Price Returns Basu (1997) Model Basu (2007) Model with lagged Oil Price Returns predicted (1) (2) VARIABLES sign EARN EARN ** ** 퐷푅푖푡 0.0611 0.0546 (2.5558) (2.3974) * 푅퐸푇푖푡 -0.0562 -0.0345 (-1.8740) (-1.1839) *** *** 퐷푅푖푡×푅퐸푇푖푡 + 0.5262 0.4589 (7.0514) (6.6528) 퐷푂푖푡−1 0.0003 (0.0194) 푂푃푅푖푡−1 -0.0023 (-0.0776) *** 퐷푂푖푡−1× 푂푃푅푖푡−1 + 0.5488 (7.4933) Firm-fix effect Yes Yes Observations 1,224 1,224 Adjusted R2 0.253 0.340 F-statistic for the full effect of *** *** 퐷푅푖푡, 푅퐸푇푖푡, 퐷푅푖푡×푅퐸푇푖푡 24.69 23.56 *** 퐷푂푖푡−1, 푂푃푅푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 53.93 F-statistics for the asymmetric effect of *** *** 퐷푅푖푡, 퐷푅푖푡×푅퐸푇푖푡 46.67 40.55 *** 퐷푂푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 60.98 *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the results for model: Column 1: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 휀푖푡 Column 3: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훿1퐷푂𝑖푡−1 + 훿2OPR𝑖푡−1 + 훿3퐷푂𝑖푡 × 푂푃푅𝑖푡−1 + 휀푖푡 The variable definitions are presented in Table 4.1. 152 4.8.2 Timing of Oil Price Changes The SEC (2009) requires firms to estimate the value of proved oil and gas reserves based on 12-month average prices, calculated as the unweighted arithmetic average of the first-day-of- the-month prices, prior to the end of the reporting period. Earnings therefore will incorporate information reflected in concurrent oil price changes. I test whether earnings respond in the similar asymmetrical fashion to concurrent oil price changes, as compared with lagged oil price changes, by replacing the lagged oil price returns (푂푃푅푡−1) with current oil price returns (푂푃푅푡) in Model 4.5. The results are reported in Column 1 of Table 4.10. The coefficient for 퐷푂푖푡× 푂푃푅푖푡 is not statistically significant, indicating that earnings do not exhibit an asymmetric pattern in timeliness of gain and loss recognition in regards to information reflected in current-year oil price changes. One possible explanation could be 푂푃푅푡 is not providing any incremental information to 푅퐸푇푡 considering the high correlation between 푅퐸푇푖푡 and 푂푃푅푖푡 (0.3843, significant at five percent level). I then re-perform the regression with both current and lagged oil price returns included in the model. As shown in Column 2, the main results hold when current oil price returns are controlled for, with the coefficient for 퐷푂푖푡−1× 푂푃푅푖푡−1 significantly positive at the 0.01 level (two-tailed). The coefficient for 퐷푂푖푡 × 푂푃푅푖푡 decreases from -0.1265 to -0.1787 (significant at five percent level, two tailed) when lagged oil price changes are controlled for. 153 Table 4.10 Asymmetric Timeliness Estimates for Multiple Indicators With Concurrent and Lagged Oil and Price Returns Extended Ball & Extended Ball & Shivakumar (2006) Shivakumar (2006) Model: Model: with current Oil with current and lagged Price Returns Oil Price Returns predicted (1) (2) VARIABLES sign EARN EARN ** ** 퐷푅푖푡 0.0455 0.0440 (2.0250) (1.9972) 푅퐸푇푖푡 -0.0215 -0.0216 (-0.6838) (-0.6894) *** *** 퐷푅푖푡×푅퐸푇푖푡 + 0.3975 0.3681 (5.3884) (5.0890) * 퐷퐶푖푡 -0.0333 -0.0096 (-1.8728) (-0.5262) 훥퐶퐹푖푡 -0.2423 -0.1720 (-1.5432) (-1.0757) *** *** 퐷퐶푖푡×훥퐶퐹푖푡 + 1.1882 1.0463 (5.3625) (4.5660) *** ** 퐷푂푖푡 -0.0610 -0.0415 (-3.0261) (-2.0954) 푂푃푅푖푡 -0.0383 0.0587 (-1.3303) (1.5659) ** 퐷푂푖푡× 푂푃푅푖푡 + -0.1265 -0.1787 (-1.5132) (-2.1346) 퐷푂푖푡−1 -0.0172 (-0.9098) 푂푃푅푖푡−1 -0.0049 (-0.1724) *** 퐷푂푖푡−1× 푂푃푅푖푡−1 + 0.2832 (3.7606) Firm-fix effect Yes Yes Observations 1,224 1,224 Adjusted R2 0.435 0.451 F-statistic for the full effect of *** *** 퐷푅푖푡, 푅퐸푇푖푡, 퐷푅푖푡×푅퐸푇푖푡 13.04 11.39 *** *** 퐷퐶푖푡, 훥퐶퐹푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 40.62 28.87 ** 퐷푂푖푡, 푂푃푅푖푡, 퐷푂푖푡× 푂푃푅푖푡 0.43 43.79 *** 퐷푂푖푡−1, 푂푃푅푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 11.45 F-statistics for the asymmetric effect of *** *** 퐷푅푖푡, 퐷푅푖푡×푅퐸푇푖푡 24.16 21.41 *** *** 퐷퐶푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 30.26 20.88 * 퐷푂푖푡, 퐷푂푖푡× 푂푃푅푖푡 0.70 2.98 *** 퐷푂푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 16.57 154 *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the results for model: Column 1: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 휃1퐷푂푖푡 + 휃2푂푃푅푖푡 + 휃3퐷푂푖푡 × 푂푃푅푖푡 + +휈푖푡 Column 2: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 휃1퐷푂푖푡 + 휃2푂푃푅푖푡 + 휃3퐷푂푖푡 × 푂푃푅푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 휈푖푡 The variable definitions are presented in Table 4.1. 