Did the EU Transparency Directive Improve Financial Reporting Transparency? an Examination of Stock Price Informativeness and Earnings Quality

Did the EU Transparency Directive improve financial reporting transparency? An examination of stock price informativeness and earnings quality

Olena V. Watanabe School of Accountancy Robert J. Trulaske Sr. College of Business University of Missouri-Columbia [email protected]

Draft Date: February 7, 2012

Preliminary and incomplete Please do not quote

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I am especially grateful to my dissertation chair, Inder Khurana, for his support and guidance in developing this paper. I thank the other members of my committee: Jere Francis, Raynolde Pereira, and Stephen Ferris, for their helpful comments. I also thank Vairam Arunachalam, Kyonghee Kim, Elaine Mauldin, Ken Shaw, Patrick Wheeler, May Zhang, Quihong Zhao, and workshop participants at the University of Missouri-Columbia, and Binghamton University for their comments and suggestions. All errors are my own.

Did the EU Transparency Directive improve financial reporting transparency? An examination of stock price informativeness and earnings quality

Abstract

This paper examines the impact of a transparency regulation on stock price informativeness and earnings quality. Specifically, it focuses on the Transparency Directive (TPD), a key securities regulation implemented by EU countries in recent years, which strives to increase and improve the flow of firm-specific information by mandating broader disclosure requirements, including greater reporting frequency by public companies listed in the EU member countries. I test whether TPD improved stock price informativeness and financial reporting quality. Using a sample of 4,768 firms from 25 EU countries during the 2001-2010 time period, I find stock price informativeness improved following the implementation of TPD and that the positive relation between TPD and stock price informativeness is more pronounced for firms in countries with (i) better staffed regulatory authorities and (ii) stronger implementation and enforcement. The analysis of accrual quality and accounting conservatism is currently in progress. Overall, my findings highlight the role of enforcement in documenting the effects of regulations requiring more transparency.

JEL Classification: F30, G15, G30, M4 Keywords: Synchronicity, Transparency, Securities Regulations, Disclosure, Enforcement

Did the EU Transparency Directive improve financial reporting transparency? An examination of stock price informativeness and earnings quality

1. Introduction

Mandatory disclosure in the form of securities regulation is an attribute of most financial markets across the world. Extant research provides several reasons why mandatory disclosure is desirable. Disclosure can increase comparability across firms and aid in valuation, particularly for firms within single industry (Admati and Pfleiderer, 2000). Disclosure can also reduce the incidence of the diversion of resources by the corporate insiders (Ferrell, 2007), increase investor protection and facilitate financial development (Black, 2001; Frost, 2006). Overall, mandatory disclosure can serve to improve a firm’s information environment.1 An implicit assumption underlying this prediction is that any regulation mandating more disclosure will be sufficiently enforced to ensure firm compliance. However, enforcement regimes vary across countries and this can influence the extent to which disclosure regulation improves firm transparency (e.g. Ball et al, 2003; Leuz and Wysocki, 2008). I empirically evaluate how mandatory disclosure affects information environment in the context of the Transparency Directive adopted by the European

Union (EU) in 2004.

The Transparency Directive (hereafter TPD) was issued with the goal of improving disclosure for public companies trading on the EU Stock Exchanges, and achieving greater harmonization with respect to financial reporting among the EU member countries in order to improve investor protection and market efficiency (Directive 2004/109/EC). The most notable changes in financial reporting include an increase in the frequency of financial reports, additional

1 While voluntary disclosure can also provide similar benefits as mandatory disclosures, extant theory points to the existence of an internal optimal level of voluntary disclosure because of costs such as the costs of information production and dissemination (Admati and Pfleiderer, 2000), and proprietary costs (Verrechia, 1993). Managers may also limit voluntary disclosure in order to conceal consumption of firm resources for personal benefit (Hope and Thomas, 2008).

semi-annual disclosures of risks and uncertainties, a statement by company executives or directors in regards to the fairness of financial information presented in the reports, and the release of a company’s annual report no later than four months after its fiscal-year end (Directive

2004/109/EC). Furthermore, TPD specifies that a competent and independent authority must be created or designated by the EU member countries to supervise compliance with directive’s provisions (Directive 2004/109/EC). Therefore, as a consequence of TPD implementation, there is a significant shift in the reporting requirements for public companies in the EU.

In this paper, I first examine the effect of TPD on stock price informativeness, which reflects the comovement of a firm’s stock return with market and its industry returns (Chen et al.,

2007). I focus on stock price informativeness because prior research (e.g., Ferreira and Laux,

2007; Fernandes and Ferreira, 2008) has identified it as a good candidate for a summary of information flow. Other recent research (e.g., Hutton et al., 2009) shows that an earnings-based opacity measure is associated with lower stock return informativeness, indicating less revelation of firm-specific information in opaque firms. Moreover, my interest in stock price informativeness around TPD adoption stems from prior research (e.g., Tobin, 1984; Morck et al.,

2000; Durnev et al., 2003; Wurgler, 2000) that views stock price as an important signal for asset allocation and regards it as crucial for efficient resource allocation. My analysis builds on prior literature that argues and finds stock price informativeness to increase in the presence of more firm-level transparency (Grossman and Stiglitz, 1980; Jin and Myers, 2006; Veldkamp, 2006;

Haggard et al., 2008). I posit stock price informativeness (stock price synchronicity) to improve

(decline) following the adoption of TPD.2 Given that greater oversight by newly appointed bodies and administrative penalties associated with a firm’s failure to comply with TPD are

2 To vary the exposition, I use the terms stock price informativeness and synchronicity interchangeably. Note that an increase in synchronicity implies a decline in stock price informativeness.

important factors that motivate firms to expend effort in implementing TPD, I posit the effect of

TPD on stock price informativeness to be greater in countries which have a stronger enforcement regime in place.

To evaluate the potential source of improvement in firms’ information environment, I also examine the impact of TPD on financial reporting quality. Schipper (1989) argues that earnings manipulation is possible in the presence of information asymmetry and limited disclosure. Consistent with this argument, prior research finds an expanded disclosure policy is associated with lower earnings management (e.g. Lobo and Zhou, 2001; Shaw, 2003; Jo and

Kim, 2007; Francis et al., 2008). The implication is that disclosure constrains managerial reporting discretion and improves financial reporting quality. Thus, I posit accruals quality and asymmetric timeliness of loss recognition to increase following the implementation of TPD.3 I also expect the effect of TPD on financial reporting quality in the form of accruals and asymmetric timely loss recognition to be more pronounced in countries which have a stronger enforcement regime in place.

I use a sample of 4,768 unique firms in 25 EU countries over the 2001-2010 time period to conduct empirical tests relating to stock price informativeness. The analysis of financial reporting quality metrics is currently in progress. Regarding stock price informativeness, I find a negative association between stock price synchronicity and the adoption of TPD, which is supportive of the argument that greater mandated disclosure contributes to greater stock price informativeness. However, I find the effect of TPD is not uniform across countries. Specifically, the relation between stock price synchronicity and TPD is more pronounced in countries with better staffing of their regulatory agencies and in countries with stronger TPD enforcement.

3 I focus on accruals quality because it provides an overall evaluation of a firm’s financial reporting quality. I focus on asymmetric timeliness of loss recognition because it captures the ability of firms to conceal bad news (Basu, 1997; Lobo and Zhou, 2006; Ball et al., 2000; Ball et al., 2003; Bushman and Piotroski, 2006).

Additional sensitivity analyses indicate that my results are robust to the inclusion of macroeconomic controls, such as GDP per capita growth and inflation, as well as firm fixed- effects. My findings are also not sensitive to the exclusion of financial firms, omission of UK firms from the sample, or alternative industry fixed-effects specification. My results are also robust to exclusion of pre-IFRS years. Furthermore, there is no change in qualitative inferences when I include non-EU firms as a benchmark sample.

To highlight the contribution of my study, it is useful to juxtapose it against prior research on disclosures. Prior research has largely focused on whether voluntary disclosure can mitigate or ameliorate information asymmetry and affect a firm’s cost of capital (e.g., Botosan,

1997). Other studies examining the consequences of mandatory disclosures focus on either how mandatory disclosure requirements affected shareholder wealth or riskiness around their enactment (Chow 1983; Jarrell, 1981; Simon, 1989). More recently, studies have focused on two

U.S. securities regulations, Regulation Fair Disclosure (Reg FD) and Sarbanes-Oxley Act of

2002 (SOX). For instance, researchers document a decline in bid-ask spreads and increase in investor trading following Reg FD to infer a decline in information asymmetry for the U.S. firms after Reg FD (e.g., Bushee et al., 2004; Eleswarapu et al., 2004). Other studies show a decline in accrual earnings management and an increase in accounting conservatism following the adoption of SOX (Lobo and Zhou, 2006; Cohen et al., 2008). Because the existing disclosure studies are normally conducted in single-country settings, they leave unresolved the question of what institutional factors drive cross-country variations in the consequences of disclosure.

I focus on TPD, a regulation that broadened the disclosure requirements including the frequency reporting for the EU firms. In a concurrent study, Christensen et al. (2011) also focus on the effects of TPD. They find TPD adoption is associated with improved liquidity (lower bid-

ask spreads and fewer zero return days) and lower cost of capital. Further, they find liquidity and cost of capital effects are more pronounced in countries where prior regulation as well as the enforcement of TPD is stronger (Christensen et al., 2011).4 My focus differs from their study in two respects. First, I focus on the impact of TPD on stock price informativeness. In part, this focus stems from the argument that mandatory disclosure, by making stock prices more informative, can improve resource allocation. Second, I directly evaluate the potential sources of improvements in firms’ information environment in the form of financial reporting quality.5

Overall, my study contributes to the literature on the consequences of laws and regulations. A typical argument of this stream of literature is that mere passage of laws and regulations to enhance transparency is not sufficient to achieve desired benefit without strong institutional environment in place (e.g. Djankov et al., 2003; Leuz and Wysocki, 2003; Hail and

Leuz, 2006; Bushman and Piotroski, 2006; LaPorta et al., 2006; Shleifer, 2005; Bhattacharya and

Daouk, 2009). For example, prior studies examine the consequences of transparency as a function of the overall level of institutional development in the form of judiciary efficiency, investor protection, or country’s legal origin (e.g. Ali and Hwang, 2000; Bushman and Piotroski,

2006). While I examine the variation in synchronicity and financial reporting quality pre- and post-TPD, conditional on the strength of regulatory quality before TPD adoption, I also focus on the strength of TPD implementation and enforcement. I find that the increase in stock price informativeness is more pronounced in countries with better staffed regulators and in those

4 Christensen et al. (2011) also investigate Market Abuse Directive (MAD), a directive passed in the EU in 2003 with the purpose to control insider dealings. Although I control for implementation of MAD in my tests, Market Abuse Directive is out of the scope of my study. 5My paper should also be distinguished from studies examining the effects of adoption of International Financial Reporting Standards (e.g. Christensen et al., 2007; Daske et al., 2008; Jeanjean and Stolowy, 2008; Byard et al., 2011; DeFond et al., 2011) because my focus is on a securities regulation that is likely to impact overall disclosure by firms.

countries where TPD is backed with sufficient enforcement. My findings highlight the importance of strong enforcement when evaluating the regulations adopted by the country.

The remainder of the paper proceeds as follows. Section 2 reviews relevant literature and develops hypotheses. Section 3 describes data sources and sample. Section 4 outlines research design and empirical models. Section 5 presents empirical results and analysis, while Section 6 concludes the paper.

