Two Essays on Media Connections and Corporate Finance Policies

TWO ESSAYS ON MEDIA CONNECTIONS

AND CORPORATE FINANCE POLICIES

Md Miran Hossain

A Dissertation Submitted to the Faculty of

College of Business

In Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Florida Atlantic University

Boca Raton, FL

August, 2018

ACKNOWLEDGEMENTS

I am highly grateful to my dissertation chair Dr. David Javakhadze for his supervision throughout the doctoral program. He has persistently encouraged me to work on high quality research projects. I feel blessed to have such an extraordinary scholar as my supervisor, mentor, instructor, and role model. Without his guidance and help this dissertation would not have been possible. I would also like to thank Dr. Luis Garcia-

Feijoo for his invaluable advice and direction. His feedbacks were instrumental in shaping my dissertation. Besides, I have learnt a lot from his courses and I thank him for his outstanding leadership in the finance PhD program. I am thankful to my other committee member, Dr. Daniel Gropper, for his advice and encouragement. I greatly appreciate his time despite his extremely busy schedule.

My sincere appreciation goes to the other faculty members of the finance department. I am thankful to Dr. Anna Agapova for her investments seminar class and collaboration in multiple research projects. Working with her has broadened my research agenda. I would like to thank Dr. Anita Pennathur for her capital markets seminar as her course furthered my understanding of research on financial markets. I am also grateful to

Dr. Zarruk, Dr. Cole and other professors for creating a supportive environment in the department. I also thank my fellow Ph.D. colleagues for their support. Your love and laughter have kept me entertained and encouraged.

iv I must express my gratitude to all my professors at North South University,

Colorado State University and Florida Atlantic University. Their relentless effort has inspired me to pursue a career in the academia.

v ABSTRACT

Author: Md Miran Hossain

Title: Two Essays on Media Connections and Corporate Finance Policies

Institution: Florida Atlantic University

Dissertation Chair: Dr. David Javakhadze

Degree: Doctor of Philosophy

Year: 2018

The study examines the effects of executives’ media connection on corporate policies. Extant literature in finance, economics and journalism provide inconclusive evidence in determining whether media works as watchdog to the financial market or whether media facilitates bias through manipulation of corporate news events. I introduce two competing hypotheses that may explain the research question. Information Efficiency

Hypothesis predicts that media connected firms mitigate information asymmetry among its investors, enjoy better governance, and are less likely to manipulate information on corporate policy choices. Manipulation Hypothesis, in contrary, suggests that firms may strategically utilize media connections to alter the information flow that may paint a tainted picture of the firm’s prospects, thereby facilitating greater misvaluation and devising of opportunistic corporate finance policies. I test these hypotheses on a set of investment policies (mergers outcomes and innovative efficiency) and financing policies

(seasoned equity offerings and share repurchases).

vi In the first essay, I find that media connection increases merger announcement return, reduces takeover premium, increases the likelihood of deal completion, although post-merger long term performance exhibit inconclusive results. Also, media connection reduces innovative efficiency and change in innovative efficiency attributable to media connections is harmful for the firm in the long run. Overall, results are consistent with the manipulation hypothesis to some extent though further investigation is required before disregarding the information efficiency effect.

In the second essay, results show that media connection increases the likelihood of an SEO event, reduces the announcement period CAR. However, analysis of post SEO long term operating and stock performance show mixed results. For repurchasing firms, media connection increases announcement returns, increases the likelihood of repurchase and the amount repurchased. Media connection also increases the likelihood that repurchase is preferred over dividends as a mode of payout. Post repurchase long term operating and stock performance, however, provide inconsistent results. In general, results are consistent with the manipulation hypothesis though information efficiency hypothesis could not be ruled out entirely.

vii

DEDICATION

I dedicate my dissertation to my beloved parents, Md Monir Hossain and Atia

Begum, for their unconditional love and support; and my dear wife, Ayrina Najib, for her sacrifices and giving me encouragement during the challenging days of PhD life. I am blessed to have them in my life.

TWO ESSAYS ON MEDIA CONNECTIONS

AND CORPORATE FINANCE POLICIES

List of Tables ...... xiii

Chapter I: Introduction ...... 1

Chapter II: Background and Primary Hypothese ...... 14

1. Media, Financial Market and Corporate Finance ...... 14

1.1. Media Disseminates Information ...... 14

1.2. Media Facilitates Investor Attention ...... 16

1.3. Media Ensures Monitoring ...... 17

1.4. Media Breeds Bias ...... 19

2. Social Capital and Its Conduits ...... 23

2.1. Information Flow ...... 24

2.2. Contract Enforcement ...... 26

2.3. Trust ...... 27

3. Primary Hypotheses ...... 28

Chapter III: Media Connection and Corporate Investment Policies ...... 31

1. Introduction ...... 31

2. Literature Review ...... 37

2.1. Review of Merger and Acquisition Literature ...... 37

2.1.1. Merger Announcement Return and Takeover Premium ...... 37

2.1.2. Price Manipulation Prior to Merger announcement ...... 39

2.1.3. Effect of Social Ties in M&A ...... 40

2.2. Review of Innovation Literature ...... 41

2.2.1. Determinants of Innovation ...... 41

2.2.2. Disclosure of Innovation Activities ...... 43

3. Hypothesis Development ...... 45

3.1. Media Connection and M&A...... 45

3.1.1. Moderating Effect of Political Connection ...... 50

3.1.2. Stock Deals ...... 51

3.1.3. Moderating Effect of Firm Recognition ...... 52

3.1.4. Geographic Proximity to Large Metropolitan Area ...... 52

3.2. Media Connection and Innovation ...... 53

4. Data and Methodology ...... 56

4.1. Media Connections Measure...... 56

4.2. Data and Sample Selection ...... 57

4.3. M&A Measures and Control Variables ...... 58

4.4. Innovative Efficiency Measures and Control Variables ...... 60

5. Empirical Results ...... 61

5.1. Mergers and Acquisitions ...... 61

5.1.1. Merger Announcement Returns ...... 62

5.1.2. Merger Takeover Premium ...... 63

5.1.3. Post-Merger Long Term Performance ...... 64

5.1.4. Effect of Political Connections ...... 65

5.1.5. Stock Deals, Acquirer Recognition and Geographic Proximity ...... 66

5.1.6. Deal Completion Likelihood and Acquisitiveness ...... 67

5.2. Innovative Efficiency ...... 69

5.2.1. Media Connections and Innovative Efficiency ...... 69

5.2.2. Media Connections, Innovative Efficiency and Firm Performance...... 70

5.3. Endogeneity Concerns ...... 72

6. Conclusions ...... 72

Chapter IV: Media Connection and Corporate Financing Policies ...... 96

1. Introduction ...... 96

2. Literature Review ...... 101

2.1. Review of SEO Literature ...... 101

2.1.1. Manipulation around SEO ...... 104

2.2. Review of Repurchase Literature ...... 105

2.2.1. Manipulation around Repurchase ...... 108

3. Hypothesis Development ...... 109

3.1. Media Connection and SEO...... 109

3.1.1. Moderating Effect of Investor Sentiment ...... 112

3.2. Media Connection and Share Repurchase ...... 113

3.2.1. Employee Option Grant ...... 116

4. Data and Methodology ...... 116

4.1. Media Connections Measure...... 116

4.2. Seasoned Equity Offerings Sample ...... 117

4.2.1. Data and Sample Selection ...... 117

4.2.2. Control Variables ...... 118

4.2.3. Descriptive Statistics ...... 118

4.3. Share Repurchase Sample ...... 119

4.3.1. Data and Sample Selection ...... 119

4.3.2. Control Variables ...... 120

4.3.3. Descriptive Statistics ...... 120

5. Empirical Results ...... 121

5.1. Seasoned Equity Offerings Sample ...... 121

5.1.1. Likelihood of SEO ...... 121

5.1.2. SEO Announcement Returns ...... 122

5.1.3. Post-SEO Long Term Performance ...... 123

5.1.4. Issuer Age, Growth Opportunity and Investor Sentiment ...... 125

5.1.5. Endogeneity Concerns ...... 126

5.2. Share Repurchases Sample ...... 127

5.2.1. Repurchase Announcement Returns ...... 127

5.2.2. Post-Repurchase Long Term Performance ...... 128

5.2.3. Employee Stock Option Plans and Firm Age ...... 129

5.2.4. Repurchase Likelihood, Amount and Payout Choice ...... 130

5.3. Endogeneity Concerns ...... 132

6. Conclusions ...... 133

Appendix ...... 159

References ...... 163

xii

LIST OF TABLES

Table 3- 1: Descriptive Statistics ...... 75

Table 3- 2: Merger Announcement Return ...... 77

Table 3- 3: Merger Takeover Premium ...... 79

Table 3- 4: Post-merger Long Term Performance ...... 80

Table 3- 5: Merger Announcement Return and Acquirers’ Political Connection ...... 83

Table 3- 6: Stock Deals, Acquirer Recognition and Geographic Proximity...... 85

Table 3- 7: Acquisitiveness and Deal Completion Likelihood ...... 88

Table 3- 8: Innovative Efficiency ...... 89

Table 3- 9: Firm Performance and Predicted Innovative Efficiency ...... 91

Table 3- 10: Endogeneity Concern – 2-SLS Instrumental Variable Regression ...... 94

Table 4- 1: Descriptive Statistics – SEO Sample ...... 135

Table 4- 2: SEO Likelihood ...... 137

Table 4- 3: SEO Announcement Return ...... 138

Table 4- 4: Post-SEO Long Term Performance ...... 140

Table 4- 5: Issuer Age, Growth Opportunity and Investor Sentiment ...... 143

Table 4- 6: Endogeneity Concern – 2-SLS Instrumental Variable Regression ...... 146

Table 4- 7: Descriptive Statistics – Repurchase Sample ...... 147

Table 4- 8: Repurchase Announcement Return ...... 149

Table 4- 9: Post-Repurchase Long Term Performance ...... 151

Table 4- 10: Employee Stock Option Plans and Firm Age ...... 154

xiii

Table 4- 11: Repurchase Likelihood and Amount ...... 156

Table 4- 12: Endogeneity Concern – 2-SLS Instrumental Variable Regression ...... 158

xiv

CHAPTER I: INTRODUCTION

“It's amazing that the amount of news that happens in the world every day always just exactly fits the newspaper.” - Jerry Seinfeld (American comedian, actor, writer, producer, and director)

A subtle characteristic of the news media is that among the numerous events happening around the globe, only the ones covered in the media comes to our attention.

