Essays on Insider Trading

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Valeriya Vitalyevna Posylnaya

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Essays on insider trading

By TITLE PAGE Valeriya Vitalyevna Posylnaya

A Dissertation Submitted to the Faculty of Mississippi State University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Finance in the Department of Finance and Economics

Mississippi State, Mississippi

August 2018

2018

Essays on insider trading

By APPROVAL PAGE Valeriya Vitalyevna Posylnaya

Approved:

______Brandon N. Cline (Major Professor/Graduate Coordinator)

______Douglas Brian Blank, II (Committee Member)

______Randall C. Campbell (Committee Member)20

______Micheal J. Highfield (Committee Member)

______Alvaro G. Taboada (Committee Member)

______Claudia R. Williamson (Committee Member)

______Sharon L. Oswald Dean College of Business

Name: Valeriya Vitalyevna Posylnaya ABSTRACT Date of Degree: August 10, 2018

Institution: Mississippi State University

Major Field: Finance

Major Professor: Brandon N. Cline

Title of Study: Essays on insider trading

Pages in Study 142

Candidate for Degree of Doctor of Philosophy

The first essay explores relations between political affiliations and illegal insider trading.

Assessing illegal insider trading is challenging due to the nature of the activity. Researchers observe and evaluate only the detected portion of illegal trading, not all illegal transactions.

This presents a problem when using traditional empirical techniques to investigate such activity. In our analysis we employ a bivariate probit model that takes into account the partial observability nature of insider trading and provides estimates for the determinants of both the commission and the detection of illegal insider trading. Among our findings, most notable is the influence of the SEC’s political structure on insider trading detection.

We show that the political party affiliation within the SEC, past indictments by the SEC, and SEC budget play a crucial role in determining current prosecution. Past SEC indictments significantly decrease the likelihood to engage in illegal insider trading as well.

Essay two investigates insider trading returns by corporate insiders in light of their firms’ lobbying activities. Lobbying is a channel firms often use to influence regulatory change.

Firms also use lobbying to obtain information on upcoming legislative and regulatory changes that are significant to the firms’ future. Establishing and maintaining these political

connections provides informational advantage not only to the firms engaged in lobbying but also to the insiders of these firms who receive an opportunity to base their trading decision on this potentially valuable information. Using data on firm lobbying activities, we provide evidence of an informational advantage acquired by corporate insiders of firms that develop these connections with policymakers. We find that insiders of lobbying firms gain additional return of 138 (156) basis points on their buys (sells) trades relative to transactions placed by insiders of firms that are not engaged in lobbying activities. We also document that the role of establishing and fostering lobbying contacts and the amounts spent on lobbying differ with type of insider transactions and length of investment horizons.

The focus of the third essay is the impact of actual trading on material non-public information on firms’ securities. Finance and law scholars present theoretical arguments both in favor of and against trading on material non-public information. However, investigating empirically the actual impact of insider trading on the insider’s firm poses significant challenges due to the lack of precision in identifying from publically available data trades that are based on private information. In this study, we utilize Securities

Exchange Commission (SEC) indictments of illegal insider trading to examine the impact of illegal insider trading on the firm. We provide evidence suggesting that illegal insider trading increases stock market liquidity for the involved firms. Our results imply that bid- ask spread following transactions based on private information is narrower for long-run windows. However, we also find results implying that informed trading is associated with

reduced liquidity, when estimated with Amihud Illiquidity proxy, reflecting price impact of trades based on private information.

ACKNOWLEDGEMENTS

I am indebted to many people without whom this dissertation would be impossible.

I am deeply grateful to each for their encouragement and support. First, I would like to thank Brandon N. Cline, my advisor and my dissertation committee chair, for his guidance, time, and patience. His tremendous help and encouragement throughout the entire doctorate program and dissertation process made this possible. I would also like to thank other members of my dissertation committee: D. Brian Blank, Randall C. Campbell,

Micheal J. Highfield, Alvaro G. Taboada, and Claudia R. Williamson for their valuable comments and insightful suggestions.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... v

LIST OF TABLES ...... viii

LIST OF FIGURES ...... x

CHAPTER

I. ILLEGAL INSIDER TRADING: COMISSION AND SEC DETECTION ...... 1

Introduction ...... 1 Literature Review ...... 7 Sample Selection ...... 11 Model Specification ...... 12 Bivariate probit model with partial observability ...... 12 Insider Trading Propensity and Insider Trading Detection ...... 14 Results 16 Bivariate probit with partial observability: Base Model ...... 17 Political Factors ...... 20 Political Factors and Purchases versus Sales ...... 27 Political Factors and Executives ...... 30 Conclusion ...... 32 References ...... 34

II. INSIDER TRADING AND CORPORATE LOBBYING ...... 58

Introduction ...... 58 Literature Review ...... 62 Insider Trading ...... 62 Corporate Lobbying Practices ...... 64 Lobbying Activities and Informed Trading ...... 65 Data 66 Insider Trading Data ...... 66 Lobbying Data ...... 66 Results 68 Descriptive Statistics ...... 68 Multivariate Analysis: 3-day CARs ...... 69

Multivariate Analysis: Long-Run Horizon ...... 72 Multivariate Analysis: Executive Rank ...... 75 Conclusion ...... 78 References ...... 80

III. IMPACT OF ILLEGAL INSIDER TRADING ...... 114

Introduction ...... 114 Literature Review ...... 117 Impact of Implied Informed Trading ...... 117 Impact of Insider Trading Regulation ...... 118 Illegal Insider Trading and Liquidity ...... 119 Sample Selection ...... 120 Measures of Insider Trading Impact ...... 121 Empirical Results ...... 122 Univariate Analysis ...... 123 Liquidity: Bid-ask spread ...... 123 Liquidity: Amihud Illiquidity Proxy ...... 124 Multivariate Results ...... 124 Liquidity: Bid-ask spread ...... 125 Liquidity: Amihud Illiquidity Proxy ...... 126 Liquidity: Announcements Effect ...... 127 Conclusion ...... 128 References ...... 131

vii

LIST OF TABLES

1.1 Distribution of Illegal Insider Trading by Corporate Event and Industry ...... 40

1.2 Descriptive Statistics ...... 41

1.3 Bivariate Probit with Partial Observability: Base Model ...... 43

1.4 Traditional Probit: Base Model ...... 45

1.5 Bivariate Probit with SEC and Political Variables: Committee Majority...... 47

1.6 Traditional Probit with SEC and Political Variables: Committee Majority...... 49

1.7 Bivariate Probit with SEC and Political Variables: Committee Majority versus Chairman...... 51

1.8 Bivariate Probit with SEC and Political Variables: Type of Transaction ...... 53

1.9 Bivariate Probit with SEC and Political Variables: Executive Rank ...... 55

2.1 Distribution of Lobbying Activities ...... 86

2.2 Spending on Lobbying Activities ...... 88

2.3 Descriptive Statistics ...... 89

2.4 CARs around Insider Transaction Dates and Lobbying ...... 90

2.5 CARs around Insider Transaction Dates and Lobbying Spending ...... 92

2.6 Purchase Monthly Returns: Lobbying ...... 94

2.7 Sale Monthly Returns: Lobbying ...... 96

2.8 Purchase Monthly Returns: Lobbying Spending ...... 98

2.9 Sale Monthly Returns: Lobbying Spending ...... 100

viii

2.10 CARs around Insider Transaction Dates and Lobbying: Executive Rank ...... 102

2.11 CARs around Insider Transaction Dates and Lobbying Spending: Executive Rank ...... 104

2.12 Purchase Monthly Returns: Executive Rank ...... 106

2.13 Sale Monthly Returns: Executive Rank ...... 108

2.14 Purchase Monthly Returns: Executive Rank and Lobbying Spending ...... 110

2.15 Sale Monthly Returns: Executive Rank and Lobbying Spending ...... 112

3.2 Distribution of Illegal Insider Trading by Corporate Event and Industry ...... 137

3.3 Changes in Liquidity ...... 138

3.4 Liquidity: Bid-Ask Spread over Long Horizons ...... 139

3.5 Liquidity: Bid-Ask Spread over Short Horizons ...... 140

3.6 Liquidity: Amihud Illiquidity over Long Horizons ...... 141

3.7 Liquidity: Amihud Illiquidity over Short Horizons ...... 142

LIST OF FIGURES

1.1 Insider Trading Time Trends ...... 57

3.1 Insider Trading Time Trends ...... 134

3.2 Bid-Ask Spread around Illegal Insider Trade Dates ...... 134

3.3 Amihud Illiquidity Proxy around Illegal Insider Trade Dates ...... 135

3.4 Bid-Ask Spread around Illegal Insider Announcement Dates ...... 135

3.5 Amihud Illiquidity Proxy around Illegal Insider Announcement Dates ...... 136

ILLEGAL INSIDER TRADING: COMISSION AND SEC DETECTION

Introduction

Insider trading has been studied for decades but still presents challenges to both researchers and regulators. Determining whether trades are made based on private information is complicated by the fact that insiders often transact with information that is not illegal. For example, insiders may trade for liquidity or diversification needs, neither of which are directly related to the possession of private information. In addition, in their trading decisions many insiders are likely to use public information combined with their superior understanding of its impact rather than private information (Piotroski and

Roulstone, 2005; Alldredge and Cicero, 2015). Consequently, inferring the use of private information solely on the basis of ex-post returns is imprecise.

Assessing the proliferation of illegal insider trading is further obscured by the fact that we cannot directly observe all illegal insider trading activity. The Security and

Exchange Commission (SEC) claims that trading based on material nonpublic information is widespread but difficult to prove due to direct evidence of the crime being rare and investigations typically relying on circumstantial evidence and drawing reasonable inferences (Bhara, 2010; Newkirk and Robertson, 1998). Consequently, we observe only the portion of insiders who get caught; those who have traded illegally but are undetected are not factored into the analysis. This implies that the models traditionally used in the

evaluation of illegal insider trading determine more about the probability of detected illegal insider trading rather than about the probability of overall illegal insider trading. Thus, limited evidence is offered regarding the actual insider trading or the detection of illegal insider trading.

In this study, we simultaneously model the commission of illegal insider trading and the detection of it using the bivariate probit model developed by Poirier (1980) and advocated by Wang (2013) and Carver et al. (2013).1 This allows us to incorporate into the analysis both the determinants of engagement in insider trading and the determinants of detection of insider trading by relating the two distinct latent processes of insider trading commission and insider trading detection. By doing so, we are better able to evaluate the factors prevalent in the individual’s decision to commit trades based on private information, as well as the factors affecting the detection process of the SEC.

The incentives that influence the decision to engage in insider trading and potentially be indicted for it are connected. According to Becker (1968), the decision to commit a crime is based on the economic assessment of the expected benefits and expected costs of the commission. A person rationally chooses to engage in criminal activity when the benefits outweigh the costs. While some crimes admittedly could be crimes of passion, the theoretical structure of Becker (1968) is especially fitting for insider trading since the decision to trade on illegal information is purely an economic decision. In the context of

Becker (1968), this means that the determinants of insider trading commitment include the

1 Wang (2013) and Carver et al. (2013) demonstrate the importance of the partial observability issue in the fraud and option backdating literature, respectively. Researching both involves problems similar to those of the investigation of illegal insider trading: we observe not all fraud and option backdating cases but only the detected ones.

expected benefits of insider trading and expected costs of engaging in the activity, which are in turn related to the expected probability of being prosecuted.

The issue of partial observability makes the empirical analysis of the factors proposed to affect illegal insider trading more challenging since it is not clear whether these factors impact insider trading commitment, detection, or both. For example, the likelihood of illegal insider trading suggested from traditional probit models could indicate either that the measured factors indeed lead to less (more) trades being committed and, because of that, less (more) trades being detected. Alternatively, the probability could imply that the factors influence the commission of illegal insider trading activity but not the detection of it, or that they effect only the detection but not the actual commission of insider trading. In other words, findings based on estimating the effects of the determinants to engage in illegal insider trading and the determinants to detect insider trading independently do not accurately measure the true interaction between the incentives to commit illegal insider trading and the detection of such activity.

In our analysis, we do not focus on the transactions reported to the SEC in accordance with Section 16(a) of the Securities and Exchange Act of 1934, since most of these transactions are unlikely to be based on material nonpublic information (Bainbridge,

2000). Instead, we utilize a sample of SEC indictments for trading while in possession of material nonpublic information – a sample of detected illegal insider trading – and compare these indictments to firms whose employees have not been accused of illegal transactions.

To identify trades based on private information, data from the SEC Litigation

Releases and the SEC Complaints are hand collected. We gather details regarding the firms and insiders prosecuted, as well as the corporate events they benefited from and the dates

of the transactions. In addition, we hand collect data on political affiliations within the

SEC, the budget of the SEC, and the intensity of the SEC enforcement activities against insider trading.

The analysis of these factors provide important insight into both the commission and detection of illegal insider trading. Prior literature on insider trading detection focuses on firm characteristics (Aboody and Lev, 2000; Frankel and Li, 2004; Huddart and Ke,

2007; Piotroski and Roulstone, 2005; Ravina and Sapienza, 2010), actual trades (Cline et al., 2016; Cohen et al., 2012; Aboody et al., 2008), and characteristics of the insiders themselves (Ravina and Sapienza, 2010; Wang et al., 2012). What has not been incorporated is the role of the SEC in the process.

It is reasonable to assume that the variables related to the very agency charged with the task of investigating trades based on material, nonpublic information play a role in illegal insider trading activity. In particular, we expect political party affiliations within the agency to have an impact on the detection process. The attitude of Republican and

Democratic Parties towards business tends to differ, especially regarding government involvement in business affairs. Corporate groups are aware of these differences and express their preferences with greater donations to Republican Party candidates for federal legislative offices (Brunell, 2005). Gimpel et al. (2014) provide detailed evidence regarding industry commitments to the political parties. They find that approximately one- third of the industry sectors lean towards Republicans and the rest have no partisan preference.

We document several novel findings. First, we show that an SEC committee comprised of a Republican majority is significantly negatively associated with the

probability of the illegal insider trading detection; however, the SEC Chairman being a

Republican does not have a significant influence on the process. This highlights the importance of the political views of the Committee as a whole but not necessarily those of the SEC Chairman.

We also show that the number of SEC insider trading litigation releases in the year prior to an insider trade decreases the propensity to trade on material, nonpublic information. On the other hand, the releases positively relate to the likelihood of the illegal trades being detected. These findings suggest that insiders are aware of recent enforcement actions taken against other insiders and that the actions themselves are an effective deterrent to current illegal trading. The budget of the SEC is also positively and significantly associated with detection.

The impact of the political variables, however, differs with the perceived severity of the illegal activity and the rank of the insider engaged in the trade. For example, the association between the SEC Committee Republican majority and propensity to be indicted remains negative and significant only for purchase transactions. For sales, we observe no significant association between the political views held by the SEC Commissioners and the likelihood of insider trading detection. This implies that public perception of the transactions has an impact on prosecution.2 We also demonstrate that top executives benefit from the Republican Party’s lighter stance towards insider trading regulation. Specifically, we find a negative association between the SEC Committee Republican majority and the likelihood of executives being indicted for trading on nonpublic information. A Republican

2 Brochet (2000) and Chen et al. (2012) point out that insider sales have higher litigation risks due to more negative reactions from the pubic and regulators. 5

Committee Majority, however, is no less likely than Democrats to indict a lower ranking insider. Interestingly, higher ranking insiders also better understand potential consequences of transacting based on private information and adjust their actions in accordance with recent SEC enforcement activities.

Finally, our findings provide empirical evidence that modeling the commitment and detection of insider trading in a simultaneous setting provides important incremental information. That is the predictions of a bivariate probit model with partial observability and the predictions from a traditional probit model differ. Most notably, the political variables are insignificant when using a traditional probit model. This provides a plausible explanation to why the literature on insider trading to date has not uncovered an association between political affiliation and illegal insider trading. That is, the models employed do not jointly estimate the commission and detection processes. Consequently, the associations between the political structure of the SEC and insider trading are left undetected. By employing the bivariate probit model, our study highlights the impact of these factors on illegal insider trading that traditional probit models fail to identify.

Other results from our model are quite similar to prior work. Cohen et al. (2012),

Cline et al. (2016), Huddart and Ke (2007), among others, demonstrate that informed trading is more likely in low book-to-market firms. Cohen et al. (2012) also show more informed trading following periods of weak performance. We too find that book-to-market and prior market returns are negatively associated with illegal insider trading commission.

Abnormal post trade performance of the insider trading firm is positively related to the propensity of detection. Prior research also demonstrates the impact of firm governance on the informational content of trades and their profitability. Specifically, Cohen et al. (2012),

along with Dai et al. (2016), show that better governed firms are less likely to be associated with informed transactions. Our results likewise indicate that illegal insider trading is more likely among firms with weaker corporate governance. For example, employees of firms with the CEO presiding as chairman of the board are more likely to engage in illegal trading activity.

Overall, our work extends the literature on insider trading by modeling simultaneously the insider trading commission process and insider trading detection process, thus providing a better understanding of illegal insider trading activity. We document new evidence regarding the importance of the political structure of the SEC and its enforcement actions. The method and findings are important for researchers seeking to better understand the decision process of economic agents contemplating whether to commit illegal insider trading and the detection of such activity by other agents.

Literature Review

Two approaches dominate the literature examining insider trading: empirical analysis of reported trades and theoretical models. The former focuses on the analysis of the informational content of open market transactions, as well as of option exercises. This literature relies on characteristics of firms, trades, and the insiders themselves to investigate the predictive power of the transactions. Information asymmetry is a key factor that determines informational advantage of insiders over outsiders. The evidence suggests that insider transactions and the associated profitability are positively related to measures of information asymmetry (Aboody and Lev, 2000; Frankel and Li, 2004; Huddart and Ke,

2007; Piotroski and Roulstone, 2005; Ravina and Sapienza, 2010). The effect of

information asymmetry could be either offset or reinforced by corporate governance. Bettis et al. (2000), Lee et al. (2014), and Roulstone (2003) demonstrate a negative impact of self- imposed trading restrictions on insider trading, while Skaife, Veenman, and Wanderin

(2013) show that ineffective internal control over financial reporting is associated with insider trading profitability. External governance plays an important role as well. Fidrmuc,

Giergen, and Renneboog (2006) find that presence of large shareholders reduces the informational content of insider trades.

The literature also identifies informative insider trading through the patterns of insider transactions: persistence of insider profitability (Cline et al., 2016), sequence of trades (Cohen et al., 2012), or timing of the transactions (Brooks et al., 2012). Others investigate characteristics of insiders and explore which insiders are likely to trade on information. Both executives and directors are found to earn significant abnormal returns

(Cline et al., 2016; Ravina and Sapienza, 2010; Wang, Shin, and Francis, 2012). Other studies demonstrate that insiders tend to be local and non-executive (Cohen et al., 2012) and that personality traits have impact on their performance (Hillier et al., 2015).

While these studies are useful in determining the types of transactions that are informative and insiders who trade profitably, the analysis in these studies suffers regarding the detection of illegal trading since it focuses on reported transactions from corporate insiders. Since corporate insiders are unlikely to report illegal transactions that violate Rule

10b5-1 under Section 16(a) (Bainbridge, 2000), much of the illegal trading activity is likely unobservable in public filings. Most of these studies therefore refer to “private information” as any information available to insiders and make no conjecture regarding the legality of its use (Seyhun, 1998; Lakonishok and Lee, 2001; Piotroski and Roulstone,

2005). The prominent difference between these studies and our work is that the insider trades employed in this study are clearly illegal in the eyes of the SEC.

The other approach to insider trading detection is based on the development of theoretical models and testing their predictions, for instance, imbalances of buy and sell flows to predict insider trading (Easley et al., 1996; Hanusek and Kopriva, 2011).3

Structural break analysis is also applied to detect potential insider trading (Olmo, Pilbeam, and Pouliot, 2011). Modeling relations between insider transactions and the time series of stock returns and applying time series analysis to identify insider transactions is used by

Park and Lee (2011).

Similar to the empirical analysis of reported trades, these studies add to our understanding of informed trading but do not offer specific insight into illegal insider trading commission or detection. Since these studies do not focus on the illegal insider trades and, particularly, on the detection of this activity, they do not take into consideration the role of the SEC. As a consequence, an analysis of the detection process with respect to the role of the SEC has not been offered in literature.

Among current studies, Ahren (2015), Bhattacharya and Marshall (2012), and

Wang (2013) are most similar to this research. Like this study, they explore illegal insider trading rather than reported transaction and utilize SEC insider trading indictment data to determine key factors associated with illegal insider trading. Ahren (2015) focuses on social networks of insiders and provides a comprehensive overview of insiders and insider

3 Easley et al. (1996) estimate the informational content of the trade flows and provide evidence of significant differences in the information content between stock trades executed on the NYSE and in Cincinnati, while Hanusek and Kopriva (2011) modify Easley et al. (1996) model to demonstrate informed trading on the Prague Stock Exchange. 9

trading activities. Bhattacharya and Marshall (2012) investigate links between the probability of being indicted for illegal insider trading and executive compensation by employing a probit and rare events logit models. Wang (2013) examines the probability of detecting insider trading in merger deals by examining the association between abnormal option trading and illegal insider trading using logit models. Neither Bhattacharya and

Marshall (2012) nor Wang (2013) account for the issue of partial observability.

The issue of partial observability with respect to illegal insider trading is recognized in the literate as early as Meulbroek (1992) and continues to be given considerable attention throughout more resent work. For example, Ahren (2015) and Bhattacharya and Marshall

(2012) both focus on insider trading indictments and note that many transactions based on private information remain undetected. The authors are particularly concerned with potential bias in estimates due to sample selection. In other words, the authors raise the question whether their results based on the SEC indictments sample are biased by how the

SEC detects and prosecutes illegal insider trading.

To address the issue, Meulbroek (1992) examines what triggers the SEC investigations by comparing the price movements around insider trading from public complaints to those of exchange referrals, a sample more likely to be impacted by price movements. Bhattacharya and Marshall (2012) also assess the motives behind SEC investigations and use instrumental variables to demonstrate that the SEC does not target based on the insiders’ wealth. Concerned with how representative the sample is of an average insider, Ahern (2015) focuses on an insider’s relatives and associates not targeted by enforcement agencies and the variation in the degree of prosecution among the detected insider traders. The authors recognize that not all illegal insider trades get detected by the

SEC; however, their concerns are not regarding the estimates for detected illegal insider trading versus overall illegal insider trading, but rather regarding which insider trades get detected and prosecuted. To our knowledge, no study has attempted to address the issue of partial observability of illegal insider trading directly through a bivariate probit model with partial observability. The proposed research fills this important gap.

Sample Selection

The primary sample consists of insiders and firms accused of insider trading. The data are hand-collected from the Litigation Releases by the SEC and associated SEC

Complaints over the period from 1996 through 2013.4 These sources provide detailed information regarding the insiders involved in investigations, their trading activities, and the resulting outcome of the litigation. In addition to the names of the insiders, the firms of their employment, the stocks traded, and the insiders’ positions in the firms are collected.

The description of the trades also allows us to determine the type of corporate events used by insiders to conduct illegal trading.