2. Relevant literature and hypothesis development

2.1. Transparency Directive

In this study I examine the effect of Transparency Directive (TPD) on the stock price informativeness and the earnings quality of the European Union countries. TPD is one of the four core directives which changed the securities regulation in the EU since the adoption of the

Financial Services Action Plan in 1999 and the implementation of the Lamfalussy Process to the law making in the EU.6 Adopted in May of 2004, TPD revises and replaces older Directive

2000/34/EC on the admission of securities to official stock exchange listings.7 The objective of

TPD is to outline the requirements in relation to the disclosure of periodic and ongoing information of public companies trading on the EU Stock Exchanges, and to achieve greater harmonization among the EU Member States related to financial information disclosure. In the view of the European Commission:

6 See Appendix I for more details in regards to the Lamfalussy Process. 7 There are three other regulations which were passed in the EU following the initiation of Financial Services Action Plan of 1999 and became the core Lamfalussy Directives related to securities regulation. Market Abuse Directive (MAD) deals with insider trading and market manipulations. I control for MAD in my analysis. Prospectus Directive (PD) was adopted in 2005 and concerns issues of securities. Member states of the EU must implement PD into national law by July 1, 2005. As a sensitivity test, I examine the effect of TPD on stock price synchronicity in the period from 2006 to 2010, effectively excluding pre-PD period. My results are not affected by this alternative sample. Finally, Markets in Financial Instruments Directive (MiFID) was passed in 2007 with the purpose of increasing competition and consumer protection in the investment services industry. This regulation seems to be of little relevance to the firms in my study.

“The disclosure of accurate, comprehensive and timely information about security issuers builds sustained investor confidence and allows an informed assessment of their business performance and assets. This enhances both investor protection and market efficiency… To that end, security issuers should ensure appropriate transparency for investors through a regular flow of information (Directive 2004/109/EC, Papa. (1, 2)).”

Specifically, TPD introduces new requirements concerning the annual financial report, which were previously guided either by the company law or specific EU countries’ regulation.

TPD also imposes limited quarterly disclosure requirements in a form of interim management statement, which complements semi-annual and annual reporting. Such interim reports must be issued no later than six weeks after the end of the first and third fiscal quarters (Directive

2004/109/EC, Article 6.1). In addition TPD revises disclosure requirements for the timing and the release of information on major holdings of voting rights.

The interim management reports must provide “an explanation of material events and transactions that have taken place during the relevant period and their impact on the financial position of the issuer” as well as “a general description of the financial position and performance the issuer…during the relevant period” (Directive 2004/109/EC, Article 6.1). Importantly, for annual and semi-annual reporting, TPD requires that within these reports “persons responsible” must make a statement that financial reports “give a true and fair view of assets, liabilities, financial position and profit or loss of the issuer…together with a description of principal risks and uncertainties that they face” (Directive 2004/109/EC, Articles 4(c) and 5(c)). These assessments are reminiscent of similar requirements for U.S. firms in the post-SOX period.

To illustrate the differences between TPD requirements and current disclosure requirements, I list below how TPD impacts public companies reporting in the UK.8 First, all UK

8 As reported in the publication of 2009 of the London Stock Exchange available at http://www.londonstockexchange.com/products-and-services/rns/tranparencydirectiveguide.pdf.

companies would be required to publish financial information four times a year, instead of two – an annual report, a half-yearly report and two interim managerial statements. Second, the half- yearly report will need to include the principal risks and uncertainties to a company’s business for the remaining six months of the year. Third, the annual report will have to be published within four months of the fiscal year end rather than six months. Thus, for the UK, TPD would result in increased disclosure and timelier release of public annual report.

2.2. Informativeness of stock prices

In this paper I examine whether recent changes in the EU securities regulation increase stock price informativeness. Stock markets play an in important economic role by generating price signals for efficient resource allocation (Tobin, 1984; Durnev et al., 2003). However, Roll

(1968) observes a low R2 statistics of common capital asset pricing models. He suggests two explanations for this observation: “existence of private information or else occasional frenzy unrelated to concrete information” (Roll, 1968, p. 566). The evidence in the existing empirical literature supports the argument that low R2 implies existence of private information.

Specifically, lower comovement (or non-synchronicity) of stock prices with market-wide returns implies that the market factors explain a smaller proportion of the variation in stock returns, thus individual stock prices reflect more firm-specific information (e.g. Morck et al., 2000; Li et al.,

2004).

The ability of synchronicity to reflect the degree of private information in stock prices has been extensively tested in both the U.S. and international settings. Durnev et al. (2003) show that U.S. firms with lower R2s exhibit higher association between current returns and future earnings, suggesting that lower synchronicity does imply more private information-rich stock prices, rather than noise trading. In the U.S. lower synchronicity has also been linked to better

capital allocation (Durnev et al., 2004), greater reliance on external financing (Durnev et al.,

2001), and stronger corporate investment sensitivity to price (Chen et al., 2007).

Examining cross-country data, Morck et al. (2000) show that stock return comovement is higher in developing and lower in developed countries, and that the relation is attributed to the level of investor protection. In a related study Li et al. (2004) find that openness of capital markets relates to lower synchronicity, and that this relation is stronger in countries with better developed governments. Wurgler (2000) examines 65 countries and shows that better capital allocation is related to lower stock price synchronicity. Overall, extant literature provides compelling evidence that stock return synchronicity captures the informativeness of stock prices.

2.3. Disclosure, transparency and synchronicity

Turning specifically to the relation between synchronicity and disclosure, there are several reasons why better disclosure may affect comovement of stock prices. Grossman and

Stiglitz (1980) offer a theory of imperfect market under the condition of costly information acquisition. Because of the costly information acquisition, prices do not reflect all relevant information, allowing for returns to private information acquirers. Changes in the firm information environment, which lead to decline in the costs of the credible information acquisition (i.e. an increase in disclosure), would imply an increase in informed trading and as a result a decline in stock price synchronicity. Veldkamp (2006) proposes a similar theory. She points out that information is costly to produce and virtually costless to reproduce. When information is costly, investors would focus more on signals that are common to many firms, thus causing greater co-movement of prices.9 When, on the other hand, firms produce credible disclosure of higher quality, the costs of private information to individual investors decline.

9 On the supply side, more common information is produced by supplier of the information (e.g. financial analysts), because it would provide highest expected returns (Veldkamp, 2006).

Therefore, one would observe negative relationship between private information and synchronicity.

Jin and Myers (2006) offer a risk-based explanation of stock price synchronicity.

According to them, if the firm is opaque, insiders bear more firm-specific risk, while the outsiders bear more market risk. The opposite would also be true: in more transparent firms, the insiders take on less firm-specific risk, while the outsiders take on less market risk. This would imply lower synchronicity for firms in more transparent regimes compared to firms in more opaque environments.10 Jin and Myers (2006) also point out that under this risk-division theory investor protection alone is not enough to explain R2s as argued by Morck et al. (2000), because in an opaque firm even under high investor protection regime the insider can still capture unexpected cash flows and therefore absorb some firm specific risk. In an international setting,

Jin and Myers (2006) document that synchronicity is higher when opaqueness on the country level is greater. Hutton et al. (2009) confirm Jin and Myers (2006) theory in the U.S. Setting and find higher synchronicity for more opaque firms, where opaqueness is proxied by abnormal accruals. Finally, Haggard et al. (2008) show that U.S. firms with better disclosure exhibit lower synchronicity. TPD impacts transparency of financial reporting and investor protection of the EU countries through its impact on the overall disclosure of the firm. My second hypothesis is stated as follows:

H1: Stock return synchronicity declines in the period after the Transparency Directive adoption, ceteris paribus.

10 In a recent paper, Dasgupta et al. (2010) argue that lumpy release of new information or release of time-invariant information, such as managerial quality, would result in an increase in synchronicity, rather than a decline. However, Dasgupta et al. (2010, p.1197) state that their findings do not negate Jin and Myers (2006) study: “…the nature of disclosure can take the form of more firm-specific information being revealed to outsiders on a regular basis, in which case the return synchronicity will decline”.

2.5. TPD adoption and financial reporting quality

In order to evaluate the potential source of improvement in firms’ information environment after the adoption of TPD, I examine the impact of TPD on financial reporting quality. Specifically I focus on two dimensions of financial reporting quality: accrual quality and asymmetric timeliness of loss recognition.

2.5.1. Accrual quality

Schipper (1989) notes that earnings manipulations are possible when less than full information is disclosed and information asymmetries are present. Disclosure can improve transparency and reduce information problems (Healy and Palepu, 2001). Thus, disclosure can also reduce incentives to manage earnings, because increased transparency will help investors detect earnings manipulations (Jo and Kim, 2007). Several empirical studies provide empirical evidence on the association between disclosure and earnings management. For instance, Francis et al. (2008) show that firms which provide more voluntary disclosure also exhibit higher quality earnings. Lobo and Zhou (2001) show that better disclosure is associated with lower earnings management. Furthermore, accrual earnings management has declined after the passage of

Sarbanes-Oxley Act of 2002 in the U.S. (Cohen et al., 2008). Greater disclosure frequency, in particular, can make earnings manipulations more difficult or can help uncover existing earnings management. Supportive of this conjecture, Jo and Kim (2007) find that accrual quality is higher for firms with more frequent disclosures around seasoned equity offerings. TPD increases overall disclosure levels and frequency of reporting, in particular. Therefore, I expect that implementation of TPD will result in reduced earnings manipulations. Specifically, I expect that accrual quality will increase in the post-TPD period.

H2: Accrual quality improves after the implementation of the Transparency Directive by

EU member countries, ceteris paribus.

2.5.2 Asymmetric timeliness of loss recognition

In addition to accrual quality another measure of financial reporting quality is asymmetric timeliness of loss recognition, or accounting conservatism (Basu, 1997; Bushman and Smith

2003). If reporting is more transparent and more frequent, managers have less opportunity to manipulate earnings and conceal losses, the end result being more timely loss recognition. For instance, Kothari et al. (2009) argue that managers tend to delay disclosure of bad news due to opportunities driven by information asymmetry and personal incentives, such as career concerns.

They find empirical evidence to this asymmetry in bad vs. good news release by managers. They also document that post-Reg FD, which leveled the plain field in terms of private information distribution, the asymmetry between bad and good news release is reduced and managers are timelier in reporting bad news (Kothari et al., 2009). Lobo and Zhou (2006) show that asymmetric timeliness of losses increased after the adoption of Sarbanes-Oxley Act of 2002 in the U.S. Cross-country level studies show that accounting conservatism is higher in countries with better financial reporting regime (Ball et al., 2000; Ball et al., 2003) and in countries where judicial systems and public enforcement of securities laws is stronger (Bushman and Piotroski,

2006). I expect that improvements in transparency created by TPD will result in increased accounting conservatism. Thus, I formulate hypothesis three as follows:

H3: Asymmetric timeliness of loss recognition increases after the implementation of the

Transparency Directive by EU member countries, ceteris paribus.

2.6. Role of implementation and prior regulation

Christensen et al. (2011) note that competing hypotheses exist in regards to the ability of existing regulation in place to influence the strength of implementation of the future regulation in a country. On the one hand, the effect of new securities regulation may be stronger in countries where prior securities regulation has been weak, such that after adoption of new law such countries “catch up” with the stronger countries. If this is the case, I expect that synchronicity

(earnings quality) effect of TPD is stronger in countries with weaker prior regulation. An alternative view is based on the notion that in countries with stronger existing regulation, there are already efficient political and institutional forces in place, which would contribute to the appropriate implementation of new securities regulation. Christensen et al. (2011) call it the

“hysterethis” hypothesis.11 Thus, the synchronicity (earnings quality) effect of TPD will be stronger in countries with better prior regulation. As such I state hypothesis four in a null form:

H4: The effect of the Transparency Directive implementation on synchronicity (financial reporting quality) does not depend on the level of prior regulation of EU countries.

The benefits of securities regulation depend fundamentally not only on their design, but also on the implementation and enforcement of the regulations. As Djankov et al. (2003, p. 603) point out “public regulation suffers from the obvious problem of public abuse of market participants by officials who are either pursuing their own interests or are captured by a particular group.” Regulators face serious information problems, are often incompetent, may be corrupt, and can be captured in the regulatory process (Christensen et al., 2011). Therefore, securities regulation is more likely to be effective in countries with better institutional

11 “Hysterethis occurs when a past temporary change of the relevant forcing variables has led to a change in economic behavior of the observed units, but a removal to the initial value of the forcing variables does not induce a complete change back to the initial behavior” (i. e. hiring costs in labor market affect demand for labor) (Gocke, 2002). 14

development, more efficient bureaucracies, stronger judicial systems, more resources, in other words, in countries with better means to implement and enforce regulations. In countries with weak institutional development, excessive or uncontrolled bureaucracies, and weak legal systems, there is a high risk that the regulation will not be enforced, which is more harmful than the absence of the regulation altogether (Shleifer 2005; Bhattacharya and Daouk, 2009). I formulate hypothesis five as follows:

H5: The effect of the Transparency Directive on synchronicity (financial reporting quality) will be stronger in countries with stricter implementation and enforcement of the

Transparency Directive.