An obvious question arises consequently that who or what determines the likelihood of a news event being covered in the media. Herman and Chomsky’s (1988) ‘propaganda model’ provides some explanation for media’s choice of topics, issues, and events. The model shows how mass public is manipulated and how public opinions on economic, social, and political events are manufactured in the public mind due to the propaganda.

Under this context, I examine media’s role in shaping different corporate finance policies in this dissertation.

Numerous studies in journalism, economics and finance literature document existence of media bias from various aspects. However, a burgeoning line of literature also demonstrate that media plays a vital role in the financial market through its monitoring and information intermediary service. This literature advocates that media works as a ‘watchdog’ to the corporate world and emphasizes media’s information dissemination role. Extant evidence is inconclusive in determining whether media works as an important intermediary in the financial market through monitoring or whether media facilitates bias through manipulation of corporate news events.

I contribute to this contemporary debate of media’s role in the financial market by investigating implications of corporate executives’ social ties with the media for corporate policy choices. I argue that conduits of social capital flowing through the ties between executives and journalists may provide an explanation for the media’s contested role. Existing studies on social network of corporate executives examine network centrality measures (including CEOs’ and directors’ networks, as well as firm social capital, ties to important players, such as financiers and politicians) and their implications on corporate finance policies and asset prices. General conclusion from this literature is that social ties at one end, may facilitate reduction of information asymmetry or on the other end, may aggravate agency problem. No study yet has investigated the effects of corporate executives’ social ties with media personnel on the media coverage driven financial outcomes.

I examine whether and how executives’ media connection may affect corporate investment and financing policies. As part of investment policy, I analyze mergers and acquisitions and investment in innovative projects. From financing side, I investigate seasoned equity offerings and share repurchases. Reviewing studies from sociology, journalism, economics and finance, I construct two competing hypotheses that may explain the effect of media connection on corporate finance policies.

Information Efficiency Hypothesis, suggests that media connections may facilitate firms with efficient flow of information and better corporate governance. This hypothesis is shaped by several arguments. First, conduits of information sharing flowing through the social ties between corporate executives and financial journalists would facilitate corporations with broadcasting of sufficient and timely information on important

corporate policies. From the executives’ perspective, social ties with a media personnel would be useful in disseminating information to outsiders. Financial journalists also would find it valuable to utilize their links with corporate executives in order to mitigate the difficulty of speed and complexity in producing financial news. An ever increasing challenge of producing news in shorter time and interpreting intricate financial concepts

(Tambini, 2010) stimulate the journalists to source more stories from corporate friends.

Second, contract enforcement channel of social capital suggests that journalists would enjoy reputation gain for accurate reporting and face reputation loss for spreading rumor or fake news. Such informal motivation would enforce journalists to scrutinize the information provided by corporate executives. As a result, media connection would ensure deterrence of opportunistic information disclosure by corporate executives.

Altogether, media connected firms are likely to exhibit efficient share prices prior to corporate events and thus facilitate value increasing corporate policies.

Manipulation Hypothesis, in contrary, predicts that social ties between financial journalists and executives may distort information flow and create opportunities for effective manipulation. Several explanations validate media’s incentive to engage in such manipulation. First, journalists’ objectivity may be impaired due to their social ties with corporate executives since journalists who have close relationship with a source may fail to verify the integrity of the information provided by the source (Pavlik, 2004).

Second, media connection may aggravate journalists’ “quid pro quo” bias (Dyck and Zingales, 2003). As journalists depend on corporate friends for sourcing news, they may become more concerned about maintaining their relationship with these sources.

Third, corporate executives’ ties with the owners of the media companies may also affect journalists’ independence. As corporations complain to the editors or owners of the media company about journalists (McNair, 2002), it creates job security concern for the journalists. Social connection between corporate executives and media owners would therefore cultivate manipulation that transpires through the hierarchy.

Finally, trust induced in social capital flowing through the ties between corporate executives and media personnel may craft dissemination of fabricated information.

Knowing that trust affects information sharing (e.g., Dyer and Chu, (2003), corporate executives may exploit journalist friends’ trustworthiness and share fictitious information with them. In summary, media connection would facilitate greater misvaluation of share prices prior to corporate events and thus contribute to devising of value destroying corporate policies.

In my first essay, I test these competing hypotheses for M&A transactions. A large number of studies examine the implications of manipulating hard information on corporate M&A activities. However, prior literature largely ignores the likelihood of price manipulation through soft information (e.g. qualitative details of deal), which is difficult to summarize in numbers. Such deceptive nature makes soft information more likely to be manipulated. Recently Ahern and Sosyura (2014) show that acquirers generate more news stories after the start of merger negotiations through ‘active media management’ to achieve price run-up in their stock price and consequently pay lower takeover premium. Though these studies focus on the role of information disclosure around merger deals, the role of media connections is unexplored. Consistent with the

disputed role of media in the financial market, media connection may aggravate or ameliorate the information disclosure process of acquirers.

According to the manipulation hypothesis, acquirer’s media connection may facilitate broadcasting good news, concealing bad news and also meddling the tone of news articles prior to merger announcements. In addition, media connection may aid in creating public opinion in favor of the merger transaction after the deal is announced.

Potential acquirers could also use media connections strategically as a bargaining tool and disseminate negative information about targets in the press effectively undervaluing these firms and thus reducing observed premium. As managers are more likely to engage in media manipulation when they devise opportunistic takeovers, these deals would underperform in the long run. In sum, manipulation hypothesis predicts that media connected acquirers would exhibit higher bid announcement return, pay lower takeover premium, be more likely to close deal, be more acquisitive and underperform in the long run relative to the non-media connected acquirers.

In contrary, information efficiency hypothesis predicts that media connections facilitate disclosure of accurate and timely information about the deal and acquirer’s future pay-offs, consequently reducing the extent of information asymmetry and market reaction on bid announcement. As the watchdog media portrays actual picture of the target and highlight the synergistic gain from potential merger bids, it would be difficult for acquirers to undervalue targets and conceal synergic gains from acquisitions, thereby raising the takeover premium. In addition, deal proposals will be more scrutinized in these firms, which leads to a decrease in the probability of deal completion. Governance role of the media would ensure that only value enhancing deals are completed. All things

considered, according to the information efficiency hypothesis, media connected acquirers would experience lower bid announcement return, pay higher takeover premium, be less likely to close deal, be less acquisitive and outperform in the long run compared to the non-media connected acquirers.

In addition to examining potential implications of media connections for M&A activities, in my first essay I also investigate the consequences of media connection on investment in innovative activities. While there is no doubt that innovation is a pivotal investment policy decision, assessing a firm’s innovative efforts is difficult for investors.

Investors are forced to look beyond the financial statements (Healy and Palepu, 2001) and seek information from alternative sources in order to assess the expected payoffs from innovation projects. News media is one such alternative source that help firms communicate the value of its innovative efforts and investors also react immediately to announcements of innovation projects (Chan et al., 1990). Therefore, I investigate whether firms exploit media connection to strategically disclose their innovation efforts, which may affect the firm’s innovation efficiency and ultimately its long-run performance and stock returns.

Information efficiency hypothesis conjectures that media connection may alleviate the information asymmetry faced by innovative projects (Lev & Zarowin, 1999;

Bhattacharya and Ritter, 1983). Media connections could facilitate broadcasting of timely and accurate information on innovation projects. Since innovative firms suffer from limited external financing (Hall and Lerner, 2010), reduction of financing constraints is another channel through which media connection may enhance innovation efficiency.

Friends in the media would help improve disclosure of innovation projects and enhance

familiarity to a broader set of investors, thereby enabling access to finance. Information efficiency hypothesis, therefore, predicts that media connected firms would exhibit greater efficiency in converting their R&D spending into successful patents.

Executives’ social ties with journalists, however, could also impair journalists’ independence and induce the journalists to refrain from the regular due diligence of the qualitative information about innovation projects shared by their corporate friends.

Manipulation hypothesis predicts that firms may strategically use media connectedness to disguise their investment in suboptimal innovation projects. Managers may find it useful to use media connections in producing false assurance to investors about the success of ongoing innovation projects. Consequently, from manipulation perspective media connected firms would exhibit poor innovation efficiency.