The total number of insider trade observations collected that are considered illegal by the SEC is 1,255. However, lack of information regarding the dates of the trades and availability of CRSP permanent numbers for the firms of the insiders’ employment and firms of securities traded limit the sample to 1,023 trade observations. The transactions are conducted by 358 unique insiders working for 267 firms, who allegedly traded in the

4 We choose 2013 as the ending year based on the average period between the insider transaction and the related SEC action (three years). Litigation release files prior to 1995 are not publicly available on the SEC website. 11

securities of 323 companies.5 Merging the sample with COMPUSTAT and Risk Metrics further limits the sample to 247 firm-year observations. For comparison, a control sample of non-insider trading firms is constructed based on the joint sample of Compustat, CRSP, and Risk Metrics. The sample consists of 22,929 firm-year observations.

Figure 1 displays the distribution of the trades conducted over the sample period.

The level is generally increasing throughout the period with a few sporadic increases. The number of trade observations is substantially lower in 2010s due to the lag between the trade date and the SEC actions.

Model Specification

Bivariate probit model with partial observability

As discussed above, one of the empirical challenges of analyzing illegal insider trading lies in the fact that illegal insider trading is not directly observable. With insider trading, there are two separate decisions by two different entities: the decision whether to engage in trading on material nonpublic information by the insider and the decision whether to prosecute by the SEC. Under full observability, we would have observed four possible distinguishable outcomes. The first case would occur when insider trading has not been committed and, thus, has not being detected. The second is when insider trading has been committed but has not being detected. The third combination is when insider trading has not been committed but has been detected. And finally, the last outcome would be when insider trading has been committed and has been detected. However, given the nature

5 The difference in numbers between firms of employment and firms traded in is explained by insiders from different firms trading in securities of the same firms. For example, insiders of both acquirer and target firms may trade in the securities of the target. 12

of insider trading, we observe only the last outcome. What we observe is not all the insider trading activities but rather only detected activity. This implies that traditional models may not be appropriate since the results and conclusions do not necessarily reflect the actual relations between insider trading and its determinants.

The importance of controlling for the partial observability problem is emphasized in the context of the detection of other activities in prior research (Carver et al., 2013;

Wang, 2013; Wang et al., 2010). Each advocates the use of a bivariate probit model with partial observability (Poirier, 1980). Following prior research, the reduced form model incorporates two distinct latent processes:

Ii* = αiβ + ui,

Di* =γiδ + vi, where I* represents incentives to engage in insider trading, D* is potential that insider trading is detected, αi is a row vector of variables that explain incentives to insider trade, γi is a row vector of variables that explain detection of insider trading, and ui and vi are zero- mean disturbances, their variances set equal to 1, with correlation equal ρ.

Both I* and D* are unobservable latent variables that are transformed into bivariate variables in the following way: Ii = 1 if Ii* > 0 and Ii = 0 otherwise. The same holds for

D*: Di = 1 if Di* > 0 and Di = 0 otherwise. Under the bivariate probit with full observability, we would have observed four cases, where Ii = 0 and Di = 0, Ii = 1 and Di = 0, Ii = 0 and

Di = 1, and Ii = 1 and Di = 1. However, we do not directly observe the realizations Ii and Di but rather the combination of them: Zi = Ii × Di, where Zi = 1 if insider trading has been committed and detected and Zi = 0 if insider trading has not been committed (and, thus, not detected) or has been committed but not detected or has not been committed but has been

detected. In other words, the latter three combinations are indistinguishable in our data. In this case, partial observability identification of the model comes from the different variables in each of the decision models (Poirier, 1980).

The theoretical nature of insider trading leads to the difference in variables for two processes. For instance, for illegal insider trading detection, there are factors that are not known at the time when an insider is contemplating whether to engage in illegal activity and, thus, do not affect this decision. These are the factors that are not included in the insider trading commission equation but only in insider trading detection equation and contribute to identification. If Φ represents bivariate standard normal cumulative distribution function, then the model for Z is:

P(Zi = 1) = P(IiDi=1) = P(Ii=1, Di=1) = Φ(αiβ, γiδ, ρ)

P(Zi = 0) = P(IiDi=0) = P(Ii=0, Di=0) + P(Ii=1, Di=0) = 1 - Φ(αiβ, ϒiδ, ρ).

The model is estimated using the maximum-likelihood method based on two separate equations that should not have exactly the same variables and are preferred to have continuous variables (Poirier, 1980).

Insider Trading Propensity and Insider Trading Detection

The probability a person engages in illegal insider trading increases with the benefits from the activity that accrue to the insider and decreases with the costs associated with it, specifically the probability and cost of getting caught. The benefits derived are primarily related to firm characteristics of an insider’s employment.

Insider transactions and their profitability are shown to be positively associated with the level of information asymmetry (Aboody and Lev, 2000; Frankel and Li, 2004;

Gider and Westheide, 2016; Huddart and Ke, 2007). To proxy for information asymmetry, 14

we use the ratio of book to market value measured at the end of fiscal year prior to the insider trade. Other determinants include return on assets as a measure of firm’s profitability (Bhattacharya and Marshall, 2012) and standard deviation of firm’s daily returns as a measure of stock return volatility. We also include the CRSP equal-weighted index to control for prior market performance (Cohen et al., 2012). Both long-term and short-term stock volatility and market performance are calculated in the year prior to the insider trades.

Strong corporate governance is typically associated with less insider trading and lower profitability of the transactions (Dai et al., 2016; Bettis et al., 2000; Cline et al., 2016;

Cohen et al., 2012; Roulstone, 2003). We include Board Size, CEO Duality, and Board

Independence as measures of firm governance. The impact of firm-level corporate governance on the detection of illegal trades by the SEC is likely to be small since most illegal insider trading is identified with help of self-regulatory organizations, informant tips, press reports, or new investigative tools employed by the SEC (Khuzami, 2011;

Meulbroek, 1992). While information used to identify insiders engaged in illegal activity could come from the firm, we have little evidence to suggest that it is a main source as very few referrals come from the issuer (Meulbroek, 1992).

Other factors are expected to affect both the probability to commit and the probability of detecting an insider trade. The effects of these factors are anticipated to load in opposite directions for insider trading violations and insider trading detection. Since size is commonly associated with the difference in information asymmetry (Seyhun, 1998;

Lakonishok and Lee, 2001), log of total assets is included. We also include leverage as a measure of external monitoring and disciplining mechanism by creditors. Insider trading is

also shown to vary by industry (Ahern, 2015). We include dummies for firms operating in industries with particularly high or low litigation exposure. These are financial, regulated, retail, and technology firms, identified at the 4-digit SIC code level (Gande and Lewis,

2009; Field et al., 2005).

In addition, there are factors unknown at the time the insider makes the decision to engage (or not) in illegal insider trading. These ex-post factors influence only the probability of insider trading detection and represent variables for identification. For example, ex-post the SEC can observe the actual stock performance following a trade. This information may play a role in determining whether or not the SEC will investigate. We therefore include a post return variable measuring the 2-day CARs following an insider trade, as the likelihood of being caught is higher when the abnormal returns attract attention of an exchange or another agency that could refer these cases to the SEC.

Results

Panel A of Table 1 presents a distribution of insider trades by the associated corporate events. Consistent with prior research, trades related to mergers and acquisitions and earning announcements dominate the sample, contributing 52.63% and 19.84%, respectively. News announcement-linked trades represent a close third at 17.81%. FDA announcement trades comprise a separate news category and are close to 6% of all indicted transactions.

Industry composition of the firms are presented in Panel B of Table 1. The industries with the greatest number of insiders caught engaging in illegal insider activity include: chemicals and allied products, electronic equipment and components, and business

services. These industries comprise 17.41%, 12.96%, and 10.12%, respectively. Machinery and computer equipment, controlling instruments and medical goods, as well as depositary institutions industries are also large contributors, with 7.69%, 6.48%, and 6.07%, respectively. Four industries are part of the manufacturing sector, which dominates the sample with 53% of firm-year observations. Finance, Insurance, and Real Estate and

Services sectors contain the second (15.81%) and third (14.43%) largest number of observations.

Table 2 compares descriptive statistics for firms indicted for illegal insider trading and firms of the control sample. Insider trading firms on average have significantly lower book-to-market and return on asset ratios. They are significantly smaller and have weaker corporate governance, as evidenced by CEO Duality. The volatility of both short term and long term returns are significantly larger for firms accused of illegal insider activities, which indicates greater opportunities to trade. Short term market returns are significantly lower prior to the illegal insider trades, and the abnormal returns 2-days following the insider trades are significantly larger. The comparisons also reveal that insider trading firms more often reside in the tech industry and are less often present in the retail industry.

Bivariate probit with partial observability: Base Model

Estimates from the bivariate probit model with partial observability are reported in

Table 3. Our baseline models do not include political variables, but rather focus on the impact of the financial and accounting variables traditionally used in the analysis of illegal insider trading.

Consistent with prior work (Cohen et al., 2012; Cline et al., 2016; Huddart and Ke,

2007), the results reported in Model 1 demonstrate that book-to-market, return on assets, 17

and prior short-term market returns are negatively and significantly related to the propensity to trade on private information. Prior long-term stock volatility is significantly positively associated with illegal insider trading. Some of the measures of corporate governance introduced in Model 2 are also consistent with prior literature, demonstrating a positive association between poor governance and illegal insider trading. CEOs presiding as the Chairman of the board, however, is the only variable significant at conventional levels. The ex-ante/detection information factors do not appear to impact the probability of engaging in illegal insider trading activity or the probability of detection in the base models.

Post insider trade returns are positive and significant in both detection models.

To determine whether the bivariate probit model provides a more accurate assessment of illegal insider trading, we estimate the correlation coefficient between the residuals of two processes. In this case, the correlation coefficient between the residuals of illegal insider trading commission and detection is statistically significantly different from zero at the 1% level, confirming that the processes should be estimated simultaneously.

Thus, consistent with our theoretical priors, the empirical estimates suggest that relying on traditional probit estimates are likely to lead to inaccurate predictions regarding the commission and detection of illegal insider trading.

To compare the findings from the bivariate probit with partial observability with those based on more traditionally used methodologies, the likelihood of committing illegal insider trading and being charged with illegal insider trading are estimated separately using a traditional probit model. These results are reported in Table 4.

Consistent with the estimates from the bivariate analysis reported in Table 3,

Models 1 and 4 show that book-to-market, return on assets, prior stock return volatility,

and prior short-term market returns remain significant and in the same direction for the probability to commit an insider trade. However, for the traditional probit, Board

Independence is significantly positively related to the probability of illegal trading in

Model 4, which is different from the result of the bivariate probit and opposite of what one would expect.

Models 2 and 5 show that post insider trade returns retain their positive and significant effect on the propensity to be detected. In Models 3 and 6, the variables that explain incentives to commit illegal insider trades and potential to be indicted are estimated together. The reported estimations are similar to the ones in separate models, with some results being counterintuitive and reflecting the need to model the processes of insider trading commitment and insider trading detection simultaneously. For example, good governance, as indicated by CEO duality and board independence, is found to increase the probability of illegal insider trading.

Overall, the comparison of the traditional probit with the bivariate probit suggests that predictions between the models can differ. This is not surprising given the significance of correlation between residuals of insider trading commission and its detection, indicating that the process of insider trading commission and process of its detection should be estimated simultaneously. This also implies inaccurate estimates when not using bivariate probit model. Overall, there are relatively few differences when including only the base variables shown in the literature to be associated with insider trading. However, the magnitude of the changes and implications based on the models becomes more apparent as we add political variables.

Political Factors

One of the distinct differences between the leading political parties in the U.S. is the parties’ stance towards the regulation of business. The Republican Party is generally perceived to be more business friendly and pro-free market, while the Democratic Party is typically considered pro-regulation with regard to business affairs as a way to protect the public interest and limit corporate misdeeds.6 This general attitude toward business regulation is likely to permeate to the regulation of insider trading.

Indeed, federal statues related to insider trading in the recent decades were introduced by members of the Democratic Party. For example, the Insider Trading

Sanctions Act of 1984 and the Insider Trading and Securities Fraud Enforcement Act of

1988 were both sponsored by Democrat members of Congress. The former increased sanctions against trading on private information to up to three times the profits gained or losses avoided. The latter extended the responsibility for illegal insider trading to controlling persons who fail to take appropriate steps to prevent this activity by insiders.7

The general preference for a party’s approach to business affairs is also witnessed by the proportion of corporate political contributions to each party. Brunell (2005) documents that corporate donations are substantially larger to Republican Party candidates.

Gimpel et al. (2016) illustrate further that among industries that have partisan preference, the Republican Party is more favored. It is also documented that the stock returns of firms that face greater regulation and are more susceptible to shifts in political positions,

6Republican Platform 2016, https://www.gop.com/platform/; The 2016 Democratic Platform, https://www.democrats.org/party-platform. 7 The Stop Trading on Congressional Knowledge Act, which states that Members of Congress, congressional employees, and federal officials are not exempt from the insider trading prohibitions, was also sponsored by a Democrat. 20

positively relate to Republican presidency and Republican majority in Congress

(Sabherwal et al., 2016).

Within the U.S. government, the SEC is the agency charged with overseeing and enforcing federal security laws. It is an independent federal agency and all five of its commissioners are appointed by the President of the U.S. and confirmed by the Senate. To mitigate partisanship of the SEC Committee, no more than three Commissioners may have the same political affiliation. However, throughout our sample, it is not uncommon to observe periods when there are less than five overall Commissioners. As a result, during these periods the SEC Committee is comprised of either a Republican or Democratic majority.8 Furthermore, prior to 2009 the Chairman of the committee was allowed to belong to a political party.

Given the difference in the parties’ attitudes towards business regulation and the corporate contributions that flow to the parties (Gimpel et al., 2014; Sabherwal et al., 2016), political affiliations are likely to have an effect on insider trading. Two variables of particular interest are whether the SEC committee majority is held by Republicans and whether the Chairman belongs to Republic Party.

Corporate insiders are not likely to be aware of the SEC’s political affiliations and consider them directly when making their decisions to engage in illegal activity. Therefore, we expect political views of the SEC Committee and those of the SEC Chairman to influence the detection of the illegal trades rather than their commission. In addition, we

8 The majority of the SEC Commissioners were Democrats in 1995, 1996, 1997, 1998, 1999, 2000 but Republicans in 2002, 2003, 2004, 2005, 2006, 2007, and 2008. 21

include a dummy indicating whether the Senate Majority is Republican to control for the overall political environment.

While insiders may be unaware of the political affiliations within the SEC, they are likely to take notice of the SEC’s actions against illegal insider trading. Jackson and Roe

(2009) find that public enforcement increases with an agency’s budget. Thus, more resources should result in more enforcement activities, which is likely to impact the behavior of insiders contemplating whether or not to trade on private information. Indeed,

Del Guercio et al. (2015) provide evidence of negative association between illegal insider trading prior to earnings and takeover announcements and the SEC enforcement efforts.

Their results imply less insider trading during aggressive enforcement. We therefore include the size of the SEC budget as a proxy for the intensity of the SEC enforcement activities and expect it to influence both insider trading commission and detection.9 The size of the SEC budget is measured in the year of the insider trade for the commitment process and in the year post insider trade for the detection process.

In addition, we include the number of insider trading-related SEC litigation releases as a direct measure of the SEC enforcement efforts against trading on private information. Litigation actions against a particular crime is likely to impact the behavior of the parties involved in similar crimes. For instance, the class-action and fraud literatures document that the propensity of a firm to be a target of litigation increases when other firms in the industry are being investigated or sued. This risk is well understood by insiders of the firms (Bradley et al., 2014; Gande and Lewis, 2009; Wang, 2013). Consequently,

9 Regulators’ official statements and actions, as well as the SEC’s coverage of high-profile illegal insider trading cases suggest that the agency considers prosecuting trading on material, nonpublic information a key priority and communicates this to potential violators (Khuzami, 2011). 22

Cohen et al. (2012) observe that opportunistic insider trading decreases in the presence of litigation activity by the SEC. We therefore include the number of the SEC litigation releases, calculated in the year prior to an insider trade, in both the commitment and detection regressions. We anticipate this variable to impact both decisions but load in opposite directions.

Corporate donations to political campaigns are shown to positively relate to firm operating performance and firm value (Cooper, Gulen, and Ovtchinikov, 2010; Ferris,

Houston, and Javkhadze, 2016; Jayachandran, 2006; Ovtchinikov and Pantaleoni, 2012).

Brunell (2005), along with Cooper, Gulen, and Ovtchinikov (2010), find that corporations on average contribute more to the Republican Party. In fact, Gimpel et al. (2014) show that roughly one-third of the economic sectors that have a partisan preference favor the

Republican Party. We therefore include Republican tilt, measured as the industry contributions to U.S. congressional campaigns, following Gimpel et al. (2014).

Estimates from the bivariate probit model containing the political variables are reported in Table 5. Model 1 includes indicator variables for firms operating in industries with extremely high or low litigation exposure; Model 2 controls for Republican tilt.

Results from Table 5 reveal that a Republican Committee Majority in the year post insider trade is negatively associated with the likelihood of illegal insider trading detection.

This suggests that insiders are less likely to be detected when the SEC Committee is dominated by Republicans. Both measures of the SEC enforcement intensity, the size of

SEC budget and the number of insider trading litigation releases prior to insider transactions, increase the likelihood of illegal insider trading detection. Litigation releases also significantly decrease the propensity to engage in illegal insider trading activity. This

implies that insiders adjust their actions in accordance with the SEC enforcement.

Republican Senate Majority in the year of the trade is significantly and positively related to the propensity to insider trade, suggesting that the overall political environment also impacts an insiders’ decisions to trade illegally.

Consistent with Model 2 of Tables 3, book-to-market, return on assets, and prior market returns remain negative and significant, while CEO duality remains positive and significant for the propensity to insider trade. Post insider trade returns likewise retains its positive and significant association with detection. Size has a significant negative effect on probability to be indicted for illegal trades in Model 1.10

Residing in the tech industry is significantly positively related to the propensity to insider trade; however, operating in a regulated industry has a negative impact on the commission of the crime. Moreover, being in either regulated or finance industry increases the probability of illegal insider trading detection. The findings by industry are intuitive since they reflect regulatory scrutiny and the level of information asymmetry.

Model 2 of Table 5 reports the results substituting the industry dummies from

Model 1 with the Republican tilt variable. The results remain qualitatively unchanged for the variables of interest. Republican Committee Majority in the year post insider trade remains significantly negatively associated with the likelihood of detection. SEC litigation decreases the likelihood of illegal insider trading, while increasing the likelihood of

10 This finding may initially appear to be counterintuitive; however, details revealed in the SEC Litigation Releases and Complaints shed some light on potential explanation behind negative relation between the firm size and illegal insider trading detection. For smaller firms, illegal insider trading transactions constitute a significant portion of the overall trades for the day. This catches greater attention by the authorities and makes it easier to show intent. 24

indictment. The association between the size of the SEC budget and propensity to be prosecuted for illegal insider trading remains significant and positive.

Overall, the results using Republican tilt are consistent with those using indicators for exposed industries. Given this lack of difference and the study’s emphasis on the role of political variables, the remainder of the analysis is conducted using only the Republican tilt variable.

Table 6 reports the findings for model specifications similar to that of Table 5, but imposes a traditional probit estimate for comparison. Model 1 and Model 2 of the Table 6 provide estimations for the probabilities to engage in illegal insider trading activity and be charged with illegal insider trading, respectively. Model 3 reports the results based on the full set of explanatory variables used in bivariate probit.

Notably, with the exception of Senate Republican, all political variables are insignificant in the probit estimates. Republican Senate Majority in Table 6 negatively associated with the probability of the detection, contradicting predictions of the bivariate probit model discussed above.

Collectively, what Tables 5 and 6 reveal is that the inference regarding the political climate of the SEC differs substantially when estimating both the commission and detection of insider trading in a bivariate setting. The insignificance of political affiliations of the SEC in the traditional probit models provides a plausible explanation to why a connection between the SEC’s political structure and the commission and detection of insider trading has not previously been uncovered.

Next, we evaluate the role of the political views of the SEC Chairman on insider trading detection. The Chairman of the Commission alone has authority over staff and

resource decisions, including the appointment of the key staff members such as Directors of SEC Divisions (Langevoort, 2006). Given this influence, the Chairman’s political views may have a greater influence on the detection and prosecution of insider trading relative to those of other Commissioners. Table 7 reports the results from a bivariate probit model that includes an indicator for the SEC Chair’s political affiliation.

The estimates reported in Panel A replace Republican majority Committee with

Republican SEC Chairman. Interestingly, the political affiliation of the Chairman does not significantly influence the detection of illegal insider trading. However, the coefficients on the control variables are qualitatively unchanged from our previous estimates, with the exception of long-term market returns and leverage on the commission and detection sides, respectively. The coefficients on the political variables are also consistent with our prior findings. The number of the SEC litigation releases regarding insider trading continues to decrease the likelihood of illegal insider trading, while increasing the likelihood of the illegal insider trading detection. Insiders also remain more likely to trade illegally when the Senate is controlled by the Republican Party. The key insight from these findings is that the role of the Chairman’s political affiliation is insignificant. This suggests that the political views of the SEC Chairman do not significantly impact the agency’s detection of illegal insider trading.

Panel B compares the relative influence of the political affiliation of the SEC

Committee to that of the Chairman by including Republican Committee Majority together with Republican SEC Chairman. Similar to Panel A, a Republican Chairman is insignificant; however, a Republican majority on the SEC Committee is negatively related to the probability of illegal insider trading detection. The size and significance of the

coefficient on the SEC Committee majority are relatively unchanged when including the

Republican Chair dummy. This suggests that the political views held by the SEC

Commissioners as a whole rather than those of the Chairman are more influential in detecting illegal insider trading.

Overall, the results presented in Tables 5 and 7 highlight the importance of the political views of the SEC Commissioners in the detection of illegal trading. They also demonstrate the influence of prior SEC enforcement intensity on an insider’s decision to trade on information and the SEC’s likelihood to detect the transaction. In addition, our findings empirically support the theoretical prediction that estimations not simultaneously taking into account both the commission and detection can provide inaccurate assessment of the insider trading process.

Political Factors and Purchases versus Sales

From a pure legal perspective, both purchases and sales based on the private information violate the same law. The literature on reported trades, however, suggests that insiders are more reluctant to trade on negative information, as the litigation risk associated with reported insider sales is greater than that of purchases (Brochet, 2010; Chen, Martin, and Wang, 2012). In theory, buying securities for profit using private information can be regarded as a value generating activity for the shareholders (Bainbridge, 2000). In this case, insider trading provides incentives for insiders to create profitable opportunities. On the other hand, selling securities to avoid the potential losses is more likely to be viewed strictly as exploiting access to the firm information. The implication is that public perception of sales based on private information is more negative and more likely to ignite a response from investors and regulators. 27

Given this difference from the public, the political and SEC variables may behave quite differently for purchases and sales. We anticipate the association between Republican

Committee Majority and the probability to detect insider trading to remain negative for purchases. However, given the public’s less tolerant attitude towards benefiting from negative information, the negative association may be attenuated for sales. To determine whether the impact of political factors vary with the perceived severity of illegal transaction, we estimate the bivariate probit model with partial observability for purchases and sales separately in Table 8.

The results show that CEOs presiding as Chairman of the Board positively relate to insider purchases. However, CEO duality has no impact on insider sales. For insider sales, an increase in the volatility of long-term returns positively relates to commission, but has no significant impact on purchases. The higher volatility indicates greater potential losses the insiders may avoid by exploiting private information. Interestingly, firm size has a significant and positive impact on the propensity to engage in illegal insider sales and significant and negative effect on propensity to be indicted for it. This suggests that employees of larger firms are more likely to trade on negative private information, while being less likely to be detected for these transactions.