3. Data sources and sample selection – synchronicity tests

Table 2 summarizes sample selection procedure. My initial sample consists of all firms for which quarterly synchronicity metrics can be computed for the period 2001-2010 using the stock return data on Compustat Global Daily Security files. I then delete firm observations with missing data for assets, revenues or owners’ equity on Annual Compustat Global files and obtain

567,677 firm-quarter observations. I then delete observations with missing control variables used in the regression models and firms with market value of equity less than US $1 million. I also require that there are at least 4 observations per unique firm similar to Christensen et al. (2010).12

I remove observations which belong to SIC2 code 99 (Nonclassifiable Establishments). Finally,

I delete firm from non-EU countries from the sample.13 My final sample has consists of 104,523 firm-quarter observations relating to 4,768 unique firms in 25 EU countries. The number of firm-

12 I repeat my analysis with a stricter requirement of at least 20 observations per firm in the sample. This requirement reduces my sample size, but does not alter the results. 13 My sample includes Iceland and Norway, which are not European Union countries, but which have agreed to adopt TPD, in order to gain access to single European Market.

quarter observations in my sample ranges from 61 in Iceland to 32,518 in the United Kingdom. I winsorize all continuous variables at 1% and 99% to reduce the effect of outliers.

4. Measurement of variables and model specification

4.1 Empirical model for test of H1.

To examine the relation between firm stock price informativeness and the implementation of TPD, I test H1 by estimating the following model:

SYNCH = β0 + β1TPD + ∑βjControlsj + ∑βiFixed Effectsi + ɛ (1)

The dependent variable SYNCH is a measure of synchronicity defined in section 4.1.2.

TPD is a test variable defined in section 4.1.1. Controlsj is a vector of control variables discussed in section 4.1.3. Fixed Effectsi represents country, industry and quarter-year fixed effects. The extensive set of fixed effects captures any time-invariant heterogeneity across countries and industries (2-digit), as well as controls for economic shocks common in time. Furthermore, country, industry and time fixed effects allow me to control for correlated omitted variables, which do not vary across countries, industries or time, respectively. In all regressions, I report robust standard errors clustered at the firm level to account for the correlation of the residuals across years for a given firm (Peterson, 2009).14 H1 predicts that synchronicity declines following the implementation of TPD, thus, I expect β1 to be less than zero.

4.1.1. Dependent variable – synchronicity

To measure stock return synchronicity I follow the approach outlined in previous studies

(e.g. Durnev et al., 2003; Hutton et al. 2009; Beuselinck et al., 2010). I use two alternative

14 Although there may also be time effect present in my dataset, Petersen (2009) states that the consistency of the clustered standard error depends on having sufficient number of clusters. “When there are only a few clusters in one dimension, clustering by the more frequent cluster yields results that are almost identical to clustering by both firm and time” (Petersen, 2009, p. 460). Similar reasoning applies to my preference of clustering on the firm level instead of the country level.

specifications of synchronicity calculated over quarterly periods. First, I regress daily returns on the current and lagged value weighted daily market return as follows:

RETi,d = α0 + α1MARETi,t+ α2MARET,i,t-1 + ɛi,t (2) where t refers to trading day, RET is daily return, calculated using daily prices and adjusted for cash distributions and reinvestment of dividends, MARET is the value weighted market return, computed using all firms in the market, excluding firm i.

Second, I expand equation (2) to include value weighted industry returns, as follows:

RETi,d = α0 + α1MARETi,t + α2MARET,i,t-1 +INDRETi,t + INDRETi,t-1 + ɛi,t (3)

Where RET and MARET are as previously defined, and INDRET is value weighted industry return, calculated for all firms in two-digit SIC industry, excluding firm i. The exclusion of firm i from industry-wide or market-wide returns prevents any spurious correlations between firm returns and market- or industry-wide returns (Durnev et al. 2003). I include lagged MARET and

INDRET following Piotroski and Roulstone (2004), who argue that the information may be incorporated into prices with a delay. For each firm, I require at least 20 returns per quarter to maximize observations from smaller EU countries.

Following prior studies, stock returns synchronicity (SYNCH1 and SYNCH2) is

( ) where R2 is the coefficient of determination obtained from estimating equations (2)

and (3) respectively. Natural logarithm transformation changes R2, which is bounded between zero and one, into a continuous and more normally distributed variable (Morck et al., 2000).

Higher values of SYNCH1 and SYNCH2 imply greater synchronicity of returns, and therefore lower informativeness of stock price.

4.1.2. Test variable

TPD is a binary variable coded 1 if the fiscal year end date for a firm is on, or after the quarter end date of the quarter during which a directive comes into force in a given EU country, zero otherwise. While TPD was adopted in 2004, the Directive’s implementation dates differ across EU member countries. The dates vary from January 2007 (Germany, Bulgaria, Romania, and UK) to August 2009 (Italy). Panel A of Table 3 reports TPD implementation dates and related calendar quarters for all countries in my sample.

The differences in implementation dates allow me to isolate the effect of securities regulation separately from any other regulation with a common adoption date which may have been passed during the time period in my study, or from economic events which affect all or most EU member countries simultaneously, such as the financial crisis of 2008. I use TPD entry into force dates as reported in Christensen et al. (2011). I use firm fiscal year end date as a cutoff, because any type of quarterly reporting in the EU is not mandatory before TPD. This allows for sufficient time for changes in firm information to be reflected in the firm’s reports.15 See Figure

1 for the example of TPD variable coding for German companies.16

4.1.3. Control variables – synchronicity

I use an extensive set of control variables shown in prior literature to explain the comovement of stock prices with the market (Piotroski and Roulstone, 2004; Chan and Hameed,

2006; Ferreira and Laux, 2007; Hutton et al. 2009; Gul et al., 2010). Following Chan and

Hameed (2006) I control for firms size (SIZE). Size is the market capitalization of a firm calculated as price per share in US dollars multiplied by number of shares outstanding. As

15 For example, Beuselinck et al. (2010) calculate annual synchronicity for the period ended 4 months after the fiscal year end to ensure that earnings-related news flows through to returns. 16 In untabulated tests I re-estimate effect of TPD on synchronicity of stock prices using TPDc variable, which is coded 1 starting at the end of calendar quarter that the directive comes into effect similar to Christensen et al. (2011). My results are similar to the ones reported in Section 5.

market wide returns used to calculate synchronicity are value weighted, market capitalization of the company determines its weight in the market index. For countries with lower number of stocks, large companies would dominate market movements (Chan and Hameed, 2006). I expect a positive coefficient on SIZE.

Actively traded stocks have faster price adjustment, react to market information on a timely basis, and therefore may have higher stock price synchronicity (Chan and Hameed, 2006).

To control for the trading activity effect on synchronicity I include share turnover (SHARE

TURNOVER) as an additional control. Firm-quarter turnover is calculated as a median of daily volume scaled by market capitalization. To control for industry concentration I include Herfindahl index (HERFINDAHL INDEX), measured annually for each 2-digit SIC industry and based on firm sales. In more concentrated industries, individual firm performances are more likely to be interdependent, and synchronicity would be higher (Piotroski and Roulstone, 2004). I expect the relation between HERFINDAHL INDEX and synchronicity to be positive.

Financial analysts produce industry-specific information though intra-industry transfers

(Piotroski and Roulstone, 2004). Following Chan and Hameed (2006) I include number of analysts (NUM_ANALYSTS) which prepare earnings forecast for a firm during the year as a control for analyst activity. I expect that ANALYSTS positively relates to synchronicity. I depend on I/B/E/S firm coverage for the NUM_ANALYSTS variable. Since a missing firm in

I/B/E/S may imply that the firm has either zero analyst coverage, or is not covered by I/B/E/S I include a dummy variable called ANALYSTS_DUMMY, which is coded 1 if the firm is missing in I/B/E/S/, zero otherwise. I do not predict a sign for the ANALYSTS_DUMMY variable.

I include firm age (AGE) as a control because Dasgupta et al. (2010) show that age is positively related to synchronicity; they argue this result is driven by the fact that market learns

more about the firm as it becomes older. I use the first year that a given firm gains coverage in

Compustat Global to calculate firm age. I expect positive regression coefficient for AGE variable. Following Hutton et al. (2009), Ferreira and Laux (2007) and Beuselinck et al. (2010) I control for leverage (LEVERAGE) and the ratio of market value of equity to book value of equity (MARKET-TO-BOOK). Beuselinck et al. (2010) suggest that high growth opportunities and leverage may be related to stock comovement with the market if these characteristics are likely to expose the firms to financial distress. Such firms would exhibit higher innate risk, therefore I expect a negative relation between synchronicity and leverage and synchronicity and market-to-book ratio. In addition, and similar to Dasgupta et al. (2010), I include standard deviation of daily returns over a quarter to control for overall firm risk (RETURN

VARIABILITY). I also control for ROE following Hutton et al. (2009), Fernandes and Ferreira

(2008), and Gul et al. (2009).

Following Jin and Myers (2006) and Hutton et al. (2009) I include quarterly measures of kurtosis (KURTOSIS) and skewness (SKEWNESS) of the daily returns used to calculate synchronicity. Because kurtosis is skewed in my sample, I take natural logarithm of that variable to normalize its distribution. Jin and Meyers (2006) note that lower skewness means that there are more negative outliers in the distribution of returns and show that skewness negatively relates to synchronicity. I expect negative sign for SKEWNESS variable. Higher kurtosis can be interpreted as a result of infrequent extreme deviations. Hutton et al. (2009, p.79) argue that such

“jump events would tend to weaken the link between firm returns and market returns” leading to a positive relation between kurtosis and stock price informativeness. Therefore, I expect a negative relation between synchronicity and KURTOSIS.

I control for number of listed firms in the market, using firm Compustat coverage

(NUM_LISTED), and for number of firms per industry used to calculate SYNCH2

(IND_FIRM_NUMBER) to control for any variation in synchronicity measures driven by the differences in samples sizes used for estimation purposes. Finally, another crucial securities regulation implemented in the EU recently is Market abuse Directive (MAD). Adopted in 2003,

MAD restricts insider dealings and market abuse (Directive 2003/6/EC). Because prior literature argues that insider trading may have an impact on the collection of private information by outsiders (i.e. Fishman and Hagerty, 1992; Manne 1996; Carlton and Fischel, 2007) and because

Fernandes and Ferreira (2009) find that first time implementation of insider trading regulation reduces stock price synchronicity, I include MAD as an additional control variable. MAD is coded 1 if a fiscal year end is greater than or equal to the quarter end during the quarter that

MAD comes into effect in EU country, zero otherwise. I expect a negative sign for the MAD variable.

I measure control variables at the firm-quarter level where possible. Because many

European countries do not provide quarterly reports before TPD, I rely on annual data for some of my control variables. Similar to Ferreira and Laux (2007) and Christensen et al. (2011), when

I need to match quarterly data with annual data, I use the most recent annual numbers to compute the variables such as Leverage, Herfindahl Index, ROE, and Market-to-Book. Definitions of all regression variables are summarized in Table 1.

4.2. Empirical model for tests of H2 and H3.

To test the relation between TPD implementation and financial reporting quality I estimate the following model:

FRQ = β0 + β1TPD + ∑βjControlsj + ∑βiFixed Effectsi + ɛ (4)

Where FRQ stands for financial reporting quality measured as accrual quality or asymmetric timeliness of loss recognition. Specific proxies for financial reporting quality are discussed in sections 4.2.1 and 4.2.2. TPD is a test variable defined in section 4.1.1. Controlsj is a vector of control variables discussed in sections 4.2.3. And 4.2.4. Fixed Effectsi represents country, industry and quarter-year fixed effects. Similar to model (1), I report robust standard errors clustered at the firm level. H2 predicts accrual quality to be higher after the implementation of TPD, and H3 predicts that accounting conservatism to be more after the adoption of TPD. Thus, I expect β1 in model (4) to be greater than zero.