Results from the first essay show that media connection increases announcement period abnormal return, reduces takeover premium, increases the likelihood of successful completion of merger deals. Further investigation reveals that the effect of media connection on merger announcement return is greater for stock deals, smaller acquirers, acquirers located in small counties. Analysis of post-merger long term performance demonstrate mixed results. Then investigation of innovative efficiency show that an increase in media connection is associated with a statiscally significant decrease in innovative efficiency and ex post firm performance is negatively related with the change in innovative efficiency attributable to media connections. Overall, results provide support for the manipulation hypothesis to some extent though the information efficiency effect could not be ruled out.

In my second essay, I examine the implications of media connection on SEOs.

While the trade-off and pecking-order theories have limitations explaining overvaluation prior to SEOs and underperformance post SEOs, market timing theory provides appealing explanations. I conjecture that SEO issuers’ connection with media could impact their strategy of market timing and the topic is overlooked in existing literature.

Information efficiency hypothesis suggests that media connection would reduce the extent of mispricing and thus constrain managerial discretion to engage in market timing through SEOs. Less mispricing of the media connected issuers also implies that these firms would experience smaller price run-ups prior to SEO announcement and reduced market reaction on SEO announcement relative to the non-media connected SEO issuers. In addition, information efficiency and monitoring through media connection would alleviate the adverse selection problem typical in SEOs. Media connected issuers would, therefore, find it difficult to undertake opportunistic SEOs. In total, information efficiency hypothesis predicts that media connected firms are less likely to conduct SEO, exhibit smaller pre-SEO price run-ups and less negative announcement return and perform better in the long run compared to the non-media connected firms.

Manipulation hypothesis contends that issuers may exploit media connection to devise opportunistic SEOs. One source of manipulation could be in the form of false certification of SEO issuer’s stock pricing. Media coverage could serve as an assurance to the investors that the SEO firm’s stocks are not mispriced. Another type of manipulation could transpire through the maneuvering of soft information prior to SEO announcement.

While there is pervasive evidence of manipulation of hard information (e.g. earnings management) prior to SEOs, I argue that managers may find it convenient to manipulate

soft information through media coverage. Manipulation hypothesis, therefore, conjectures that media connection would exacerbate the extent of mispricing and influence managers to engage in market timing through SEOs resulting in greater likelihood of conducting

SEOs, higher pre-SEO price run-ups, more negative announcement returns and worse long-term performance compared to non-media connected firms.

In addition, in the second essay I also examine the role of media connection on share repurchases. Lax regulatory requirement coupled with flexibility of repurchase program makes repurchase another ideal corporate financing policy to investigate in the context of media connection. Prior studies on share repurchases broadly agree that managers tend to buy back shares when their shares are undervalued and also embark on price manipulation schemes to artificially depress stock price prior to repurchase announcement1. Yet the role of media connection in aiding such undervaluation is unexplored in the literature.

Information efficiency hypothesis suggests that reduced information asymmetry between managers and investors would diminish managerial incentive of signaling through share repurchase. Reduced information asymmetry also implies that the information content of the repurchase announcement would be lower for media connected firms. As media connected firms have more efficient prices and low level of information asymmetry, neither signaling nor managerial private incentives could motivate these firms to conduct share repurchase. A plausible purpose of repurchase for these firms could be distribution of free cash flow, which predicts long term underperformance after repurchasing (Grullon and Michaly, 2004). Information

1 Bens et al. (2003), Gong et al. (2008), Brockman et al. (2008) 9

efficiency hypothesis, hence, predicts that media connected firms are less likely to repurchase share, experience less positive announcement return and underperform in long run than the non-media connected firms.

Manipulation hypothesis, on the other hand, envisages that managers may exploit media connection to articulate opportunistic repurchases. Media connection may facilitate broadcasting of more bad news prior to the intended repurchase announcement in order to deflate stock price. Furthermore, media manipulation may provide biased certification of the repurchasing firm’s undervalued stock price and thereby mitigate valuation concern of the investors during the pre-repurchase period. As media manipulation facilitates greater mispricing and greater information asymmetry, a plausible purpose of repurchase for these firms could be signaling of future prospects. Manipulation hypothesis, thus, predicts that media connected firms are more likely to repurchase shares, experience higher announcement return and exhibit better long-term performance than non-media connected firms.

Empirical analysis show that media connection is likely to increase the probability of an SEO event, is negatively associated with the announcement period CAR and this effect is greater for younger firms and firms with high growth opportunities. Though results suggest that managers take advantage of media connection to engage in opportunistic SEOs, results on post SEO long term operating and stock performance are inconclusive. For repurchasing firms, media connection is positively associated with announcement returns, the likelihood of repurchase and the amount repurchased. Media connection also increases the likelihood that repurchase is preferred over dividends as a mode of payout. In addition, the effect of media connection on repurchase announcement

return is greater for firms in which top executives have greater than median option exercise value. Post repurchase long term operating and stock performance, however, provide inconsistent results. In general, results are consistent with the manipulation hypothesis though further investigation is required before disregarding the information efficiency hypothesis.

My dissertation contributes broadly to literatures on social capital and finance as well as media’s role in finance. First, the dissertation is related to the emerging research on social networks in corporate finance and asset pricing. Extant evidences illustrate that well connected VCs experience better fund performance and are more likely to survive

(Hochberg et al., 2007), mutual funds perform better on connected holdings (Cohen et al.,

2008), analyst recommendations outperform on connected firms (Cohen et al., 2010), acquirer returns are higher for acquirer-target connected bids (Cai and Sevilir, 2012), borrower-lender connection reduces loan spreads (Engelberg et al., 2012), financier connection enhances the sensitivity of investment and external finance to Q (Javakhadze et al., 2016), political connection increases the likelihood of deal completion and increases post-merger performance (Ferris et al. 2016). I contribute to this literature by studying another type of social connections (i.e. media connections), which has not been explored yet. I argue that conduits of social capital flowing through the social ties between corporate executives and media personnel may add to the understanding of social network in corporate finance.

Second, my study contributes to the literature that debates on media’s role in corporate finance and asset pricing. One line of research conclude that media facilitates efficient flow of information and acts as a ‘watchdog’ to the corporate world. These studies show that media coverage reduces information asymmetry around earnings announcements (Dyck and Zingales, 2003; Bushee et al., 2010), tone and content of

media coverage predicts stock returns (Tetlock, 2007; Tetlock et al., 2008; Engelberg,

2008; Garcia, 2013), media coverage generates investor attention (Huberman and Regev,

2001; Barber and Odean, 2008; Engelberg et al., 2012), media detects accounting fraud

(Miller, 2006), enhances corporate governance (Dyck and Zingales, 2002; Djankov et al.,

2003; Dyck et al., 2008), monitors CEO compensation (Core et al., 2008; Kuhnen and

Niessen, 2012). Another stream of studies contend that media breeds biases such as readership bias (Baron, 2005; Mullainathan and Shleifer, 2005; Gentzkow and Shapiro,

2006), advertising bias (Reuter and Zitzewitz, 2006), ownership bias (Besley and Pratt,

2006), and quid pro quo bias (Dyck and Zingales, 2003). My study adds to this literature by examining whether corporate executives’ social ties with media personnel fosters information efficiency and monitoring or act as another potential source of media bias, and its implications on corporate investment and financing policies.

I use the social network database from Boardex of Management Diagnostics Ltd. as the major source to estimate the measure of media connection. First, I identify the firms that are part of the ‘Media and Communications’ industry in BoardEx. Then I categorize all the directors/ executives working in these media firms as ‘media directors’.

BoardEx contains a director connection file that plots connections among directors formed through employment, education or social activities. I merge this director connection file with the media directors identified in the previous step to obtain the connections of directors with the media directors. Then I count the number of current and prior connections of directors of a firm by each year and then aggregate the number of connections for each firm-year. Finally, I construct the measure of media connection

(Log_MC) by taking log of one plus the number of media connections for a firm-year.

My sample expands from January 2000 through December 2016. I obtain acquisition data from SDC Platinum’s M&A database and patent citation data from the National Bureau

of Economic Research (NBER) patent database. I collect the sample of SEOs and repurchases from the SDC Platinum database.

Subsequent chapters in this dissertation include review of relevant literature and construction of primary hypothesis in chapter II, the essay on the effect of media connection on corporate investment policies in chapter III, and the essay on association between media connection and corporate financing policies in chapter IV.

CHAPTER II: BACKGROUND AND PRIMARY HYPOTHESE

1. Media, Financial Market and Corporate Finance

Finance and economics literature provides pervasive evidence of diverse roles played by media in the financial market. Business media influences asset pricing by mitigating information asymmetry, affects investor sentiment by growing visibility, provides monitoring of corporations by ensuring corporate governance and by detecting corporate frauds. Following sections discuss these roles in detail.

1.1. Media Disseminates Information

Information intermediary role of media is deeply rooted in finance literature. The

Efficient Market Hypothesis posits that asset prices reflect market information and introduction of additional information is instantaneously adjusted in the asset prices

(Fama, 1970). Business media mediates the information disclosure process of corporations by disseminating information to a wide range of readers and interpreting technical details into readable content. A well-functioning media can, therefore, mitigate information asymmetry among different players in the financial market.

Numerous studies document that media disseminates information to the financial market. Dyck and Zingales (2003) examine the association between media coverage and market reactions to earnings announcements. They conclude that market reaction is greater for the earnings announcements that are highlighted by media and the effect is inversely related with the number of analysts and positively related with the credibility of the media outlet. Bushee et al. (2010) investigates information environment of a firm and

finds that media coverage is a determinant in reducing information asymmetry around earnings announcements.