Perhaps most interestingly, Table 8 documents stark differences for the political variables. For buy transactions, Republican Committee Majority in the year following the insider trade is significantly negatively associated with the probability of illegal insider trading detection. In contrast, for sales, no significant association between Republican

Committee Majority and the probability of detection is observed. These findings suggest that the Republican Party’s preference for lighter regulation among the SEC

Commissioners benefits insiders when they engage in transactions that are perceived less egregious by the public. However, if they trade on negative information, the impact of political views gets eliminated. Republican Senate Majority also has the opposite effect on the propensity to engage in purchases versus sales based on private nonpublic information.

The association is positive and significant for the purchases, which is consistent with prior results, but negative and significant for the sales. This implies that insiders consider the impact of public perception.

The influence of SEC action also differs for purchases and sales. Consistent with prior estimations, for purchases, insider trading litigation releases prior to insider transactions reduce commission while increasing the probability of detection. However, for sales, there is no significant association between the number of litigation releases and either the likelihood to insider trade or be indicted for it. Insiders engaged in both purchase and sale transactions probably take notice of the SEC illegal insider trading efforts communicated in the media. Nevertheless, their reactions may differ due to loss aversion.

Even when insiders are aware of the recent related enforcement actions, taking losses may be harder psychologically than letting the gains go (Tversky and Kahneman, 1992). In particular, sellers may be more likely to dismiss increases in the SEC enforcement as it is harder to accept losses if they were to take no action. On the other hand, the SEC budget in the year following the insider trade has a significant positive effect on the probability to detect insider trading sales.

Table 8 also reveals that for sale transactions, being in a Republican tilt sector is significantly negatively related to the propensity to commit an illegal transaction and significantly positively related to the probability of indictment. These findings suggest that

political connections with entities more lenient on insider trading could hurt rather than benefit insiders when they engage in transactions negatively perceived by the public. The results also imply that insiders in these industries are likely to factor this in and adjust their behavior accordingly.

Overall, the results presented in Table 8 highlight the differential impact of the political influences on both illegal insider trading commission and detection for buy versus sale transactions. The findings suggest that public perception of the crime affects prosecution. Specifically, the Republican Committee majority negatively relates to the likelihood of indictments only for insider purchases. The Committee’s political views do not provide any benefits when insiders are involved in insider sales – transactions that are perceived as more egregious.

Political Factors and Executives

Access to private information within a firm is a source of profitability for corporate insiders (Cline, Gokkaya, and Liu, 2017; Niehaus and Roth, 1999; Fidmuc, Goergen, and

Renneboog, 2006; Wang, Shin, and Francis, 2012). This access varies according to insider rank, with top executives having better and more opportunities to benefit from upcoming changes (Wang, Shin, and Francis, 2012). In order to evaluate how the rank of an insider impacts illegal insider trading, we bifurcate our sample into executives and non-executives.

The executive subsample includes members of C-suite, presidents and vice-presidents, as well as heads of divisions and departments.

Table 9 reports the results. The financial variables are generally consistent with the findings for the full sample reported in Table 3. Prior stock return volatility, however, is significantly negatively related to an executives’ propensity to trade illegally. In addition, 30

leverage has a different effect on the likelihood to be detected for illegal transactions for executives and non-executives. For executives, leverage positively impacts the likelihood to be indicted for trading on private information. This could imply potential monitoring role of debt. For non-executives, leverage negatively relates to the propensity of indictment. The estimates for non-executives also reveal that firm size is significantly positively associated with the probability to insider trade, but negatively associated with detection.

We also observe that, for top executives, Republican Committee Majority in the year following the trade has a significant negative association with the likelihood of illegal insider trading detection. However, it is insignificant for non-executives. This suggests the impact of the political views of the Commissioners depends on the rank of the insider. The findings regarding the number of litigation releases also differ between the subsamples.

Litigation releases decrease the propensity to engage in illegal insider trading and increase the probability of detection only for executives. Estimates for non-executives are insignificant. The results imply that executives better understand potential consequences of the SEC enforcement activity and adjust their behavior. Moreover, the findings indicate easier detection of executive transactions by the authorities. Panel A also reports that

Republican Senate Majority as of the year of insider trade increases the probability to insider trade for executives.

In addition, for the executives subsample, being in sectors that have contributed to the Republican Party has a positive and significant impact on the likelihood of committing an illegal transaction. However, for non-executives, being in these sectors decreases the probability of illegal insider trading, while increasing the probability of detection. These

suggest a different impact of the political connections for insiders of different ranks.

Executives are more likely to be better connected and benefit from the Party’s more favorable attitude. The effect is reverse for non-executives, who appear to be aware of this.

Overall, the findings for the executive and non-executive subsample show that the role the SEC commissioners’ political affiliation, as well as of SEC enforcement effort varies with insiders’ rank. The results suggest that executives better understand potential consequences of their actions and also are more likely benefit from their higher positions.

Conclusion

The problem of partial observability presents significant challenges to any empirical analysis of illegal insider trading. Specifically, researchers are able to evaluate only detected illegal transactions, not all illegal trades committed by insiders. This problem is further exacerbated due to the fact that most insiders engaging in illegal trading are unlikely to report their trades in accordance with Section 16(a) of the Securities and

Exchange Act of 1934. As a result, prior research focuses only on detected insider trading and is likely to shed little light on the overall nature of actual illegal insider trading. In addition, models that estimate the commission and detection processes independently fail to identify the role of the SEC’s political structure and their enforcement actions.

To address these issues we use a bivariat probit model with partial observability, which allows us to model both the insider trading commission and detection processes simultaneously. By doing so we demonstrate that an SEC committee comprised of a

Republican majority has a negative and significant impact on insider trading detection, highlighting the importance of political affiliations within the Committee. The number of

prior illegal insider trading litigation releases negatively impacts the probability to trade on material, nonpublic information. However, insider trading litigation releases and the SEC budget are positively associated to the propensity to be indicted. These results indicate that insiders are aware of SEC efforts to investigate and prosecute insider trading and take this information into account when deciding whether to trade.

We also show that the impact of political preference varies both with the severity of the illegal trade and the rank of the insider. The severity of the crime impacts the attitude towards regulation within the SEC as implied by the difference in signs and significance for sales when the Committee majority is held by Republicans. Insiders engaged in sales transactions also appear to be more concerned with potential losses and ignore recent SEC actions regarding illegal insider trading. However, insiders purchasing stock based on private information take the SEC efforts into account. Executives are shown to be more favorably impacted by a Republican majority within the SEC. They also pay closer attention to the SEC investigative efforts than non-executives.

Finally, our analysis indicates that the evidence provided from the bivariate analysis differs from the results of traditional probit models. In particular, analysis from a bivariate probit reveals that the political structure of the SEC and prior enforcement actions by the

SEC are significant factors in determining the detection and commission of illegal insider trading.

Collectively, the results from our analysis suggest that similar to other institutions, the SEC is not immune to political influence. While our research takes no stance on the merits or costs of illegal insider trading, it highlights that political factors are likely at play when determining the level of commission and prosecution.

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Table 1.1 Distribution of Illegal Insider Trading by Corporate Event and Industry

Panel A: Distribution of Illegal Insider Trades by Corporate Event Percent N Mergers and acquisitions 52.63 130 Quarterly earnings announcements 19.84 49 News announcements 17.81 44 FDA announcements and clinical trials 5.67 14 Fraud related events 4.05 10 Total 100.00 247

Panel B: Distribution of Illegal Insider Trades by Industry Percent N Mining: 2.83 7 Oil and Gas Extraction 2.02 5 Construction 1.21 3 Manufacturing: 53.04 131 Chemicals and Allied Products 17.41 43 Machinery and Computer Equipment 7.69 19 Electric Equipment and Components 12.96 32 Controlling Instruments and Medical Goods 6.48 16 Transportation and Public Utilities: 9.31 23 Communications 4.45 11 Wholesale and Retail Trade: 7.29 18 Wholesale Trade Durable Goods 2.43 6 Finance, Insurance, Real Estate 12.55 31 Depository Institutions 6.07 15 Services 13.77 34 Business Services 10.12 25

The table presents frequency of illegal insider trades by corporate event and according to industry for the industries containing the largest number of indictments.

Panel A reports the total number and percentage of illegal insider trade according to the event around which the transaction was conducted for the total sample of 247 indictments from 1996-2013. Panel B reports the percentages of insider trade firms by industry for the industry and sub-industry classifications with the largest percentage of illegal insider trading within each division.

Table 1.2 Descriptive Statistics

Firms indicted for Firms not indicted for Difference illegal insider trading illegal insider trading Ex-ante information: BM 0.3734 0.5226 -0.1491 *** ROA -0.0183 0.0414 -0.0597 *** Prior Mkt Return, LT 0.2090 0.2103 -0.0013 Prior Mkt Return, ST 0.0171 0.0400 -0.0229 *** Pre Volatility, ST 0.0301 0.0249 0.0052 *** Pre Volatility, LT 0.0329 0.0268 0.0061 *** SEC Budget, t 0.7109 0.7351 -0.0242 Board Size 9.0954 9.3882 -0.2928 CEO Duality 0.3392 0.1932 0.1460 *** Board Independence 0.8410 0.8316 0.0094 Ex-ante/detection information: Leverage 0.2267 0.2232 0.00357 Size 7.2573 7.5591 -0.3018 ** Reg 0.0954 0.1055 -0.0101 Finance 0.1237 0.1333 -0.0097 Tech 0.2792 0.1434 0.1357 *** Retail 0.0495 0.0795 -0.0300 ** Rep Tilt 0.2403 0.2219 0.0184 Senate Rep, t 0.6254 0.6325 -0.0071 Litigation Releases, t 71.4170 71.3395 0.0774 Ex-post detection information: Post Returns 0.0996 0.0347 0.0649 *** SEC Budget, t+1 0.7685 0.7929 -0.0244 Com Majority, Rep, t+1 0.3675 0.3675 0.0000 Chairman, Rep, t+1 0.4558 0.4313 0.0245

This table presents mean descriptive statistics for the sample of litigation releases

from the SEC for insiders indicted for illegal insider trading and for the comparative

sample of firms not indicted for illegal insider trading over the period 1996-2013. BM is

the ratio of book value to market value, and ROA is the return on assets at the prior fiscal

year-end. Prior Mkt Return, LT and Prior Mkt Return, ST are the performance of the

CRSP equal-weighted index in the year leading up to twenty one days prior to the

indictment and twenty days preceding the indictment, respectively. PreVolatility, ST and

PreVolatility, LT are the standard deviations of the stock returns over the twenty trading

days leading up to the indictment and year leading up to twenty one days prior to the

indictment, respectively. SEC Budget, t is budget of the SEC for the year of the insider

trade, in millions. SEC Budget, t+1 is the budget of the SEC for the year post insider trade, in millions. Leverage is the ratio of long-term debt to total assets for the prior fiscal year. Size is the natural log of total assets for the prior fiscal year. Board Size is the number of board members, and CEO Duality is a dummy variable equal to one if the

CEO is the chairman of the board. Board Independence is a dummy variable equal to one if more than 50 percent of the board is independent. Reg is a dummy variable equal to one if a firm's four-digit SIC is between 4000 and 4999, Finance a dummy indicating if a firm's four-digit SIC is between 6000 and 6999, Tech a dummy variable equal to one if a firm's four-digit SIC is within 2833-2836, 3570-3577, 3600-3674, 7371-7379, or 8731-

8734, and Retail is dummy indicating if a firm's four-digit SIC is between 5200 and 5961.

Rep Tilt is a dummy variables indicating that the firm operates in republican-tilting sector. Senate Rep, t indicates if the U.S. Senate majority is Republican in the year of insider trade, and 0 otherwise. Litigation Releases, t is the number of the SEC litigation releases regarding insider trading in the year leading up to the indictment. Post Returns is absolute value of the (0,2)-day CAR around insider trade date. SEC Budget, t+1 is the budget of the SEC for the year after the insider trade, in millions. Com Majority, Rep, t+1 is dummy variable equal to one if in the year after the insider trade the SEC Committee majority is Republican. Chairman, Rep, t+1 indicates that the Chairman of the SEC

Committee belongs to the Republican Party in the year after the insider trade. A difference in means test is conducted in the final column. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

Table 1.3 Bivariate Probit with Partial Observability: Base Model

(1) (2) [P(I)] [P(D|I)] [P(I)] [P(D|I)] Ex-ante information: BM -0.341*** -0.353*** (-2.867) (-2.95) ROA -0.594** -0.531** (-2.33) (-2.445) Prior Mkt Return, LT -0.045 -0.055 (-0.578) (-0.674) Prior Mkt Return, ST -2.994*** -3.100*** (-3.14) (-3.214) Pre Volatility, ST -4.13 -4.814* (-1.561) (-1.727) Pre Volatility, LT 5.275* 5.600* (1.903) (1.937) Board Size 0.004 (0.446) CEO / Chair Duality 0.254*** (2.715) Board Independence 0.106 (1.599) Ex-ante/detection information: Leverage -0.217 0.01 -0.129 -0.059 (-0.402) (0.024) (-0.26) (-0.156) Size 0.013 -0.002 0.035 -0.023 (0.335) (-0.052) (0.852) (-0.742) Ex-post detection information: Post Returns 2.673*** 2.859*** (3.701) (3.869) Constant 0.687 -1.194*** 0.226 -0.998*** (1.304) (-3.054) (0.49) (-2.784) Log likelihood -1383.1 -1369.7 χ2 22.54 23.83 N 23,212 23,212

[P(I=1)] and the probability that the illegal activity is subsequently identified

[P(D=1|I=1)]. BM is the ratio of book value to market value, and ROA is the return on assets at the prior fiscal year-end. Prior Mkt Return, LT and Prior Mkt Return, ST are the performance of the CRSP equal-weighted index in the year leading up to twenty one days prior to the indictment and twenty days preceding the indictment, respectively.

PreVolatility, ST and PreVolatility, LT are the standard deviations of the stock returns over the twenty trading days leading up to the indictment and year leading up to twenty one days prior to the indictment, respectively. Leverage is the ratio of long-term debt to total assets, and Size is natural log of total assets for the prior fiscal year. Board Size is the number of board members, and CEO Duality is a dummy variable equal to one if the

CEO is the chairman of the board. Board Independence is a dummy variable equal to one if more than 50 percent of the board is independent. Post Returns is absolute value of the

(0,2)-day CAR around insider trade date. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

Table 1.4 Traditional Probit: Base Model

Commit Detect Full Commit Detect Full (1) (2) (3) (4) (5) (6) Ex-ante information: BM -0.313*** -0.328*** -0.316*** -0.331*** (-5.396) (-5.708) (-5.408) (-5.691) ROA -0.373*** -0.360*** -0.363*** -0.354*** (-3.528) (-3.334) (-3.429) (-3.271) Prior Mkt Return, LT 0.028 0.016 0.014 0.004 (0.338) (0.190) (0.169) (0.046) Prior Mkt Return, ST -4.127*** -3.742*** -4.126*** -3.739*** (-8.040) (-7.069) (-7.957) (-6.995) Pre Volatility, ST 2.435 -6.204*** 2.361 -6.293*** (1.209) (-2.683) (1.157) (-2.688) Pre Volatility, LT 6.162*** 5.908** 6.286*** 5.997** (2.824) (2.566) (2.841) (2.568) Board Size 0.007 0.004 (0.693) (0.418)

45 CEO / Chair Duality 0.286*** 0.279*** (5.460) (5.204) Board Independence 0.120* 0.132* (1.813) (1.912) Ex-ante/detection information: Leverage -0.084 -0.020 -0.192 -0.075 -0.020 -0.184 (-0.687) (-0.164) (-1.516) (-0.605) (-0.164) (-1.435) Size 0.000 -0.012 0.002 -0.008 -0.012 -0.003 (0.016) (-0.882) (0.165) (-0.432) (-0.882) (-0.180) Ex-post detection information: Post Returns 3.250*** 3.682*** 3.250*** 3.675*** (12.646) (10.514) (12.646) (10.416) Constant -2.206*** -2.313*** -2.143*** -2.387*** -2.313*** -2.322*** (-15.885) (-21.741) (-15.245) (-15.454) (-21.741) (-14.806) Log likelihood -1448.79 -1447.17 -1385.38 -1433.59 -1447.17 -1371.37 χ2 159.30 162.55 286.13 189.71 162.55 314.14 N 23,212 23,212 23,212 23,212 23,212 23,212

This table presents traditional probit models for our sample of firms indicted for illegal insider trading and the comparative sample. BM is the ratio of book value to market value, and ROA is the return on assets at the prior fiscal year-end. Prior Mkt

Return, LT and Prior Mkt Return, ST are the performance of the CRSP equal-weighted index in the year leading up to twenty one days prior to the indictment and twenty days preceding the indictment, respectively. PreVolatility, ST and PreVolatility, LT are the standard deviations of the stock returns over the twenty trading days leading up to the indictment and year leading up to twenty one days prior to the indictment, respectively.

Leverage is the ratio of long-term debt to total assets, and Size is natural log of total assets for the prior fiscal year. Board Size is the number of board members, and CEO

Duality is a dummy variable equal to one if the CEO is the chairman of the board. Board

Independence is a dummy variable equal to one if more than 50 percent of the board is independent. Reg, Finance, Tech, and Retail are dummy variables indicating that the firm operates in a regulated, financial, technology, and retail industry, respectively. Post

Returns is absolute value of the (0,2)-day CAR around insider trade date. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

Table 1.5 Bivariate Probit with SEC and Political Variables: Committee Majority

(1) (2) [P(I)] [P(D|I)] [P(I)] [P(D|I)] Ex-ante information: BM -0.484*** -0.505*** (-5.076) (-5.299) ROA -1.109*** -1.118*** (-4.230) (-4.111) Prior Mkt Return, LT -0.199* -0.192* (-1.831) (-1.794) Prior Mkt Return, ST -4.361*** -4.501*** (-6.796) (-6.550) Pre Volatility, ST -4.923 -5.917 (-1.480) (-1.549) Pre Volatility, LT 4.640 4.864 (1.412) (1.493) SEC Budget, t 0.140 0.260 (0.490) (0.919) Board Size 0.016 0.012 (1.177) (0.891) CEO / Chair Duality 0.459*** 0.436*** (4.144) (4.270) Board Independence 0.038 0.060 (0.372) (0.611) Ex-ante/detection information: Leverage -0.394* 0.283 -0.528** 0.291* (-1.655) (1.622) (-2.221) (1.657) Size 0.042 -0.065*** 0.017 -0.026 (1.572) (-3.161) (0.632) (-1.317) Regulated -0.362* 0.383** (-1.783) (2.462) Finance -0.153 0.328*** (-1.069) (2.797) Tech 0.405*** -0.050 (4.138) (-0.498) Retail -0.080 0.041 (-0.486) (0.278) Rep Tilt 0.129 -0.118 (1.404) (-1.414) Senate Rep, t 3.382*** -7.333 3.247*** -7.207 (8.070) (-0.072) (4.882) (-0.090) Litigation Releases, t -0.009** 0.009** -0.011** 0.011** (-2.049) (2.310) (-2.357) (2.380) Ex-post detection information: Post Returns 3.588*** 3.965*** (6.586) (3.642) SEC Budget, t+1 0.835*** 0.758** (2.589) (2.254) Com Majority, Rep, t+1 -0.438*** -0.448*** (-3.409) (-3.162) Constant -1.843*** 4.935 -1.580*** 4.606 (-4.077) (0.049) (-3.367) (0.058) Log likelihood -1322.4 -1340.9 χ2 430.41 401.91 N 23,212 23,212

This table presents the bivariate probit model with partial observability for our sample of firms indicted for illegal insider trading and the comparative sample of firms not indicted for illegal insider trading including SEC and political variables. We estimate both the propensity to insider trade [P(I=1)] and the probability that the illegal activity is subsequently identified [P(D=1|I=1)]. SEC Budget, t is budget of the SEC for the year of the insider trade, in millions. SEC Budget, t+1 is the budget of the SEC for the year post insider trade, in millions. Senate Rep indicates if the U.S. Senate majority is Republican in the year of insider trade, and 0 otherwise. Litigation Releases is the number of the SEC litigation releases regarding insider trading in the year leading up to the indictment. Com

Majority, Rep, t+1 is dummy variable equal to one if in the year after the insider trade the

SEC Committee majority is Republican Rep Tilt is a dummy variables indicating that the firm operates in a republican-tilting sector. All other variables are as described in Table 2 and in the text. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

Table 1.6 Traditional Probit with SEC and Political Variables: Committee Majority

Commit Detect Full (1) (2) (3) Ex-ante information: BM -0.325*** -0.345*** (-5.564) (-5.922) ROA -0.353*** -0.345*** (-3.302) (-3.155) Prior Mkt Return, LT 0.041 0.011 (0.491) (0.134) Prior Mkt Return, ST -4.148*** -4.023*** (-8.013) (-7.086) Pre Volatility, ST 2.868 -5.621** (1.400) (-2.382) Pre Volatility, LT 6.222*** 5.389** (2.748) (2.194) SEC Budget, t 0.087 -0.505 (0.651) (-1.003) Board Size 0.012 0.010 (1.096) (0.906) CEO / Chair Duality 0.319*** 0.315*** (5.846) (5.616) Board Independence 0.070 0.076 (1.004) (1.051) Ex-ante/detection information: Leverage -0.048 -0.003 -0.155 (-0.383) (-0.025) (-1.198) Size -0.017 -0.017 -0.015 (-0.957) (-1.201) (-0.794) Rep Tilt 0.039 0.056 0.021 (0.687) (0.985) (0.355) Senate Rep, t -0.094 -0.211** -0.006 (-1.088) (-2.019) (-0.056) Litigation Releases, t 0.002 0.000 0.000 (0.704) (0.041) (0.156) Ex-post detection information: Post Returns 3.242*** 3.703*** (12.489) (10.431) SEC Budget, t+1 -0.170 0.712 (-1.239) (1.378) Com Maj, Rep, t+1 0.091 -0.053 (1.475) (-0.733) Constant -2.468*** -2.067*** -2.440*** (-10.261) (-9.693) (-9.140) Log likelihood -1429.64 -1444.21 -1366.30 χ2 197.61 168.48 324.28 N 23,212 23,212 23,212

This table presents traditional probit models for our sample of firms indicted for illegal insider trading and the comparative sample including SEC and political variables.