4.8.3 O&G Firm Sensitivity to Oil Price Changes Prior studies show that the impact of commodity prices on O&G firms’ stock returns differs based on the activities that firms engage in (Boyer & Filion, 2007; Jin & Jorion, 2006). Boyer and Filion (2007) show that oil price returns are more significantly associated with stock returns for oil producers than other firms in the sector. To test whether the asymmetrical timeliness of earnings recognition in response to oil price changes differs for different types of O&G firms, I re-perform Model 4.5 in two subsamples: Crude Petroleum and Natural Gas (3-digit SIC=131) and other firms. The majority of the firms in “Crude Petroleum and Natural Gas” are engaged in exploration and development (E&P) activities. Other firms include the classifications of “Natural Gas Liquids (SIC code of 132)” and “Oil and Gas Field Services (SIC of 138)”.75 Regression results are tabulated in Table 4.11. Results indicate that E&P firms are more sensitive to bad news reflected in lagged changes in oil prices, with a positive coefficient for 퐷푂푖푡−1× 푂푃푅푖푡−1 of 0.3489 (significant at the 0.05 level, two-tailed), than firms engaged in filed services. 75 “Oil and Gas Field Services” includes further classification of “Drilling Oil and Gas Wells (1381)”, “Oil and Gas Field Exploration Services (1382)” and “Oil and Gas Field Services, Not Elsewhere Classified (1389)”. 155 Table 4.11 Estimates for Earnings within Subsamples: Crude Petroleum and Natural Gas Firms vs. Oil and Gas Field Service Firms Crude Petroleum Other firms and Natural Gas (3-digit SIC=132 or 138) (3-digit SIC=131) predicted (1) (2) VARIABLES sign EARN EARN *** 퐷푅푖푡 0.1495 0.0187 (3.0509) (0.5538) 푅퐸푇푖푡 0.1601 0.0546 (1.2319) (1.2145) *** ** 퐷푅푖푡×푅퐸푇푖푡 + 0.5366 0.1931 (2.9279) (2.2509) ** 퐷퐶푖푡 0.0205 0.0753 (0.5061) (1.9829) 훥퐶퐹푖푡 -1.0518* -0.1123 (-1.9167) (-0.4410) *** *** 퐷퐶푖푡×훥퐶퐹푖푡 + 2.7132 2.1343 (3.4478) (2.9613) ** 퐷푂푖푡−1 0.0496 0.0660 (1.4785) (2.5296) 푂푃푅푖푡−1 0.1006 0.0809 (0.9988) (1.3921) ** 퐷푂푖푡−1× 푂푃푅푖푡−1 + 0.3489 0.1014 (2.2905) (0.8207) Firm-fix effect Yes Yes Observations 970 353 Adjusted R2 0.435 0.262 F-statistic for the full effect of *** ** 퐷푅푖푡, 푅퐸푇푖푡, 퐷푅푖푡×푅퐸푇푖푡 13.00 3.94 *** *** 퐷퐶푖푡, 훥퐶퐹푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 13.91 11.53 ** 퐷푂푖푡−1, 푂푃푅푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 3.60 0.35 F-statistics for the asymmetric effect of ** ** 퐷푅푖푡, 퐷푅푖푡×푅퐸푇푖푡 4.14 4.67 *** ** 퐷퐶푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 12.12 8.75 * 퐷푂푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 3.82 0.08 *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the results for model: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 휈푖푡 The variable definitions are presented in Table 4.1. 156 4.8.4 Full-Cost Method vs. Successful-Effort Method As suggested in Section 4.7, asset write-downs for firms under the full-cost method are more sensitive to oil price changes than for firms utilizing the successful-effort method. To further test whether earnings exhibit similar differentiation between these two methods, I re-perform Model 4.5 in two subsamples of observations under the full-cost and successful-effort methods. As shown in Table 4.12, the coefficient of 퐷푂푖푡−1× 푂푃푅푖푡−1 (0.9724) for firms utilizing the full- cost method is positive and significant at the 0.01 level (two-tailed) but not for successful-effort firms, suggesting that full-cost method firms are more sensitive to bad news reflected in oil price returns, which is consistent with earlier findings. 157 Table 4.12 Estimates for Earnings within Subsamples: Full-Cost Firms vs Successful- Effort Firms Full Cost Successful-Effort predicted (1) (2) VARIABLES sign EARN EARN ** * 퐷푅푖푡 0.2070 0.0915 (2.3162) (1.7367) 푅퐸푇푖푡 0.3877 0.0427 (1.2820) (0.7123) ** 퐷푅푖푡×푅퐸푇푖푡 + 0.4456 0.3865 (1.1637) (1.9857) * 퐷퐶푖푡 0.0065 0.0623 (0.0853) (1.7789) 훥퐶퐹푖푡 -1.5883 -0.1023 (-1.5650) (-0.3942) ** *** 퐷퐶푖푡×훥퐶퐹푖푡 + 3.1605 1.7403 (2.3386) (2.9746) * 퐷푂푖푡−1 0.1246 -0.0089 (1.7931) (-0.2901) 푂푃푅푖푡−1 0.1173 0.0052 (0.6557) (0.1112) *** 퐷푂푖푡−1× 푂푃푅푖푡−1 + 0.9724 0.0541 (2.9218) (0.4070) Firm-fix effect Yes Yes Observations 429 513 Adjusted R2 0.435 0.238 F-statistic for the full effect of *** ** 퐷푅푖푡, 푅퐸푇푖푡, 퐷푅푖푡×푅퐸푇푖푡 8.03 3.34 *** *** 퐷퐶푖푡, 훥퐶퐹푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 5.95 9.00 ** 퐷푂푖푡−1, 푂푃푅푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 3.91 0.43 F-statistics for the asymmetric effect of ** 퐷푅푖푡, 퐷푅푖푡×푅퐸푇푖푡 0.32 3.23 ** *** 퐷퐶푖푡, 퐷퐶푖푡×훥퐶퐹푖푡 5.69 8.71 ** 퐷푂푖푡−1, 퐷푂푖푡−1× 푂푃푅푖푡−1 7.25 0.23 *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the results for model: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 훿1퐷푂퐼푡−1 + 훿2OPR푖,푡−1 + 훿3퐷푂퐼푡 × 푂푃푅푖푡−1 + 휈푖푡 The variable definitions are presented in Table 4.1. 