The literature also explores the tone and sentiment of media contents using textual analysis. Tetlock (2007) conducts textual analysis of media contents and demonstrate that the number of negative words in the “Abreast of the Market” column of the Wall Street

Journal predicts stock returns. Tetlock et al. (2008) show that the negative words in firm- specific news stories significantly explains firm earnings and that stock prices are responsive to the information content of negative words. Engelberg (2008) differentiates news into hard or quantitative information and soft or qualitative information and study the tone of earnings announcements. He finds that qualitative earnings information has greater explanatory power in predicting asset prices in addition to the predictability in quantitative information. Garcia (2013) considers both positive and negative words in general financial news columns of the New York Times and the Wall Street Journal, and show that media content predicts trading volume.

The association between media coverage and asset prices may suffer from endogeneity since unobservable factors may influence both media coverage and asset prices or reverse causality may arise as media may cover better/ worse performing firms.

Engelberg and Parsons (2011) establish causality by showing that the probability and magnitude of local trading are strongly associated with the local patterns of media coverage. Dougal et al. (2012) also documents causality by showing that bullish or bearish sentiment conveyed by Wall Street Journal columnists predicts the next-day return. Peress (2014) conducts a quasi-natural experiment by examining the effect of media coverage on the financial market by using newspaper strikes in different countries.

He demonstrates causality by showing that trading volume decreases 12% on national newspaper strike days.

1.2. Media Facilitates Investor Attention

The second stream of literature argue that media affects asset prices by not only supplying information but also attracting investor attention and increasing firm recognition. Kahneman’s (1973) Limited Attention Hypothesis states that attention is a scarce cognitive resource and investors may need to be selective in processing vast amount of available information. On a similar note, Merton (1987) proposes the Investor

Recognition Hypothesis which states that investors buy and hold only those securities about which they have information. A common way to facilitate investors' recognition is to promote firm visibility through the media. Media coverage, therefore, can contribute in broadening a firm’s visibility and increasing investor attention.

Consistent with the theoretical predictions, empirical evidences show that media coverage generates investor attention. Huberman and Regev (2001) find that an already publicly available information of a cancer-cure drug reported in the New York Times produced significant abnormal return on the following day. Busse and Green (2002) investigates the effect of a stock being highlighted on the Morning Call or Midday Call segment of CNBC and find that stock prices reflect the information revealed through the

CNBC discussion almost instantaneously. Chan (2003) examines newspaper headlines to identify prominent news and finds that stocks with news particularly for stocks with bad news experience subsequent drift. Antweiler and Frank (2006) also provide evidence of momentum and subsequent reversal in future for the stocks that are covered by the media.

Meschke (2004) finds that when a CEO is interviewed on CNBC it leads to a significant

price increase and higher trading volume on that day. Barber and Odean (2008) show that individual investors are net buyers of stocks featured in the news.

Next, Engelberg et al. (2012) show that stock recommendations by Jim Cramer in television show Mad Money causes abnormal overnight return and subsequent reversal.

Fang and Peress (2009) show that the informative role of the media increases the depth of information, thereby causing lower return for media covered firms. Hillert et al. (2014) show that investor biases are aggravated by media coverage, media covered firms experience stronger momentum. Liu et al. (2014) show that IPOs with greater media coverage experience lower future expected returns and greater liquidity, analyst coverage, institutional investor ownership, long-term performance.

Another stream of literature investigates the effect of media induced investor attention on mutual fund flows and performances. Sirri and Tufano (1998) show that media coverage of a mutual funds is associated with faster growth and stronger flow performance sensitivity. Kaniel, Starks, and Vasudevan (2007) show that fund flows are related to the existence and frequency of media coverage. Solomon, Soltes, and Sosyura

(2012) find that stocks with high past returns in funds’ portfolios attract investors’ flows only if they are recently featured in the media. Fang, Peress, and Zheng (2014) demonstrate that mutual funds are likely to buy the media covered stocks. Their conclusion implies that fund managers also exhibit limited attention. Kaniel and Parham

(2015) reports that mutual funds featured in a Wall Street Journal “Category Kings" ranking list experience substantially increased flows and also a spillover effect to the other funds in the complex.

1.3. Media Ensures Monitoring

Though financial market is monitored by regulatory authorities, Keller (1998) argues that the business media also plays a ‘watchdog’ role. Miller (2006) argues that accounting frauds could be detected by media through investigation and dissemination of information from other intermediaries. Dyck et al. (2010) finds corroborating result and conclude that media’s role in detecting fraud is more powerful than the other corporate governance entities.

Several papers find that media creates pressure on firms to alter governance structures, acquisition decisions, structure of executive compensation etc. Dyck and

Zingales (2002) argue that media can reducing the costs of contracting since media facilitates information aggregation and communication to the public. They investigate the role of the media in international setting and find that media affects overall corporate governance and economic development in a country. Djankov et al. (2003) corroborates the monitoring role of media by showing that countries with greater state ownership of the media have biased media, fewer political rights for citizens, poor corporate governance and underdeveloped capital markets. Dyck et al. (2008) study the Anglo-

American press coverage of Russian firms and report that negative press coverage is associated with improvements in governance. They also conclude that better governance is attributable to the broader international dissemination of the governance problems that pressures the firm and Russian regulators to reform. Joe et al. (2009) show that firms are likely to take remedial actions and protect shareholder wealth when board ineffectiveness is exposed in the media.

Media can influence executives’ human capital by publicizing their actions and by influencing perceptions of those actions. Core et al. (2008) examine whether the media

monitors executive compensation. They discover that media plays an important role in detecting excess compensation, and firms with large stock option exercises are more likely to get exposed. However, further investigation suggest that the monitoring is not effective as firms do not change compensation in response to the media coverage on excess compensation.

A more recent study by Kuhnen and Niessen (2012) finds that CEO stock option grants are reduced when the press produces negative coverage of CEO compensation. Liu and McConnell (2013) argue that managers’ reputational capital is affected by the media coverage of value-reducing acquisitions. In line with their predictions, they find that value reducing acquisitions are more likely to be abandoned if they are covered by the media. Dai et al. (2015) examines the media coverage of SEC filings regarding executives’ insider trading. They find that information dissemination through press reduces executives’ future trading profits. Reduction of information asymmetry coupled with litigation risk are attributed to the corrective mechanism. Liu et al. (2017) find a positive relation between the level and tone of media coverage given to CEOs’ firms while the CEOs are “on the job” and the number of outside board seats held by the CEOs following their departures as CEOs. Results support the conjecture that media plays a significant role in corporate governance by influencing the value of CEOs’ human capital.

1.4. Media Breeds Bias

“They determine, they select, they shape, they control, they restrict – in order to serve the interests of dominant, elite groups in the society.” (Herman and Chomsky,

1988)

Though a large number of studies document that media disseminates important information, facilitates investor attention and ensures monitoring of corporations, there is also equivalent concern that business media lacks in-depth research, tends toward sensationalism and may even produce biased coverage in certain circumstances. A survey by the American Society of Newspaper Editors (ASNE, 1999) reveal that 78% of the public believe there is bias in news reporting. Several studies criticize financial journalists for failure to conduct investigations (Davis, 2005; Wilby, 2007) and for not being skeptical (Doyle, 2006).

Media bias may arise from demand side or supply side. On the demand side, media outlets tend to maximize profit by catering to their readers’ preferences.

Communications literature provides several evidences which suggests that readers enjoy and remember stories consistent with their beliefs. Graber (1984) surveys 200 people who were followed through 1976 in a number of interviews in which they were asked open ended questions about what had appeared in the media. She finds that readers’ knowledge abstracted from experience processes new information and makes it possible to retrieve stored information. Readers disregard anything that does not match the schema. Severin and Tankard (1992) suggest that newsworthiness of an event is determined by the demand for cognitive consistency.

Psychology literature also confirms that readers tend to remember less, find it less credible and update less the information that are inconsistent with their beliefs (Bartlett,

1932; Lord et al., 1979; Klayman, 1995, Fiske, 1995). George and Waldfogel (2003) study the daily newspaper purchase behavior among groups with different preferences in

US and find that reader heterogeneity is associated with less media bias. As readers share

various beliefs and tastes, it becomes difficult for the media to produce the correspondent biased information useful to confirm their ideas and so to be liked. Mullainathan and

Shleifer (2005) build a model on determinants of media bias. The model assumes that readers tend to see their beliefs confirmed and newspapers can slant stories toward these beliefs. They find that media bias is driven by reader diversity. Gentzkow and Shapiro

(2006) build a model of media bias in which media firms tend to build reputation by manipulating their reports toward the prior beliefs of their customers. The model predicts that bias will be smaller when predictions are concrete and outcomes are immediately observable. Also, Media firm’s incentives to distort information are weakened when consumers have access to a source that can provide ex-post verification of reality.

Next, Ahern and Sosyura, (2015) study the accuracy of merger rumors and argue that newspapers tend to publish sensational news in order to achieve greater readership.

While there is a trade-off between readership appeal and accuracy, media coverage of merger rumors is biased toward newsworthy firms that appeal to a broad audience.

Likelihood of rumor is positively associated with firm size, brand value and advertising expenditures.