All variables are as described in Table 2 and in the text. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

Table 1.7 Bivariate Probit with SEC and Political Variables: Committee Majority versus Chairman

SEC Chairman SEC Chairman and Committee Majority [P(I)] [P(D|I)] [P(I)] [P(D|I)] Ex-ante information: BM -0.520*** -0.494*** (-5.402) (-5.199) ROA -1.101*** -1.109*** (-4.020) (-4.110) Prior Mkt Return, LT -0.126 -0.201* (-1.160) (-1.845) Prior Mkt Return, ST -4.540*** -4.727*** (-6.447) (-6.814) Pre Volatility, ST -6.228 -6.672** (-1.524) (-1.976) Pre Volatility, LT 3.981 4.095 (1.162) (1.246) SEC Budget, t 0.308 0.302 (1.112) (1.057) Board Size 0.011 0.012 (0.747) (0.877) CEO / Chair Duality 0.461*** 0.437*** (4.237) (4.312) Board Independence 0.061 0.063 (0.580) (0.650) Ex-ante/detection information: Leverage -0.520** 0.281 -0.507** 0.279 (-2.226) (1.571) (-2.209) (1.556) Size 0.017 -0.028 0.013 -0.026 (0.634) (-1.387) (0.490) (-1.292) Rep Tilt 0.123 -0.113 0.128 -0.122 (1.307) (-1.308) (1.377) (-1.406) Senate Rep, t 3.060*** -7.964 3.067*** -7.242 (4.474) (-0.056) (6.041) (-0.085) Litigation Releases, t -0.012*** 0.011** -0.012*** 0.011*** (-2.581) (2.534) (-2.605) (2.642) Ex-post detection information: Post Returns 4.302*** 4.290*** (2.993) (4.083) SEC Budget, t+1 -0.000 0.688* (-0.000) (1.951) Com Maj, Rep, t+1 -0.506*** (-3.334) Chairman, Rep, t+1 -0.059 0.096 (-0.587) (0.886) Constant -1.545*** 5.610 -1.498*** 4.662 (-3.180) (0.039) (-3.322) (0.055) Log likelihood -1347.6 -1340.5 χ2 411.73 432.08 N 23,212 23,212

This table presents the bivariate probit model with partial observability for our

sample of firms indicted for illegal insider trading and the comparative sample of firms

not indicted for illegal insider comparing the influence of the political affiliation of the

SEC Committee with one of the SEC Chairman. We estimate both the propensity to insider trade [P(I=1)] and the probability that the illegal activity is subsequently identified

[P(D=1|I=1)]. Com Majority, Rep, t+1 indicates if in the year after the insider trade the

SEC Committee majority is Republican. Chairman, Rep, t+1 indicates that the Chairman of the SEC Committee belongs to the Republican Party in the year after the insider trade.

Rep Tilt is a dummy variables indicating that the firm operates in republican-tilting sector. All other variables are as described in Table 2 and in the text. ***, **, and * denote significance at 1%, 5%, and 10%, respectively

Table 1.8 Bivariate Probit with SEC and Political Variables: Type of Transaction

Buy Trades Sell Trades [P(I)] [P(D|I)] [P(I)] [P(D|I)] Ex-ante information: BM -0.417*** -2.843*** (-3.915) (-3.728) ROA -1.366*** 0.015 (-5.020) (0.019) Prior Mkt Return, LT -0.286** -0.860 (-2.100) (-1.638) Prior Mkt Return, ST -4.417*** -23.419*** (-5.485) (-4.323) Pre Volatility, ST -5.314 -12.657 (-1.127) (-1.059) Pre Volatility, LT -2.308 97.065*** (-0.473) (3.421) SEC Budget, t 0.243 -3.066 (0.740) (-1.596) Board Size 0.010 0.095 (0.531) (1.215) CEO / Chair Duality 0.632*** 0.662 (5.497) (1.507) Board Independence -0.056 0.533 (-0.310) (1.025) Ex-ante/detection information: Leverage -0.685** 0.249 -1.021 0.276 (-2.554) (1.296) (-1.295) (1.065) Size 0.034 -0.014 0.388*** -0.158*** (1.088) (-0.644) (3.230) (-4.465) Rep Tilt 0.080 -0.127 -0.803* 0.475*** (0.756) (-1.381) (-1.905) (3.193) Senate Rep, t 3.899*** -7.336 -1.410* 0.652 (5.381) (-0.100) (-1.724) (1.480) Litigation Releases, t -0.010* 0.009** 0.037 -0.015 (-1.937) (2.406) (1.022) (-0.974) Ex-post detection information: Post Returns 3.323*** -5.855*** (5.610) (-8.103) SEC Budget, t+1 0.497 1.889** (1.302) (2.154) Com Maj, Rep, t+1 -0.356** -0.143 (-2.076) (-0.879) Constant -1.570*** 4.537 -3.015 -2.162*** (-2.665) (0.062) (-1.357) (-3.126) Log likelihood -380.19 -999.23 χ2 128.87 281.84 N 23,123 23,006

This table presents the bivariate probit model with partial observability for our

sample of firms indicted for illegal insider trading and the comparative sample of firms

not indicted for illegal insider comparing the impact of the SEC and political variables

depending on type of illegal insider transaction. We estimate both the propensity to insider trade [P(I=1)] and the probability that the illegal activity is subsequently identified

[P(D=1|I=1)]. Post Returns is (0,2)-day CAR around insider trade date. SEC Budget, t is budget of the SEC for the year of the insider trade, in millions. SEC Budget, t+1 is the budget of the SEC for the year post insider trade, in millions. Senate Republican indicates if the U.S. Senate majority is Republican in the year of insider trade, and 0 otherwise.

Litigation Releases is the number of the SEC litigation releases regarding insider trading in the year leading up to the indictment. Com Majority, Rep, t+1 is indicates in the year after the insider trade the SEC Committee majority is Republican. All other variables are as described in Table 2 and in the text. ***, **, and * denote significance at 1%, 5%, and

10%, respectively.

Table 1.9 Bivariate Probit with SEC and Political Variables: Executive Rank

Executives Non Executives [P(I = 1)] [P(D = 1|I = 1)] [P(I = 1)] [P(D = 1|I = 1)] Ex-ante information: BM -0.515*** -0.358** (-3.959) (-2.337) ROA -0.988*** -0.718** (-2.849) (-2.530) Prior Mkt Return, LT -0.368** 0.072 (-2.189) (0.719) Prior Mkt Return, ST -3.240*** -3.992*** (-3.788) (-2.676) Pre Volatility, ST -10.244** -7.769 (-2.030) (-1.595) Pre Volatility, LT 2.705 4.364 (0.485) (1.258) SEC Budget, t -0.108 -0.171 (-0.286) (-0.363) Board Size 0.007 0.004 (0.286) (0.331) CEO / Chair Duality 0.627*** 0.283** (4.841) (2.353) Board Independence 0.091 0.046 (0.392) (0.547) Ex-ante/detection information: Leverage -0.454 0.593*** 0.875 -0.982** (-1.549) (2.676) (1.582) (-2.300) Size -0.003 -0.040 0.106** -0.085** (-0.072) (-1.342) (2.311) (-2.523) Rep Tilt 0.297*** -0.152 -1.926*** 1.597*** (2.666) (-1.210) (-3.748) (3.193) Senate Rep, t 2.562*** -7.167 -0.084 0.190 (3.789) (-0.078) (-0.217) (0.605) Litigation Releases, t -0.014** 0.012** 0.008 -0.006 (-2.349) (2.138) (0.823) (-0.759) Ex-post detection information: Post Returns 5.101*** 3.108*** (4.454) (3.326) SEC Budget, t+1 0.737 0.417 (1.589) (1.018) Com Majority, Rep, t+1 -0.394* -0.083 (-1.928) (-1.199) Constant -1.025 3.734 -0.096 -0.882 (-1.570) (0.041) (-0.104) (-1.190) Log likelihood -738.61 -762.21 χ2 244.9 60.22 N 23,065 23,074

This table presents the bivariate probit model with partial observability for our

sample of firms indicted for illegal insider trading and the comparative sample of firms

not indicted for illegal insider comparing the impact of the SEC and political variables

depending on an insider’s rank. We estimate both the propensity to insider trade [P(I=1)] and the probability that the illegal activity is subsequently identified [P(D=1|I=1)]. SEC

Budget, t is budget of the SEC for the year of the insider trade, in millions. SEC Budget, t+1 is the budget of the SEC for the year post insider trade, in millions. Senate

Republican indicates if the U.S. Senate majority is Republican in the year of insider trade, and 0 otherwise. Litigation Releases is the number of the SEC litigation releases regarding insider trading in the year leading up to the indictment. Com Majority, Rep, t+1 is indicates in the year after the insider trade the SEC Committee majority is Republican.

All other variables are as described in Table 2 and in the text. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

140

120

100

40 NUMBER OF ILLEGAL TRADESOF NUMBER

0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 YEAR

Figure 1.1 Insider Trading Time Trends

This figure plots the number and trend of litigation releases from the SEC for insiders investigated for illegal insider trading for the period 1996 through 2013.

INSIDER TRADING AND CORPORATE LOBBYING

Introduction

Government regulations and policies shape the legal and business environment where firms operate. It is reasonable to assume then that firms seek to influence these regulations and policies to their advantage. Corporate lobbying is a strategic decision by firms to invest in establishing contacts with government officials in hope of acquiring certain benefits. Unlike the donations to political campaigns, spending on lobbying is not legally limited and substantially exceeds contributions to political action committees, another avenue firms use to build connections with policymakers (Kerr et al., 2014; Milyo et al., 2000). U.S. corporations’ spending is not only about twenty times greater on lobbying activities relative to the amounts spent on political action committees but also, together with trade associations, accounts for approximately 85% of total spending by all interest groups attempting to influence U.S. federal government (de Fegueiredo and

Richter, 2014; Hill et al., 2013; Milyo et al., 2000). This implies that lobbying is a primary way that U.S. firms employ to build political connections in order to obtain competitive advantage with help of government officials.

The importance of lobbying practices for the firms is reflected by the amount of attention these activities receive in academic research. A significant body of work explores various aspects of lobbying and the resulting outcomes for firms engaged in lobbying

practices. However, the findings are not exactly conclusive. While Chen, Parsley, and Yang

(2015), Hill et al. (2013), and Unsal et al. (2017) among others find positive association between lobbying and firm performance and value, Cao et al. (2018) provide evidence of negative relation between the two. In addition, other studies claim that there is no link between lobbying and firm’s future performance (Hersh et al., 2008). Beyond the impact on the firm performance, literature also shows that lobbying can be helpful in gaining specific desired outcomes. For instance, lobbying is associated with government contracts

(Schuler et al., 2002), faster patent approvals (Unsal and Rayfiels, 2017), and lower effective tax rates (Richter et al., 2009). However, one aspect of lobbying that has not received significant attention is the potential for insiders to exploit private information acquired through lobbying efforts.

Lobbying as a way to pursue interests that are crucially important to the firm can be exercised in two forms. First, firms could attempt to influence formation and modification of the federal government policies. Second, they could acquire information regarding the upcoming legislative and regulatory changes that could impact performance and value of their firms overall, as well as lead to specific benefits or losses for the firms.

Regardless of which of the two the firm employs and whether the lobbying outcomes for the firm are positive or negative, lobbying practices embody information. This information regarding these practices being in place, potential outcomes, and their impact on the firms’ future provide potentially valuable trading opportunities.

According to the Lobbying Disclosure Act of 1995, lobbying practices of the individuals and entities that intend to influence either executive or legislative branches of the federal government have to be disclosed to the government. The entities involved in

lobbying activities must register with the Secretary of the State and the Clerk of the House of Representatives once they make initial lobbying contact and provide detailed information regarding lobbying practices and incurred lobbying expenses to the Offices of

Public records and Public Disclosure documents on semiannual basis. In other words, firms are mandated to disclose their lobbying practices making this information public knowledge. However, there are two aspects that make this information more valuable to certain parties, e.g., the insiders. First, there is a delay between the lobbying contact, any oral or written communications with a government official, and the disclosure of this activity. This provides insiders with a potential window of opportunity to take advantage of this information. Second, even when a particular lobbying contact or practice is disclosed, actual magnitude of its impact may be relatively less clear to outside investors, placing corporate insiders with an information advantage. Indeed corporate insiders are able to employ the knowledge of their firms and their superior understanding of public information to make profitable investment decisions (Piotroski and Roulstone, 2005;

Alldredge and Cicero, 2015). In this study, we evaluate insider trading activities by corporate insiders and investigate whether informational channels formed through lobbying help them make more informative trade decisions.

We identify U.S. firms that engage in lobbying practices and show significant negative association between lobbying and 3-day returns around insider sale transactions.

We document evidence implying that insiders of firms that lobby avoid a loss of 16 basis points compared to insiders of firms that do not foster political connections. We further show that this negative and significant association holds when lobbying firm identifier is replace with the amount spent on these lobbing activities averaged over the previous four

years. However, we do not find evidence of the same link between insider purchases and either lobbying activities or lobbying expenses. This suggests that lobbying connections allow insiders to gain access to the information regarding negative changes in firms’ future and take advantage of this knowledge.

We also document that the association between lobbying practices and returns on insider transactions becomes greater and more significant as investment horizons increase.

We provide evidence of negative and highly significant associations between one-, two-, and three-month returns following insider sells and both measures of lobbying activity.

Insider of firms that lobby avoid loss of up to 156 basis points relative to insides of firms that do not engage in lobbying. In addition, we find the link between long-run returns for buys and lobbying practices but not spending on lobbying. Purchase transactions by insiders of lobbying firms earn additional return of up to 138 basis points compared to non- lobbying insiders. The results further imply the importance of informational content of lobbying interactions.

Overall, our study extends the literature on the connection between lobbying practices by U.S. firms and trading by corporate insiders of these firms providing better understating of lobbying as a way to maintain informational channel with the federal government that could benefit the insiders. We find greater impact of this channel for the events that have longer lasting impact on the firms’ performance. We also find that establishing lobbying contacts plays bigger role for certain transactions types than the amount spent on lobbying activities.

Literature Review

Insider Trading

The majority of insider trading research attempts to determine whether the trades are informative based on ex-post returns. A significant body of this work focuses on the corporate insider transactions reported to the SEC in accordance with Section 16(a) of the

Securities Exchange Act of 1934. Insider purchases are typically shown to contain more information about future stock returns versus insider sales (Jeng et al., 2003; Jenter, 2005;

Lakonishok and Lee, 2001; Lin and Howe, 1990; Seyhun, 1986; Seyhun, 1998). The difference in predictive power is suggested to be attributed to the insiders’ reluctance to trade on negative information due to higher ligation risks (Brochet, 2010; Chen, Martin, and Wang, 2012). In addition, informational content of the trades generally varies with firm characteristics, trade characteristics, and characteristics of the insiders.

Finance literature reveals that profitability of insider transactions is related to the firm’s level of information asymmetry. Informative trades are associated with different measures of information transparency ranging from analyst following to R&D intensity

(Aboody and Lev, 2000; Frankel and Li, 2004; Huddart and Ke, 2007; Piotroski and

Roulstone, 2005; Ravina and Sapienza, 2010). Literature also finds corporate governance to be an important determinant of explanatory power of insider transactions. Unlike internally imposed insider trading restrictions being negatively associated with insider trading (Bettis et al., 2000; Lee et al., 2014; Roulstone, 2003), poor internal controls over financial reporting have positive impact on insider sale transactions. External governance plays an important role as well. Fidrmuc, Giergen, and Renneboog (2006) find that

presence of large shareholders who monitor the firm insiders reduces informational value of insider trades.

Other studies investigate predictive ability of insider transaction by focusing on the patterns of trades by corporate insiders: timing of these transactions (Brooks et al., 2012), persistence of insider profitability (Cline et al., 2017), or sequence of trades (Cohen et al.,

2012). They find that persistently profitable (Cline et al., 2017) and not-routine transactions

(Cohen et al., 2012), as well as early option exercises exercised not on the vest day and when least in-the-money (Brooks et al., 2012) contain information regarding the firm’s future.

Another strand of literatures explores characteristics of insiders whose trades are likely to have explanatory power. Access to the firm’s private information can be a source of more informational trades. Cline et al. (2017), Ravina and Sapienza (2010), and Wang,

Shin, and Francis (2012) find that top executives and directors earn significant abnormal returns. Other studies show that insiders with profitable trades are likely to be local and non-executive (Cohen et al., 2012). Their personality traits may have impact on informational content of their trades as well. (Hillier et al., 2015).

In addition to studies suggesting that corporate insiders utilize some kind of firm specific private information, others provide evidence implying that corporate insiders may have superior understanding of publicly available data. They are better able to either recognize impact of public information on the future performance of their firms (Piotroski and Roulstone, 2005; Alldredge and Cicero, 2015) or identify outsiders’ pricing errors and take advantage of them (Rozefff and Zaman, 1998; Lakonishok and Lee, 2001).

Corporate Lobbying Practices

Literature on the impact of lobbying activities on the future of the firms can be broadly partitioned into two strands. The first explores whether a link exists between lobbying and firm performance and value and shows findings suggesting significant and positive relation between them. Chen at el. (2015) provide evidence of a positive association between lobbying and financial statement measures of firm performance, while

Hill at el. (2013) demonstrate that lobbying is related to increases in shareholder wealth. In addition, Kim (2008) finds a positive impact of lobbying practices on firms’ returns compared to the industry and the overall market, and Unsal et al. (2017) document positive association between lobbying and firm value in the pharmaceutical industry. On the other hand, there is empirical evidence of the negative impact lobbying could have on the firm, for instance, Coates (2012) discusses shareholder value in the light of the Supreme Court decision in the Citizens United case. He shows results suggesting a negative association between lobbying and firm value, measured as Tobin’s Q, for a sample of S&P 500 firms that are not from heavily-regulated or government dependent industries. Furthermore, lobbying practices may have neutral relations with firm value as suggested by lack of association between lobbying contributions and Tobin’s Q (Hersh et al., 2008).

In addition to research on the overall relation between lobbying practices and firm performance and value, other studies investigate specific benefits of lobbying. Richter et al. (2008) demonstrates that lobbying can be helpful in obtaining tax benefits when spending on lobbying is associated with lower effective tax rates. Unsal et al. (2017) and

Unsal and Rayfield (2017) investigate lobbying benefits in the pharmaceutical industry and find lobbying predictive of a larger number of patent, medical device, and drug approvals

and lower wait time for these approvals. Schuler et al. (2002) shows the importance of lobbying and maintaining close connections with policymakers for firms securing U.S. military contracts. Firms actively employ lobbying political connections to acquire protection from foreign competition (Gawande and Bandyopadhyay, 2000; Lee and Baik,

2010). Moreover, lobbying is found to be effective with other forms of the governance assistance as well. While Blau at el. (2013) provide evidence of positive associations between lobbying practices and higher likelihood of receiving Troubled Assets Relief

Program funds in greater amounts, Duchin and Sosyura (2012) find that the likelihood of being approved for Capital Purchase Program is positively related to lobbying activities.

In addition, lobbying is associated with lower fraud detection rate (Yu and Yu, 2001) and better outcomes in employee litigations (Unsal et al., 2017).

Lobbying Activities and Informed Trading

Academic researchers expressed interest in the relation between lobbying practices and insider trading as early as the 1980s. King and O’Keefe (1986) investigate whether there is a link between insider transactions and the firms’ lobbying activities on Financial

Accounting Standard No. 19 Exposure Draft that was to affect full cost and successful efforts firms and their executives’ wealth in opposite directions. They compare trading decisions by insides of both firm types that engage in lobbying activities regarding

Financial Accounting Standard No.19 Exposure Draft to the ones whose firms do not lobby. Their findings reveal that insiders of full cost lobbying firms are on average net sellers and insiders of successful efforts firms are net buyers, while trading decisions by insiders of firms that do not lobby are reversed. More recent studies focus on either lobbying in specific industries (Kim et al., 2016) or by specific groups of investors (Gao 65

and Huang, 2016). Kim et al. (2016) investigate lobbying activities in the pharmaceutical industry targeting the Food and Drug Administration approvals and provide evidence of increased numbers of insider purchases prior to the FDA approval announcements. Gao and Huang (2016) show that lobbying by hedge fund managers is positively associated with hedge fund performance in their politically sensitive stocks. They do not, however, find relation between lobbying practices and performance of not-politically sensitive stocks suggesting that lobbyists provide hedge fund managers with private political information.

Data

Insider Trading Data

Section 16 (a) of the Securities Exchange Act of 1934 requires corporate insiders to file Forms 3, 4, and 5 reflecting changes in the ownership of their shares. For our insider transactions data, we utilize Thomson Financial Insider Trading Data from January 1998 to December 2016, focusing on Form 4, Table I reported open market purchases and sales by corporate insiders. We restrict the dataset to trades with cleanse codes R, H, C, L, and I to ensure data accuracy (Liu and Swanson, 2016; Otto, 2014). We further limit the sample to exclude amended filings, transactions where the price deviates more than 20% from the

CRSP price on the trade date (Lakinishok and Lee, 2001), and trades resulting from option exercises.

Lobbying Data

The Lobbying Disclosure Act of 1995 was introduced with the purpose of improving transparency of and increasing accountability in corporate political practices.

The Act mandates any organization with lobbying spending above $20,000 and any 66

lobbyist with total income from lobbying activities higher than $5,000 to register with the

Secretary of the State and the Clerk of the House of Representatives. Registration must be completed within 45 days of the first lobbying contact, defined as any oral or written communication with a government official or the first day of the lobbyist’s employment with an organization. Both lobbying organizations and individuals are also required to file semiannual reports detailing their lobbying activities, including names of clients, general issue area, list of specific issues, list of employees acting as lobbyists, description of any foreign interest, and good faith estimate of total lobbying expenses. The individual filings are available through the Senate’s Office of Public Records or the Clerk’s Public

Disclosure Records.

We obtain lobbying data from the Center for Responsive Politics, a non-profit, nonpartisan research group operating in Washington, D.C., that maintains all the above filings in a database provided on their website OpenSecrets.org. Our dataset covers the period from 1998 to 2016 and is comprised of U.S. firms that lobby and in accordance with the Lobbying Disclosure Act 1995 have to register their lobbying activities and provide details of their lobbying practice. The utilized database includes the name of the sponsor, the amount spent on lobbying in a given year, and the lobbying year. The total spending on lobbying represents total amounts the parent company spends on lobbying its interest and interests of its subsidiaries if it has any.

Our insider transaction data are merged with lobbying data based on the name of the company and the year of the insider transaction for the former and the year of the lobbying activity for the latter. We further merge the combined lobbying/insider trading data with CRSP and Compustat, as well as RiskMetrics to obtain stock returns, accounting,

and governance information, respectively. The resulting dataset contains 133,383 sale trade

40,662 purchase trade observations.

Results

Descriptive Statistics

Table 1 presents the distribution of lobbying activities by U.S. firms by year for the period of the study and by the Standard Industrial Classification Divisions. According to

Panel A, there are some fluctuations in the number of lobbying firms but, overall, the number of firms involved in lobbying activities has been gradually increasing. In 1998 there were 405 U.S. firms actively lobbying. By 2016 that number increased to 655, an increase of over 60%. Since 2009 approximately 17% of U.S. firms engage in lobbing each year.

Panel B of Table 1 reveals that firms attempting to promote their interest through lobbying are not equally distributed among the industries. The panel shows that almost

85% of the firms belong to only four SIC divisions. About 40% of lobbying firms come from the Manufacturing division, one of the largest sectors. Firms in Services division contribute approximately 18.5%. They are followed by Transportation, Communications,

Electric, Gas, and Sanitary Services with almost 14% of lobbying firms. Finance, Insurance and Real Estate division comprises about 12%. However, Panel B also demonstrates that industry composition is not as imbalanced when we focus on ratio of lobbying firms to all firms in the sample. Manufacturing division includes close to 21% of firms with political connections, while Transportation, Communications, Electric, Gas, and Sanitary Services contains approximately 29% of lobbying firms. Roughly 15%and 7% of Services and 68

Finance, Insurance, and Real Estate firms, respectively, are engaged in lobbying activities.

Tables 2 provides summary statistics of the firms’ spending on lobbying by year.

The table indicates an average amount spent on lobbying each year has grown substantially over the sample period from approximately $400,000 in the late 1990s to almost one million dollars since 2012. Median lobbing expenses have increased from $120,000 a year to a quarter of a million over the same period.