158 4.8.5 Additional Controls for Economic Factors and Reporting Factors This study also tests whether the main results hold with controls for additional “economic factors” and “reporting factors” following Riedl (2004). Economic factors include changes in pre-write-down earnings (∆퐸푖푡) and GDP growth (∆퐺퐷푃푡). Reporting factors include indicators for “big bath” charges (퐵퐴푇퐻푖푡), earnings smoothing (푆푀푂푂푇퐻푖푡) and private debt (퐷퐸퐵푇푖푡). Results are presented in Table 4.13, and are consistent with main analyses when these additional factors are controlled for. 159 Table 4.13 Asymmetric Timeliness Estimates for Multiple Indicators with Additional Controls Three-Indicator Model: Three-Indicator Model: Lagged Oil Price Lagged Oil Price Return Return predicted (1) (2) VARIABLES sign EARN EARN 퐷푅푖푡 0.0056 -0.0048 (0.2928) (-0.2638) 푅퐸푇푖푡 -0.0144 -0.0114 (-0.4892) (-0.3887) *** *** 퐷푅푖푡×푅퐸푇푖푡 + 0.3332 0.1797 (5.2363) (3.6190) 퐷퐶푖푡 -0.0267 -0.0002 (-1.6187) (-0.0138) 훥퐶퐹푖푡 -0.1347 0.1587 (-0.8494) (1.0507) 퐷퐶푖푡×훥퐶퐹푖푡 + 0.3075 -0.0485 (1.4609) (-0.2059) 퐷푂푖푡−1 0.0061 0.0209 (0.3560) (1.3469) 푂푃푅푖푡−1 -0.0359 -0.0317 (-1.3200) (-1.4121) ** *** 퐷푂푖푡−1× 푂푃푅푖푡−1 + 0.2072 0.2441 (2.3829) (2.7868) *** 훥퐸푖푡 0.2047 0.4221 (1.1765) (2.6855) 퐷퐸푖푡 -0.0233 -0.0214 (-1.4859) (-1.5150) *** 훥퐺퐷푃푡 -0.8791 -0.4954 (-2.7033) (-1.5323) 퐵퐴푇퐻푖푡 0.1423 -0.0466 (0.9155) (-0.3264) *** *** 푆푀푂푂푇퐻푖푡 -0.3025 -0.3320 (-5.4391) (-6.3070) 퐷퐸퐵푇 0.0058 푖푡 (omitted) (0.2582) Constant 0.1429*** (5.6131) Firm-Fix effect - Yes Observations 902 870 Adjusted R2 0.596 0.709 *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the results for model: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + +훿1퐷푂푖푡−1 + 훿2OPR푖푡−1 + 훿3퐷푂푖푡 × 푂푃푅푖푡−1 + 𝜌1∆퐸푖푡 + 훽2퐷퐸푖푡 + 훽3∆퐺퐷푃푡 + 훽4퐵퐴푇퐻푖푡 + 훽5푆푀푂푂푇퐻푖푡 + 𝜌6퐷퐸퐵푇푡 + 휈푖푡 The variable definitions are presented in Table 4.1. 160 Discussion and Conclusion This study examines the role of product markets in asymmetrically timely gain and loss recognition. I argue that product market prices provide complementary information for future cash flows, in addition to stock returns and operating cash flow changes and even sales changes. I therefore propose product market price change as a new indicator for conditional conservatism, especially for industries where the overall business performance and outlook are closely associated with product market prices. Employing a sample of firms in the U.S. O&G sector for the period 2002–2016, this study shows that earnings respond asymmetrically to lagged crude oil price changes, that bad news (negative oil price shocks) is recognised more fully and in a timelier fashion than good news (positive oil price shocks). The results however suggest that the asymmetric impact of changes in product market prices on earnings is subsumed in the effect of the changes in sales when indicators of changes in sales and changes in prices are both included in the model. Empirical results are robust when additional economic and reporting factors are controlled for. This study also examines the role of a product market index on firms’ impairment decisions. The results suggest an asymmetric association between goodwill impairment and lagged oil price returns incremental to the asymmetric effect of stock returns. When oil price decreases, firms recognize goodwill impairment in a timelier manner compared with oil price increase periods. This study also provides evidence that firms under the full-cost method are more sensitive to oil price returns by recognizing tangible asset write-downs caused by oil price movements in a more timely and fuller fashion compared with successful-effort firms. This study contributes to the literature by examining the role of product markets in accounting decisions. The results show that accounting reflects changes in product market prices in a 161 delayed fashion. It appears to take time (one-year lag) before news of earnings raised from changes in product market prices is fully reflected in financial reporting. Product market price changes are able to signal potential sacrifices of earnings quicker than other accounting-based performance measures, providing investors with an opportunity to reduce their downside risk by taking protective actions early. This study has several limitations. First, this study examines a single industry; generalization to other industries would require caution. Further research is needed to examine how the findings extrapolate to other industries. Second, this study focuses on conditional conservatism. Further research could consider the impact of product markets on other types of conservatism. In addition, some of the measures used in this study may contain non-trivial errors. Qiang (2007) suggests that book-value-based measures of conditional conservatism are subject to error introduced by offsetting news. Third, the sample of this study consists of well-established firms and results may not generalize to less-established firms. Moreover, the sample for this study is extracted from the U.S. market. Further study may re-examine the research question in other countries such as the U.K. and Canada, as firms in oil-exporting and oil-importing countries may react differently to oil price movements. Last but not least, this study did not differentiate the reasons for oil price shocks (e.g., supply vs. demand shocks). Future research could further address this issue to investigate in more detail whether firms’ earnings react differently to different types of price shocks. 