Supply side bias, on the other hand, may reflect the preferences or career concerns of journalists, editors or owners. Baron (2006) presents a theory of media bias and shows that journalists may bias their stories if their career prospects can be advanced by being published on the front page. McNair (2002) propose that journalists may be concerned about retaining their job or prospect of future employment if corporations complain to their editors. Dyck and Zingales (2003) argue that media bias may stem from the quid pro quo relationship between journalists and corporate executives. Corporate insiders are an

important source of stories for journalists. Hence journalists may find it beneficial to spin stories to reward their sources. In exchange of the positive spin, corporate insiders would provide more stories of corporate events to the journalists. They also hypothesize that the positive spin is positively associated with the demand for the source’s information and negatively with the alternative sources of that information. They test these predictions using stock market returns and company releases of GAAP and pro forma estimates of earnings. Results are consistent with the predictions of quid pro quo bias.

Ownership of the media is also considered as a source of bias. Government ownership may hinder freedom of the media, however, private ownership may facilitate manipulation and favoritism toward corporate elites. Herman and Chomsky (1988) argue that media’s role is influenced by interlocks in ownership, shared goals and market forces. Jamieson and Campbell (2000) and Herman and Chomsky (1988) argue that media’s willingness to scrutinize the market is reduced since media company’s interests are aligned with that of the market. Djankov et al. (2003) propose two theories of government ownership of the media. The public interest theory hypothesizes that government ownership mitigates market failures, and the public choice theory conjectures that government ownership impedes political and economic freedom. Their analysis show that countries with greater government ownership of the media have less freedom of press, fewer political rights for citizens, inferior governance and less developed capital markets. Results suggest that media bias is driven by the state ownership of media. Besley and Prat (2004) propose a model of media bias in which the government can buy the silence of the media and hide bad news about its quality.

Media may be biased towards the advertisers as advertisement constitutes a significant portion of newspaper revenues. Several studies investigate effect of advertising on news coverage and tone. Blasco, Pin, and Sobbrio (2014) propose a model in which advertisers may pay a media outlet to hide negative information about the quality of their products. The model shows that competition among advertisers determine the advertisers' success in influencing media outlets. Stromberg (2004) models the incentives of the media to deliver news to different groups. This model show that advertising financing of media firms induces them to provide more coverage to groups that are valuable to advertisers.

Further, Ellman and Germano (2009) develop a game theory based model in which advertisers are also concerned about the content of articles surrounding their advertisements. Reuter and Zitzewitz (2006) test advertising bias within the financial media and find that recommendation for mutual funds is highly correlated with past advertising by those funds in three personal finance publications. Gurun and Butler

(2012) propose catering hypothesis which suggests that local media may slant local firm news since local newspaper subscription is likely to be driven by local firm employees.

They find that local media reporting about local companies contain fewer negative words than that about nonlocal companies. More importantly, the local media bias increases with the local advertising expenditures.

2. Social Capital and Its Conduits

The concept of social capital has become popular in various social science disciplines. Social science scholars have conceptualized social capital as a set of social resources embedded in relationships. Bourdieu (1985) provides one of the earliest

definitions, which states that social capital is “the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance or recognition”. Loury (1977) argue that economic theories were individualistic and concentrated on individual human capital. He considers social capital as “an asset which may be as significant as financial bequests in accounting for the maintenance of inequality in our society”. According to

Coleman (1988), social network becomes social capital when an actor effectively uses it to pursue his interests. Putnam (1995) advocates a broader definition of social capital, which included social relationships, norms of reciprocity and trustworthiness. According to Woolcock (1998), social capital is the aggregation of information, trust, and norms of reciprocity inherent in social network. Social capital may exist in different forms: information flow, contract enforcement, trust etc. Following sections discuss these channels.

2.1. Information Flow

Transfer of information is an inherent component of social interactions, even if it is not intended. Social capital in the form of networks of social relationships can, therefore, influence economic transactions by alleviating information asymmetry and reducing contracting costs through circulation of information.

A wide array of social science research demonstrate information sharing as the key benefits of social capital. Coleman et al. (1966) show that the more connections a physician had in her local medical community, the earlier she got information about a new drug. Granovetter (1995) find that relationships base networks allow job seekers to obtain important information on job opportunities. Burt (1997) finds that social capital

facilitates information flow from other actors to the focal person through brokering activities Uzzi (1997) studies the apparel industry and shows that information dissemination through social capital helps them forecast future demands and anticipate customer preferences. Fafchamps and Minten (1999) survey the agricultural traders in

Madagascar and find that relationships are an important factor for success as social capital facilitates the circulation of information about prices and market conditions.

Fernandez et al. (2000) find that economic payoff are greater for employee referral based recruitments relative to other recruitments. Rauch and Casella (2001) document that trade is promoted in ethnic networks through providing market information and supplying referral services.

There is ample evidence of information sharing through social network in finance literature. Hochberg et al. (2007) argue that syndication networks facilitate the sharing of information, contacts, and resources among venture capital firms and find that well connected VCs experience better fund performance and are more likely to survive. Cohen et al. (2008) examine connections between mutual fund managers and corporate board members through shared education networks. They find that portfolio managers hold more stocks of the firms they are connected to through their network, and perform significantly better on these holdings relative to their non-connected holdings.

Further, Cohen et al., (2010) investigate the educational ties between sell side analysts and corporate officers. They hypothesize that analysts’ networks provide them with valuable information. Consistent with the hypothesis, they find that analysts outperform on their stock recommendations when they have an educational link to the company. Cai and Sevilir (2012) examines merger transactions between firms with

current board connections and argues that such connections may facilitate information flow and communication between acquirer and target. They find that acquirers’ announcement returns when a common director sits on the board of both the acquirer and the target. Acquirer returns are also higher in transactions in which one acquirer director and one target director serve on the same third board. Engelberg et al. (2012) examine the effect of social ties between borrower and lender on loan interest rates. They argue that relationship based banking may facilitate improved information flow and reduce monitoring cost. Their results are consistent with the hypothesis that firms that have social ties with banks obtain loans with lower interest rate and fewer covenants. Hong et al. (2004, 2005) hypothesize information about stocks transmits among investors by word of mouth. They show that trades of mutual fund managers are influenced by the trades of other managers in the same city.

2.2. Contract Enforcement

Contract enforcement is an important component of social capital. Social relationships form reputation for honest dealing and impose reputation loss on dishonest dealing. Hence, despite the presence of formal contract enforcement in the form of legal actions, economic agents rely on informal arrangements that are enforced through social capital. Macaulay (1963) shows that application of social pressure and reputation are more pervasive than formal contracts and lawsuits. Kandori (1992) shows that the social norm of cooperation can be sustained as an equilibrium even when there is no information available about the individuals in a network. Johnson, McMillan and

Woodruff (1999) investigate formal and informal forms of contract enforcement using survey data from Russia, Ukraine, Romania, Poland, and Slovakia. They find that

relational contracting can be substituted for courts when suppliers can avail information about the customer through a social network. Greif (1993) examine that the trading practices of the 11th century Maghribi traders and show that agents accused of dishonesty were punished by the entire community.

Overall, these studies depict that contract enforcement plays pivotal role in circumstances of imperfect monitoring and deterring opportunistic behavior since legal system may seem inefficient due to the cost of litigation.

2.3. Trust

Social capital fosters trust that facilitates norms of reciprocity within a network.

Coleman (1988) defines trust as an important form of social capital on which future obligations and expectations may be based. He suggests that trust can only be produced in informal, small, closed and homogeneous communities which are able to enforce normative consents. Putnam (1993) regards trust as a source of social capital that sustains economic dynamism and governmental performance. Fafchamps (2004) argues that trust is an optimistic expectation or belief regarding other agents' behavior.

Importance of trust in economic transactions is inextricable as trustworthiness ensures reliance on future actions at a lower cost. Arrow (1972) argues that almost every economic transaction contains element of trust and attribute that lack of trustworthiness for much of the economic backwardness in the world might. Fukuyama (1995) suggest that the level of trust in a society strongly influences its economic success. Knack and

Keefer (1997) show that economic growth is a direct function of country level trust.

LaPorta, Lopez-de-Silaries, Shleifer and Vishny (1997) find that more trustworthy countries experience greater judicial efficiency and lower government corruption. Guiso,

Sapienza, and Zingales (2004, 2008) document that trust derived from social capital facilitates greater participation in the stock market. Dyer and Chu (2003) find that trust has a significant positive effect on information sharing between a supplier and a buyer.

Trust is also effective in increasing employee cooperation (Smith et al., 1995) and solving agency problems (Jones, 1995).

3. Primary Hypotheses

I construct two competing hypotheses that may explain the effect of media connection on corporate finance policies. Information Efficiency Hypothesis, suggests that media connection may facilitate firms with broadcasting of sufficient and timely information on important corporate finance policies. Several arguments construct the hypothesis. First, information flow channel of social capital would facilitate efficient transmission of information between corporate executives and journalists. Tambini

(2010) argues that speed and complexity are the key challenges faced by financial journalists. They have been experiencing ever increasing pressure to write more stories in less time. With the advent of internet and social media, journalists are forced to produce news materials within short intervals. Complexity of the financial stories is another challenge faced by the journalists2. Social ties with corporate executives would, therefore, ameliorate journalists’ challenge of sourcing news materials and alleviate their lack of skills to interpret financial news.