Table 3 compares descriptive statistics for lobbying firms and firms not engaged in lobbying activities. Lobbying firms are significantly larger with average size of $15,198 million versus only $1,940 million for non-lobbying firms. Lobbying firms are also significantly more levered (0.26 compared to 0.225) and experience better prior year returns. Regarding corporate governance, the firms’ boards of directors consist of significantly more members, which could be related to their size. In addition, lobbying firms have boards classified as independent and have fewer CEOs presiding as Chairmen.

Multivariate Analysis: 3-day CARs

Results from OLS regressions on stock performance around transactions by insiders working for the firms engaged in lobbying are reported in Table 4. We estimate regressions for insider purchases and sales separately. The dependent variable in each regression is a

3-day cumulative market-adjusted abnormal return around the date of the trade by a corporate insider. To control for other determinants of the stock returns, we include measures of information asymmetry, risk, and prior performance, as well as the quality of corporate governance. Prior research shows positive associations between level of information asymmetry and insider trading profitability (Aboody and Lev, 2000; Frankel and Li, 2004; Huddart and Ke, 2007). To proxy for information asymmetry, we use the 69

ratio of book to market value estimated at the end of fiscal year prior to the insider trade.

Size is natural logarithm of the firm’s market equity in the prior fiscal year. Leverage is computed as the ratio of long-term debt to total assets. Firm’s profitability is defined as ratio of net income to total assets in the prior fiscal year (Bhattacharya and Marshall, 2012).

We also include prior year and prior month market-adjusted firm returns. Strong corporate governance is found to be associated with lower profitability of corporate insider trades

(Dai et al., 2016; Bettis et al., 2000; Cline et al., 2016; Cohen et al., 2012; Roulstone, 2003).

To control for the level of corporate governance, we include the size of the board, board independence, and CEO/Chairman duality. In addition, we include firm and industry fixed effects. The model takes the following form:

Ri,t = β1Lobbying + β2BM + β3Size + β4Leverage + β5ROA + β6Prior Year Return + β7Prior Month Return + β8Board Size + β9Board Independence + β10CEO Duality + firm dummies + industry dummies + ε (1)

The primary variable of interest is Lobbying indicating whether a trade is place by an insider working for a firm engaged in lobbying activities in the year prior to the insider trade. Lagged values are used to account for the time it takes a firm to become more effective at lobbying process and practices, as well as time it takes lobbying efforts to impact policymakers in an intended way (Drutman, 2010; Hall and Deardorf, 2016).

Models 1 and 4 control for size, book-to-market, and corporate governance quality. In

Models 2 and 5, we add prior firm performance, and in Models 3 and 6 we include firm’s profitability and leverage. The findings demonstrate that there is no association between profitability of the insider purchase transactions and firms’ lobbying efforts. However, the coefficient on Lobbying identifier for sell transactions indicates significant negative

association between abnormal performance and lobbying. Marginal effects suggest that corporate insiders avoid a 16 basis points loss relative to insiders whose firms are not engaged in lobbying.

Table 5 reports OLS regression estimates of the stock performance around transactions by insiders of lobbying firms. The regressions are performed separately for buy and sell transactions. The dependent variable in each is a 3-day CAR around the insider transaction date.:

Ri,t = β1Lobbying Spending + β2BM + β3Size + β4Leverage + β5ROA + β6Prior Year Return + β7Prior Month Return + β8Board Size + β9Board Independence + β10CEO Duality + firm dummies + industry dummies + ε (2)

The key explanatory variable in these models, however, is Lobbying Spending, which is equal to the average amount a firm spent on its lobbying efforts in the prior four years. Spending on lobbying may vary from year to year depending on political cycles and the amount of attention the federal government is giving to issues of corporate interest

(Antia et al., 2013; Milyo, 2000). To account for these fluctuations, we employ average rather than raw amounts.

Similar to Table 4, we report Models 1 and 4, controlling only for book-to-market, size, and level of corporate governance. We include prior firm performance in Models 2 and 5 and profitability and leverage in Models 3 and 6. The results of all the models indicate no relation between profitability of insider buys in lobbying firms and average amounts the firms has given to lobbying activities in the prior four years. Models 4 through 6 of the

Table reveal that the level of spending on lobbying does impact profitability of insider sells as suggested by significant negative association between CARs around sales by corporate

insiders and average amount spend on lobbying in the prior four years. The insiders of lobbying firms avoid a loss of 5 basis points at sample mean increase of lobbying spending relative to not lobbying firms. The findings indicate that having connections with government officials and investing in these connections allows corporate insiders acquire information on upcoming changes that could impact their firms in negative ways.

Overall, the results of the multivariate analysis on stock performance around corporate insider transactions shows that there is negative relation between profitability of three day market adjusted abnormal returns of insider sells and lobbying activities in the prior year, as well as average amounts spent on these lobbying activities in the prior four years. The relation does not hold, however, for insider buys over short-run and either lobbying activities or average amounts spent on them. The findings suggest the importance of information insiders learn through lobbying activities regarding negative rather than positive changes in the firm’s future.

Multivariate Analysis: Long-Run Horizon

Next, we examine the relation between lobbying activities and long-run returns following insider transactions. The dependent variables in this analysis are one-, two-, or three-month market-adjusted abnormal returns, estimated from the day of the transaction over 20, 40, and 60 trading days, respectively. We control for the same determinants of stock returns as in prior analysis. Table 6 presents OLS regression results on stock performance post insider purchases. Similar to equation (1), the key variable of interest is the lobbying indicator.

Models 1 through 3 demonstrate a positive significant association between lobby- active firms and the returns in the first month following an insider trade. Models 4, 5, and 72

6 show that this relation holds for the two-months returns following purchases as well.

Insiders of firms that foster connections with government officials earn additional 99 basis points and additional 138 basis point over non-lobbying insiders on their one-month and two-month returns, respectively. Comparison of the estimates for one-month and two- month purchase transactions also demonstrates that the impact of lobbing on profitability of the trades is similar in significance. The association between profitability of insider transactions and lobbying activities, however, becomes smaller and only marginally significant in our examination of three-months insider purchase returns. Nevertheless, these purchases of lobbying insiders are associated with additional return of 104 basis points relative to their non-lobbying counterparts.

Table 7 presents estimates of the post insider sells stock performance. The dependent variables in the models are the one-, two-, or three-month market-adjusted abnormal returns, while independent variable is lagged value of Lobbying identifier. We use equation (1) to model the relation between lobbying and long-run horizon insider sales returns.

Controlling for book-to-market, size, prior performance, and corporate governance does not appear to have impact on significance of the relation between lobbying and post insider sale stock performance as demonstrated in all models of Table 7. Consistent with our prior findings regarding profitability of 3-day CARs, we report negative and significant association between lobbying activities by the firms and sale transactions by the insiders of these firms. Insiders whose firms engage in lobbying avoid loss of 85, 156, and 154 basis points on one-, two-, and three-month trades, respectively, compared to insiders

whose firms do not have lobbying contacts. The results for the effect of lobbying are similar in their significance across all model specifications.

Having established that firms’ spending on lobbying is significantly associated with

3-days CARs around insider sale transactions but not the returns around buy transactions, we explore whether this relation remains the same for the returns over longer investment horizons. Tables 8 and Table 9 demonstrate the findings on post insider purchase and sale transactions, respectively, and the average amount spent on lobbying practices over the previous four years. We continue employing equation (2); however, 3-day CARs are replaced with one-, two-, or three-month market-adjusted abnormal returns as dependent variables.

Table 8 reports that the association between firms’ lobbying expenses and profitability of insider buys is only marginally significant only in Models 2, 5, and 8, where we control for book-to-market, size, corporate governance quality, and prior stock returns but do not include impact of profitability and level of leverage in the prior fiscal year.

Overall, we continue to find no significant link between the amounts firms spent on lobbying and either one-, two-, or three-month returns following insider purchase. The finding suggests that creating and fostering lobbying contacts is a more significant predictor of returns following insider purchases than the actual amount spent on these contacts.

In Table 9, we investigate the link between the amount firms dedicate to lobbying activities and post insider sale transaction stock performance. Unlike the findings for the purchases, we observe a negative and significant association between firms’ spending on lobbying and returns following insider sells. The results stay unchanged for all three

investment horizons and when controlling for information asymmetry, prior profitability, and corporate governance. Models demonstrate that benefits to insiders of lobby-active firms relative to insiders of non-lobbying firms vary with investment horizons, from 27 to

38 basis points additional earnings with sample mean of spending on lobbing. The findings in Table 9 further imply the importance of the lasting informational advantage resulting from political connections.

Overall, the estimates for monthly returns demonstrate that, in contrast to the findings for daily returns around insider trades, lobbying contacts provide informational advantage to insiders considering purchase decisions in light of positive upcoming changes that have longer lasting impact on the firm. With the sales, the association between returns and lobbying remains negative and significant.

Multivariate Analysis: Executive Rank

Insider trading literature provides evidence that access to information is one of the factors that determine profitability of insiders’ transactions (Cline, Gokkaya, and Liu,

2017; Niehaus and Roth, 1999; Fidmuc, Goergen, and Renneboog, 2006; Wang, Shin, and

Francis, 2012). In addition, insiders of different ranks have different access and different resulting outcomes (Wang, Shin, and Francis, 2012). However, in case of lobbying practices the importance may lie not in having higher rank with better access but in simply having knowledge that the firm is engaged in lobbying activities. Under existing corporate rules, decisions to engage in lobbying in attempt to affect government officials and to spend resources on these activities are treated as other business decisions and made solely by the firms’ insiders without input from other potentially interested parties. (Bebchuk and

Jackson, 2010). In other words, the contrasting groups may be not high ranked executives 75

versus non-executives but rather insiders of different ranks versus outsiders. To assess whether what matters is being an insider or the rank of insiders, we explore whether the associations between the returns either around or following insiders transactions differ with insiders’ ranks. We divide our sample into three subgroups: C-level executive, executives, and not executives. To be included into C-level group, an insider has to belong to C-suite.

Executive insiders are the ones who have Chief, President, or Vice President in their titles, while non-executives group includes all other insiders.

Table 10 and Table 11 report the results for 3-day stock performance around insider transactions and lobbying activities and spending on lobbying, respectively. In each table, purchase and sales are estimated separately. We employ equation (1) for Table 10 estimates and equation (2) for Table 11 estimates, where dependent variable is 3-day CAR around either buy or sell insider trade. In Table 10, we observe that there exist no relation between lobbying practices and profitability of the returns around insider purchase transactions by any of the insiders of lobbying firms, which is consistent with our overall findings for returns around insider buys. However, we show association between lobbying and sale transaction returns to be negative and significant for non-executives, marginally significant for executives, and non-significant for C-suite insiders. This could imply that higher ranked insiders may tend to abstain from this type of sell trades, probably due to higher probability of being watched by the authorities and associated potential litigation risks (Brochet, 2010;

Chen, Martin, and Wang, 2012).

Table 11 reveals no relation between spending on lobbying practices and returns around either purchase or sale transactions by any of the groups of lobbying firms’ insiders.

The only exception is negative association between lobbying expenses and CARs around sells by corporate non-executives significant at 10% level.

Next, in Tables 12 and Tables 13, we evaluate whether the links between lobbying practices by the firms and long-term post insider transaction returns of their insiders are impacted by the rank of the insider. We again utilize equation (1) and equation (2) to model the relations with lobbying activities, replacing 3-day CARs with one-, two-, or three- month market-adjusted abnormal returns. We control for the same determinants of stock returns as in prior analysis. Table 12 shows that in case of purchases, the associations are significant and in expected direction for all insider ranks for one-month returns.

Significance, however, diminishes for some insiders for two-month returns, and disappeares for three-month returns (for two-month for c-level buys).

According to Table 13, unlike findings for 3-day sale transactions and monthly purchase transactions, the results for associations between lobbying activities and sale transactions are significant and remain negative and highly significant for all three groups for one-, two-, and three-month returns for insiders of different ranks. Interestingly, non- executives of lobbying firms gain more additional basis points in case of one- and two- month returns but the situation reverses in case of three-month returns. C-level insiders earn additional 328 basis points compared to 128 of non-executives, both relative insiders of firms that do not lobby. This could indicate better understanding of the impact of the upcoming changes but not trading on them earlier. Overall, the results suggest that insiders of all ranks are aware of their firms’ lobbying activities and their lasting impact on the firms’ future.

Tables 14 and 15 provide estimates of the impact of spending on lobbying contacts and monthly returns following buy and sell trades by different insider groups, respectively.

We show lack of any effect of the amounts spent on lobbing on either one-, two-, or three- month returns for any of the ranks. However, the analysis of sales transactions reveals the importance of the lobbying expenses for insider sells. The effect of the amounts is not equally significant for all ranks and appears to play a bigger role for non C-suite insiders suggesting potential effect of litigation risks for higher ranked executives.

Overall, the findings for the subsamples of insiders of different ranks imply the importance of being an insider versus an outside investor rather than of having higher high rank. However, the role of rank somewhat resurfaces in case of insider sale transactions by C-suite executives when the associations between 3-day CARs and lobbying activities are found to be insignificant only for that subgroup.

Conclusion

Lobbying allows firms to influence the environment they operate in by establishing political contacts with government officials. The firms attempt to impact formation and modification of the political processes and policies. They also attempt to acquire timely information regarding upcoming legislative and regulatory changes that could have effect the firms’ future. Resulting outcomes could be positive or negative for the firms; however, regardless of the direction of the impact lobbying activities could have on the firms, they represent informational advantage that insiders of these lobbying firms could utilize.

In our study, we employ U.S. firms lobbying activities database to document evidence that lobbying indeed provides insiders of lobby-active firms with informational

advantage that allows them to earn additional returns on both their buy and sell transactions. Insiders of firms that lobby earn up to 138 basis points more on their purchase transactions and avoid loss of up to 156 basis points on their sale transactions relative to insiders whose firms do not lobby. We further find that establishing and maintaining these political connections matter more than the amounts firms spent on the lobbying activities in case of purchases. Both, however, are critical for sales placed by insiders of firms engaged in lobbying. The significance of lobbying, as well as lobbing expenditures, increases with the time of the investment horizon.

We also document findings implying that rank of insiders does not have a big effect on profitability of the insider trades for lobbying firms. Transactions placed by insiders of all ranks of lobbying firms are associated with additional gains relative to transactions of insiders working for non-lobbying firms. However, the rank appears to play a role when the transactions might lead to higher litigation risk.

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Table 2.1 Distribution of Lobbying Activities

Panel A: Distribution of Lobbying Activities by Year Number of Percent of Percent of Total Year Lobbying Firms Lobbying Firms Number of Firms 1998 405 3.82 6.73 1999 425 4.00 7.42 2000 403 3.80 7.36 2001 420 3.96 8.54 2002 445 4.19 9.88 2003 511 4.82 11.93 2004 516 4.86 11.82 2005 562 5.30 13.00 2006 586 5.52 13.72 2007 594 5.60 13.72 2008 631 5.95 15.07 2009 638 6.01 16.83 2010 606 5.71 17.49 2011 624 5.88 16.77 2012 626 5.90 17.43 2013 627 5.91 17.01 2014 653 6.15 17.12 2015 685 6.45 17.75 2016 655 6.17 17.90 Panel B: Distribution of Lobbying Activities by SIC Divisions Number of Percent of Percent of Total Division Lobbying Firms Lobbying Firms Number of Firms

Agriculture, Forestry and Fishing 3 0.16 9.09 Mining 82 4.36 16.33 Construction 20 1.06 18.69 Manufacturing 761 40.50 21.07 Transportation, Communications, Electric, Gas and Sanitary service 262 13.94 28.82 Wholesale Trade 33 1.76 9.02 Retail Trade 61 3.25 10.41 Finance, Insurance and Real Estate 221 11.76 7.05 Services 348 18.52 15.49 Public Administration 1 0.05 33.33 Non classifiable 87 4.63 12.61

The table presents frequency of lobbying activities over the period from 1998 to

2016 by year and according to the Standard Industrial Classification Divisions. Panel A reports total number of firms involved in lobbying activates each year, percentages of each year lobbying firms in total number of lobbying firms, and percentages of each year lobbying firms in total number of firms in our sample. Panel B reports number and percentages of lobbying firms in each SIC Division, and percentage of lobbying firms in total number of firms in our sample in each SIC Division.

Table 2.2 Spending on Lobbying Activities

Year Mean Median 25th 75th Standard Percentile Percentile Deviation 1998 422,003 120,000 50,000 395,503 834,523 1999 356,337 120,000 60,000 340,000 687,790 2000 394,613 120,000 40,000 370,000 724,235 2001 430,254 140,000 60,000 400,000 867,488 2002 461,977 140,000 60,000 420,000 905,507 2003 493,395 160,000 60,000 436,860 1,012,218 2004 547,599 160,000 73,421 480,000 1,085,851 2005 562,676 180,000 80,000 480,000 1,245,958 2006 629,968 160,000 75,000 470,000 1,633,132 2007 699,202 200,000 80,000 600,000 1,665,803 2008 912,657 210,000 80,000 665,000 2,270,020 2009 828,437 210,000 80,000 660,000 1,801,766 2010 1,030,941 237,500 80,000 790,000 2,879,679 2011 952,640 260,000 87,500 876,427 2,004,288 2012 989,530 250,000 90,813 920,000 2,053,728 2013 1,022,722 280,000 110,000 950,000 2,062,920 2014 921,980 240,000 96,000 850,000 1,819,488 2015 961,616 240,000 90,000 919,000 1,924,983 2016 1,015,533 240,000 118,209 940,000 2,050,529

The table presents a firm’s annual spending on lobbying activities for the period from 1998 to 2016. The sample consists of 10,612 firm-year observations that reflect information regarding lobbying activities by U.S. firms for the period.

Table 2.3 Descriptive Statistics

Lobbying Non-lobbying Difference Firms Firms

BM 0.3779 5.8146 -5.4367 Size 15198.3 1940.0 13258.3 *** Leverage 0.2618 0.2250 0.0368 *** ROA -0.00416 -0.0675 0.0633 *** Prior Year Return 0.0644 0.0493 0.0151 ** Prior Month Return -0.0071 -0.0079 0.000845

Board Size 10.3642 9.1136 1.2506 *** CEO Duality 0.1586 0.1696 -0.0110 ** Board Independence 0.9225 0.8569 0.0656 ***

This table presents mean descriptive statistics for the sample of lobbying versus

non-lobbying firms over the period from 1998-2018. Lobbying, which equals one is a

firm lobbied in the year prior to transaction. BM is the ratio of book value to market

value, and Size is a firm’s market equity. Leverage is the ratio of long-term debt to total

assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year-end.

Prior Year Return and Prior Month Return are market adjusted returns for a firm in the

prior one and twelve months, respectively. Board Size is the number of board members,

and CEO Duality is a dummy variable equal to one if the CEO is the chairman of the

board. Board Independence is a dummy variable equal to one if more than 50 percent of

the board is independent. ***, **, and * denote significance at 1%, 5%, and 10%,

respectively.

Table 2.4 CARs around Insider Transaction Dates and Lobbying

Buys Sells (1) (2) (3) (4) (5) (6) Lobbying -0.0008 -0.0010 -0.0009 -0.0019*** -0.0016*** -0.0016*** (-0.515) (-0.648) (-0.626) (-3.635) (-3.024) (-3.097) BM 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.239) (0.164) (0.200) (0.682) (0.432) (0.379) Size 0.0006 0.0002 0.0001 0.0032*** 0.0027*** 0.0024*** (0.925) (0.228) (0.101) (11.894) (10.122) (8.624) Leverage -0.0098** -0.0012 (-2.278) (-0.768) ROA -0.0044 0.0079*** (-1.515) (7.377) Prior Year Return -0.0063*** -0.0064*** 0.0011*** 0.0008*** 90 (-6.704) (-6.749) (4.111) (3.011) Prior Month Return 0.0502*** 0.0501*** 0.0386*** 0.0380*** (21.315) (21.239) (35.493) (34.764) Board Size 0.0001 0.0002 0.0002 -0.0007*** -0.0006*** -0.0006*** (0.402) (0.488) (0.629) (-5.860) (-4.754) (-4.752) Board Independence 0.0033** 0.0031** 0.0032** -0.0019*** -0.0011** -0.0011** (2.119) (1.992) (2.048) (-3.409) (-2.008) (-2.025) CEO Duality -0.0010 -0.0009 -0.0010 0.0016*** 0.0012** 0.0014*** (-0.768) (-0.704) (-0.726) (3.018) (2.359) (2.698) Constant -0.0092* -0.0040 -0.0013 -0.0111*** -0.0108*** -0.0082*** (-1.665) (-0.700) (-0.215) (-4.839) (-4.755) (-3.474)

Fixed Effects Yes Yes Yes Yes Yes Yes Observations 40,662 40,656 40,549 133,383 133,364 132,716 R2 0.103 0.115 0.115 0.079 0.088 0.089

This table presents OLS regression results on around insider-trade stock performance for the period from 1998 to 2016. The regressions are performed for buys and sells separately. The dependent variables is (-1,1)-day CAR around the transaction date. The key explanatory variable is Lobbying, which equals one is a firm lobbied in the year prior to transaction. BM is the ratio of book value to market value, and Size is natural logarithm of the firm’s market equity. Leverage is the ratio of long-term debt to total assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year- end. Prior Year Return and Prior Month Return are market adjusted returns for a firm in the prior one and twelve months, respectively. Board Size is the number of board members, and CEO Duality is a dummy variable equal to one if the CEO is the chairman of the board. Board Independence is a dummy variable equal to one if more than 50 percent of the board is independent. ***, **, and * denote significance at 1%, 5%, and

10%, respectively.

Table 2.5 CARs around Insider Transaction Dates and Lobbying Spending

Buys Sells (1) (2) (3) (4) (5) (6) Lobbying Spending 0.0003 0.0002 0.0003 -0.0007*** -0.0005** -0.0005** (0.272) (0.224) (0.308) (-2.733) (-1.971) (-1.964) BM 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.234) (0.158) (0.193) (0.674) (0.423) (0.371) Size 0.0006 0.0001 0.0000 0.0031*** 0.0027*** 0.0023*** (0.858) (0.146) (0.009) (11.776) (9.989) (8.487) Leverage -0.0100** -0.0013 (-2.304) (-0.828) ROA -0.0043 0.0078*** (-1.499) (7.290) 92 Prior Year Return -0.0063*** -0.0064*** 0.0011*** 0.0008***

(-6.687) (-6.735) (4.150) (3.048) Prior Month Return 0.0502*** 0.0501*** 0.0386*** 0.0380*** (21.315) (21.238) (35.497) (34.768) Board Size 0.0001 0.0002 0.0002 -0.0007*** -0.0006*** -0.0006*** (0.432) (0.520) (0.666) (-5.956) (-4.829) (-4.831) Board Independence 0.0032** 0.0030* 0.0031** -0.0020*** -0.0012** -0.0012** (2.077) (1.939) (1.996) (-3.652) (-2.213) (-2.238) CEO Duality -0.0009 -0.0008 -0.0009 0.0016*** 0.0012** 0.0014*** (-0.717) (-0.645) (-0.666) (3.002) (2.367) (2.707) Constant -0.0091* -0.0038 -0.0011 -0.0110*** -0.0107*** -0.0080*** (-1.648) (-0.675) (-0.178) (-4.792) (-4.691) (-3.403) Fixed Effects Yes Yes Yes Yes Yes Yes Observations 40,662 40,656 40,549 133,383 133,364 132,716 R2 0.103 0.115 0.115 0.079 0.088 0.089

10%, respectively.