162 CHAPTER 5: CONCLUSION Introduction This thesis comprises three studies that examine financial accounting issues. The first study focuses on conflict risk. Conflict risk has been identified as a key risk factor for MNEs. Managers of MNEs operating in conflict-prone environments need to effectively manage their investment projects. Study one explores the impact of conflict risk on investment efficiency for MNEs. Continuing my line of interest in conflict events, the second study examines the influence of a recent series of ISIS terrorist attacks on the foreign exchange market. This study employs intra-day data to examine the real-time reactions of foreign exchange returns to news announcements of terrorist attacks. The foreign exchange market was chosen as it is one of the largest and most liquid financial markets, and likely to incorporate information from unexpected shocks on a timely basis. The third study examines the relation between product markets and conditional conservatism. This study builds from a recent result in the literature that earnings are asymmetrically associated with changes in sales after controlling for stock returns and changes in operating cash flows (Banker et al., 2017). Study three extends this prior study to examine whether earnings respond asymmetrically to information contained in product market prices for a sample of firms from the O&G sector. The reminder of this chapter is organized as follows. Section 5.2 provides an overview of these three studies. Section 5.3 summarizes the contributions of this thesis. Each study is subject to limitations inherent in the study designs, and in particular, in the availability of data to study uncertainties relating to business operations in conflict zones. The limitations for these three studies are discussed in the Discussion and Conclusion sections of Chapters 2, 3 and 4 respectively. 163 Overview of the Three Studies 5.2.1 Study 1: The Influence of Conflict Risk on Investment Efficiency for Multinational Enterprises This study explores the impact that conflict risk has on firms’ investment efficiency using a sample of U.S.-listed MNEs that have subsidiaries in conflict-affected regions; specifically, I test whether conflict risk results in suboptimal investment. The results indicate that when MNEs are exposed to relatively high conflict risk, they are more likely to defer or bypass investment opportunities. In addition, the results suggest that the higher the conflict risk that MNEs are exposed to, the poorer the overall quality of their financial reports. To operate in conflict-intense regions, firms may need to rely on local social networks, increasing off-the-book transactions. These transactions can be difficult to track and monitor, which creates opportunities for manipulation of financial reports. This study finds supporting evidence for a significant negative association between conflict intensity and financial reporting quality when using an aggregated proxy for overall financial reporting quality. This study also tests the relationship between financial reporting quality and investment efficiency. Consistent with prior research (e.g., Biddle et al., 2009; Chen et al., 2011), financial reporting quality is found to be negatively associated with both underinvestment and overinvestment, showing that better financial quality helps to mitigate both underinvestment and overinvestment. 5.2.2 Study 2: The Impact of Terrorism on Financial Markets: Intra-day Evidence from Foreign Exchange Market Reactions to ISIS Attacks Study two examines the influence of ISIS terrorist attacks on the highly liquid foreign exchange market. Most announcements of ISIS terrorist events showed no systematic pattern of returns 164 in response to the attacks; only a few ISIS attacks created a negative impact on foreign exchange returns. Specifically, the first major ISIS attack in the region at the Jewish Museum in Belgium, the London Bridge attacks, and to a lesser extent, the November Paris Attack were associated with a depreciation in exchange rates. Depreciation was most pronounced around the announcement of the confirmation of casualties. The effects are mostly short-lived and foreign exchange markets are typically able to recover within a day of the attack. There is also an increase in volatility around some attacks, however, any reaction is short-lived and markets recover within a day. 5.2.3 Study 3: The Role of Product Markets in Asymmetrically Timely Gain and Loss Recognition: Evidence from the U.S. Oil and Gas Industry This study examines the role of product markets in asymmetrically timely gain and loss recognition. I argue that product market prices provide complementary information for future cash flows, in addition to stock returns and operating cash flow changes. Employing a sample of firms in the U.S. O&G sector for the period 2002–2016, this study shows that earnings respond asymmetrically to lagged