Second, contract enforcement mechanism of social capital would ensure strict scrutiny and verification of the information provided by the corporate executives to the

2 BBC Business Editor Robert Peston mentions in an interview that explaining Collateralized Debt Obligations (CDOs) through his reporting was quite challenging. Moreover, even bankers creating the CDOs were unable to describe them in simple terms. 28

journalists. Journalists would enjoy informal reputation for accurate reporting and face reputation loss for spreading rumor or fake news. Therefore, journalists would enforce extra due diligence and investigation of news articles of the firms in which they have social ties. Altogether, if media publishes more timely and accurate news articles of the connected firms and stock prices of these firms are more efficient due to better information flow, information efficiency hypothesis predicts that media connected firms would be able to reduce information asymmetry among its investors and enjoy better governance. These firms are also less likely to be able to manipulate share price prior to corporate events.

Manipulation Hypothesis, in contrary, suggests that social ties between financial journalists and executives may catalyze the manipulation of information prior to implementation of corporate finance policies. Several reasons could explain media’s incentive to engage in such manipulation. First, journalists’ relationship with firms may impair journalists’ independence and thus create conflict of interest between journalists and investors. Pavlik (2004) argues that journalists who have close relationship with a source may fail to verify the integrity of the information provided by the source.

Second, as journalists become dependent on a specific source, they may become more concerned about maintaining their relationship with the source. Dyck and Zingales

(2003) argue that media slant may occur due to such quid pro quo relationship between journalists and corporate executives.

Third, journalists’ career concerns could make them less skeptical (Doyle, 2006) and discourage them from scrutinizing corporations (Jamieson and Campbell, 2000;

Herman and Chomsky, 1988). Journalists may be concerned about retaining their job or prospect of future employment if corporations complain to their editors (McNair, 2002).

Finally, trust is an important form of social capital (Coleman, 1988) and has a significant effect on information sharing (Dyer and Chu, 2003). Trust induced through the social ties would convince the journalists to the information shared by their corporate friends. Manipulation hypothesis, thus, suggests that firms may strategically utilize media connection to alter the information flow that may not paint true picture of the firm’s prospects and facilitate greater misvaluation of stock prices prior to implementation of corporate finance policies.

CHAPTER III: MEDIA CONNECTION AND CORPORATE INVESTMENT

POLICIES

1. Introduction

In this essay, I investigate the effects of executives’ media connections on corporate investment policies. As part of the investment policy, I focus on corporate investments in acquisitions and innovative projects. Specific research questions include:

Do acquirers’ media connections affect bid announcement return, takeover premium, likelihood of deal completion and post-merger long term performance? Do firms exploit media connection to improve innovative efficiency?

While extant literature provides several hypotheses explaining firms’ acquisition motives and the impact of deal and merger characteristics, Ahern and Sosyura (2014) have recently proposed the “Active Media Management Hypothesis,” which substantiates the role of the media as a vital player in M&A transactions. The hypothesis predicts that firms tend to inflate stock prices prior to merger announcements by strategically manipulating information through media coverage. Their findings suggest that active media management reduces the average takeover premium by between $230 million and

$558 million. Given that acquirers can strategically use the media to manipulate the quantity and tone of coverage, social ties may offer additional advantages to the media connected acquirers. An acquirer’s inflated share price lowers acquisition costs by increasing the value of the deal currency.

Several studies investigate the consequences of social ties in corporate mergers and provides mixed evidence on the effectiveness of social ties in merger transactions.

Ferris et al. (2016) specifically looks at acquirer’s political connections and conclude that politically connected acquirers find it helpful to overcome regulatory barriers in the merger process. As social ties with regulators seem beneficial in M&A deals, it also appears interesting to investigate whether acquirer’s connection with media personnel affect the information flow during merger negotiations. Moreover, the media’s ability to shift public opinion is a widely-documented phenomenon (DellaVigna and Kaplan, 2007;

Gerber et al., 2009) and thus media connections may aid in creating public opinion in favor of the merger transaction after the deal is announced. Given these motivations, I examine the effect of acquirers’ media connections on bid announcement returns, takeover premiums, deal completion likelihood, acquisitiveness, and long-term performance following corporate merger transactions.

Corporate investment in innovation projects facilitates growth and enhances firm value3. While there is no doubt that innovation is a pivotal investment policy decision, assessing a firm’s innovative efforts is not straight orward. R&D expenditure, available in financial statements, is an input to the process of innovation and does not convey information about the output and the nature of the innovative projects (Lev & Zarowin,

1999). In an attempt to assess the expected payoffs from innovation projects, investors are forced to look beyond the financial statements (Healy and Palepu, 2001) and seek information from alternative sources. News media is one such alternative source that help

3 R&D expenditure is associated with growth in firm-level productivity (Griliches, 1986), long-term abnormal operating performance (Eberhart et al., 2004). Patents and patent citations also create long term value (Trajtenberg, 1990; Harhoff et al., 1999). 32

firms to communicate the value of innovative efforts and investors also react immediately to announcements of innovation projects (Chan et al., 1990). Therefore, I investigate whether media connection affects efficiency of the innovation projects and result in value creation in the long run.

Reviewing studies from sociology, journalism, economics and finance, I construct two competing hypotheses that may explain the effect of media connection on corporate merger activities and innovative efficiency.

The Information Efficiency Hypothesis suggests that media connections may offer acquirers an efficient flow of information and an improved corporate governance environment. Acquirers’ social ties with media outlets is likely to promote efficient share prices prior to merger announcements and thus facilitate value increasing merger deals.

Information Efficiency Hypothesis also predicts that media connection would facilitate greater efficiency in firms’ investments in innovative projects. As media connection facilitates broadcasting of sufficient and timely information on innovation projects, it should mitigate information asymmetry and thereby improve innovative efficiency.

Media connection could also enhance innovative efficiency by reducing financing constraints. Friends in the media could help improve disclosure of innovation projects and publicize firm to a broader set of investors, thereby facilitating access to finance and reduce financing costs.

The Manipulation Hypothesis, in contrast, postulates that social ties between financial journalists and acquirers may distort the information flow to the public and create opportunities for effective manipulation. Testable predictions suggest that media connection would be positively associated with merger announcement return, likelihood

of deal completion, and acquisitiveness. Also, media connection would be negatively related with takeover premium, and post-merger long term operating performance. Firms may also strategically utilize media connection to camouflage their investment in suboptimal innovation projects. Agency problems inherent in innovative projects and information asymmetry of R&D investment entice managers to seek private benefits and invest in value decreasing innovation projects. Managers seeking private benefits through innovation projects may find it useful to exploit media connection in manufacturing false assurance to firms’ investors about the ongoing innovation projects. Manipulation hypothesis, thus, predicts that media connection would result in poor innovative efficiency.

I estimate the media connection variable by using the BoardEx social network database. First, firms in the ‘Media and Communications’ industry in BoardEx are identified as ‘media firms’. Then I categorize all the directors/ executives working in these media firms as ‘media directors’. I use the director connection file to find the directors who have prior or current employment, education or social connection with the media directors. Finally, I aggregate the number of media connections of all directors in a firm-year. Media connection measure (Log_MC) is the natural log of one plus the raw count of number of media connections for a firm-year.

My sample of mergers consist of the 3,157 deals made by 1,929 unique US acquirers that acquire targets in domestic market from 2000 to 2016. I use SDC

Platinum’s Mergers and Acquisitions database for acquisition data, Compustat for fundamentals and CRSP for stock price data. I analyze innovative efficiency by using the firm-year patent counts and patent citations data from the National Bureau of Economic

Research (NBER) patent database. Final sample for innovative efficiency tests contains

6,521 firm-year observations of 1,590 unique firms.

Empirical analysis show that media connection is an important determinant in merger transactions. Multivariate regression analysis reveals that media connection is positively associated with Cumulative Abnormal Return (CAR) around the bid announcements. This result is consistent with the Manipulation Hypothesis, which predicts that media connection facilitates acquirers with successful media management prior to bid announcement, thereby fostering greater mispricing of the acquirers’ shares and consequently resulting in greater bid announcement return. In line with the

Manipulation Hypothesis, I find that media connection reduces the takeover premium, increases the likelihood of successful completion of merger deals. Results also support the view that acquirers take advantage of the media connection to engage in more acquisitions as media connection increases the likelihood of more bids and acquisitions in a year. Cross-sectional analysis of merger announcement returns reveals that the effect of media connection on merger announcement return is greater for stock deals, smaller acquirers, acquirers located in small counties.

Analysis of innovative efficiency show that media connections affect innovative efficiency. An increase in media connection is associated with a statistically significant decrease in innovative efficiency. This result suggests that firms strategically utilize media connection to camouflage their investment in suboptimal innovation projects resulting in a negative relation between media connection and innovation efficiency.

Further empirical analysis illustrates that ex post firm performance is affected by the change in innovative efficiency attributable to media connections which suggests that the

effect of media connection on innovative efficiency is harmful for future firm performance.