Table 2.6 Purchase Monthly Returns: Lobbying

One Month Two Months Three Months (1) (2) (3) (4) (5) (6) (7) (8) (9) Lobbying 0.0095*** 0.0092*** 0.0099*** 0.0130*** 0.0119*** 0.0138*** 0.0099 0.0078 0.0104* (3.170) (3.080) (3.321) (2.780) (2.576) (2.968) (1.581) (1.262) (1.685) BM -0.0000 0.0000 0.0000 -0.0026*** -0.0035*** -0.0040*** -0.0020** -0.0030*** -0.0041*** (-0.111) (0.450) (0.417) (-3.998) (-5.339) (-6.086) (-2.086) (-3.187) (-4.362) Size -0.0000 0.0083*** 0.0059*** 0.0172*** 0.0334*** 0.0274*** 0.0285*** 0.0486*** 0.0370*** (-0.021) (5.814) (3.939) (8.024) (15.067) (11.750) (9.928) (16.468) (11.931) Leverage -0.0446*** -0.0875*** -0.1408*** (-5.099) (-6.409) (-7.752) ROA 0.0087 0.0388*** 0.1045*** (1.501) (4.247) (8.620) 94 Prior Year Return -0.0279*** -0.0289*** -0.0635*** -0.0664*** -0.0799*** -0.0856*** (-14.635) (-15.086) (-21.452) (-22.291) (-20.105) (-21.432) Prior Month Return -0.0962*** -0.0972*** -0.1316*** -0.1352*** -0.1685*** -0.1762*** (-20.194) (-20.346) (-17.772) (-18.218) (-17.098) (-17.866) Board Size -0.0026*** -0.0032*** -0.0030*** -0.0041*** -0.0053*** -0.0047*** -0.0059*** -0.0075*** -0.0065*** (-4.030) (-5.022) (-4.626) (-4.013) (-5.259) (-4.713) (-4.359) (-5.612) (-4.849) Board Independence -0.0059* -0.0055* -0.0053* -0.0221*** -0.0217*** -0.0207*** -0.0295*** -0.0287*** -0.0250*** (-1.898) (-1.784) (-1.685) (-4.535) (-4.487) (-4.275) (-4.533) (-4.461) (-3.876) CEO Duality 0.0006 0.0011 0.0009 0.0066 0.0077* 0.0065 0.0148*** 0.0163*** 0.0134** (0.238) (0.400) (0.323) (1.587) (1.853) (1.578) (2.670) (2.962) (2.435) Constant 0.0523*** -0.0079 0.0177 39,515 39,509 39,404 -0.0801*** -0.2221*** -0.1153*** (4.680) (-0.693) (1.440) 0.203 0.218 0.220 (-3.440) (-9.363) (-4.540) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 40,662 40,656 40,549 39,515 39,509 39,404 32,921 32,915 32,821 R2 0.187 0.199 0.200 0.203 0.218 0.220 0.239 0.255 0.259

This table presents OLS regression results on post insider purchase transaction stock performance for the period from 1998 to 2016. The dependent variables are one, two, and three months CARs following the transaction date. The key explanatory variable is Lobbying, which equals one is a firm lobbied in the year prior to transaction. BM is the ratio of book value to market value, and Size is natural logarithm of the firm’s market equity. Leverage is the ratio of long-term debt to total assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year-end. Prior Year Return and Prior Month

Return are market adjusted returns for a firm in the prior one and twelve months, respectively. Board Size is the number of board members, and CEO Duality is a dummy variable equal to one if the CEO is the chairman of the board. Board Independence is a dummy variable equal to one if more than 50 percent of the board is independent. ***,

**, and * denote significance at 1%, 5%, and 10%, respectively.

Table 2.7 Sale Monthly Returns: Lobbying

One Month Two Months Three Months (1) (2) (3) (4) (5) (6) (7) (8) (9) Lobbying -0.0080*** -0.0085*** -0.0085*** -0.0140*** -0.0153*** -0.0156*** -0.0134*** -0.0155*** -0.0154*** (-6.637) (-7.031) (-7.040) (-8.190) (-8.941) (-9.125) (-5.935) (-6.842) (-6.838) BM 0.0000* 0.0000* 0.0000* 0.0000** 0.0000** 0.0000** -0.0217*** -0.0246*** -0.0276*** (1.819) (1.893) (1.697) (2.217) (2.391) (2.084) (-12.313) (-13.916) (-15.412) Size 0.0146*** 0.0154*** 0.0130*** 0.0250*** 0.0269*** 0.0217*** 0.0263*** 0.0290*** 0.0211*** (23.654) (24.611) (20.332) (28.484) (30.417) (23.977) (22.046) (24.197) (17.111) Leverage -0.0005 -0.0016 -0.0352*** (-0.130) (-0.311) (-5.138) ROA 0.0460*** 0.0939*** 0.1204*** (18.581) (26.916) (25.462) Prior Year Return -0.0053*** -0.0068*** -0.0124*** -0.0147*** -0.0202*** -0.0235*** (-8.576) (-10.925) (-14.174) (-16.693) (-17.459) (-20.122)

96 Prior Month Return -0.0039 -0.0069*** -0.0368*** -0.0425*** -0.0664*** -0.0729***

(-1.524) (-2.731) (-10.284) (-11.848) (-14.038) (-15.422) Board Size -0.0024*** -0.0026*** -0.0026*** -0.0052*** -0.0057*** -0.0056*** -0.0067*** -0.0074*** -0.0071*** (-8.399) (-8.975) (-8.806) (-12.677) (-13.835) (-13.605) (-12.232) (-13.611) (-13.085) Board Independence -0.0048*** -0.0057*** -0.0056*** -0.0072*** -0.0097*** -0.0080*** -0.0130*** -0.0168*** -0.0142*** (-3.805) (-4.508) (-4.402) (-4.054) (-5.437) (-4.472) (-5.498) (-7.116) (-6.002) CEO Duality -0.0046*** -0.0043*** -0.0034*** -0.0104*** -0.0095*** -0.0075*** -0.0109*** -0.0095*** -0.0077*** (-3.783) (-3.515) (-2.734) (-5.993) (-5.466) (-4.341) (-4.739) (-4.150) (-3.367) Constant -0.0841*** -0.0864*** -0.0701*** -0.1353*** -0.1403*** -0.1049*** -0.1163*** -0.1201*** -0.0591*** (-15.884) (-16.293) (-12.798) (-17.993) (-18.644) (-13.555) (-11.291) (-11.676) (-5.550) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 133,373 133,354 132,706 129,428 129,409 128,782 107,509 107,493 106,974 R2 0.097 0.098 0.100 0.120 0.122 0.127 0.151 0.155 0.160

This table presents OLS regression results on post insider sale transaction stock performance for the period from 1998 to

2016. The dependent variables is one, two, and three months CARs following the transaction date. The key explanatory variable

Lobbying, which equals one is a firm lobbied in the year prior to transaction. BM is the ratio of book value to market value, and Size is natural logarithm of the firm’s market equity. Leverage is the ratio of long-term debt to total assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year-end. Prior Year Return and Prior

Month Return are market adjusted returns for a firm in the prior one and twelve months, respectively. Board Size is the number of board members, and CEO Duality is a dummy variable equal to one if the CEO is the chairman of the board. Board Independence is a dummy variable equal to one if more than 50 percent of the board is independent. ***,

**, and * denote significance at 1%, 5%, and 10%, respectively.

Table 2.8 Purchase Monthly Returns: Lobbying Spending

One Month Two Months Three Months (1) (2) (3) (4) (5) (6) (7) (8) (9) Lobbying Spending -0.0023 -0.0035* -0.0030 -0.0029 -0.0052* -0.0042 -0.0031 -0.0062* -0.0044 (-1.103) (-1.700) (-1.447) (-0.957) (-1.737) (-1.408) (-0.853) (-1.715) (-1.220) BM 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 (0.093) (0.652) (0.581) (0.181) (0.879) (0.799) (0.187) (0.967) (0.805) Size 0.0033** 0.0120*** 0.0092*** 0.0176*** 0.0335*** 0.0280*** 0.0271*** 0.0484*** 0.0379*** (2.271) (8.035) (5.820) (8.393) (15.458) (12.305) (10.645) (18.498) (13.816) Leverage -0.0378*** -0.0812*** -0.1252*** (-4.086) (-6.068) (-7.766) ROA 0.0213*** 0.0345*** 0.0896*** (3.461) (3.875) (8.356) Prior Year Return -0.0309*** -0.0322*** -0.0629*** -0.0655*** -0.0824*** -0.0871*** (-15.335) (-15.879) (-21.582) (-22.330) (-23.415) (-24.613)

98 Prior Month Return -0.0922*** -0.0936*** -0.1291*** -0.1321*** -0.1868*** -0.1925***

(-18.301) (-18.529) (-17.716) (-18.083) (-21.249) (-21.879) Board Size -0.0026*** -0.0033*** -0.0030*** -0.0041*** -0.0054*** -0.0049*** -0.0054*** -0.0070*** -0.0061*** (-3.812) (-4.822) (-4.437) (-4.161) (-5.448) (-4.905) (-4.471) (-5.906) (-5.132) Board Independence -0.0052 -0.0049 -0.0043 -0.0209*** -0.0204*** -0.0193*** -0.0273*** -0.0265*** -0.0240*** (-1.591) (-1.483) (-1.307) (-4.371) (-4.300) (-4.059) (-4.704) (-4.627) (-4.173) CEO Duality 0.0007 0.0012 0.0006 0.0053 0.0062 0.0052 0.0127** 0.0138*** 0.0114** (0.265) (0.415) (0.195) (1.297) (1.521) (1.268) (2.555) (2.815) (2.321) Constant 0.0289** -0.0340*** -0.0068 -0.0340** -0.1466*** -0.0928*** -0.0753*** -0.2273*** -0.1316*** (2.450) (-2.814) (-0.521) (-1.984) (-8.384) (-4.949) (-3.634) (-10.766) (-5.821) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 40,662 40,656 40,549 40,654 40,648 40,541 40,530 40,524 40,417 R2 0.183 0.194 0.194 0.202 0.217 0.218 0.233 0.251 0.254

Lobbying Spending, which equals the average amounts a firm spent on lobbying in the year prior four years. BM is the ratio of book value to market value, and Size is natural logarithm of the firm’s market equity. Leverage is the ratio of long-term debt to total assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year-end.

Prior Year Return and Prior Month Return are market adjusted returns for a firm in the prior one and twelve months, respectively. Board Size is the number of board members, and CEO Duality is a dummy variable equal to one if the CEO is the chairman of the board. Board Independence is a dummy variable equal to one if more than 50 percent of the board is independent. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

Table 2.9 Sale Monthly Returns: Lobbying Spending

One Month Two Months Three Months (1) (2) (3) (4) (5) (6) (7) (8) (9) Lobbying Spending -0.0028*** -0.0031*** -0.0031*** -0.0023*** -0.0030*** -0.0027*** -0.0031*** -0.0043*** -0.0039*** (-4.713) (-5.138) (-5.197) (-2.739) (-3.578) (-3.172) (-3.067) (-4.231) (-3.841) BM 0.0000* 0.0000* 0.0000* 0.0000** 0.0000** 0.0000** 0.0000** 0.0000*** 0.0000** (1.801) (1.875) (1.681) (2.167) (2.337) (2.028) (2.394) (2.637) (2.340) Size 0.0145*** 0.0152*** 0.0129*** 0.0241*** 0.0260*** 0.0207*** 0.0296*** 0.0327*** 0.0258*** (23.409) (24.360) (20.100) (27.888) (29.829) (23.222) (28.327) (31.084) (23.993) Leverage -0.0009 -0.0033 -0.0226*** (-0.235) (-0.648) (-3.713) ROA 0.0454*** 0.0936*** 0.1143*** (18.379) (27.196) (27.525) Prior Year Return -0.0053*** -0.0068*** -0.0122*** -0.0145*** -0.0196*** -0.0225*** (-8.509) (-10.870) (-14.134) (-16.699) (-18.906) (-21.546) 100 Prior Month Return -0.0038 -0.0069*** -0.0360*** -0.0415*** -0.0670*** -0.0732*** (-1.519) (-2.728) (-10.185) (-11.736) (-15.727) (-17.164)

Board Size -0.0025*** -0.0027*** -0.0026*** -0.0053*** -0.0058*** -0.0057*** -0.0071*** -0.0078*** -0.0077*** (-8.571) (-9.153) (-8.996) (-13.132) (-14.291) (-14.063) (-14.416) (-16.026) (-15.686) Board Independence -0.0053*** -0.0063*** -0.0062*** -0.0079*** -0.0105*** -0.0087*** -0.0159*** -0.0201*** -0.0181*** (-4.249) (-4.967) (-4.864) (-4.503) (-5.928) (-4.960) (-7.512) (-9.463) (-8.524) CEO Duality -0.0046*** -0.0043*** -0.0034*** -0.0105*** -0.0096*** -0.0076*** -0.0114*** -0.0099*** -0.0077*** (-3.780) (-3.525) (-2.753) (-6.123) (-5.618) (-4.439) (-5.485) (-4.809) (-3.742) Constant -0.0836*** -0.0858*** -0.0696*** -0.1302*** -0.1352*** -0.0992*** -0.1491*** -0.1571*** -0.1067*** (-15.772) (-16.182) (-12.691) (-17.549) (-18.225) (-12.982) (-16.673) (-17.590) (-11.606) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 133,373 133,354 132,706 133,333 133,314 132,666 132,628 132,610 131,965 R2 0.097 0.098 0.100 0.119 0.121 0.126 0.147 0.151 0.156

This table presents OLS regression results on post insider sale transaction stock

performance for the period from 1998 to 2016. The dependent variables are one, two, and

three months CARs following the transaction date. The key explanatory variable

Lobbying Spending which equals the average amounts a firm spent on lobbying in the

year prior four years n. BM is the ratio of book value to market value, and Size is natural

logarithm of the firm’s market equity. Leverage is the ratio of long-term debt to total

assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year-end.

Prior Year Return and Prior Month Return are market adjusted returns for a firm in the

prior one and twelve months, respectively. Board Size is the number of board members,

and CEO Duality is a dummy variable equal to one if the CEO is the chairman of the

101 board. Board Independence is a dummy variable equal to one if more than 50 percent of

the board is independent. ***, **, and * denote significance at 1%, 5%, and 10%,

respectively.

Table 2.10 CARs around Insider Transaction Dates and Lobbying: Executive Rank

Buys Sells (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec) Lobbying -0.0009 -0.0056 -0.0004 0.0003 -0.0014* -0.0019*** (-0.209) (-1.636) (-0.210) (0.262) (-1.685) (-2.863) BM 0.0015 0.0000 0.0001 -0.0061*** 0.0000 0.0016*** (0.770) (0.690) (0.274) (-3.709) (0.423) (6.311) Size 0.0048** 0.0002 -0.0002 0.0010 0.0025*** 0.0025*** (2.107) (0.138) (-0.274) (1.507) (5.583) (6.960) Leverage -0.0017 -0.0223** -0.0103** -0.0113*** 0.0001 -0.0018 (-0.134) (-2.286) (-2.024) (-3.074) (0.034) (-0.879) ROA -0.0208*** -0.0227*** 0.0018 0.0071*** 0.0086*** 0.0057*** (-2.669) (-3.285) (0.522) (2.580) (5.352) (3.836)

102 Prior Year Return -0.0094*** -0.0057*** -0.0058*** 0.0001 0.0012*** 0.0001

(-3.803) (-2.842) (-5.149) (0.249) (2.941) (0.345) Prior Month Return 0.0338*** 0.0394*** 0.0542*** 0.0124*** 0.0287*** 0.0410*** (5.661) (8.374) (19.252) (5.255) (16.805) (28.752) Board Size -0.0003 0.0003 0.0002 -0.0004 -0.0006*** -0.0005*** (-0.311) (0.467) (0.581) (-0.993) (-2.923) (-3.265) Board Independence 0.0078* 0.0007 0.0037** -0.0015 -0.0013 -0.0013* (1.709) (0.199) (2.041) (-1.021) (-1.335) (-1.956) CEO Duality -0.0038 -0.0014 -0.0003 -0.0004 0.0011 0.0013** (-0.963) (-0.464) (-0.166) (-0.299) (1.252) (1.960) Constant -0.0422** -0.0049 0.0008 0.0054 -0.0080** -0.0109*** (-2.033) (-0.343) (0.107) (0.909) (-2.090) (-3.505) Fixed Effects Observations 7,684 12,706 27,843 28,830 54,882 77,834 R2 0.275 0.223 0.147 0.271 0.187 0.104

This table presents OLS regression results on around insider-trade stock performance for the period from 1998 to 2016. The regressions are performed for buys and sells by executives of different ranks separately. The dependent variables is (-1,1)- day CAR around the transaction date. The key explanatory variable is Lobbying, which equals one is a firm lobbied in the year prior to transaction. BM is the ratio of book value to market value, and Size is natural logarithm of the firm’s market equity. Leverage is the ratio of long-term debt to total assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year-end. Prior Year Return and Prior Month Return are market adjusted returns for a firm in the prior one and twelve months, respectively. Board Size is the number of board members, and CEO Duality is a dummy variable equal to one if the

CEO is the chairman of the board. Board Independence is a dummy variable equal to one if more than 50 percent of the board is independent. ***, **, and * denote significance at

1%, 5%, and 10%, respectively.

103

Table 2.11 CARs around Insider Transaction Dates and Lobbying Spending: Executive Rank

Buys Sells (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec)

Lobbying Spending -0.0077 0.0023 0.0002 0.0002 -0.0005 -0.0007* (-1.080) (0.609) (0.155) (0.256) (-1.261) (-1.933) BM 0.0016 0.0000 0.0001 -0.0061*** 0.0000 0.0016*** (0.808) (0.813) (0.264) (-3.708) (0.421) (6.315) Size 0.0048** 0.0009 -0.0003 0.0010 0.0025*** 0.0025*** (2.146) (0.571) (-0.316) (1.487) (5.545) (6.823) Leverage -0.0015 -0.0227** -0.0103** -0.0113*** 0.0000 -0.0019 (-0.121) (-2.357) (-2.033) (-3.070) (0.015) (-0.922) ROA -0.0210*** -0.0213*** 0.0018 0.0071*** 0.0086*** 0.0055***

104 (-2.702) (-3.134) (0.531) (2.588) (5.330) (3.719)

Prior Year Return -0.0094*** -0.0068*** -0.0058*** 0.0002 0.0012*** 0.0001 (-3.808) (-3.538) (-5.140) (0.254) (2.940) (0.395) Prior Month Return 0.0337*** 0.0389*** 0.0542*** 0.0124*** 0.0287*** 0.0411*** (5.638) (8.355) (19.252) (5.259) (16.775) (28.797) Board Size -0.0003 0.0002 0.0002 -0.0004 -0.0006*** -0.0005*** (-0.327) (0.264) (0.595) (-0.985) (-2.948) (-3.391) Board Independence 0.0080* 0.0025 0.0037** -0.0014 -0.0014 -0.0014** (1.761) (0.731) (2.028) (-1.001) (-1.487) (-2.086) CEO Duality -0.0039 -0.0021 -0.0002 -0.0004 0.0010 0.0014** (-0.984) (-0.737) (-0.149) (-0.279) (1.204) (1.993) Constant -0.0427** -0.0047 0.0009 0.0054 -0.0080** -0.0106*** (-2.066) (-0.362) (0.128) (0.917) (-2.089) (-3.413) Fixed Effects Yes Yes Yes Yes Yes Yes Observations 7,684 12,706 27,843 28,830 54,882 77,834 R2 0.275 0.221 0.147 0.271 0.187 0.104

Spending, which equals the amount a firm spent on lobbying in the year prior to transaction. BM is the ratio of book value to market value, and Size is natural logarithm of the firm’s market equity. Leverage is the ratio of long-term debt to total assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year-end. Prior Year

Return and Prior Month Return are market adjusted returns for a firm in the prior one and twelve months, respectively. Board Size is the number of board members, and CEO

Duality is a dummy variable equal to one if the CEO is the chairman of the board. Board

Independence is a dummy variable equal to one if more than 50 percent of the board is independent. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

105

Table 2.12 Purchase Monthly Returns: Executive Rank

One Month Two Months Three Months (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec) Lobbying 0.0261*** 0.0169** 0.0086** 0.0220 0.0252** 0.0102* 0.0069 0.0142 0.0081 (2.621) (2.472) (2.362) (1.488) (2.456) (1.919) (0.352) (1.046) (1.139) BM -0.0088* 0.0000 -0.0103*** -0.0222*** -0.0032*** -0.0061*** -0.0186** -0.0024 -0.0155*** (-1.935) (1.009) (-9.058) (-3.322) (-2.647) (-3.662) (-2.094) (-1.584) (-6.838) Size 0.0129** 0.0200*** 0.0044** 0.0424*** 0.0504*** 0.0256*** 0.0615*** 0.0706*** 0.0280*** (2.518) (5.882) (2.328) (5.543) (9.702) (9.276) (6.145) (10.356) (7.555) Leverage -0.0729** -0.0606*** -0.0380*** -0.1790*** -0.1171*** -0.0675*** -0.1401** -0.1324*** -0.1561*** (-2.524) (-2.916) (-3.518) (-4.172) (-3.751) (-4.283) (-2.460) (-3.235) (-7.399) ROA -0.0385** -0.0446*** 0.0351*** -0.0607** -0.0727*** 0.0409*** -0.0011 -0.0716*** 0.0927*** (-2.176) (-3.041) (4.857) (-2.122) (-3.128) (3.861) (-0.033) (-2.611) (6.403)

106 Prior Year Return -0.0432*** -0.0367*** -0.0330*** -0.0948*** -0.0783*** -0.0609*** -0.1216*** -0.0882*** -0.0860***

(-7.664) (-8.857) (-13.868) (-11.382) (-12.678) (-17.572) (-11.060) (-10.810) (-18.477) Prior Month Return -0.0622*** -0.0836*** -0.0964*** -0.1059*** -0.1561*** -0.1148*** -0.1646*** -0.2012*** -0.1537*** (-4.590) (-8.325) (-16.167) (-5.252) (-10.370) (-13.202) (-6.007) (-9.971) (-13.403) Board Size -0.0037* -0.0044*** -0.0018** -0.0052* -0.0066*** -0.0031*** -0.0073* -0.0085*** -0.0035** (-1.748) (-2.726) (-2.295) (-1.651) (-2.770) (-2.743) (-1.745) (-2.677) (-2.342) Board Independence -0.0066 -0.0095 -0.0044 -0.0247 -0.0315*** -0.0238*** -0.0692*** -0.0538*** -0.0149** (-0.639) (-1.304) (-1.137) (-1.606) (-2.899) (-4.250) (-3.390) (-3.715) (-2.013) CEO Duality 0.0177** 0.0076 -0.0006 0.0192 0.0190** 0.0009 0.0349** 0.0275** -0.0022 (1.978) (1.208) (-0.176) (1.449) (2.030) (0.198) (1.981) (2.214) (-0.346) Constant 0.0214 -0.0551** 0.0167 -0.1297** -0.2018*** -0.0975*** -0.2057** -0.2910*** -0.0822*** (0.455) (-1.960) (1.069) (-2.125) (-4.735) (-4.272) (-2.522) (-5.121) (-2.692) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 7,684 12,706 27,843 7,484 12,351 27,053 6,210 10,262 22,559 R2 0.336 0.280 0.240 0.350 0.296 0.263 0.387 0.364 0.304

This table presents OLS regression results on post insider purchase transaction stock performance. The dependent variables are one, two, and three months CARs following the transaction date. The regressions are performed for different ranks separately. The key explanatory variable is Lobbying, which equals one is a firm lobbied in the year prior to transaction. BM is the ratio of book value to market value, and Size is natural logarithm of the firm’s market equity. Leverage is the ratio of long-term debt to total assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year- end. Prior Year Return and Prior Month Return are market adjusted returns for a firm in the prior one and twelve months, respectively. Board Size is the number of board members, and CEO Duality is a dummy variable equal to one if the CEO is the chairman of the board. Board Independence is a dummy variable equal to one if more than 50 percent of the board is independent. ***, **, and * denote significance at 1%, 5%, and

10%, respectively.