4.2.1. Dependent variables – accrual quality

To capture accrual quality, I follow the approach adopted by DeFond and Park (2001) and Francis and Wang (2008) to estimate a firm-year measure of total accrual and abnormal accruals. Francis and Wang (2008) note that typical abnormal accruals measure based on Jones

(1991) model do not perform well with the international data, possibly because the number of industry observations per country may be small. Therefore they estimate predicted accruals based on a firm’s current and past sales, current accruals, current and past property plant and equipment, and depreciation (Francis and Wang, 2008). I present detailed calculation of accrual quality metrics in Appendix II. I multiply total and abnormal accruals by minus one so that the metric increases with financial reporting quality.

4.2.2. Dependent variables – asymmetric timeliness of loss recognition

To measure asymmetric timeliness of earnings, I use the CSCORE measure developed by

Khan and Watts (2009). The CSCORE metric is calculated on a firm-year basis and is derived using the Basu (1997) measure of asymmetric timeliness. Jayaraman (2011) successfully extends

the use of the CSCORE to the cross-country setting.17 See Appendix III for details on the

CSCORE calculation.

4.2.3. Control variables – accrual quality

Control variables for accrual quality tests are similar to those used in prior research (e.g.

DeFond and Jiambalvo, 1994; Francis and Wang, 2008; Dechow et al., 2010). I control for firm size (SIZE) measured as natural logarithm of revenues. I also control for level of operating cash flows scaled by lagged assets (CFO). Further, I include leverage (LEVERAGE) calculated as total liabilities divided by total assets, sales growth and growth in property plant and equipment as control variables. I control for prior losses with LAG_LOSS which is a dummy variable equal to 1 if firm reports negative income before extraordinary items in prior year, zero otherwise. I also include Market-to-book ratio (MTB) as a control variable. I control for variance in sales and cash flows from operations in the past three years (STD_SALE, STD_CFO). Finally, I control for the absolute value of prior year total accruals (ABS_TA). In addition, I include several time- varying country-level control variables: level of GDP per capita, per capita GDP growth, and inflation similar to Jayaraman (2011).

4.2.4. Control variables – asymmetric timeliness of loss recognition

Control variables for conservatism tests are similar to those reported by Callen et al.

(2010) and Jayaraman (2011). To control for growth opportunities I include annual sales growth

(S_GROWTH) and R&D expenditures (RD). I also control for investment cycle length

(INV_CYCLE) measured as depreciation divided by assets. I control for the firm age (AGE), and firm specific uncertainty (STD_RET) measured as the standard deviation of returns over a year. I

17 In addition to using CSCORE as a proxy for asymmetric timeliness of loss recognition, I also examine the impact of TPD on accounting conservatism by estimating directly Basu (1997) reversed earnings-returns regression and Ball and Shivakumar (2005) model based on the behavior of changes in earnings. See Appendix IV for the description of these models.

include return on equity (ROE) to control for the firm performance. I also include market to book ratio (MB), size (SIZE) and leverage (LEV) as those variables are commonly used as controls in conservatism studies. Specifically, leverage is included to control for bondholders’ demand for conservative accounting (Zhang, 2008), and SIZE is known to be associated negatively with conservatism (LaFond and Watts, 2008). I also include several country-level time-varying control variables, specifically, GDP growth per capita, inflation and level of GDP per capita.

4.3. Empirical model for tests of H4

To test H4, I estimate empirical models (1) and (4) separately for a sample of countries classified as having weak and strong regulatory quality prior to the implementation of TPD. I use three measures of regulatory quality similar to Christensen et al. (2011). My first measure of prior regulation is the Regulatory Quality index from Kaufman et al. (2009). Regulatory quality measures the “ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development” (Kaufmann et al., 2009, p.6).

My second measure of regulatory quality is Supervisory Staff which measures the number of full- time employees working for the supervisory authority in charge of securities regulation, scaled by the number of listed companies in a given country (Christensen et al., 2011). Jackson and Roe

(2009, p.210) argue that “greater staffing allows the regulator to examine the allegations of wrongdoing, to write its rules carefully, to conduct market surveillance and review filings, and to act more often to remedy, prevent, and punish wrongdoing.” Thus, greater number of supervisory staff would imply stronger intensity of public enforcement of securities regulation.

My third and last measure of regulatory quality is the percentage change in full-time employees working for the local securities regulator from 2004 to 2009, Staff Growth, as defined in

Christensen et al. (2011). The sample of EU countries is split into strong and weak regulatory quality countries by median of the respective variable.

4.4. Empirical model for tests of H5

To test H5, I estimate empirical models (1) and (4) separately for a sample of countries classified as having weak and strong implementation and enforcement of TPD. I use three implementation strength measures for TPD. First, Maximum Fine is the maximum monetary penalty that the supervisory authority of a given EU country can impose on public companies for violations of Articles 4 to 6 of TPD (Christensen et al., 2011; CESR 2009a).18,19 Second, Shift in

Enforcement is based on the survey sent to the enforcement authority of EU country and local

PWC office with the inquiry about the implementation of comment and review process

(Christensen et al., 2011).20 Finally, Compliance with CESR Std.1 represents a subset of EU countries which by September 30th of 2008 fully complied with all of the enforcement principles of CERS Standard No. 1.21 As with regulatory quality, I partition the sample into weak and strong enforcers of TPD by splitting the sample by median of the related enforcement variable.

18 Articles 4 to 6 deal with the periodic reporting requirements. Specifically, Article 4 manifests a release of annual report within four months after the fiscal year end, Article 5 regulates semi-annual reporting, and Article 6 requires public interim management statements to be released between semi-annual report and annual report dates (Directive 2004/109/EC). 19 For example, maximum amount of administrative fines for violating Articles 4-6 of TPD is €10,000,000 in France, unlimited in United Kingdom and Norway, while a maximum fine in Austria is €100,000, and only €20,000 in Belgium, Lithuania and Romania (CESR, 2009a, p. 43). 20 The variable is coded one if the authority indicated a shift in enforcement and PWC agreed, zero otherwise, including cases where PWC disagreed with the local authority in terms of their assessment of the shift in intensity of enforcement (Christensen et al., 2011). 21 CESR Standard No. 1 outlines 21 principles of enforcement of standards on financial information, most of which became law within TPD. For instance Principle 1 states that “the purpose of enforcement of standards on financial information provided by the issuers... is to protect investors and promote market confidence by contributing to the transparency of financial information relevant to the investors’ decision making process” (CESR, 2009b, p.19). CESR Standard No. 1 requires the establishment of new or assignment of existing authorities to oversee and enforce implementation of the Standard, proposes risk-based model for selection of issues to be reviewed, defines checking procedures, etc. (CESR, 2009b).

5. Empirical results

5.1 Descriptive statistics

Panel B of Table 3 presents the descriptive statistics for the variables used in my empirical tests. Mean and median SYNCH1 is lower than mean and median SYNCH2, suggesting that industry adjusted model explains more variation in quarterly returns. That same pattern is observed for R2’s of models (1) and (2): R2 statistic of industry adjusted model (mean of 0.178) is higher than R2 statistic of market model (mean of 0.122). The summary statistics for

SYNCH1 and SYNCH2 in my study are similar to those reported in prior studies (e.g.

Beuselinck et al. 2010; Piotroski and Roulstone, 2004).

The mean market value is US $967.7 million; the median is US $106.6 million. Because of the skewed distribution of the SIZE variable, I use the natural logarithm of SIZE in regressions. The mean and median return variability is reported as log values of -3.733 and

-3.729 respectively. Reversing the log, the mean and median are both 0.024. Share turnover, de- logged, has mean and median value of zero. Overall, summary statistics for size, share turnover and return variability are very similar to the descriptive statistics of these same variables reported by Christensen et al. (2011) for their EU sample.

My sample has a mean (median) value of the Herfindahl index of 0.340 (0.284), which is higher than reported in prior studies and implies more concentrated industries in my sample. This inference is further supported by the mean number of firms in 2-digit industry of 37.93 (median of 14.00). Low number of firms per industry is comparable to Beuselink et al. (2010) EU sample.22 The average (median) number of analyst coverage in my sample is 1.748 (0.000). Low average number of analyst following is due to the incomplete coverage of firms in I/B/E/S. Sixty

22 I re-estimate model (3) with a restriction of at least 5 (at least 10) firms per industry. My results hold and are qualitatively similar to those reported in Section 5.3.

six percent of my firm-quarter observations are not covered in this database. For the sub-sample of EU firms, which are followed by at least one analyst, unreported mean (median) is 4.4 (2.0) with a range of 1 to 42 analysts.

The mean (median) age of a firm in my sample is 9.99 (9.00) years, with 1% of firms being younger than 3 years and 1% of firms older than 22 years.23 The mean (median) leverage is

0.190 (0.167), and the mean (median) market-to-book ratio is 1.618 (0.912). An average firm is unprofitable, with the ROE of -0.032, although half of the observations are above 0.037, and half are below. On average, there are 893.52 firms listed in the market, but the variable is skewed with a standard deviation of 719. Finally, Panel B of Table 3 reports summary statistics for three country-level macroeconomic variables: GPD growth, GDP per capita in US dollars, and inflation. The average GDP per capita is $24,497, the average annual GDP growth is 0.95%, and mean inflation is 2.04%.

Panel C of Table 3 reports the mean and median of synchronicity variables and the number of observations by country. Iceland, Latvia and Czech Republic have the smallest representation in the sample, while Great Britain, France and Germany have the highest coverage among all countries. Median SYNCH2 ranges from -0.25 for Czech Republic to -2.27 for

Germany. Panel D reports quarterly adoption dates for TPD. The majority of countries implemented TPD during the four quarters of 2007 (82.13% of observations). Panel E reports the frequency of firm-quarter observations by year and quarter. The observations are relatively evenly distributed among four quarters. There is a monotonic increase in the number of observations up until 2008, followed by a decline in 2009 and 2010, most likely caused by the firms exiting the market as a result of the financial crisis.

23 In untabulated tests, I re-estimate model (3) with a requirement that every firm has at least 20 firm-quarter observations, to mitigate the impact of young firms entering into the markets. My results are not affected by this restriction.

[Insert Table 3 here]

5.2. Correlations

Table 4 presents Pearson pairwise correlations among regression variables. SYNCH1 and

SYNCH2 are correlated at 73%, suggesting that about 27% of the unexplained relationship relates to the industry specific return variation. SYNCH1 (SYNCH2) is positively correlated to

TPD with the correlation values of 0.07 (0.04). A positive correlation is inconsistent with my prediction. However, the univariate correlations do not control for other firm- or country-level factors which may influence the comovement of stock prices, nor do they account for the time fixed effects.

Consistent with prior research (e.g. Dasgupta et al., 2009; Chan and Hameed, 2006), synchronicity measures are positively correlated to the number of analysts, age, size, Herfindahl

Index, and share turnover, with size having the largest correlation coefficient of 0.42 with

SYNCH1 and 0.36 with SYNCH2. Number of firms listed in the country has a negative correlation with SYNCH1 of 14% reflective of a conjecture that more concentrated markets have stronger co-moving stocks. The growth in the GDP per capita has a strong negative correlation with TPD of 44%. This is most likely due to the fact that many TPD observations belong to the period when the recent financial crisis impacted the EU. At the same time, SYNCH1 and

SYNCH2 have small positive correlations with the growth in GDP per capita.

To exclude the possibility that any of the large correlations impose multicollinearity in the regressions, I estimate the variance inflation factors (VIFs) for model (1). The absolute majority of the VIFs are less than 12 suggesting that multicollinearity is unlikely to be of concern when interpreting regression results. Only one control variable, NUM_LISTED exhibits the VIF

of 42. I re-estimate the model without this variable; results reported in Section 5.3 are qualitatively unchanged.