The essay contributes to the finance literature on social capital as well as the literature on media’s role in finance. First, the paper is related to the emerging research on social networks in corporate finance. Extant evidence illustrates that analyst recommendations outperform for connected firms (Cohen et al., 2010), acquirer returns are higher for acquirer-target connected bids (Cai and Sevilir, 2012), borrower-lender connection reduces loan spreads (Engelberg et al., 2012), financier connection enhances the sensitivity of investment and external finance to Q (Javakhadze et al., 2016), and political connection increases the likelihood of deal completion and increases post- merger performance (Ferris et al. 2016). I contribute to this literature by studying another type of social connections (i.e. media connections), which has not been explored to date. I argue that conduits of social capital flowing through the social ties between corporate executives and media personnel may determine merger transaction outcomes and innovative efficiency. Second, this paper contributes to the debate on the media’s role in corporate finance and asset pricing. One line of research concludes the media facilitates the efficient flow of information and act as a ‘watchdog’ to the corporate world. These studies show that media coverage reduces information asymmetry around earnings announcements (Dyck and Zingales, 2003; Bushee et al., 2010), tone and content of media coverage predicts stock returns (Tetlock, 2007; Tetlock et al., 2008; Engelberg,

2008; Garcia, 2013), media coverage generates investor attention (Huberman and Regev,

2001; Barber and Odean, 2008; Engelberg et al., 2012), media detects accounting fraud

(Miller, 2006), enhances corporate governance (Dyck and Zingales, 2002; Djankov et al.,

2003; Dyck et al., 2008), monitors CEO compensation (Core et al., 2008; Kuhnen and

Niessen, 2012). Another stream of studies contend the media breeds biases such as readership bias (Baron, 2005; Mullainathan and Shleifer, 2005; Gentzkow and Shapiro,

2006), advertising bias (Reuter and Zitzewitz, 2006), ownership bias (Besley and Pratt,

2006), and quid pro quo bias (Dyck and Zingales, 2003). This study adds to this literature by examining whether corporate executives’ social ties with media personnel fosters information efficiency and monitoring or acts as another potential source of media bias, and its implications on corporate merger transactions and innovative efficiency.

The essay proceeds by reviewing relevant literature in section 2, explaining the hypothesis development in section 3, describing the data and methodology in section 4, analyzing empirical results in section 5, and providing concluding remarks in section 6.

2. Literature Review

2.1. Review of Merger and Acquisition Literature

2.1.1. Merger Announcement Return and Takeover Premium

The literature on M&A has identified several determinants of merger announcement return and takeover premium. Eckbo (2009) reviews M&A literature and reports that acquirers, on average, experience significantly negative bid announcement return. Key drivers of negative bid announcement return include acquirer’s size, target’s public status, method of payment etc. Moeller, Schlingemann, and Stulz (2004) show that return for acquiring firm shareholders is about 2% higher for small acquirers and it is independent of method of payment and whether the target is public or private. Several other studies also report similar negative relation between announcement return and

acquirer size4. Fuller et al. (2002) find that private targets are associated with positive abnormal announcement returns. They attribute the abnormal return to the lower acquisition prices or premiums in acquisitions of private target. Similar results are also demonstrated by Faccio et al (2006) for non-US acquirers.

Acquirers may pay the target shareholders in cash, stock, or a combination of the two. Existing literature shows that cash is the prevailing mode of payment in US merger deals (Travlos, 1987; Martin, 1996), followed by acquirer stock, and mixed deals. Chang

(1998) study the acquirer’s announcement returns of the deals with private targets and report that acquirers experience a positive abnormal return in stock deals and no abnormal return in cash deals. This is contrary to the negative abnormal return observed in deals with public targets.

Acquirers, in general, pay significant acquisition premium (Eckbo, 2009) and several factors affect takeover premium, such as: synergistic gain, managerial hubris, diversification benefit etc. Bradley et al. (1988) provide evidence of about 7.4% increase in the combined value of the target and acquiring firms. More recent evidences of synergistic gain include Andrade, Mitchell, and Stafford (2001), Bhagat, Dong,

Hirshleifer, and Noah (2005) and Betton, Eckbo, and Thorburn (2008). Another stream of literature argues that excess premium may be paid by acquirers suffering from hubris

(Roll, 1986) or overconfidence (Malmendier and Tate, 2005). Moeller (2005) report that powerful target CEOs are associated with lower takeover premiums.

4 Ahern (2010), Humphery-Jenner and Powell (2011), Harford et al. (2012) Golubov et al. (2015) 38

2.1.2. Price Manipulation Prior to Merger announcement

Shleifer and Vishny (2003) present a model of valuation driven acquisitions and show that acquirers use of overvalued stock to purchase real assets at a relatively cheaper price increases shareholder wealth in the long run. Such incentive motivates acquirers to manipulate their share price in merger transactions. As target shareholders receive acquirer’s shares in exchange for target’s shares, an inflated share price of acquirers reduces acquisition cost by increasing the value of deal currency.

Consistent with theoretical prediction, empirical literature provides evidence of price manipulation by acquirers prior to acquisitions through different techniques.

Earnings management is one of the prominent ones among these practices. Erickson and

Wang (1999) investigate earnings management by acquirers in stock merger deals and find that acquirers tend to inflate their stock price prior to merger transactions by engaging in earnings management. In addition, earnings management increases with the relative deal size. Louis (2004) shows that acquirers inflate their earnings in the quarter prior to merger announcement and pre-merger earnings management is associated with both the short-term and the long-term performance of acquirers in stock offers. Gong et al. (2008) investigate the association between pre-merger earnings management and likelihood of post-merger lawsuits. Their results show that earnings management increases the probability of lawsuits against stock-for-stock acquirers. Botsari and Meeks

(2008) conduct similar analysis on a sample of UK firms and find that acquirers engage in income increasing accrual manipulation in the year prior to the bid announcement. Ge and Lenox (2011) conjecture that acquirers can temporarily inflate their share price by revealing good news or evade stock price decline by remaining silent about bad news.

Results show that acquirers are more likely to conceal bad news about future earnings and are less likely to issue favorable earnings forecasts. Higher litigation risk associated with act of commission relative to act of omission explains this result.

Further, Kimbrough and Louis (2011) show that acquirers in stock-for-stock mergers are more likely to hold conference calls to announce the bid. Results further show that acquirers use merger related supplemental disclosure at the time of the merger announcement in order to create stock prices run-up. Ahern and Sosyura (2014) propose active media management hypothesis which predicts that firms’ strategic incentives may motivate manipulation of the relationship between information and stock prices through media coverage. They examine stock swap acquisitions and show that acquirers strategically initiate and distribute information to inflate their stock price during merger deals. Particularly, acquirers tend to withhold negative news or adjust the tone to make it appear less negative during merger transactions.

2.1.3. Effect of Social Ties in M&A

A number of recent studies document whether and how social ties affect the likelihood of merger and subsequent value consequences to the acquirer and target.

Schonlau and Singh (2009) show that firms’ network centrality is positively associated with acquisition performance and also with the likelihood of cash as the method of payment. Similarly, Bouwman and Xuan (2010) document that the likelihood of announcing a merger bid is associated with the existence of interlocking directors who sit on firms experienced in merger transactions. Cai and Sevilir (2012) examine the performance of merger deals in which a common board member sits in both the acquirer and the target. Their results suggest that acquirers experience greater announcement

returns in interlocking deals and attribute this abnormal return to the acquirer's ability to pay lower takeover premiums and lower advisory fees in such transactions.

Next, Ishii and Xuan (2014) test the impact of social ties between targets and acquirers on acquisition outcomes. They use educational background and past employment as a proxy for social ties between the acquirer and the target. This measure of social ties is related to agency issues because of favoritism among the entities in a social network. Khatib et al. (2015) investigate the effect of CEO network centrality on

M&A outcomes. Their results show that high network centrality CEOs engage in more frequent M&A deals and these deals are value destructing to the acquirer deals initiated by low centrality CEOs which is consistent with the view that high network centrality

CEOs use their power and influence to increase entrenchment and reap private benefits.

Ferris, Houston and Javakhadze (2016) demonstrate that politically connected acquirers increase the likelihood of avoiding regulatory delay and play higher merger premium.

More importantly, political connections create value for the acquirers as documented in both announcement period returns and post-merger financial and operating performance.

2.2. Review of Innovation Literature

2.2.1. Determinants of Innovation

A voluminous literature investigates the determinants of innovation both theoretically and empirically. Holmstrom (1989) constructs a theoretical model which shows that innovation projects may mix poorly with regular projects in an organization.

Aghion and Tirole (1994) argue that it is difficult to contract managers optimally ex ante since innovation outcomes are unpredictable. Their model suggests that firms’ organizational structure is an important determinant of innovation. Manso (2010) argues

that innovation is induced by managerial contracts that provide tolerance for short-term failure and reward for long-term success. Ferreira et al. (2012) develops a model that shows that private ownership stimulates innovation.

Empirical studies identify several firm characteristics and market conditions that motivate innovation. Francis and Smith (1995) find that innovation increases with ownership concentration. This result indicates that monitoring by concentrated owners mitigates agency costs and facilitates investment in innovation. Lerner et al. (2011) examine the effect of private equity investment on firm innovation. They find that patents filed after the LBO transaction generate more citations than those filed in the year of the

LBO transaction which implies that private equity ownership is conducive to innovation.

Aghion et al. (2013) argue that institutional investors may promote innovation by providing reassurance to managers who are concerned about their careers. Their results show support for the career concern hypothesis which suggests that firms with higher institutional ownership innovate more. Fang et al. (2014) find that stock liquidity is associated with a decrease in future innovation since liquid firms are exposed to greater threat of hostile takeovers. Chemmanur et al. (2014) report that firms backed by corporate venture capitals (CVC) are more innovative than those backed by independent venture capitals (IVC). They attribute CVC’s greater industry knowledge and greater tolerance for failure as possible mechanisms.

Other studies show that product market competition, market conditions, firm boundaries, banking competition, CEO overconfidence, external financial dependence, basic education of workers, and investors’ attitudes toward failure all affect firm innovation (Aghion et al., 2005; Nanda and Rhodes-Kropf, 2013, 2016; Tian and Wang,

2011; Hirshleifer et al., 2012; Seru, 2014; D’Acunto, 2014; Cornaggia et al., 2015,

Bloom et al., 2016).