107

Table 2.13 Sale Monthly Returns: Executive Rank

One Month Two Months Three Months (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec) Lobbying -0.0066** -0.0088*** -0.0096*** -0.0110*** -0.0133*** -0.0173*** -0.0328*** -0.0258*** -0.0128*** (-2.197) (-4.500) (-6.018) (-2.646) (-4.836) (-7.675) (-6.101) (-7.169) (-4.373) BM -0.0153*** 0.0000** -0.0125*** -0.0265*** 0.0000*** -0.0209*** -0.0563*** -0.0224*** -0.0384*** (-4.004) (2.048) (-10.320) (-4.995) (2.603) (-11.017) (-8.413) (-7.580) (-16.170) Size 0.0191*** 0.0157*** 0.0092*** 0.0368*** 0.0280*** 0.0151*** 0.0427*** 0.0341*** 0.0149*** (12.079) (15.261) (10.392) (16.675) (19.323) (11.800) (15.175) (17.780) (9.000) Leverage -0.0218** -0.0076 -0.0033 -0.0745*** -0.0288*** -0.0065 -0.1301*** -0.0791*** -0.0120 (-2.546) (-1.311) (-0.681) (-6.268) (-3.523) (-0.936) (-8.537) (-7.290) (-1.316) ROA 0.0729*** 0.0546*** 0.0442*** 0.1381*** 0.1149*** 0.0663*** 0.1349*** 0.1056*** 0.0793*** (11.444) (14.582) (12.542) (15.635) (21.813) (13.238) (12.387) (15.192) (11.326)

108 Prior Year Return -0.0081*** -0.0060*** -0.0092*** -0.0131*** -0.0108*** -0.0182*** -0.0156*** -0.0137*** -0.0272***

(-5.846) (-6.281) (-10.721) (-6.805) (-8.094) (-14.931) (-6.279) (-7.833) (-17.030) Prior Month Return -0.0352*** -0.0202*** -0.0140*** -0.0898*** -0.0867*** -0.0418*** -0.0880*** -0.0926*** -0.0876*** (-6.439) (-5.095) (-4.165) (-11.828) (-15.509) (-8.736) (-9.072) (-12.739) (-14.043) Board Size -0.0066*** -0.0040*** -0.0013*** -0.0131*** -0.0084*** -0.0038*** -0.0184*** -0.0119*** -0.0047*** (-8.011) (-8.283) (-3.559) (-11.347) (-12.272) (-7.217) (-12.285) (-13.300) (-6.815) Board Independence -0.0174*** -0.0109*** -0.0036** -0.0314*** -0.0159*** -0.0063*** -0.0360*** -0.0233*** -0.0157*** (-5.136) (-4.998) (-2.248) (-6.686) (-5.171) (-2.764) (-5.915) (-5.800) (-5.330) CEO Duality -0.0159*** -0.0080*** 0.0001 -0.0415*** -0.0197*** 0.0003 -0.0585*** -0.0275*** 0.0016 (-5.018) (-3.997) (0.075) (-9.397) (-6.974) (0.139) (-10.250) (-7.387) (0.529) -0.0594*** -0.0721*** -0.0446*** -0.1074*** -0.1178*** -0.0539*** -0.0699*** -0.1006*** -0.0232 Constant (-4.320) (-8.104) (-5.836) (-5.601) (-9.395) (-4.896) (-2.858) (-6.002) (-1.632) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 28,828 54,877 77,829 28,007 53,292 75,490 23,264 44,246 62,728 R2 0.181 0.144 0.145 0.236 0.185 0.167 0.288 0.217 0.204

10%, respectively.

109

Table 2.14 Purchase Monthly Returns: Executive Rank and Lobbying Spending

One Month Two Months Three Months (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec) Lobbying Spending -0.0031 -0.0009 -0.0033 -0.0123 -0.0060 -0.0032 -0.0311 -0.0162 -0.0021 (-0.193) (-0.111) (-1.564) (-0.511) (-0.495) (-1.072) (-1.007) (-1.048) (-0.509) BM -0.0077* 0.0000 -0.0102*** -0.0211*** -0.0032*** -0.0059*** -0.0178** -0.0025 -0.0154*** (-1.697) (1.031) (-8.947) (-3.169) (-2.715) (-3.574) (-2.003) (-1.612) (-6.785) Size 0.0146*** 0.0206*** 0.0052*** 0.0440*** 0.0516*** 0.0265*** 0.0624*** 0.0715*** 0.0287*** (2.858) (6.086) (2.791) (5.780) (9.948) (9.682) (6.262) (10.505) (7.792) Leverage -0.0662** -0.0582*** -0.0368*** -0.1727*** -0.1135*** -0.0661*** -0.1367** -0.1303*** -0.1550*** (-2.297) (-2.803) (-3.406) (-4.040) (-3.637) (-4.195) (-2.406) (-3.187) (-7.342) ROA -0.0388** -0.0444*** 0.0344*** -0.0614** -0.0728*** 0.0402*** -0.0021 -0.0722*** 0.0922*** (-2.192) (-3.021) (4.763) (-2.147) (-3.125) (3.789) (-0.066) (-2.629) (6.366)

110 Prior Year Return -0.0429*** -0.0368*** -0.0331*** -0.0945*** -0.0785*** -0.0610*** -0.1216*** -0.0884*** -0.0861***

(-7.602) (-8.871) (-13.935) (-11.347) (-12.697) (-17.619) (-11.058) (-10.827) (-18.506) Prior Month Return -0.0623*** -0.0840*** -0.0964*** -0.1061*** -0.1567*** -0.1147*** -0.1653*** -0.2021*** -0.1536*** (-4.590) (-8.358) (-16.159) (-5.261) (-10.410) (-13.196) (-6.033) (-10.017) (-13.398) Board Size -0.0038* -0.0046*** -0.0019** -0.0054* -0.0070*** -0.0032*** -0.0073* -0.0088*** -0.0035** (-1.798) (-2.866) (-2.449) (-1.695) (-2.934) (-2.851) (-1.754) (-2.772) (-2.391) Board Independence -0.0051 -0.0080 -0.0035 -0.0232 -0.0293*** -0.0228*** -0.0674*** -0.0520*** -0.0142* (-0.494) (-1.104) (-0.928) (-1.511) (-2.699) (-4.097) (-3.295) (-3.597) (-1.923) CEO Duality 0.0165* 0.0060 -0.0012 0.0181 0.0164* 0.0003 0.0341* 0.0253** -0.0026 (1.839) (0.957) (-0.355) (1.365) (1.765) (0.060) (1.939) (2.051) (-0.412) Constant 0.0100 -0.0557** 0.0132 -0.1372** -0.2033*** -0.1015*** -0.2111*** -0.2923*** -0.0853*** (0.212) (-1.979) (0.845) (-2.252) (-4.768) (-4.459) (-2.592) (-5.143) (-2.802) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 7,684 12,706 27,843 7,484 12,351 27,053 6,210 10,262 22,559 R2 0.335 0.279 0.240 0.350 0.296 0.263 0.387 0.364 0.304

This table presents OLS regression results on post insider sale transaction stock performance for the period from 1998 to 2016. The dependent variables are one, two, and three months CARs following the transaction date. The regressions are performed for different ranks separately. The key explanatory variable Lobbying Spending, which equals the amount a firm spent on lobbying in the year prior four years. BM is the ratio of book value to market value, and Size is natural logarithm of the firm’s market equity.

Leverage is the ratio of long-term debt to total assets for the prior fiscal year. ROA is the return on assets at the prior fiscal year-end. Prior Year Return and Prior Month Return are market adjusted returns for a firm in the prior one and twelve months, respectively.

Board Size is the number of board members, and CEO Duality is a dummy variable equal to one if the CEO is the chairman of the board. Board Independence is a dummy variable equal to one if more than 50 percent of the board is independent. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

111

Table 2.15 Sale Monthly Returns: Executive Rank and Lobbying Spending

One Month Two Months Three Months (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec) (C) (Exec) (Non-Exec) Lobbying Spending -0.0042*** -0.0035*** -0.0029*** -0.0024 -0.0027** -0.0025** -0.0053* -0.0046*** -0.0035** (-2.649) (-3.835) (-3.473) (-1.068) (-2.132) (-2.079) (-1.733) (-2.632) (-2.230) BM -0.0184*** 0.0000** -0.0160*** -0.0337*** 0.0000*** -0.0240*** -0.0642*** -0.0244*** -0.0367*** (-4.938) (2.076) (-13.214) (-6.463) (2.619) (-12.651) (-9.433) (-8.120) (-15.556) Size 0.0193*** 0.0167*** 0.0087*** 0.0351*** 0.0285*** 0.0142*** 0.0379*** 0.0309*** 0.0166*** (12.341) (16.148) (9.809) (16.062) (19.860) (11.176) (13.199) (15.766) (10.112) Leverage -0.0147* -0.0018 -0.0046 -0.0632*** -0.0183** -0.0021 -0.1102*** -0.0689*** -0.0143 (-1.756) (-0.310) (-0.930) (-5.398) (-2.264) (-0.305) (-7.119) (-6.241) (-1.575) ROA 0.0636*** 0.0489*** 0.0363*** 0.1348*** 0.1142*** 0.0551*** 0.1579*** 0.1298*** 0.0765*** (10.186) (13.046) (10.336) (15.482) (21.949) (11.028) (14.259) (18.328) (11.009)

112 Prior Year Return -0.0085*** -0.0061*** -0.0097*** -0.0151*** -0.0124*** -0.0197*** -0.0222*** -0.0197*** -0.0310***

(-6.263) (-6.434) (-11.325) (-7.904) (-9.420) (-16.248) (-8.782) (-11.096) (-19.469) Prior Month Return -0.0204*** -0.0135*** -0.0146*** -0.0572*** -0.0653*** -0.0386*** -0.0668*** -0.0694*** -0.0885*** (-3.818) (-3.400) (-4.336) (-7.649) (-11.835) (-8.084) (-6.762) (-9.375) (-14.278) Board Size -0.0062*** -0.0039*** -0.0014*** -0.0117*** -0.0077*** -0.0040*** -0.0155*** -0.0106*** -0.0047*** (-7.619) (-7.932) (-3.767) (-10.282) (-11.355) (-7.597) (-10.212) (-11.556) (-6.823) Board Independence -0.0164*** -0.0110*** -0.0028* -0.0288*** -0.0144*** -0.0048** -0.0309*** -0.0186*** -0.0129*** (-4.979) (-5.027) (-1.727) (-6.242) (-4.784) (-2.123) (-5.017) (-4.560) (-4.406) CEO Duality -0.0152*** -0.0079*** -0.0002 -0.0364*** -0.0166*** 0.0002 -0.0461*** -0.0207*** 0.0001 (-4.880) (-3.899) (-0.119) (-8.341) (-5.928) (0.095) (-7.923) (-5.440) (0.033) Constant -0.0671*** -0.0849*** -0.0406*** -0.1134*** -0.1374*** -0.0515*** -0.0752*** -0.1038*** -0.0453*** (-4.964) (-9.508) (-5.338) (-5.989) (-11.076) (-4.688) (-3.019) (-6.067) (-3.206) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 28,828 54,877 77,829 28,007 53,292 75,490 23,264 44,246 62,728 R2 0.176 0.144 0.134 0.222 0.182 0.162 0.276 0.221 0.201

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IMPACT OF ILLEGAL INSIDER TRADING

Introduction

Buying or selling securities based on information considered material and non- public is established to be illegal and is heavily regulated in the U.S. The restrictions were first introduced with the Securities and Exchange Act of 1934. They were later complemented by the Insider Trading Sanctions Act of 1984 and the Insider Trading and

Securities Fraud Enforcement Act of 1988. Not only these laws increased penalties for trading on private information for up to three times gains earned or losses avoided, they also extended the responsibility to control persons increasing the number of parties that could be charged. Court interpretations of federal statues and decisions add to the regulations prohibiting insider trading. Careful analysis of the regulations and theories behind them, however, indicates that they are associated with certain doctrinal and policy issues that in many cases lack sound economic theory. Nevertheless, a more important question is regarding the actual impact of what they regulate – impact of trading based on private information (Bainbridge, 2000, 2013b; Carlton and Fischel, 1983).

Scholars from a range of disciplines have debated the regulation of insider trading for decades. Bainbridge (2013a) and Carlton and Fischel (1983) support the deregulation of insider trading as a way to improve price informativeness. They suggest that insider trading could be a timely and safer alternative to public disclosure of sensitive information.

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In addition, Manne (2009) advocates allowing insiders-entrepreneurs to trade based on the information they create as a way to encourage and compensate for their special contributions to the firms’ performance. Bainbridge (2000) argues that letting managers benefit from incremental information can be a less costly and more appropriate way to incentivize the production of new and valuable information than going through costly compensation negotiations.

Other scholars contend that insider trading opportunities can lead to moral hazard issue as insiders attempt to benefit from negative information, consequently encouraging them create “bad” information rather than create value for the shareholders. Insider trading could also be detrimental if it incentivizes the delay of information transmission, corporate action, interference with corporate plans, managers’ manipulations with stock prices, or reputational injury (Bainbridge, 2000, 2013b). As argued by Leland (1992), however, one of more serious issues, is the impact of insider trading on the liquidity of the stock market.

Insider trading represents a situation of clear information asymmetry due to informational advantage of insiders over non-insiders. Outside investors realizing this would be more reluctant to trade since the likelihood of trading with an informed trader is high. This disincentive to trade would likely result in a smaller number of uninformed traders in the market and lower both the volume and liquidity of the stock (Carlton and Fischel, 1983).

Insiders’ informational advantage also leads to higher transaction costs of trading in the stock market. If some traders has superior knowledge, other uninformed traders - market- makers increase their bid-ask spread in order to offset the losses of trading against those information motivated traders - insiders. (Copeland and Galai, 1983). Both situations could consequently cause reduced liquidity (Bainbridge, 2000, 2013b; Carlton and Fischel,

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1983). In our study, we explore whether insider trading while on procession of private information indeed leads to these negative consequences for liquidity of the firms’ shares.

Due to lack of precision in distinguishing transactions based on private information from publicly available data, investigating the impact of insider trading empirically imposes challenges. The majority of the studies explore either effects of implied informed trading (Aktas et al., 2008; Cao et al., 2004; Chung and Charoenwong, 1998; Piotroski and

Roulstone, 2004) or the impact of insider trading regulation and enforcement. Most evidence the latter comes from cross-country analysis (Beny, 2007; Bhattacharya and

Daouk, 2002; Bris, 2005; Fernadndes and Ferreira, 2008). Illegal insider trading cases derived from the Security and Exchange Commission (SEC) indictments represent a direct measure of trading on material non-public information in the U.S. market. However, those few studies that attempt to analyze consequences of these illegal insider trading activities focus on single cases, for instance transactions around Campbell Taggart (Cornell and Sirri,

1992) or Carnation Company acquisitions (Chakravarty and McConnell, 1997), and provide mixed findings.

In our analysis, we employ a sample of SEC indictments from 1995 to 2015. Our findings suggest changes in liquidity following trades based on material non-public information. We observe that liquidity is not impaired by illegal insider trading when measured as Corwin-Schultz bid-ask spread over the windows of 30 and 20 days around the transaction dates. However, we find evidence of decreased liquidity when estimated with Amihud Illiquidity proxy, for both long-run and short-run windows. In addition, we document that the SEC announcements regarding illegal insider trading are not associated with changes in liquidity.

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Literature Review

Impact of Implied Informed Trading

A limited number of studies examine the relation between potentially informed trading and its impact of the firms’ stocks. Some research price discovery and accuracy.

These studies focus on reported transactions by corporate insiders as defined by the SEC –

“company's officers and directors, and any beneficial owners of more than ten percent of a class of the company's equity securities”, who must file any changes in their securities’ ownership. Aktas et al. (2008) use intraday data to investigate whether trades from these insiders contribute to quicker price discovery on the days of the trades. They study the change in sensitivity of the return to the relative order imbalance induced by insider trading and find that insider abnormal purchases and sales are associated with faster price discovery. Piotroski and Roulstone (2004) analyze the impact of trading by analysts, institutional investors, and insiders on firm’s information environment. They demonstrate an inverse association between insider transactions and stock price synchronicity, a measure of firm-specific information. They also provide evidence of insider trading contributing to improved timing of firm-specific earnings information incorporation into prices. Other studies analyze relations between reported insider trading and liquidity.

Chung and Charoenwong (1998) examine transactions by corporate insiders and intraday data during 1988 to analyze the relation their impact on bid-ask spread. Their cross- sectional findings reveal that greater intensity of insider trading is associated with larger spreads; however, time-series results demonstrate a lack of evidence for spread changes on the insider trading days. Cao et al. (2004) investigate the impact of insider trading on

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market liquidity in the light of IPO lockup expirations. These expirations result in pre- announced, large scale rises of sales by insiders which lead to temporary small increases in effective bid-ask spread, as well as substantial increases in quote depth, average trade size, and number of trades per day.

Impact of Insider Trading Regulation

A stream of international research on insider trading focuses on the introduction of insider trading regulation versus enforcement of the law (Bhattacharya and Daouk, 2002;

Bris, 2005). Bhattacharya and Daouk (2002) investigate the changes in the cost of equity in 103 countries that have stock markets to evaluate whether existence or enforcement of trading laws affect the efficiency of stock market. The authors examine returns, turnover, and volatility in the descriptive statistics of their sample for before and after the introduction of the insider trading laws and around the first enforcement of the law. They also use a simplified version of international asset pricing factor model and calculate changes in dividend yields to measure changes in the cost of equity. Their overall conclusion is that the introduction of insider trading laws has little effect on the cost of equity; it is the enforcement of the laws that plays the key role in lowering the cost of equity. Bris (2005) reaches a similar conclusion regarding the role of enforcement using a sample of international acquisitions and abnormal volume and price movements around their announcements as proxies for insider trading. He employs CARs around the announcement dates before and after first insider trading prosecution to draw a conclusion that it is not the existence of insider trading laws but the enforcement of the laws that influences market reactions and contributes to higher market liquidity. Rather than studying enforcement of the law, Beny (2007) focuses on the toughness of insider trading 118

laws. She finds that outside ownership is positively related with expected monetary and criminal sanctions for insider trading law violations and so is accuracy of stock prices. The relation between liquidity of a country’s stock market and toughness of insider trading law is also found to be positive. Fernadndes and Ferreira (2008) focuses solely on stock price informativeness and whether it is affected by enforcement of insider trading regulation.

They find evidence of greater price informativeness following insider trading law enforcement but also show that the results are subject to quality of legal institutions.

Illegal Insider Trading and Liquidity

Both Cornell and Sirri (1992) and Chakravarty and McConnell (1997) analyze the impact of the insider trades on liquidity. Cornell and Sirri (1992) show that bid-ask spreads do not widen in connection with insider transactions around acquisition of Campbell

Taggart. Based on detailed records of the purchases of Carnation stocks, Chakravarty and

McConnell (1997) demonstrate that Boesky’s purchases have no significant negative effect on liquidity. While these trades do not significantly affect quoted and effective bid and ask spreads, the transactions either improve or do not significantly affect market depths. More recent study by Fishe and Robe (2004) explores how transactions based on information from advanced copies of BusinessWeek from June of 1995 to February of 1996 impact liquidity and provide evidence of significant negative impact on measures of market liquidity. The authors show that bid and ask spread widens on NYSE and AMEX (not on

NASDAQ) and market depth falls, the decrease in latter is stronger on NYSE and AMEX as compared to NASDAQ.

Theory of insider trading predicts that trading based on material non-public information could lead to both positive and negative consequences. Current empirical 119

evidence regarding impact of illegal insider trading on prices and liquidity does not solve the debate as the findings are mixed, overall limited to studies based on single cases of illegal insider trading indictments, and require further research.

Sample Selection

The sample of trades based on private information is hand-collected from the SEC

Litigation Releases and relevant SEC Complaints for the period from 1995 to 2015. They provide details regarding each case of trading while in possession of material non-public information and resulting outcome of the litigation. The information includes the names of the insiders, the firms of their employment, the stocks traded, and the insiders’ positions in the firms. In addition, we utilize description of the trading activities and preceding events to classify the trades according to the corporate events that allowed insiders to benefit from access to the private information.

The total number of insider trade observations collected that are considered illegal by the SEC is 1,184; however, lack of information regarding the dates of insider trades limits the sample to 1,009 observations. The sample is limited further by the availability of

CRSP permanent numbers for the firms involved in trading while in procession of private information. The securities of 297 firms were traded based on the information from 351 insiders working at 333 firms. The discrepancy between the number of firms of employment and firms traded in is due to insiders working for different firms transacting in securities of the same firms. An acquisition is an example of the situation where insiders of both acquirer and target firms could trade in the securities of the target.

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Figure 1 demonstrates the distribution of the transactions based on private information over the sample period. The number of trades is increasing, however, we observe the number going down in 2010s. This is explained by the lag between the insider trading commission and insider trading detection by the SEC. The agency tends to charge insiders on average three years after the trades, with most charges being typically settled around the same time.

Measures of Insider Trading Impact

Based on prior research, we identify potential measures that could reflect effects of insider trading on the firms. Illegal insider trading represents trading at informational advantage relative to other less informed traders, including market makers, who could attempt to compensate for this information asymmetry by widening bid-ask spread.

Following Corwin and Schultz (2012), we employ bid-ask spread estimations based on daily high and low prices as one of the liquidity measures. The methodology is created on assumptions that daily high (low) prices are almost always buy (sell) trades and high–low ratio reflects both the stock's variance and its bid-ask spread. The authors point out that while the variance component of the high–low ratio is proportional to the return interval, the spread component is not, which means that a spread estimator could be derived as a function of high–low ratios over 1-day and 2-day intervals (Corwin and Schultz, 2012).

Similar to other bid-ask spread estimations, wider Corwin and Schultz bid-ask spread reflects less liquidity.

Illegal insider trading as trading on superior information could undermine uniformed investor confidence, who, as a result, could choose not to participate in trading.

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We also employ Amihud illiquidity proxy (2002) as another measure of liquidity. The proxy is a measure of the relative price change associated with trading volume. It is calculated as a ratio of absolute value of a stock’s return of on the day to the dollar volume of this stock on this day. Higher trading volume would lead to lower values of Amihud proxy, so less liquid stocks are associated with higher value of Amihud illiquidity proxy.