[Insert Table 4 here]

5.3. Multivariate regression results

I next proceed to the multivariate analysis. In Table 5, Panel A I report the regression estimations. Columns II and IV include control for Market Abuse Directive (MAD). TPD coefficient is negative and statistically significant for both measures of synchronicity, supportive of H1. For instance, last model (column IV) reports TPD coefficient of -0.083 (p-val. 0.001).

Therefore, there is a decline in synchronicity of 8.3% after the EU countries implement TPD.

Thus, an entry into force of TPD results in the increase in stock price informativeness.

Most of the control variables are statistically significant and behave as predicted. For instance, larger firms, more profitable and older firms, as well as firms with greater share turnover tend to have greater synchronicity consistent with Chan and Hameed (2006), Hutton et al. (2009) and Dasgupta et al. (2010). Synchronicity is higher in more concentrated industries and for firms with higher analyst coverage consistent with Piotroski and Roulstone (2004).

Skewness is negatively related to synchronicity consistent with Jin and Myers (2006) and Hutton et al. (2009). MAD has a negative and significant coefficient, supporting prior literature findings that anti-insider trading regulation reduces stock return synchronicity (e.g. Fernandes and

Ferreira, 2009). Adjusted R2 of the model is 0.35 when SYNCH1 is the dependent variable and

0.33 when SYNCH2 is the dependent variable, thus, at least 33% of the variation in the synchronicity is explained by the model.

[Insert Table 5 here]

In the following set of empirical results, I present a full model where SYNCH1 is the dependent variable, but only report TPD estimated coefficient when SYNCH2 is the dependent variable, omitting the reporting of control variables for brevity. I next test H4, by examining the importance of prior regulation on the effect of TPD on synchronicity. Table 6 Panel A reports estimation of model (1) where the sample is split into countries with weak and strong regulatory quality. Regulatory quality is based on the index by Kaufmann et al. (2009), which measures a country’s ability to formulate and implement sound policies and regulations and promote private sector development. In examining coefficient TPD where SYNCH2 is a dependent variable, weaker countries exhibit larger negative effect -0.161 vs. -0.097, with p-values of 0.000 and

0.005 respectively. This finding is consistent with “catching up” hypothesis. Countries with weaker prior regulatory quality enjoy a stronger effect of TPD. Although securities markets in those countries were weakly regulated prior to the implementation of TPD, the TPD itself results in a greater change in synchronicity than in countries with strong prior regulation.

Panel B of Table 6 presents estimation of model (1) when the sample is divided into countries with weak or strong levels of supervisory staff in 2003. In regards to SYNCH1 TPD has a negative and statistically significant coefficient in strong sub-sample. For SYNCH2 TPD is also negative and significant (coef. -0.106, p-value 0.000). Coefficient TPD is insignificant for weak subsample for both SYNCH1 and SYNCH2. Finally, Panel C of Table 6 reports the estimation of model (1) dividing the sample into strong and weak percentage change in supervisory staff from 2004 to 2009. The negative effect of TPD on synchronicity is pronounced in a strong sub-sample, with statistically significant at 1% level coefficients TPD for both measures of synchronicity. On the contrary, weak changes in supervisory staff result in TPD coefficient being statistically insignificant. I interpret the findings in panels B and C to suggest

that supervisory staff levels and growth do influence the strength of TPD implementation, with the end result being lower synchronicity in the period after TPD implementation in countries with better staffed regulators.

[Insert Table 6 here]

In Table 7, I test H5 and report regression results splitting sample of the EU countries into weak and strong enforcers of TPD. In Panel A I show that the negative effect of TPD on stock return synchronicity is driven by those EU countries, which impose above median fines for violating periodic reporting requirements of TPD. In contrast, Panel B shows that the effect of

TPD is stronger in countries which have weaker shift in enforcement. However, when defining this variable, Christensen et al. (2011) classify countries as weak enforcers if there is a disagreement between a regulator and PWC in their perception of the enforcement shift. My findings can be biased if actual strong enforcers are classified as weak enforcers. Finally, Panel

C shows that TPD impacts synchronicity of returns to the greater extent in countries which by the end of 2008 fully comply with the provisions of CESR Standard No 1. Firms in countries which do not comply with CESR Standard No 1 fully, do not exhibit a change in synchronicity in post-TPD period. Overall, Table 7 provides evidence in support of H5 – stock price informativeness post-TPD depends on the level of the Directive’s enforcement by the EU country.

[Insert Table 7 here]

I support my empirical analysis of the effect of TPD on stock price synchronicity with several sensitivity tests presented in Table 8. I include full set of controls and fixed effects in all regressions, as well as cluster by firm, but report only TPD coefficient for brevity. First, I remove financial firms from the sample, as those firms are subject to additional set of

regulations. There is no change to my findings. I control for GDP per capita, GDP per capita growth, and inflation to capture macroeconomic effects which may not be controlled for by country or time fixed effects.24 My inferences are unchanged. Because UK comprises a large portion of my sample, I estimate model (3) separately for UK and all other countries. This does not change my inferences. Similar effect is observed if I split sample into firms with December year-end and all other firms: TPD has a negative and significant coefficient in both sub-samples.

The inclusion of the firm fixed effects also does not alter my inferences. My results are not sensitive to the type of industry definition I use for industry fixed effect, such as Campbell’s

(1996) industry definition25 as used by Christensen et al. (2011) or 1-digit SIC code as in

Beuselink et al. (2010). My results do not change if I exclude pre-IFRS period from the sample, and only retain observations from 2006 to 2010, – a period when EU countries report using

IFRS. This period also covers post Prospectus Directive years. Finally, I follow the approach by

Christensen et al (2011) and create a benchmark sample of all non-EU countries that I can calculate regression variables for. I set TPD as zero for firms in all non-EU countries and include in my regression separate quarter-year-EU fixed effects in addition to quarter-year fixed effects.

My findings are unchanged in terms of negative effect of TPD on synchronicity. Overall, I find strong evidence that the negative relation between Transparency Directive and synchronicity is robust to alternative model specifications.

[Insert Table 8 here]

24 One caveat to my sample is that post-TPD period largely coincides with the recent financial crisis. Some may argue that the negative coefficient of my variables of interest is driven by the crisis period when the stocks may reflect less synchronicity due to noise. I control for GDP per capita growth in an attempt to control for the fluctuations in stock price comovement which may be caused by bad economic conditions. Furthermore, Brockman et al. (2010) find that stock co-movement is counter-cyclical in relation to business cycle: when aggregate economic activity is low, co-movement is high. Therefore, my results are unlikely to be driven by the recent financial crisis. 25 Campbell (1999) defines 12 industries as follows: petroleum, finance/real estate, consumer durables, basic, food/tobacco, construction, capital goods, transportation, utilities, textiles/trade, services, and leisure.

6. Conclusion

This paper examines the effect of securities regulation on quality of firm information.

Specifically, I examine the recently implemented Transparency Directive (TPD) which governs financial reporting requirements for issuers of public securities in the European Union. I argue that an increase in mandatory disclosure which follows adoption of TPD results in the increase in quality of firm financial information. I draw on the study by Christensen et al. (2011) and use the staggered adoption of the directives to test their impact on stock price synchronicity and earnings quality.

Using the sample of 4,768 unique firms in 25 EU countries from 2001 to 2010, I find that stock price synchronicity declines following the implementation of TPD. I conduct extensive sensitivity analysis which shows that my results are robust to inclusion of macroeconomic controls, firm fixed effects, use of alternative industry definition, and exclusion of financial firms. Furthermore, I document similar inference when I include non-EU firms as a benchmark sample and re-estimate my empirical model with separate EU-quarter-year fixed effects. I find strong empirical evidence that the effect of TPD on synchronicity is present only when the supervisory authority of EU member country is adequately staffed prior to the implementation of

TPD. Furthermore, I find that stock price informativeness improves more around TPD when EU countries invest in substantial enforcement efforts. Overall, my findings are supportive of the argument that transparency regulation can improve stock price informativeness. However, such regulation will be more effective if supported with stronger enforcement.

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Figure 1. Timeline for defining independent variables (pre-post period for TPD implementation)

Transparency Directive (TPD) entry into force in Germany

Q1-2007 Q2-2007 Q3-2007 Q4-2007

Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 TPD = 0 TPD = 1

TPD = 1 for firm with 3/31 FYE TPD = 0 TPD = 1 TPD = 1 for firm with 7/30 FYE

Appendix I. Recent Developments in Securities Regulation in the EU

In May of 1999 the European Commission launched the Financial Service Action Plan

(FSAP) with the purpose to promote an integrated financial market for the EU countries

(European Commission, 1999). The Plan was well received and endorsed by the Council of the

European Union in April of 2000 (Hansen, 2004). Later in 2000 the Council established a committee of seven “wise men”, led by Baron Alexandre Lamfalussy, former President of the

European Monetary Institute in Frankfurt. The group of “wise men” prepared and delivered the report on the regulation of European securities markets to the Council of European Union in

March of 2001, where it was adopted by a resolution (CWM, 2001).

An undeniably crucial portion of the Lamfalussy report was its recommendation for a four-step approach to law-making in the EU, now commonly known as the Lamfalussy Process.

The four levels are 1) framework directives, 2) implementing measures, 3) joint interpretation and implementation, and 4) enforcement (Ferran, 2004). Additionally, two new committees were formed to oversee the process of drafting and implementing securities regulation, namely,

European Securities Commission (ESC) and the Committee of European Securities Regulators

(CESR)26 (Avgouleas, 2005). The ESC deals with the Securities Regulation process at Level 2

(technical implementing details), while the CESR is responsible for Level 3, specifically, joint interpretation, recommendations, consistent guidelines and common standards, and peer review to ensure consistent implementation and application of the regulation. National Regulators of the

EU member countries impose level 3 rules, following consultation with CESR (Avgouleas,

2005). The Lamfalussy Process resulted in several new securities regulations, and changed the overall approach to policy-making and enforcement in the European Union.

26 CESR became European Securities and Markets Authority (ESMA) as of January 1, 2011.

Appendix II. Accounting Quality Measures.

I measure accounting quality using absolute value of total accruals and absolute value of abnormal accruals scaled by lagged total assets. Both measures are multiplied by minus one, so that they increase in accounting quality. Total accruals and abnormal accruals are estimated following Francis and Wang (2008) as follows (italicized items represent Compustat Global variable names):

Abnormal accrualst = Actual total accrualst – Predicted accrualst

Predicted accruals = {Salest (revt)*(Current Accrualst-1/Salest-1) – PPEt (ppegt)*(Depreciationt(dp-am)/PPEt-1)}/Total Assets(at)t-1

Current Accruals = change in non-cash working capital = Δtotal current assets (act) – cash and short term investments (che) – treasury stock shown in current assets (tsca) – Δtotal current liabilities (lct) – total debt in current liabilities (dlc) – proposed dividends (prodv)

Total Accruals = (Earnings before extraordinary itemst (ib) – Operating cash flowst)/Total Assetst-1(at)

Operating Cash Flows = Earnings before extraordinary items (ib) + depreciation and amortization (dp+am) + change in deferred income tax (txdb) + change in untaxed reserve (rvutx) + change in other liabilities (lo) + minority interest (mii) – current accruals (defined above)

My measures of accounting quality are ABS_TA and ABS_ABNAC, calculated as follows:

ABS_TA = -1*abs(Total Accruals)

ABS_ABNAC = -1*abs(Abnormal Accruals)

Appendix III. CSCORE measure of accounting conservatism

The CSCORE metric, developed by Khan and Watts (2009) is calculated on a firm-year basis and is derived using the Basu (1997) measure of asymmetric timeliness.

Basu (1997) cross-sectional regression is specified as follows:

Xit= β1 + β2Dit + β3Rit+ β4DitRit + eit (a) where i indexes the firm, X is earnings, R is the annual stock returns, calculated nine months before fiscal year end and three months after, and D represents a dummy variable, coded 1 if returns are less than zero, and zero otherwise. Here, the good news timeliness is captured by β3, while the incremental timeliness of bad news over good news, or conservatism, is represented by the estimate β4 and total news timeliness is β3+β4. Given that accounting conservatism is argued to entail asymmetric timelines of economic loss recognition, coefficient β4 is expected to be larger than coefficient β3.