crude oil price changes, that bad news (negative oil price shocks) are recognized more fully and in a more timely fashion than good news (positive oil price shocks) after controlling for stock returns and changes in operating cash flows. The results however suggest that the asymmetric impact of changes in product market prices on earnings is subsumed in the effect of the changes in sales when indicators of changes in sales and changes in prices are both included in the model. Empirical results are robust when additional economic and reporting factors are controlled for. This study also examines the role of the product market index on firms’ write-down and impairment decisions. The results suggest an asymmetric association between goodwill impairment and lagged oil price returns incremental to the asymmetric effect of stock returns. 165 When oil prices decrease, firms recognize goodwill impairments in a timelier manner compared with oil price increase periods. This study shows evidence that, in comparison to successful- effort, firms under the full-cost method are more sensitive to changes in oil prices. For full-cost firms, both tangible asset write downs and earnings are asymmetrically associated with oil price returns. Contributions The contributions of the three studies presented in this thesis include the following. Study one contributes to three streams of literature. First, it advances understanding on the link between conflict risk and firm-level investments. Driffield et al. (2013) studied the prevalence of firms investing in conflict countries and found that countries with weaker institutions and fewer concerns about corporate social responsibility are more likely to invest in conflict regions. Dai et al. (2006) suggest that conflict risk reduces the likelihood of survival for foreign subsidiaries in conflict regions. This study takes a step further, to explore MNEs’ investment performance in conflict-affected environments. Second, study one contributes to the literature in terms of the relation between uncertainties and economic outcomes. Most research has focused on political uncertainties (e.g., Bialkowski et al., 2008; Cao et al., 2017; Jens, 2017; Kesten & Mungan, 2015; Pástor & Veronesi, 2013). This study extends the current understanding on the impact of violent conflicts (within the broad category of political uncertainties) on economic outcomes. Third, study one contributes to the literature examining the association between reporting quality and investment efficiency (e.g., Biddle et al., 2009; Lara et al., 2015) and ownership structure (e.g., Chen et al., 2011; Chen et al., 2011). This study adds to the literature by examining the impact of geographically defined risks, rarely studied, on firms’ investment decisions and performance. 166 Risk disclosure has received attention because of apparent increased uncertainty in the business environment (Brown et al., 2018). SEC (2013) suggested that a more appropriate approach to risk disclosures needs to be considered and highlighted the importance of disclosures relating to non-U.S. operations. By examining the association between conflict risks and investment efficiency, study one provides evidence that sufficient disclosure on overseas operations, especially those in conflict zones, is of importance to assessing investment efficiency. Study two contributes to the literature to provide a better understanding of the dissemination of information in financial markets. First, this study extends prior research focusing on the aftermath of 9/11 (e.g., Carter & Simkins, 2004; Charles & Darne, 2006; Coleman, 2012; Karolyi & Martell, 2010), to examine a recent series of ISIS attacks in Europe. This study shows that in the post-9/11 period, the economic importance of terrorist attacks in Western countries is declining. While terrorist attacks attract much attention in the media, their impacts on financial markets are limited. Second, this study adds to the literature examining the impact of unexpected exogenous shocks on financial markets. Terrorist attacks have a clearly identifiable event time, attract wide and immediate media coverage and are free from privileged information (e.g., Coleman, 2012, Kollias et al., 2012), which enables more precise testing and understanding of market reactions to new information. Third, this study contributes to the literature employing intra-day foreign exchange return data to explore the immediate influence of terrorism shocks on markets. Many of the prior studies employing daily data to examine the impact of terrorist attacks on financial markets can be problematic. Using daily data is unlikely to capture the instant movement in stock prices and returns immediately after attacks occur. Financial markets are able to recover more quickly from terrorist attacks after 9/11 (Chen & Siems, 2004). This study shows that for 167 the recent attacks, the impact