Empirical Results

Table 1 provides information regarding the firms whose securities were traded based on material non-public information. Panel A of Table 1 presents a distribution of the insider transactions by type of corporate events that allowed insiders to either gain profits or avoid losses. The findings are consistent with prior research: the majority of illegal insider trades are associated with mergers and acquisitions, followed by earning announcements. Trades based on these two event types contribute 57.68 % and 21.37 % to all sample transactions, respectively. While news announcement associated trades comprise almost 14 % of the sample, FDA announcement trades represent a separate news category and are close to 5 percent of all insider trades.

Panel B of Table 1 reports industry composition of the transactions. The trades are divided based on both firms where insider are employed and securities of which firms they trade in. The industries with the largest number of trading activities while in position of material non-public information are chemicals and allied products and electronic/electric components and equipment, with 16.54% and 10.98% of transactions, respectively. They are closely followed by depository institutions, business services, and machinery and computer equipment that comprise 16.54%, 10.98%, 8.14%, 7.88%, and 7.62% of all

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sample trades, respectively. The top three industries belong to manufacturing sector and cover approximately 48 % of all insider trades. The distribution of the trades according to the firms of insiders’ employment is qualitatively similar. The majority of trades are conducted by insiders related to the firms in manufacturing, finance, or business services sectors.

Univariate Analysis

Liquidity: Bid-ask spread

Figure 2 provides preliminary analysis of the changes in our first measure of liquidity. It demonstrates the changes in the bid-ask spread 30 days preceding and following trades based on material non-public information. The graph shows an increase in the spread prior to the transactions followed by a decline several days after suggesting enhanced liquidity.

We follow with the univariate analysis of the same changes over a range of windows prior and post the trades while in possession of private information. Panel A of

Table 3 provides univariate analysis of findings for Corwin-Schultz bid-ask spread. We show that the bid-ask spreads preceding and following transactions based on private information are significantly different for most of windows specifications. Post insider trades bid-ask spreads are narrower for long-run windows (30 days) and wider for shorter horizons (5 days and less). The results suggest difference in the liquidity around the transaction date, with higher transaction costs closer to the trades by informed insiders but lower ones as we increase the time horizon.

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Liquidity: Amihud Illiquidity Proxy

Figure 3 demonstrates changes in averages of Amihood liquidity measure 30 days before and after the trades by informed insiders. It shows that the changes are more drastic in comparison to changes in bid-ask spread overall but demonstrate similar trend closer to the illegal insider transaction dates with liquidity decreasing as informed insiders engage in informed trading.

Panel B of Table 3 shows that, similar to bid-ask results, the Amihum illiquidity proxy values are greater prior to transactions that reveal private information to the market.

The significance of the differences between the measures before and after varies in a different way. The results are significant for longer windows (30 and 20 days) but insignificant for shorter periods (5 days or less). The measure implies enhanced liquidity following the informative trades as the time passes.

Multivariate Results

Results of the univariate analysis provide some preliminary evidence that there is difference in liquidity before and after illegal transactions in the securities of these firms.

The findings suggest that firms experience enhanced liquidity following the trades by the insiders as we move further away from the dates of the transactions that were made while in procession of material non-public information. To draw more definite conclusions, we conduct multivariate analysis of the impact of these measures of insider trading on the involved firms’ stocks. To account for other factors that could alter firms’ stock liquidity, we control for commonly used liquidity determinants (Chan et el, 2013; Chung, 1998).

Size is estimated as natural logarithm of market capitalization at the end of the prior fiscal

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year, while volume is measured on a daily basis. We calculate Price Inverse as one over daily price of the stock. Volatility is estimated as standard deviation of the daily stock returns in the prior year.

Following Cao, Field, and Hanka (2004), we estimate the influence of the informed trading on the liquidity by including two observations per firm per trade. First observation contains the average over each of the specified windows before, while second one is for the periods after the transaction. Since the overall results for long-run and short-run windows were inconclusive and in opposite direction potentially implying different conclusion regarding the role of the illegal insider trading in changes in liquidity, we run the analysis for two groups separately.

Liquidity: Bid-ask spread

Table 3 reports OLS regression estimates of the liquidity of firms’ stocks traded by the insiders while in procession of private information. The dependent variable are averages of Corwin-Schultz bid-ask spread proceeding and following illegal insider transactions. Each model is estimated for 30-, 20-, 10-, and 7- days windows. The key explanatory variable is Post Trade, which is equal to one following the transactions and reveals impact of illegal transactions on the liquidity for involved firms’ shares. Models

(1) and (2) demonstrate significant associations between post trade period identifier and bid-ask spread for the averages over 30 and 20 days around the trades by insiders with superior information. The findings imply that trading by the informed insiders leads to narrower bid-ask spread suggesting improved liquidity for long-run windows. However, as

Models (3) and (4) reveal we do not observe any significant influence of the illegal trading

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on the liquidity as measured by bid-ask spreads over 10 and 7 days windows prior and post the informed trades.

Next, we examine the relation between illegal insider trading and Corwin-Schultz bid-ask spread over short-run windows by focusing on the week proceeding and following the informed transactions. The key explanatory variable remains Post Trade, equal to one for the periods post the trades placed by the insiders with superior information. Model (1) through Model (5) demonstrate no significant association between liquidity and informed trading for any of the windows suggesting lack of influence for the situations specified in our models.

Liquidity: Amihud Illiquidity Proxy

We repeat the analysis for the impact of trading while in procession of material non-public information on liquidity of the involved firms’ stocks with Amihud Illiquidity proxy, our another measure of liquidity. Table 5 presents the results estimated for 30-, 20-

, 10-, and 7- days windows, where dependent variable is replaced with Amihud proxy. The primary variable of interest is Post Trade, which is equal to one following the transactions to show the effect of informed transactions on the liquidity for involved firms’ shares.

Model (1) through Model (4) provide evidence of significant positive association between illegal insider trading and liquidity as measured by Amihud Illiquidity proxy. The results suggests less liquidity for the stocks traded by insiders with access to private information.

We continue our investigation of informed trading’s influence on Amihud

Illiquidity proxy by estimating the impact illegal insider trading has on it over short-run windows. We estimate the regressions for the Amihud Illiquidity for 5-, 4-, 3-, 2-, and 1- days around the transactions by the insiders who are found to be informed. Models (1) and 126

(2) show significant positive association between informed trading and Amihud Illiquidity proxy implying reduced liquidity following transactions by insiders with superior knowledge. However, the results for shoter windows, closer to the dates of the transactions do not reveal any significance.

Liquidity: Announcements Effect

Upon the completion of the illegal insider trading cases, either through the court ruling or settlement, the SEC publishes litigation releases on its website detailing the civil lawsuits brought to the court by the Commission. As discussed in the Sample Selection section, these releases contain information regarding trades made while in possession of material non-public information, including the names of the firms, securities of which were traded, who traded them, their positions in the firms, and in some cases provide an idea of the size of those trades. The releases disseminate knowledge regarding the situation of information asymmetry and informed trading by the insiders among outside investors.

They are made explicitly aware of others trading on superior information, which could lead to changes in their trading behavior (Bainbridge, 2013b; Carlton and Fischel, 1983;

Copeland and Galai, 1983; Leland, 1992). We evaluate whether the SEC announcements of trading while in possession of private information has effect on our measures of liquidity. These announcements by the SEC are not likely to be proceeded by other media announcements. In most cases, the SEC attempts not to share the information regarding on-going cases until they are close to the end of the investigation. However, what should be pointed out that there is a significant lag between the time the trades while in procession of material non-public information are placed and investigations into them are closed.

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Figures 4 and 5 present graphs picturing the changes in Corwin-Schultz bid-ask spread and Amihud Illiquidity, respectively, 30 days prior and post announcements regarding processing of insiders who traded based on private information. Unlike the graphs for the liquidity measures around the dates of actual illegal insider trades, we do not observe significant movements or trends providing preliminary results for lack of relation between the announcements of informed trading and liquidity of stocks traded based on non-public information.

In untabulated results, we repeat our analysis focusing on impact on Corwin-

Schultz bid-ask spread, as well as on Amihud Illiquidity, both around the SEC announcement dates. The key variable of interest is Post Announcement, which is equal to one for the periods following the SEC announcements on illegal insider trading. We analyze both long-run and short-run windows but find no evidence of significant associations between announcements regarding informed trading and either of the liquidity measures in any of the specifications. This suggests that post-factum information about informed trading occurring has no effect on the liquidity for the firms whose share were traded by informed insiders.

Conclusion

The law heavily limits trading while in possession of material non-public information. The regulations were introduce the Security and Exchange Act of 1934 and have been reinforced through out the century. The penalties for insider trading were increased to up to three times the profits acquired or losses avoided and included controlling parties who failed to take needed steps to prevent this activity as responsible

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for these transactions. Both tippers and tippees can be considered responsible. However, these regulations are based on theories and doctrines that in many cases lack sound economic theory. The question arises regarding the actual impact is of what they are designed to regulate. Finance and law scholars present theoretical arguments both supporting and opposing insider trading based on private information and its regulation.

One of the detrimental effects illegal insider trading could have is on liquidity of the stock market. Trading based on non-public information creates situations when a group of investors has informational advantage over other market participants. This could undermine their confidence in the market and lead to adverse selection cost of trading resulting in reduced liquidity.

One of the challenges of assessing the consequences of trading while in possession of material non-public information lie in difficulty to identify these transactions from the ones based not on private information. The majority of prior studies employ implied informed trading or focus on the impact of insider trading regulation. Unlike them, we utilize a sample of the SEC indictments from 1995 to 2015 as a direct measure of insider trading activities to analyze how stock liquidity changes following trading based on private information. In our univariate tests, we find evidence that our measures of liquidity are significantly different post insider transactions relative to the period preceding the activity.

We observer increased liquidity for long-run windows following trades based on private information but decline in liquidity for short-run windows. Our multivariate analysis reveals a significant associations between illegal insider trading and both Corwin-Schultz bid-ask and Amihud Illiquidity proxy. We find that transactions based on private information are significantly negatively associated with Corwin-Schultz bid-ask spread for

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the periods of 30 and 20 days around the transactions. This suggests enhanced liquidity over those periods following the trades by insiders. We also documents significant positive association between transactions based on private information and Amihud Illiquidity for long-run and short-run windows, implying less liquidity as a consequence of the trades by informed insiders and reflecting price impact of their trades. In addition, we demonstrate that the trades themselves not the announcement of the trades have impact on liquidity.

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Amihud, 2002, Illiquidity and stock returns Cross-section and time-series effects, Journal of Financial Markets

Bainbridge, M. Stephen, 2000. Insider Trading-An Overview, The Encyclopedia of Law & Economics, 1 (5650), 772-812

Bainbridge, M. Stephen, 2013a, An overview of insider trading law and policy- An introduction to the Research Handbook on Insider Trading, Research Handbook on Insider Trading,

Bainbridge, M. Stephen, 2013b, Regulating insider trading in the post-fiduciary duty era: equal access or property rights?, Research Handbook on Insider Trading, 80-98

Beny, N. Laura, 2007. Insider Trading Laws and Stock Markets Around the World: An Empirical Contribution to the Theoretical Law and Economics Debate. Journal of Corporate Law 32, 237-300.

Bhattacharya, Utpal and Hazem Daouk, 2002, The World Price of Insider Trading, Journal of Finance, 57(1), 75-108.

Bris, Arturo, 2005. Do Insider Trading Laws Work? European Financial Management 11, 267-312.

Cao, Field, and Hanka, 2004, Does insider trading impair market liquidity Evidence from IPO lockup expirations, Journal of Financial and Quantitative Analysis

Carlton and Fischel, 1983, The regulation of insider trading, Stanford Law Review 131

Chakravarty and McConnell, 1997, An analysis of prices, bid ask spreads and bid and ask depths surrounding Ivan Boesky's illegal trading in Carnation's stock, Financial Management

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Insider Trades Time Trend 140

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100

NUMBER OF ILLEGAL TRADESOF NUMBER 20

YEAR

Figure 3.1 Insider Trading Time Trends

This figure plots the number and trend of litigation releases from the SEC for insiders investigated for illegal insider trading for the period 1990 through 2016.

Bid - Ask Spread Changes 0.045 0.04 0.035 0.03 0.025

0.02 Ask Spread Ask

- 0.015

0.01 Bid Bid 0.005

0 2 4 6 8

-8 -6 -4 -2

10 12 14 16 18 20 22 24 26 28 30

-28 -30 -26 -24 -22 -20 -18 -16 -14 -12 -10 Day

Figure 3.2 Bid-Ask Spread around Illegal Insider Trade Dates

The figure presents the changes in daily average values of Bid-Ask spread 30 days preceding and following insider trades while in possession of material private information. The trades are placed either by both tippers or tippees but limited to one trade per insider trading day per firm.

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Amihud Illiquidity Changes 0.004 0.0035 0.003 0.0025 0.002 0.0015

0.001 Amihud Illiquidity Amihud 0.0005

0 2 4 6 8

-4 -8 -6 -2

10 12 14 16 18 20 22 24 26 28 30

-30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 Day

Figure 3.3 Amihud Illiquidity Proxy around Illegal Insider Trade Dates

The figure presents the changes in daily average values of Amihud Illiquidity proxy 30 days preceding and following insider trades while in possession of material private information. The trades are placed either by both tippers or tippees but limited to one trade per insider trading day per firm.

Bid - Ask Spread Changes 0.035 0.03 0.025 0.02

Ask Spread Ask 0.015 -

0.01 Bid Bid 0.005

0 2 4 6 8

-8 -6 -4 -2

10 12 14 16 18 20 22 24 26 28 30

-16 -30 -28 -26 -24 -22 -20 -18 -14 -12 -10 Day

Figure 3.4 Bid-Ask Spread around Illegal Insider Announcement Dates

The figure presents the changes in daily average values of Bid-Ask spread 30 days preceding and following SEC insider trade announcements while in possession of material private information. The trades are placed either by both tippers or tippees but limited to one trade per insider trading day per firm. 135

Amihud Illiquidity Changes 0.006

0.005

0.004

0.003

0.002

Amihud Illiquidity Amihud 0.001

0 2 4 6 8

-8 -6 -4 -2

10 12 14 16 18 20 22 24 26 28 30

-28 -30 -26 -24 -22 -20 -18 -16 -14 -12 -10 Day

Figure 3.5 Amihud Illiquidity Proxy around Illegal Insider Announcement Dates

The figure presents the changes in daily average values of Amihud Illiquidity proxy 30 days preceding and following SEC insider trade announcements while in possession of material private information. The trades are placed either by both tippers or tippees but limited to one trade per insider trading day per firm.

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Table 3.1 Distribution of Illegal Insider Trading by Corporate Event and Industry

Panel A: Distribution of Illegal Insider Trades by Corporate Event Percent Mergers and acquisitions 57.68 Quarterly earnings announcements 21.37 News announcements 13.61 FDA announcements and clinical trials 4.63 Fraud related events 2.76 Total 100.00

Panel B: Distribution of Illegal Insider Trades by Industry Traded Firm Employment Mining: 1.91 Firm2.2 Firm Oil and Gas Extraction 1.15 1.42 Construction 0.89 0.52 Manufacturing: 47.83 49.35 Chemicals and Allied Products 16.54 16.67 Machinery and Computer Equipment 7.62 6.11 Elect. Equipment and Components 10.98 11.58 Controlling Instruments and Medical Goods 5.94 4.83 Transportation and Public Utilities: 7.51 9.31 Communications 4.39 3.31 Wholesale and Retail Trade: 12.47 9.58 Wholesale Trade Durable Goods 2.33 4.96 Finance, Insurance, Real Estate 16.54 17.7 Depository Institutions 8.14 6.62 Services 12.85 11.36 Business Services 7.88 9.03

The table presents frequency of illegal insider trades by corporate event and according to industry for the industries containing the largest number of indictments.

Panel A reports the percentage of illegal insider trades according to the event around which the transaction was conducted for the total sample of trades based on material non- public information from 1995-2016. Panel B reports the percentages of insider trades by industry for the industry and sub-industry classifications with the largest percentage of illegal insider trading within each division.

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Table 3.2 Changes in Liquidity

Panel A: Bid-Ask Spread Panel B: Amihud Illiquidity Proxy Window Mean Window 0.0921 -30, -1 0.0291 -30, -1 0.0544 0, +29 0.0258 0, +29 0.0377 (0.0079) Diff 0.00330 (0.0126) Diff

-20, -1 0.0291 -20, -1 0.0922 0, +19 0.0273 0, +19 0.0540 Diff 0.00179 (0.1870) Diff 0.0382 (0.0082)

-10, -1 0.0292 -10, -1 0.0744 0, +9 0.0306 0, +9 0.0565 Diff -0.00140 (0.3675) Diff 0.0179 (0.1827)

-7, -1 0.0293 -7, -1 0.0688 0, +6 0.0325 0, +6 0.0512 Diff -0.00321 (0.0580) Diff 0.0176 (0.1952)

-5, -1 0.0293 -5, -1 0.0724 0, +4 0.0342 0, +4 0.0525 Diff -0.00483 (0.0081) Diff 0.0199 (0.2208)

-4, -1 0.0298 -4, -1 0.0634 0, +4 0.0341 0, +4 0.0511 Diff -0.00431 (0.0292) Diff 0.0124 (0.3189)

-3, -1 0.0298 -3, -1 0.0629 0, +2 0.0341 0, +2 0.0545 Diff -0.00431 (0.0292) Diff 0.00847 (0.5509)

-2, -1 0.0299 -2, -1 0.0670 0, +1 0.0352 0, +1 0.0498 Diff -0.00532 (0.0111) Diff 0.0172 (0.2747)

The table presents univariate results for the difference between liquidity measures preceding and following the insider trades while in possession of material private information. In Panel A, bid-ask spread is estimated following Corwin and Schultz

(2012). In Panel B, Amihud illiquidity proxy is estimated following Amihud (2002). The measures are averaged over the specified windows.

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Table 3.3 Liquidity: Bid-Ask Spread over Long Horizons

30-days 20-days 10-days 7-days (1) (2) (3) (4) Post Trade -0.0041*** -0.0031** -0.0019 -0.0008 (-2.676) (-1.986) (-1.124) (-0.411) Size -0.0008 -0.0009 0.0008 0.0017 (-0.350) (-0.412) (0.294) (0.573) Volume 0.0038** 0.0043*** 0.0078*** 0.0101*** (2.358) (2.917) (5.412) (6.839) Price Inverse 0.0973*** 0.0837*** 0.0935*** 0.1002*** (5.943) (4.664) (4.020) (3.671) Volatility 0.6034*** 0.5964*** 0.6732*** 0.8280*** (3.487) (3.315) (3.192) (3.514) Constant -0.0398 -0.0440* -0.1009*** -0.1429*** (-1.617) (-1.817) (-3.877) (-5.047) Fixed Yes Yes Yes Yes Effects Observations 995 995 994 994 R2 0.599 0.587 0.557 0.545

The table reports OLS regression for the liquidity preceding and following the insider trades while in possession of material private information. The dependent variable is Bid-Ask Spread proxy estimated following Corwin and Schultz (2002) based on daily

CRSP prices. Size equals the log of market capitalization at the end of the previous fiscal year end. Volume is measured as daily trading volume. Price Inverse is the inverse of the price on day t. Volatility is estimated as standard deviation of the daily stock returns in the prior year. ***, ** and *denote significance at 1%, 5%, and 10%, respectively.

139

Table 3.4 Liquidity: Bid-Ask Spread over Short Horizons

5-days 4-days 3-days 2-days 1-day (1) (2) (3) (4) (5) Post Trade -0.0007 -0.0014 -0.0017 -0.0017 -0.0019 (-0.330) (-0.662) (-0.807) (-0.781) (-0.886) Size 0.0030 0.0048 0.0050 0.0008 -0.0001 (0.980) (1.465) (1.449) (0.212) (-0.027) Volume 0.0127*** 0.0145*** 0.0163*** 0.0184*** 0.0160*** (8.499) (9.316) (10.186) (10.928) (9.085) Price Inverse 0.1248*** 0.1503*** 0.1975*** 0.1296*** 0.0562 (4.053) (4.330) (4.968) (2.666) (0.985) Volatility 0.9608*** 1.0903*** 1.1986*** 1.0735*** 0.8315*** (3.810) (4.056) (4.200) (3.541) (2.735) Constant -0.1902*** -0.2316*** -0.2625*** -0.2510*** -0.1994*** (-6.401) (-7.373) (-7.893) (-7.109) (-5.655) Fixed Effects Yes Yes Yes Yes Yes Observations 994 991 990 990 982 R2 0.543 0.525 0.518 0.491 0.504

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Table 3.5 Liquidity: Amihud Illiquidity over Long Horizons

30-days 20-days 10-days 7-days (1) (2) (3) (4) Post Trade 0.0015*** 0.0014*** 0.0014** 0.0015** (3.147) (3.270) (2.494) (2.550) Size 0.0004 0.0006 0.0011 0.0005 (0.578) (0.964) (1.272) (0.544) Volume -0.0044*** -0.0040*** -0.0038*** -0.0030*** (-8.807) (-10.176) (-8.263) (-6.400) Price Inverse 0.0343*** 0.0155*** 0.0236*** 0.0211** (6.667) (3.242) (3.150) (2.446) Volatility -0.0973* -0.0900* -0.0389 -0.0689 (-1.787) (-1.875) (-0.572) (-0.923) Constant 0.0554*** 0.0504*** 0.0420*** 0.0365*** (7.185) (7.834) (5.032) (4.081) Fixed Effects Yes Yes Yes Yes Observations 996 996 996 996 R2 0.781 0.786 0.740 0.620

The table reports OLS regression for the liquidity preceding and following the insider trades while in possession of material private information. The dependent variable is Amihud illiquidity proxy estimated following Amihud (2002) as absolute value of return over dollar volume in dollars. Size equals the log of market capitalization at the end of the previous fiscal year end. Volume is measured as daily trading volume. Price

Inverse is the inverse of the price on day t. Volatility is estimated as standard deviation of the daily stock returns in the prior year. ***, ** and *denote significance at 1%, 5%, and

10%, respectively.

141

Table 3.6 Liquidity: Amihud Illiquidity over Short Horizons

5-days 4-days 3-days 2-days 1-day (1) (2) (3) (4) (5) Post Trade 0.0023*** 0.0023** 0.0013 0.0004 -0.0005 (2.895) (2.382) (1.511) (0.703) (-0.857) Size 0.0006 0.0005 0.0001 0.0008 0.0018 (0.453) (0.348) (0.083) (0.871) (1.591) Volume -0.0039*** -0.0042*** -0.0038*** -0.0032*** -0.0043*** (-6.447) (-5.966) (-5.926) (-7.493) (-8.282) Price Inverse 0.0226* 0.0259 0.0201 0.0286** 0.0363** (1.816) (1.623) (1.220) (2.318) (2.105) Volatility -0.0833 -0.1073 -0.1422 -0.0553 -0.0563 (-0.818) (-0.870) (-1.224) (-0.719) (-0.612) Constant 0.0475*** 0.0530*** 0.0524*** 0.0359*** 0.0433*** (3.953) (3.687) (3.896) (4.020) (4.110) Fixed Effects Yes Yes Yes Yes Yes Observations 996 996 995 995 992 R2 0.544 0.523 0.491 0.491 0.327

Inverse is the inverse of the price on day t. Volatility is estimated as standard deviation of the daily stock returns in the prior year. ***, ** and *denote significance at 1%, 5%, and

10%, respectively.

142