Khan and Watts (2009) extend the Basu (1997) model by arguing that the good news timeliness

(GSCORE) as well as the bad news timeliness (CSCORE) is a linear function of firm size, market-to-book ratio and leverage:

GSCORE = β3 = µ1 + µ2Sizei + µ3M/Bi + µ4Levi (b)

CSCORE = β4 = λ1 + λ2Sizei + λ3M/Bi + λ4Levi (c)

Then, substituting equations (b) and (c) into equation (a), and including main effects, the following regression is estimated using annual cross-sectional regressions:

Xi = β1 + β2Di + β3Ri (μ1 + μ2Sizei + μ3M/Bi + μ4Levi)

+ β4DiR i(λ1 + λ2Sizei +λ3M/Bi + λ4Levi)

+ δ1Sizei + δ2M/Bi + δ3Levi + δ4DiSizei + δ5DiM/Bi +δ6DiLevi+ εi (d)

Larger values of the CSCORE are assumed to reflect high accounting conservatism.

Appendix IV. Alternative Models of Accounting Conservatism

As an alternative to the CSCORE measure of accounting conservatism I estimate two models. First, I evaluate the change in the asymmetric loss recognition after TPD adoption using the Basu (1997) model directly27:

Xit= β1 + β2Dit + β3Rit + β4DitRit +

+ β5TPD + β6RitTPD + β7DitRitTPD + ∑βjControlsj + ∑βiFixed Effectsi eit (i) where i indexes the firm, X is the earnings, R is the annual stock returns, calculated eight months before fiscal year end and four months after, and D represents a dummy variable, coded 1 if the returns are less than zero, and zero otherwise. Here, the incremental timeliness of bad news over good news, or conservatism, is represented by the estimate β4 for pre-TPD period, and the incremental timeliness of loss recognition after TPD is represented by coefficient β7. I expect a positive β7 to indicate an increase in conservatism in the post-TPD period consistent with H3.

Controlsj is a vector of control variables: Size, Market-to-Book and Leverage, defined above. I include each of the above three control variables as main effects, and also interact them with Dit,

Rit and DitRit similar to Francis and Martin (2009).

Because Basu (1997) based measure relies on the market efficiency and the behavior of returns in the relation to earnings, my second alternative measure of accounting conservatism follows method outlined by Basu (1997) and implemented by Ball and Shivakumar (2005). Basu

(1997) argues that under conservative accounting regime negative earnings changes have a greater tendency to reverse than positive earnings changes. This relation is reflected in the following model:

ΔNIit= β1 + β2Dit + β3ΔNIit-1 + β4DitΔNIit-1 + eit (ii)

27 This approach is similar to the one used by Lobo and Zhou (2006) in evaluating change in accounting conservatism post-SOX. 45

Where ∆NI is the change in net income excluding extraordinary items, scaled by beginning of the period total assets, and D is coded one if ∆NI is less than zero, zero otherwise. A negative coefficient β4 represents more timely recognition of economic losses than gains (Basu, 1997; Ball and Shivakumar, 2005). I augment model (ii) to examine the impact of TPD implementation on asymmetric loss timeliness, as follows:

ΔNIit= β1 + β2Dit + β3ΔNIit-1 + β4DitΔNIit-1 + β5TPD +

+ β6 ΔNIit-1TPD + β7 Dit ΔNIit-1TPD + ∑βjControlsj + ∑βiFixed Effectsi eit (iii)

The coefficient of interest is β7, I expect it to be negative if TPD increases conditional accounting conservatism. Similar to model (i) Controlsj is a vector of control variables: Size,

Market-to-Book and Leverage. I include each of the above three control variables as main effects, and also interact them with Dit, ΔNIit-1 and DitΔNIit-1. I estimate models (i) and (iii) with industry, year and country fixed-effects and report robust standard errors clustered at the firm level to account for the correlation of the residuals across years for a given firm (Petersen, 2009).

Table 1. Variable Definitions.

Synch1 Synchronicity measure, calculated using daily market wide returns. Synch2 Synchronicity measure calculated using daily market and industry wide returns. Rsq1 R-squared from the regression using market wide returns. Rsq2 R-squared from the regression using market and industry wide returns. Dummy variable which turns to 1 if the fiscal year end date for a given firm equals to or is TPD greater than the quarter end date in the calendar quarter that the Transparency Directive was passed, zero otherwise.

Natural logarithm of market value of equity (price in U.S. dollars times number of shares Size, t-4 outstanding at the end of the quarter), lagged by 4 quarters (log(PRCCD*CSHOC))

Return Variablilityt-4 Standard deviation of daily returns over a quarter. Natural logarithm of the median trading volume for a quarter, where trading volume is Share Turnovert-4 Compustat reported daily volume scaled by market capitalization log (CSHTRD/CSHOC*PRCCD). Herfindahl Index Revenues based Herfindahl Index, calculated for each two-digit SIC industry. Ind_firm_number Number of firms per industry-quarter used to calculate Synch2. Num_Analysts Average number of analysts providing forecasts for a firm during the year. Dummy which is equal to one if analyst information is missing in I/B/E/S for a given firm Analysts_dummy in the sample, zero otherwise. Age of the firm, at the end of the year, calculated based on the first year that the firm is Age covered in Compustat Global. Leverage Ratio of total Assets to Total Liabilities, annual, lagged by year (AT/(DLT+DLCC)). ROE Return on Equity, annual, lagged by year (IB/CEQ) Firm-quarter kurtosis of the daily returns distribution used to calculate synchronicity Kurtosis measures. Firm-quarter skewness of the daily returns distribution used to calculate synchronicity Skewness measures.

Num_Listed Number of listed firms in a given EU country, based on Compustat Global coverage.

Dummy variable which turns to 1 if the fiscal year end date for a given firm equals to or is MAD greater than the quarter end date in the calendar quarter that the Market Abuse Directive was passed, zero otherwise. Market-to-book Ratio of market value of assets to book value of assets, lagged by year. Market Value, millions, Price per share in US dollars times number of shares outstanding (PRCCD*CSHOC). USD Annual Growth in GDP per GDP per capita growth from World Bank economic indicators. Capita GDP per Capita, USD GDP per capita in US dollars from World Bank economic indicators. Annual Inflation rate, %, from World Bank economic indicators. Inflation

Table 2. Sample selection

Dataset of quarterly synchronicity observations from 2001 to 2010 730,864

Merge with Compustat Global annual dataset (2001-2010) with non-missing and 567,677 positive assets, revenues and owner’s equity

Less observations with insufficient data to calculate control variables (128,895) 438,782 Less observations of market value < $1 million (846) 437,936 Less firms with fewer than 4 observations from 2001 to 2010 (12,584) 425,352 Less observations from SIC2 = 99 (6,386) 418,966 Less non-EU countries (314,443) 104,523

Table 3. Descriptive Statistics.

Panel A. Sample composition, entry into force dates, and institutional variables Prior Regulation TPD implementation

TPD entry Regulatory Compliance TPD quarter Supervisory Supervisory Maximum Enforcement Country N into force Quality with CESR date Staff 2003 Staff growth Fine Shift date 2003 No 1

Austria 1,331 Apr-07 6/30/2007 1 0 1 0 0 0 Belgium 2,082 Aug-08 9/30/2008 1 0 0 1 0 1 Cyprus 137 Mar-08 3/31/2008 0 0 0 0 0 0 Czech Republic 98 Aug-09 9/30/2009 0 1 1 0 0 0 Denmark 2,198 Jun-07 6/30/2007 1 0 0 1 1 1 Estonia 111 Dec-07 12/31/2007 1 1 0 1 1 0 Finland 2,977 Feb-07 3/31/2007 1 0 0 0 1 1 France 14,822 Dec-07 12/31/2007 0 1 0 1 1 1 Germany 15,837 Jan-07 3/31/2007 1 1 1 0 1 0 Greece 3,240 Jul-07 9/30/2007 0 1 0 1 0 0 Hungary 408 Dec-07 12/31/2007 0 1 0 0 1 0 Iceland 61 Nov-07 12/31/2007 1 0 1 0 0 0 Ireland 930 Jun-07 6/30/2007 1 1 1 1 1 1 Italy 5,349 Aug-09 9/30/2009 0 1 1 1 0 1 Latvia 77 Apr-07 6/30/2007 0 0 1 0 1 1 Lithuania 506 Feb-07 3/31/2007 0 1 0 0 1 0 Luxembourg 265 Jan-08 3/31/2008 1 1 1 0 0 0 Netherlands 3,364 Jan-09 3/31/2009 1 1 1 0 1 0 Norway 3,309 Jan-08 3/31/2008 1 0 1 1 0 1 Poland 4,067 Mar-09 3/31/2009 0 1 1 1 0 1 Portugal 845 Nov-07 12/31/2007 0 1 0 1 0 1 Slovenia 153 Sep-07 9/30/2007 0 0 1 0 0 0 Spain 3,078 Dec-07 12/31/2007 1 0 1 0 0 1 Sweden 6,760 Jul-07 9/30/2007 1 0 1 1 1 0 United Kingdom 32,518 Jan-07 3/31/2007 1 1 1 1 1 1 49

Table 3. Continued. Panel B. Summary statistics for variables used in regressions.

Variable Mean Std. Dev. P25 Median P75 Dependent Variables SYNCH1 -2.648 1.515 -3.564 -2.552 -1.603 SYNCH2 -1.895 1.297 -2.703 -1.979 -1.208 Rsq1 0.122 0.134 0.028 0.072 0.167 Rsq2 0.178 0.173 0.063 0.121 0.230 Explanatory Variable TPD 0.37 0.48 0.00 0.00 1.00 Control Variables Size,t-4 4.902 1.902 3.540 4.716 6.098 Market Value, millions, USD 967.67 3410.34 31.33 106.59 445.33 Rerturn Variablilityt-4 -3.733 0.506 -4.071 -3.729 -3.384 Share Turnovert-4 -8.893 2.416 -10.600 -8.870 -7.251 Herfindahl Index 0.340 0.245 0.153 0.284 0.467 Ind_firm_number 37.93 56.63 5.00 14.00 41.00 Num_Analysts 1.75 4.11 0.00 0.00 1.00 Analysts_dummy 0.66 0.47 0.00 1.00 1.00 Age 9.99 4.69 6.00 9.00 13.00 Leveraget-4 0.19 0.17 0.03 0.17 0.31 ROE -0.03 0.38 -0.03 0.04 0.08 Kurtosis -3.31 1.12 -4.06 -3.36 -2.59 Skeweness 0.45 1.35 -0.15 0.37 1.02 Market-to-bookt-4 1.62 12.80 0.51 0.91 1.60 Number of listed firms 894 719 241 731 1754 MAD 0.60 0.49 0.00 1.00 1.00 Macroeconomic Variables GPD per capita, USD 24,497 6,687 22,797 25,547 27,755 Annual Growth in GDP per Capita, % 0.95 2.55 0.19 1.57 2.16 Inflation, % 2.04 1.09 1.36 2.05 2.46

Panel C. Mean and median synchronicity and related R2 values by EU country.