on foreign exchange markets is short-lived and can be recovered within one day. Study three contributes to the literature by examining the role of product market prices in accounting decisions. This study extends prior research to show that accounting-based earnings respond asymmetrically to changes in product market prices. Studies of conditional conservatism typically rely on firm-reported performance measures, such as cash flows and sales (e.g., Ball & Shivakumar, 2006; Banker et al., 2017), and examine their impact on concurrent earnings. This study shows that one-year lagged oil price returns have an asymmetric impact on O&G firms’ net income, indicating that product market prices are able to signal changes in future cash flows ahead of accounting-based performance measures. This study is also of value to stakeholders who demand accounting conservatism, for example lenders and investors. This shows that accounting reflects changes in product market prices in a delayed fashion. Product market price changes are able to signal potential sacrifices of earnings quicker than other accounting-based performance measures, providing investors with an opportunity to reduce their downside risk by taking protective actions early. 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Australian Accounting Review, 27(2), 195–213. 184 APPENDIX 2.1 RELATION BETWEEN CONFLICT INTENSITY AND UNDERINVESTMENT (OVERINVESTMENT) WITH CONTROL FOR FIRM-FIXED EFFECT Dependent Variable = UNDER Dependent Variable = OVER VARIABLES FRQ DisAccr DisRev DD FRQ DisAccr DisRev DD Conflict_Intensity -0.0005 -0.0002 -0.0004 -0.0001 -0.0013** -0.0012* -0.0014** -0.0014** (-1.4048) (-0.4951) (-0.8566) (-0.1401) (-2.1464) (-1.8957) (-2.0411) (-2.1328) FRQ -0.0018 -0.0002 (-1.4388) (-0.0687) Conflict_Intensity×FRQ 0.0027*** 0.0009 (3.9531) (0.6689) DisAccr -0.0024** -0.0003 (-2.0428) (-0.1575) Conflict_Intensity×DisAccr 0.0023*** 0.0010 (3.3178) (0.7456) DisRev -0.0249*** -0.0040 (-3.2002) (-0.2813) Conflict_Intensity×DisRev 0.0052 -0.0013 (1.0180) (-0.1462) DD -0.0064*** 0.0001 (-4.5730) (0.0243) Conflict_Intensity×DD 0.0035*** -0.0002 (4.1319) (-0.1268) Size 0.0051*** 0.0051*** 0.0051*** 0.0050*** 0.0036*** 0.0036*** 0.0036*** 0.0035*** (12.1903) (12.2271) (12.2702) (12.1297) (4.8289) (4.8484) (4.8322) (4.7777) MTB -0.0001 -0.0001* -0.0001** -0.0001** 0.0001 0.0001 0.0001 0.0001 (-1.5363) (-1.7614) (-2.3067) (-2.0007) (0.9653) (1.0131) (1.0029) (1.0072) ROA 0.0020*** 0.0022*** 0.0020*** 0.0021*** -0.0002 -0.0002 -0.0002 -0.0001 (2.9445) (3.2828) (3.0170) (3.0941) (-0.1274) (-0.1653) (-0.1309) (-0.0945) LOSS 0.0011 0.0011 0.0011 0.0011 0.0002 0.0002 0.0003 0.0002 (1.4860) (1.5204) (1.5818) (1.5073) (0.1867) (0.1634) (0.2316) (0.1387) Leverage 0.0105*** 0.0104*** 0.0104*** 0.0105*** -0.0112*** -0.0116*** -0.0115*** -0.0115*** (4.9441) (4.8453) (4.8182) (4.9007) (-2.7805) (-2.8898) (-2.8632) (-2.8516) 185 Tangibility 0.0552*** 0.0552*** 0.0557*** 0.0558*** -0.0739*** -0.0722*** -0.0732*** -0.0729*** (15.0633) (15.0584) (15.1481) (15.2611) (-11.4598) (-11.2664) (-11.3780) (-11.3361) Slack -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001** -0.0001** -0.0001** -0.0001** (-2.9908) (-2.9906) (-2.9047) (-2.9980) (-2.2672) (-2.2419) (-2.2874) (-2.2607) CFO_SALE 0.0000 -0.0000 -0.0000 0.0000 -0.0002 -0.0002 -0.0002 -0.0002 (0.1832) (-0.0756) (-0.3341) (0.0278) (-1.2529) (-1.2127) (-1.3190) (-1.1917) OPERATING_CYCLE 0.1557 0.1641 0.1207 0.1480 -0.2647 -0.3039 -0.3260 -0.2665 (1.0783) (1.1267) (0.8266) (1.0257) (-1.0938) (-1.2592) (-1.3427) (-1.1023) Z_SCORE 0.0000 -0.0000 -0.0000 -0.0000 0.0001 0.0001 0.0001 0.0001 (0.0631) (-0.1576) (-0.1565) (-0.0431) (0.7818) (0.7899) (0.7699) (0.7685) 𝜎CFO 0.0047** 0.0040** 0.0037** 0.0036** 0.0083*** 0.0071** 0.0069** 0.0079*** (2.5024) (2.2206) (2.0480) (1.9807) (2.7666) (2.4442) (2.3886) (2.6954) 𝜎SALE 0.0029*** 0.0030*** 0.0029*** 0.0029*** 0.0021** 0.0023** 0.0022** 0.0021** (4.5349) (4.6937) (4.5872) (4.6065) (1.9794) (2.2081) (2.1459) (2.0088) 𝜎Invest 0.0232*** 0.0226*** 0.0226*** 0.0228*** 0.0410*** 0.0409*** 0.0411*** 0.0408*** (4.7641) (4.6449) (4.6431) (4.6877) (5.6787) (5.6839) (5.7107) (5.6466) Constant -0.0220*** -0.0224*** -0.0230*** -0.0226*** 0.0262*** 0.0260*** 0.0262*** 0.0263*** (-7.7936) (-7.8948) (-8.1213) (-8.0174) (5.3776) (5.3843) (5.4000) (5.4213) Firm-fix effect Yes Yes Yes Yes Yes Yes Yes Yes Year-fix effect Yes Yes Yes Yes Yes Yes Yes Yes Observations 22,902 23,073 23,066 22,920 18,928 19,084 19,088 18,941 Adj R-squared 0.053 0.052 0.052 0.054 0.050 0.049 0.049 0.049 FRQ is the aggregated financial reporting quality proxy calculated as the average of standardized DisAccr, DisRev and DD. DisAccr is discretionary accruals estimated following Kothari et al. (2005). DisRev is discretionary revenue estimated following McNichols and Stubben (2008) and Stubben (2010). DD is discretionary current accruals predicted by