SYNCH1 SYNCH2 R-sq1 R-sq2 Country n Mean Median Mean Median Mean Median Mean Median Austria 1,331 -2.88 -2.76 -1.92 -2.04 0.10 0.06 0.17 0.12 Belgium 2,082 -2.84 -2.71 -1.80 -1.98 0.10 0.06 0.19 0.12 Cyprus 137 -2.64 -2.47 -1.41 -1.64 0.12 0.08 0.23 0.16 Czech Republic 98 -1.33 -1.08 0.00 -0.25 0.27 0.25 0.47 0.44 Denmark 2,198 -2.49 -2.35 -1.70 -1.82 0.13 0.09 0.20 0.14 Estonia 111 -2.80 -2.67 -1.65 -1.81 0.10 0.06 0.20 0.14 Finland 2,977 -2.39 -2.26 -1.54 -1.70 0.14 0.09 0.22 0.15 France 14,822 -2.77 -2.69 -2.07 -2.12 0.11 0.06 0.16 0.11 Germany 15,837 -2.98 -2.90 -2.20 -2.27 0.10 0.05 0.14 0.09 Greece 3,240 -1.45 -1.24 -0.87 -0.92 0.25 0.22 0.33 0.28 Hungary 408 -2.30 -2.20 -1.58 -1.59 0.14 0.10 0.21 0.17 Iceland 61 -2.46 -2.27 -1.06 -1.11 0.12 0.09 0.29 0.25 Ireland 930 -2.91 -2.77 -2.05 -2.08 0.09 0.06 0.16 0.11 Italy 5,349 -2.12 -1.95 -1.56 -1.57 0.16 0.12 0.22 0.17 Latvia 77 -2.97 -2.66 -1.75 -1.86 0.09 0.07 0.19 0.13 Lithuania 506 -2.69 -2.54 -1.32 -1.61 0.11 0.07 0.24 0.17 Luxembourg 265 -3.11 -2.89 -1.02 -1.77 0.08 0.05 0.30 0.15 Netherlands 3,364 -2.25 -2.14 -1.57 -1.69 0.16 0.11 0.22 0.16 Norway 3,309 -2.14 -2.00 -1.34 -1.44 0.17 0.12 0.25 0.19 Poland 4,067 -2.29 -2.15 -1.66 -1.74 0.14 0.10 0.20 0.15 Portugal 845 -2.42 -2.26 -1.55 -1.63 0.15 0.09 0.23 0.16 Slovenia 153 -2.27 -2.30 -0.78 -1.36 0.17 0.09 0.33 0.20 Spain 3,078 -1.98 -1.81 -1.09 -1.31 0.19 0.14 0.29 0.21 Sweden 6,760 -2.44 -2.29 -1.74 -1.81 0.13 0.09 0.19 0.14 United Kingdom 32,518 -2.90 -2.80 -2.14 -2.17 0.10 0.06 0.14 0.10

Table 3. Continued.

Panel. D. Frequency of quarterly adoption dates in the sample

TPD n % 3/31/2007 51,838 49.59% 6/30/2007 4,536 4.34% 9/30/2007 10,153 9.71% 12/31/2007 19,325 18.49% 3/31/2008 3,711 3.55% 9/30/2008 2,082 1.99% 3/31/2009 7,431 7.11% 9/30/2009 5,447 5.21% 104,523 100.00%

Panel E. Sample composition by year and calendar quarter.

Year n % Quarter n % 2001 8,650 8.3 I 25,420 24.3 2002 9,827 9.4 II 25,911 24.8 2003 9,847 9.4 III 26,457 25.3 2004 10,060 9.6 IV 26,735 25.6 2005 10,361 9.9 104,523 100% 2006 11,035 10.6 2007 11,713 11.2 2008 11,735 11.2 2009 11,428 10.9 2010 9,867 9.4 104,523 100%

Table 4. Correlations.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1 SYNCH1 1.00 2 SYNCH2 0.73 1.00 3 MAD 0.08 0.06 1.00 4 TPD 0.07 0.04 0.63 1.00

5 Sizet-4 0.42 0.36 0.05 0.03 1.00

6 Rerturn Variablilityt-4 -0.04 -0.02 -0.12 0.07 -0.32 1.00

7 Share Turnovert-4 0.03 0.00 0.06 0.05 -0.26 0.23 1.00 8 Herfindahl Index 0.07 0.24 0.01 0.00 0.14 -0.05 -0.23 1.00 9 Ind_firm_number -0.09 -0.13 0.02 0.05 -0.19 0.12 0.29 -0.53 1.00 10 Num_Analysts 0.28 0.24 0.12 0.26 0.43 -0.02 0.04 0.04 -0.03 1.00 11 Analysts_dummy -0.09 -0.04 -0.13 -0.34 -0.18 0.02 -0.07 0.04 -0.06 -0.59 1.00 12 Age 0.12 0.10 0.25 0.25 0.33 -0.22 -0.07 0.07 -0.11 0.26 -0.18 1.00

13 Leveraget-4 0.09 0.11 0.00 0.00 0.19 -0.08 -0.11 0.18 -0.25 0.09 -0.03 0.12 1.00 14 ROE 0.07 0.07 0.05 0.00 0.15 -0.19 -0.17 0.06 -0.10 0.08 -0.05 0.14 0.07 1.00 15 Kurtosis 0.03 0.04 0.00 0.13 -0.25 0.53 0.22 -0.07 0.11 -0.06 0.05 -0.21 -0.06 -0.25 1.00 16 Skewness -0.09 -0.08 0.01 0.01 -0.11 0.07 0.07 -0.01 0.02 -0.04 0.01 -0.03 -0.02 -0.01 0.15 1.00

17 Market-to-bookt-4 0.03 0.03 0.00 -0.01 0.07 0.01 -0.02 0.01 0.00 -0.01 0.01 -0.03 0.00 0.00 0.01 -0.01 1.00 18 Num_Listed -0.14 -0.18 0.00 0.07 -0.15 -0.05 0.53 -0.42 0.43 0.02 -0.17 0.06 -0.20 -0.09 0.01 -0.01 -0.01 1.00 19 GDP_per_capita (USD) -0.07 -0.07 0.07 0.10 0.00 0.05 0.15 -0.08 0.16 0.02 -0.06 0.12 -0.09 -0.07 0.06 0.00 0.01 0.30 1.00 20 GDP_per_capita_grth 0.01 0.02 -0.19 -0.44 -0.05 -0.14 -0.03 0.02 -0.03 -0.19 0.24 -0.13 -0.04 0.05 -0.18 -0.04 0.03 -0.05 -0.15 1.00 21 Inflation 0.11 0.11 0.14 0.12 0.05 -0.12 0.03 0.05 -0.04 -0.05 0.05 -0.05 0.02 0.02 0.01 -0.04 0.04 -0.02 -0.23 0.17 1.00

Bolded numbers indicate correlation with a p-value of 5% or less

Table 5. Multivariate Analysis - effect of Transparency Directive on stock price informativeness

Y = Synch1 Y = Synch1 Y = Synch 2 Y = Synch 2 Pred. I II III IV Variable Sign Coef. t-stat P-val Coef. t-stat P-val Coef. t-stat P-val Coef. t-stat P-val Intercept +/- -2.683 -11.05 <.0001 *** -2.610 -10.60 <.0001 *** -1.615 -2.61 0.009 *** -1.552 -2.50 0.012 *** TPD - -0.124 -5.07 <.0001 *** -0.125 -5.11 <.0001 *** -0.082 -3.21 0.001 *** -0.083 -3.25 0.001 ***

Sizet-4 + 0.336 60.64 <.0001 *** 0.336 60.65 <.0001 *** 0.231 37.41 <.0001 *** 0.232 37.42 <.0001 ***

Rerturn Variablilityt-4 - 0.046 3.39 0.001 *** 0.046 3.40 0.001 *** 0.102 7.25 <.0001 *** 0.102 7.26 <.0001 ***

Share Turnovert-4 + 0.157 33.96 <.0001 *** 0.157 33.97 <.0001 *** 0.103 18.67 <.0001 *** 0.103 18.67 <.0001 *** Num_Analysts + 0.046 20.07 <.0001 *** 0.046 20.08 <.0001 *** 0.040 17.20 <.0001 *** 0.040 17.21 <.0001 *** Analysts_dummy +/- 0.101 6.37 <.0001 *** 0.103 6.45 <.0001 *** 0.132 8.02 <.0001 *** 0.133 8.09 <.0001 *** Age + 0.007 3.77 0.000 *** 0.007 3.77 0.000 *** 0.007 2.95 0.003 *** 0.007 2.94 0.003 *** Herfindahl Index + 0.060 1.47 0.142 0.061 1.48 0.138 0.999 11.54 <.0001 *** 1.000 11.54 <.0001 ***

Leveraget-4 +/- 0.034 0.76 0.449 0.033 0.75 0.453 0.130 2.27 0.023 ** 0.130 2.27 0.023 ** Kurtosis - 0.061 9.88 <.0001 *** 0.061 9.87 <.0001 *** 0.065 11.63 <.0001 *** 0.065 11.63 <.0001 *** Skewness - -0.038 -11.63 <.0001 *** -0.038 -11.64 <.0001 *** -0.032 -11.74 <.0001 *** -0.032 -11.75 <.0001 *** ROE + 0.161 6.84 <.0001 *** 0.161 6.84 <.0001 *** 0.121 6.78 <.0001 ** 0.122 6.78 <.0001 ***

Market-to-bookt-4 - 0.000 -1.19 0.236 0.000 -1.19 0.234 -0.001 -1.34 0.182 -0.001 -1.34 0.181 Num_Listed - 0.000 -2.45 0.014 ** 0.000 -2.52 0.012 ** 0.000 -2.58 0.010 *** 0.000 -2.64 0.008 *** Ind_firm_number +/- 0.001 5.58 <.0001 *** 0.001 5.57 <.0001 *** MAD - -0.069 -2.29 0.022 ** -0.060 -1.90 0.058 *

Adj. R-sq 0.35 0.35 0.33 0.33 N 104,523 104,523 104,523 104,523 Industry Fixed Effects Yes Yes Yes Yes Quarter-year Fixed Effects Yes Yes Yes Yes Country Fixed Effects Yes Yes Yes Yes Cluster by firm Yes Yes Yes Yes SYNCH1 is log transformation of synchronicity measure calculated using the regression of daily returns of market wide returns. SYNCH2 is a log transformation of synchronicity measure which is R2 from the regression of daily returns on market-wide and industry-wide returns. TPD is a dummy variable which turns to 1 if the fiscal year end date for a given firm equals to or is greater than the quarter end date of the calendar quarter that the Transparency Directive was passed, zero otherwise. All other control variables are defined in Table 1. ***, **, and * denote significance at the 1%, 5% and 10% levels (two-tail), respectively. All p-values are based on robust standard errors clustered at the firm level. 54

Table 6. The effect of TPD on stock price informativeness when prior regulation differs

Panel A. Level of Regulatory Quality in 2003

Y = Synch1 Regulatory Quality 2003 - weak Regulatory Quality 2003 - strong Variable Coef. t-stat P-val Coef. t-stat P-val Intercept -1.241 -4.42 <.0001 *** -2.743 -10.62 <.0001 *** TPD -0.219 -5.59 <.0001 *** -0.098 -2.85 0.004 *** 0.326 29.31 <.0001 0.338 53.08 <.0001 Sizet-4 *** 0.044 1.66 0.098 0.063 3.97 <.0001 Return Variablilityt-4 * *** 0.201 24.51 <.0001 0.137 24.87 <.0001 Share Turnovert-4 *** *** Num_Analysts 0.030 7.80 <.0001 *** 0.051 18.76 <.0001 *** Analysts_dummy -0.095 -3.11 0.002 *** 0.167 9.11 <.0001 *** Age 0.012 2.83 0.005 *** 0.005 2.36 0.018 ** Herfindahl Index 0.165 1.96 0.050 ** 0.020 0.40 0.688 -0.004 -0.05 0.964 0.028 0.54 0.587 Leveraget-4 Kurtosis 0.165 12.89 <.0001 *** 0.034 4.99 <.0001 *** Skewness -0.059 -7.22 <.0001 *** -0.033 -9.47 <.0001 *** ROE 0.137 1.85 0.065 * 0.145 6.49 <.0001 *** 0.000 -0.56 0.573 -0.001 -0.23 0.819 Market-to-bookt-4 Num_Listed 0.000 -0.69 0.491 0.000 -4.84 <.0001 *** MAD 0.178 2.90 0.004 *** -0.121 -3.60 0.000 ***

Adj. R-sq. 0.399 0.330 N 29,702 74,821 Industry Fixed Effects Yes Yes Quarter-year Fixed Effects Yes Yes Country Fixed Effects Yes Yes Cluster by firm Yes Yes

Y= Synch2 TPD -0.161 -3.99 0.000 *** -0.097 -2.84 0.005 ***

Adj. R-sq 0.42 0.33

SYNCH1 is log transformation of synchronicity measure calculated using the regression of daily returns of market wide returns. SYNCH2 is a log transformation of synchronicity measure which is R2 from the regression of daily returns on market-wide and industry-wide returns. TPD is a dummy variable which turns to 1 if the fiscal year end date for a given firm equals to or is greater than the quarter end date of the calendar quarter that the Transparency Directive was passed. All other control variables are defined in Table 1. ***, **, and * denote significance at the 1%, 5% and 10% levels (two- tail), respectively. All p-values are based on robust standard errors clustered at the firm level.