Dechow and Dichev’s (2002) model as modified by McNichols (2002) and Francis et al. (2005). Other variables are defined in Table 2.1. Coefficients are estimated using standard errors adjusted for two-dimensional clustering at the firm and year level. *** p < 0.01, ** p < 0.05, * p < 0.1 186 APPENDIX 2.2 REGRESSION ANALYSIS FOR CONFLICT INTENSITY AND FINANCIAL REPORTING QUALITY WITH CONTROL FOR FIRM-FIXED EFFECT VARIABLES FRQ DisAccr DisRev DD Conflict_Intensity 0.0003 0.0061** 0.0003 0.0011 (0.1344) (2.2413) (0.8737) (0.4540) Size -0.0294*** -0.0116*** -0.0032*** -0.0195*** (-10.1143) (-3.8786) (-7.2817) (-6.7604) MTB -0.0027*** -0.0037*** -0.0002*** -0.0031*** (-6.1194) (-8.2961) (-3.5965) (-6.9306) ROA 0.0085* 0.0339*** -0.0016** 0.0037 (1.8877) (7.3138) (-2.2970) (0.8202) LOSS -0.0105** 0.0003 0.0006 -0.0050 (-2.0513) (0.0616) (0.7859) (-0.9839) Leverage -0.0882*** -0.0106 -0.0111*** -0.0294* (-5.6705) (-0.6503) (-4.6703) (-1.9114) Tangibility 0.1196*** 0.0838*** 0.0345*** 0.0723*** (4.7829) (3.1597) (8.8840) (2.9279) Slack 0.0001 -0.0003** 0.0000 -0.0005*** (0.6841) (-2.1011) (1.6100) (-3.0917) CFO_SALE -0.0000 -0.0003 -0.0013*** 0.0040*** (-0.0605) (-0.5720) (-14.6195) (7.2057) OPERATING_CYCLE -2.2066** -3.8750*** -1.9596*** 1.4430 (-2.3240) (-3.8788) (-13.4012) (1.5394) Z_SCORE 0.0005 0.0055*** -0.0002** 0.0005 (0.8177) (9.0893) (-2.3852) (0.8552) 𝜎CFO -0.5874*** -0.2666*** -0.0203*** -0.2152*** (-49.7802) (-21.4438) (-11.1672) (-18.4221) 𝜎SALE 0.0466*** 0.0101** -0.0072*** 0.0043 (10.9387) (2.2381) (-10.9335) (1.0173) 𝜎Invest -0.0748** -0.1574*** 0.0007 -0.1514*** (-2.3921) (-4.6982) (0.1423) (-4.8930) Constant 0.2083*** -0.0955*** -0.0205*** -0.0214 (10.7943) (-4.7623) (-6.9874) (-1.1197) Firm-fix effect Yes Yes Yes Yes Year-fix effect Yes Yes Yes Yes Observations 42,190 45,890 45,903 42,249 Adj R-squared 0.165 0.091 0.051 0.062 This table presents the results from the regression model: 퐹푅푄푖푡+1=훽0 + 훽1퐶표푛푓푙𝑖푐푡_퐼푛푡푒푛푠𝑖푡푦푖푡 + Σ훾푗퐶표푛푡푟표푙푗푖푡 + 휀푖푡 Financial reporting quality is measured using four proxies. FRQ is the aggregated financial reporting quality proxy calculated as the average of standardized DisAccr, DisRev and DD. DisAccr is discretionary accruals estimated following Kothari et al. (2005). DisRev is discretionary revenue estimated following McNichols and Stubben (2008) and Stubben (2010). DD is discretionary current accruals predicted by Dechow and Dichev’s (2002) model as modified by McNichols (2002) and Francis et al. (2005). The regression is estimated with controls for firm-fix and year-fix effect. Variables are defined in Table 2.1. Coefficients are estimated using standard errors adjusted for two-dimensional clustering at the firm-year level. *** p < 0.01, ** p < 0.05, * p < 0.1 187 APPENDIX 3.1 EVENT EXCHANGE RATE RETURNS (GBP/USD) Panel A Cumulative Exchange Rate Returns for a 10 minute window around when a Terrorist Attack is First Reported (1) Cumulative (2) (3) (4) (5) Event return Benchmark 1 Difference† Benchmark 2 Difference† -5 to +5 Cumulative return =(1)-(2) Previous Day =(1)-(4) Event Date minutes -15 to -5 minutes -5 to +5 min. 12 Westminster attack 22-Mar-17 -1.3358 0.1813 -1.5171 0.6019 -1.9377 14 Manchester Arena Bombing 23-May-17 -0.0184 -0.0010 -0.0174 0.2039 -0.2223 London Bridge and Borough Market 15 attack 2-Jun-17 0.4657 0.1940 0.2717 0.2424 0.2233 Panel B Cumulative Exchange Rate Returns for a 10 minute window around when Confirmed Casualties are First Reported 12 Westminster attack 22-Mar-17 -0.0398 0.3489 -0.3887 -0.8359 0.7961 14 Manchester Arena Bombing 23-May-17 -0.5914 0.2719 -0.8633 0.7414 -1.3328 London Bridge and Borough Market 15 attack 3-Jun-17 0.4657 0.1940 0.2717 0.2424 0.2233 Cumulative returns are multiplied by 1,000 for ease of presentation. The event window is -5 minutes to +5 minutes where t = 0 when a terrorist attack is first reported. Column (1) shows cumulative returns for the event window. Returns are calculated every 30 seconds. Therefore, the -5 minutes/+5minutes event window includes 10 time buckets on each side of time 0. The length for each time bucket is 30 seconds. Negative returns indicate a depreciation of GBP relative to the USD. Column 2 shows the cumulative return for the 10-minute time period before the event window. Column 4 shows the cumulative return for the same time period as the event window a day before the event occurred. † I performed the binomial probability test for the signs of differences in mean returns for the 15 attacks to test whether the terrorist attacks were likely to reduce returns around the event comparing with the benchmark windows, with probability of success of 0.5. The results did not show any significant evidence that mean returns in event windows are likely to be lower than mean returns in the comparison windows. The insignificant statistical results could be attributed to the small sample. 