Table 6. Continued

Panel. B. Strength in Supervisory Staff in 2003

Supervisory Staff 2003 - Y = Synch1 Supervisory Staff 2003 - weak strong Variable Coef. t-stat P-val Coef. t-stat P-val Intercept -1.899 -6.44 <.0001 *** -3.031 -11.43 <.0001 *** TPD 0.049 0.86 0.393 -0.180 -6.48 <.0001 *** 0.339 29.16 <.0001 0.333 53.14 <.0001 Sizet-4 *** *** 0.107 3.18 0.002 0.039 2.58 0.010 Return Variablilityt-4 *** *** 0.127 13.36 <.0001 0.164 31.45 <.0001 Share Turnovert-4 *** *** Num_Analysts 0.035 7.41 <.0001 *** 0.048 18.69 <.0001 *** Analysts_dummy -0.007 -0.15 0.877 0.110 6.46 <.0001 *** Age 0.009 1.99 0.047 ** 0.007 3.18 0.002 *** Herfindahl Index 0.076 1.08 0.279 0.089 1.69 0.091 * 0.081 0.87 0.386 -0.001 -0.02 0.982 Leveraget-4 Kurtosis 0.045 2.70 0.007 *** 0.062 9.39 <.0001 *** Skewness -0.054 -6.36 <.0001 *** -0.035 -10.00 <.0001 *** ROE 0.170 2.23 0.026 ** 0.154 6.84 <.0001 *** -0.006 -2.21 0.027 0.000 -1.01 0.312 Market-to-bookt-4 ** Num_Listed -0.003 -4.68 <.0001 *** 0.000 0.72 0.471 MAD -0.095 -1.41 0.158 -0.057 -1.68 0.093 *

Adj. R-sq. 0.35 0.350 N 22,163 82,360 Industry Fixed Effects Yes Yes Quarter-year Fixed Effects Yes Yes Country Fixed Effects Yes Yes Cluster by firm Yes Yes

Y= Synch2 TPD 0.013 0.18 0.855 -0.106 -3.94 0.000 ***

Adj. R-sq 0.34 0.35

Table 6. Continued.

Panel C. Supervisory Staff Growth of the securities regulator 2004-2009

Y = Synch1 Staff growth - below median Staff growth - above median Variable Coef. t-stat P-val Coef. t-stat P-val Intercept -2.256 -9.88 <.0001 *** -2.845 -11.16 <.0001 *** TPD -0.131 -2.22 0.027 ** -0.111 -4.02 <.0001 *** 0.329 27.50 <.0001 0.335 53.29 <.0001 Sizet-4 *** *** 0.048 1.74 0.082 0.057 3.64 0.000 ReturnVariablilityt-4 * *** 0.197 21.61 <.0001 0.142 26.48 <.0001 Share Turnovert-4 *** *** Num_Analysts 0.029 6.75 <.0001 *** 0.053 19.92 <.0001 *** Analysts_dummy -0.033 -1.10 0.272 0.146 7.90 <.0001 *** Age 0.011 2.55 0.011 ** 0.007 3.03 0.002 *** Herfindahl Index 0.155 1.87 0.062 * 0.016 0.32 0.753 -0.099 -1.11 0.268 0.057 1.12 0.264 Leveraget-4 Kurtosis 0.128 9.42 <.0001 *** 0.043 6.42 <.0001 *** Skewness -0.052 -6.58 <.0001 *** -0.034 -9.59 <.0001 *** ROE 0.119 2.38 0.018 ** 0.162 7.30 <.0001 *** 0.000 0.03 0.980 -0.003 -1.09 0.278 Market-to-bookt-4 Num_Listed 0.001 2.91 0.004 *** 0.000 -3.98 <.0001 *** MAD -0.211 -3.78 0.000 *** 0.003 0.10 0.924

Adj. Rsq. 0.35 0.340 N 27,326 77,197 Industry Fixed Effects Yes Yes Quarter-year Fixed Effects Yes Yes Country Fixed Effects Yes Yes Cluster by gvkey Yes Yes

Y= Synch2 TPD 0.013 0.18 0.855 -0.106 -3.94 0.000 ***

Adj. Rsq 0.34 0.35

Table 7. The effect of TPD on stock price informativeness when the strength of implementation and enforcement differs

Panel A. Level of Maximum Fine imposed for noncompliance

Y = Synch1 Max Fine - low Max Fine - high Variable Coef. t-stat P-val Coef. t-stat P-val Intercept -2.122 -7.99 <.0001 *** -2.763 -8.66 <.0001 *** TPD -0.028 -0.51 0.610 -0.129 -4.64 <.0001 ***

Sizet-4 0.373 34.60 <.0001 *** 0.318 49.96 <.0001 ***

Return Variablilityt-4 0.016 0.59 0.557 0.060 3.79 0.000 ***

Share Turnovert-4 0.190 20.15 <.0001 *** 0.141 26.60 <.0001 *** Num_Analysts 0.033 8.11 <.0001 *** 0.049 18.07 <.0001 *** Analysts_dummy 0.007 0.16 0.869 0.125 7.21 <.0001 *** Age 0.000 0.12 0.903 0.010 4.64 <.0001 *** Herfindahl Index 0.061 0.67 0.500 0.078 1.61 0.107

Leveraget-4 -0.074 -0.87 0.384 0.061 1.17 0.240 Kurtosis 0.113 8.74 <.0001 *** 0.049 7.08 <.0001 *** Skewness -0.059 -7.18 <.0001 *** -0.032 -8.92 <.0001 *** ROE 0.271 6.19 <.0001 *** 0.139 6.00 <.0001 ***

Market-to-bookt-4 -0.005 -1.48 0.139 0.000 -0.96 0.336 Num_Listed -0.001 -3.78 0.000 *** 0.000 -1.83 0.068 *

Adj. R-sq 0.38 0.34 N 28,292 76,231 Industry Fixed Effects Yes Yes Quarter-year Fixed Effects Yes Yes Country Fixed Effects Yes Yes Cluster by firm Yes Yes

Y= Synch2 TPD -0.067 -1.01 0.315 -0.067 -2.41 0.016 **

Adj. R-sq 0.38 0.33

Table 7. Continued

Panel. B. Shift in Enforcement effect

Y = Synch1 Shift in enforcement - weak Shift in enforcement - strong Variable Coef. t-stat P-val Coef. t-stat P-val Intercept -1.133 -3.69 0.000 *** -2.766 -11.48 <.0001 *** TPD -0.246 -5.37 <.0001 *** -0.093 -2.48 0.013 ** 0.310 27.36 <.0001 0.343 54.06 <.0001 Sizet-4 *** *** 0.049 1.51 0.130 0.055 3.64 0.000 Return Variablilityt-4 *** 0.178 19.78 <.0001 0.150 28.62 <.0001 Share Turnovert-4 *** *** Num_Analysts 0.019 4.43 <.0001 *** 0.052 20.21 <.0001 *** Analysts_dummy -0.137 -3.24 0.001 *** 0.156 9.36 <.0001 *** Age 0.016 3.22 0.001 *** 0.003 1.61 0.108 Herfindahl Index 0.025 0.34 0.732 0.052 1.02 0.308 -0.067 -0.69 0.493 0.020 0.42 0.678 Leveraget-4 Kurtosis 0.179 10.72 <.0001 *** 0.044 6.77 <.0001 *** Skewness -0.084 -8.49 <.0001 *** -0.032 -9.36 <.0001 *** ROE 0.337 6.45 <.0001 *** 0.144 6.46 <.0001 *** 0.000 0.11 0.910 -0.006 -1.73 0.083 Market-to-bookt-4 * Num_Listed 0.000 -0.32 0.752 0.000 -3.19 0.001 ***

Adj. R-sq 0.37 0.33 N 24,015 80,508 Industry Fixed Effects Yes Yes Quarter-year Fixed Effects Yes Yes Country Fixed Effects Yes Yes Cluster by firm Yes Yes

Y= Synch2 TPD -0.191 -3.90 0.000 *** -0.035 -0.98 0.328

Adj. R-sq 0.33 0.32

Table 7. Continued

Panel. C. Compliance with CESR Standard No 1

Y = Synch1 Compliance - bad Compliance - good Variable Coef. t-stat P-val Coef. t-stat P-val Intercept -2.810 -13.600 <.0001 *** -3.199 -9.700 <.0001 *** TPD -0.067 -1.090 0.275 -0.099 -3.600 0.000 *** 0.363 36.690 <.0001 0.324 48.340 <.0001 Sizet-4 *** *** 0.015 0.570 0.565 0.071 4.420 <.0001 Return Variablilityt-4 *** 0.181 19.830 <.0001 0.142 26.940 <.0001 Share Turnovert-4 *** *** Num_Analysts 0.033 7.670 <.0001 *** 0.050 19.040 <.0001 *** Analysts_dummy -0.030 -0.810 0.418 0.137 7.880 <.0001 *** Age 0.003 0.690 0.492 0.009 4.110 <.0001 *** Herfindahl Index 0.089 1.120 0.264 0.056 1.110 0.266 -0.070 -0.870 0.387 0.071 1.340 0.180 Leveraget-4 Kurtosis 0.102 8.000 <.0001 *** 0.050 7.320 <.0001 *** Skewness -0.058 -7.320 <.0001 *** -0.032 -9.060 <.0001 *** ROE 0.307 8.190 <.0001 *** 0.127 5.840 <.0001 *** 0.000 -1.150 0.251 -0.006 -1.600 0.110 Market-to-bookt-4 Num_Listed 0.000 -0.360 0.722 0.000 -3.830 0.000 ***

Adj. R-sq 0.38 0.34 N 32,271 72,252 Industry Fixed Effects Yes Yes Quarter-year Fixed Effects Yes Yes Country Fixed Effects Yes Yes Cluster by firm Yes Yes

Y= Synch2 TPD -0.009 -0.13 0.894 -0.062 -2.20 0.027 **

Adj. R-sq 0.39 0.32

Table 8. Robustness tests

Y = SYNCH1 Y = SYNCH2 Variable Coef. t-stat Coef. t-stat n Delete financial firms TPD -0.125 -5.10 *** -0.078 -3.09 *** 103,974

Control for logGDP_US, GDP per capita growth, and inflation TPD -0.113 -4.70 *** -0.074 -2.86 *** 104,507

Exclude UK firms TPD -0.073 -3.15 *** -0.059 -2.26 ** 72,005

UK firms only TPD -0.210 -3.47 *** -0.126 -2.67 *** 32,518

Firm Fixed Effects TPD -0.113 -4.69 *** -0.069 -3.00 *** 104,523

December year end firms only TPD -0.090 -3.19 *** -0.070 -2.20 ** 76,817

non-December year end firms TPD -0.139 -2.64 *** -0.089 -2.05 ** 27,706

Alternative Industry Definition - Campbell (1996) TPD -0.118 -4.80 *** -0.077 -2.95 *** 104,523

Alternative Industry Definition - 1 digit sic code TPD -0.118 -4.77 *** -0.078 -2.99 *** 104,523

Using benchmark of non-EU firms and quarter-year-EU fixed effects TPD -0.135 -5.60 *** -0.084 -3.32 *** 418,966

Post-IFRS period (2006-2010) TPD -0.112 -4.58 *** -0.070 -2.77 *** 55,778

All control variables including fixed effects are included, but omitted for brevity. ***, **, and * denote significance at the 1%, 5% and 10% levels (two-tail), respectively. P-values are based on robust standard errors clustered at the firm level. 61