188 APPENDIX 3.2 TERRORIST ATTACKS’ INFLUENCE ON GBP/USD EXCHANGE RATE RETURN VOLATILITIES First Media Report Confirmed Casualty 퐷_푒푣푒푛푡푡 퐷_푒푣푒푛푡푡 Coef. z-stat n Coef. z-stat n 12 Westminster attack 2.8833*** (5.43) 5761 1.1485* (1.66) 5761 14 Manchester Arena Bombing 2.2400*** (2.09) 5760 2.0181* (1.85) 5761 London Bridge and Borough 15 Market attack 3.0939*** (3.27) 4356 3.4226*** (3.71) 4356 *p<0.10 **p<0.05 ***p<0.01 (two-tailed) This table reports coefficients (훾) estimated using a GARCH (1, 1) model with the conditional mean model of 푅푡 = 휕0 + 훽1푅푡−1 + 휀푡 and conditional variance model ℎ푡 ≡ 2 2 푉푎푟(푅푡|퐼푡−1) = 휔 + 훼휀푡−1 + 훽ℎ푡−1 + 훾퐷_푒푣푒푛푡푡. D_event is an indicator variable which equals to 1 if the observation is within the event window, 0 otherwise. The event window is -5 minutes to +5 minutes where t = 0 is when a terrorist attack is first reported. The full sample period is -1 day to +1 day around the event. The length of each time period to calculate the foreign exchange return is 30 seconds and therefore the sample for each regression analysis consists of 5,761 time periods with 2880 of them before t = 0 and 2880 after t = 0. 189 APPENDIX 3.3 CONTEMPORANEOUS EFFECTS OF TERRORIST ATTACKS ON GBP/USD EXCHANGE RATE RETURNS First Media Report Announcement of Confirmed Casualties 퐷_푒푣푒푛푡푡 퐷_푒푣푒푛푡푡 Coef. z-stat n Coef. z-stat n 12. Westminster attack 0.1253 (0.46) 5761 -0.0410 (-0.23) 5761 14. Manchester Arena Bombing -0.0006 (0.00) 5759 -0.2911 (-0.99) 5761 15. London Bridge and Borough Market attack 0.0731 (0.39) 4205 0.1528 (0.86) 4356 *p<0.10 **p<0.05 ***p<0.01 (two-tailed) This table reports estimates for a GARCH (1,1) model that has the following form 푅푡 = 휕0 + 훽1푅푡−1 + 훽1퐷_푒푣푒푛푡푡 + 휀푡. The coefficients for D_event are expressed in basis points (1 basis point equals to 0.0001) for ease of presentation. D_event is an indicator variable which equals to 1 if the observation is within the event window, 0 otherwise. The event window is -5 minutes to +5 minutes where t = 0 is when a terrorist attack is first reported. The full sample period is -1 day to +1 day around the event. The length of each time period is 30 seconds and the foreign exchange return is calculated every 30 seconds. Therefore the sample for each regression analysis consists of 5761 time periods with 2880 of them before t = 0 and 2880 after t = 0. 190 APPENDIX 4.1 REPLICATION OF BANKER ET AL. (2017) This section reports the replication results of Banker et al. (2017), using the sample detailed in their paper. The replication results are largely consistent with estimates reported in Banker et al. (2017). Appendix 4.1 Replication of Banker et al. (2017) Asymmetric Timeliness Estimates for Multiple Indicators Ball and Basu (1997) Banker et al. Shivakumar Model (2017) Model (2006) Model predicted (1) (2) (3) VARIABLES sign EARN EARN EARN DR 0.0036 0.0074*** 0.0075*** (1.4981) (3.2204) (3.4353) RET 0.0070*** 0.0184*** 0.0103*** (2.9239) (7.6591) (4.4103) DR×RET + 0.3209*** 0.2861*** 0.2572*** (43.4445) (40.9534) (38.4610) ** DC -0.0046 -0.0013 (-2.5480) (-0.7207) *** *** ΔCF -0.2434 -0.1573 (-17.9978) (-11.9026) *** *** DC× ΔCF + 0.7235 0.4950 (33.3780) (22.3550) DS -0.0278*** (-12.8859) ΔSALES 0.0394*** (9.0843) DS×ΔSALES + 0.1346*** (15.1144) Constant 0.0399** 0.0795*** 0.0649*** (2.2451) (4.6338) (3.8466) Industry-fix effect Yes Yes Yes Year-fix effect Yes Yes Yes Observations 59,282 59,282 59,282 Adjusted R2 0.174 0.152 0.212 F-statistic for the full effect of DR, RET, DR×RET 35.55*** 20.35*** 20.85*** DC, ΔCF, DC×ΔCF 56.33*** 18.94*** DS, ΔSALES, DS×ΔSALES 16.30*** F-statistics for the asymmetric effect of DR, DR×RET 2353.85*** 2005.84*** 1749.71*** 191 DC, DC×ΔCF 1145.05*** 509.23*** DS, DS×ΔSALES 360.16*** *** p < 0.01, ** p < 0.05, * p < 0.1 The t-statistics in parentheses are based on standard errors clustered by firm and year. This table presents the pooled regression estimates on a sample of 59,282 firm-year observations from 1987– 2007 for models: Column 1: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 휀푖푡 Column 2: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푖푡 × 훥퐶퐹푖푡 + 휈푖푡 Column 3: 퐸퐴푅푁푖푡=휕0 + 휕1퐷푅푖푡 + 휕2푅퐸푇푖푡 + 휕3퐷푅푖푡 × 푅퐸푇푖푡 + 훽1퐷퐶푖푡 + 훽2훥퐶퐹푖푡 + 훽3퐷퐶푡 × 훥퐶퐹푖푡 + 훾1퐷푆푖푡 + 훾2훥푆퐴퐿퐸푆푖푡 + 훾3퐷푆푖푡 × 훥푆퐴퐿퐸푆푖푡 + 𝜍푖푡 퐸퐴푅푁푖푡 is net income in year t, scaled by the market value of equity at the beginning of the year; 푅퐸푇푖푡 is stock return for the 12-month period of fiscal year t; 퐷푅푖푡 is a dummy variable that equals 1 if stock return 푅퐸푇푖푡 is negative, and 0 otherwise; 훥퐶퐹푖푡 is change in operating cash flow from year t-1 to year t, scaled by market value of equity at the beginning of the year; 퐷퐶푖푡 is a dummy variable that equals 1 if cash flow change 훥퐶퐹푖푡 is negative, and 0 otherwise; 훥푆퐴퐿퐸푆푖푡 is change in sales from year t-1 to year t, scaled by market value of equity at the beginning of the year; 퐷푆푖푡 is a dummy variable that equals 1 if sales change 훥푆퐴퐿퐸푆푖푡 is negative, and 0 otherwise. 192