2.2.2. Disclosure of Innovation Activities

Disclosures of innovation efforts are useful to investors since financial information based on traditional accounting statements are inadequate in illustrating the value creation through investment in innovation. Lev and Zarowin (1999) find a decline in the value relevance of earnings as a consequence of the increased R&D intensity, suggesting that the reporting of R&D activities does not effectively reveal the value and economic consequences of R&D investments. Amir and Lev (1996) document that nonfinancial indicators of performance in the wireless telecommunication sector have a significant association with stock prices. They argue that the inadequate accounting treatment of intangibles causes firms with a significant level of intangibles to employ non-financial information to supplement their financial statement information. Merkley

(2013) argues that the inadequate mandatory disclosure requirements of R&D activities, and its accounting measures do not reflect the performance of R&D activities. His results indicate that current earnings performance (adjusted for R&D expense) is negatively related to the quantity of narrative R&D disclosure.

Information problem regarding innovation activities may create a demand for more information beyond the financial statements and encourage management to introduce alternate modes of communication. Healy and Palepu (2001) show that outside investors who are interested in evaluating the economic performance of firms and potential future benefits from innovation projects are forced to look beyond the financial statements. Gelb (2002) finds that firms with high levels of R&D expenditure and with

insufficient disclosure will probably turn to other forms of disclosure. Franzen, Rodgers and Simin (2007) argue that earnings and book values of R&D intensive firms are distorted due to the full expensing of R&D expenses for financial reporting purposes.

Consequently, they find that R&D expenditure is positively associated with the likelihood of misclassifying solvent firms.

A firm’s decision to disclose innovation efforts is based on the trade-off between the benefits and costs of disclosure. Benefits of innovation disclosure include winning patent races (De Fraja, 1993), facilitating reciprocity in knowledge exchange (Hicks,

1995; Simeth and Raffo, 2013), and attracting talented human capital (Stern, 2004;

Sauermann and Roach, 2013).

Moreover, a large stream of literature shows that disclosure reduces cost of capital. Botosan (1997) and Botosan and Plumlee (2001) report that cost of equity capital decreases with the level of disclosure. Sengupta (1998) show that greater disclosure is associated with lower cost of debt. Welker (1995) finds bid-ask spread is directly proportional to disclosure and suggest that disclosure reduces information asymmetry.

In contrary, a major concern in innovation disclosure is the threat of revealing sensitive information to competitors (Verrecchia, 1983, 2001; Darrough and Stoughton,

1990), knowledge spillover (Arrow, 1962), and risk of losing talented employees to competitors (Stuart and Liu, 2014; Kim and Marschke, 2005). Therefore, voluntarily disclosure of innovation activities is optimal when the benefit of disclosure exceeds the corresponding cost. James et al. (2013) report that firms value the benefits from reducing information asymmetry in firm valuation more than the potential losses from knowledge spillovers to rivals. Guo et al. (2013) also find similar result.

Timing of the disclosure is also an important consideration for firms. A firm may disclose its patent application several months after it has applied for the patent or may choose to disclose an awarded patent months after the PTO’s official issuance.

Prior studies on voluntary disclosure find that the disclosure of good news decreases the cost of equity and the disclosure of bad news increases it (Clement et al.,

2003; Brown and Caylor, 2005). Managers, aware of such incentives, strategically disclose good news to reduce the cost of equity (Lang and Lundholm, 2000). According to the survey finding by Graham et al. (2005), CFOs believe that when firm’s nonfinancial leading indicators sketch bright picture of future earnings, market response to

EPS miss is less pronounced. Managers’ personal welfare concerns are also likely to define the disclosure timing decision. Extant literature demonstrate that managers use their discretion in voluntary disclosure decisions for personal gain (Aboody and Kasznik,

2000; Cheng and Lo, 2006).

3. Hypothesis Development

This section develops a set of testable hypotheses to examine the effect of media connection on corporate investment policies. I use the two primary hypotheses – information efficiency hypothesis and manipulation hypothesis – developed in chapter II to construct the testable hypotheses for this essay.

3.1. Media Connection and M&A

Examining the implications of hard information on corporate M&A activities is a long-standing issue in academic finance. Hard information is quantitative in nature and its information content does not depend on the interpretation. Prior literature shows that acquirers engage in earnings management (Erickson and Wang, 1999; Louis, 2004; Gong

et al., 2008), hold more conference calls (Kimbrough and Louis, 2011), and withhold imminent bad news about future earnings (Ge and Lenox, 2011) prior to merger announcements in an attempt to create price run-ups. These evidences substantiate manipulation of hard information (i.e. earnings, earnings forecast) which has costly consequences. Gong et al. (2008) show that the likelihood of post-merger lawsuits increases with the pre-merger earnings management in stock-for-stock acquisitions.

Thompson and Thomas (2003) find that plaintiffs receive large monetary settlements in acquisition related class action lawsuits.

Prior literature largely ignores the likelihood of price manipulation through soft information (i.e. qualitative details of deal), which is difficult to summarize in numbers.

Such deceptive nature makes soft information more likely to be manipulated. Solomon

(2012) finds corroborating evidence that firms find it easier to spin non-earnings announcement news than earnings announcement news. He argues that non-earnings news contains more soft information which allows firms to manipulate interpretation of qualitative information on corporate events. Ahern and Sosyura (2014) provide direct evidence in this regard and show that bidders in stock mergers generate significantly more news stories after the start of merger negotiations which generates run-up in acquirer’s stock price. The incentive for such strategic news disclosure is to achieve an overpriced deal currency in stock deals.

In line with the above arguments, the presence of social ties between acquirer’s executives and media personnel would catalyze the manipulation of soft information in

M&A deals. According to the manipulation hypothesis discussed in previous chapter

(chapter 3.1), media-connected acquirers would be successful in broadcasting good news

and concealing bad news prior to merger announcements. Along with the timing of good/ bad news, media connected acquirers may also find it beneficial to manipulate the tone of their news articles prior to merger announcements. Such media management would foster greater mispricing of the acquirers’ shares and consequently result in greater bid announcement return than that experienced by non-media connected acquirers.

Alternatively, information efficiency hypothesis suggests that social ties between acquirer’s executives and media personnel may provide the media with access to more accurate, relevant and timely information about the deal and acquirer’s future pay-offs, thereby reducing information asymmetry and consequently the extent of price surprise on merger announcement. If media publishes more accurate news articles of the connected acquirer and stock prices of these firms are more efficient due to better information flow, information efficiency hypothesis predicts that abnormal merger bid announcement return would be lower for media connected acquirers than for non-media connected acquirers. Consistent with this argumentation, I hypothesize the following:

H1a: CAR around merger announcements are higher for media-connected acquirers (Manipulation). H1b: CAR around merger announcements are lower for media-connected acquirers (Information Efficiency).

Media connection may also affect acquirer’s takeover premium. Manipulation hypothesis predicts a negative relationship between media connection of acquirer and takeover premium. Well-connected acquirers could have bargain advantage during merger negotiations. Prior evidence (e.g., Schwert 2000) suggests that target company directors often resist potential mergers in an attempt to improve merger terms (e.i. shareholder interest hypothesis5) that forces bidders to raise offered premium. However,

5 Bugeja and Walter (1995), Hubbard and Palia (1995). 47

potential acquirers could use media connections strategically as a bargaining tool and disseminate negative information about targets in the press effectively undervaluing these firms and thus reducing observed premium.

Information efficiency hypothesis, in contrary, predicts a positive relationship between media connection of acquirer and takeover premium. Acquirers usually target undervalued firms in an attempt to capture synergistic gains. Betton et al. (2014) show that market receives informative signals about potential acquisitions with synergistic gains. Information flow and monitoring mechanism would motivate media to reveal the true value of the target upon announcement. In addition, media may also highlight the synergistic gain from potential merger bids. Consequently, it would be difficult for acquirers to undervalue targets and conceal synergic gains from acquisitions. Thus, observed premium paid to target will be higher. This analysis lead to the following set of hypotheses:

H2a: Media connected acquirers pay lower takeover premiums (Manipulation). H2b: Media connected acquirers pay higher takeover premiums (Information Efficiency). Acquirer’s media connection would be also beneficial in successful completion of merger deals. Once a merger deal is announced, shareholders of both acquirers and targets continue to learn about the transaction through additional information. Becher et al. (2015) show that the likelihood of deal completion increases as analysts provide more upgrade recommendations on acquirer stocks. I conjecture that news media could be another important channel for acquirers to broadcast favorable deal sensitive information to the investors. In fact, market do react negatively to those M&A deals that generated extensive negative media coverage (Dutta, John, Saadi, and Zhu, 2014).

Media connection may aid in creating public opinion in favor of the merger transaction after the deal is announced. Media’s ability to shift public opinion is a widely- documented in the literature (DellaVigna and Kaplan, 2007; Gerber et al., 2009). Studies beyond the finance literature also illustrate media’s power in influencing public opinion with regard to when and what to think about (Entman, 2007; Couldry, 2003). Stronger public opinion is likely to catch attention of the politicians and regulators, and create thereby an incentive for them to respond to public opinion (Culpepper, 2010). Therefore, from manipulation perspective, acquirer’s social ties with media would contribute to the likelihood of deal completion.

Information efficiency hypothesis, however, predicts a lower likelihood of deal completion for media connected acquirers. Efficient flow of information and strict monitoring would ensure that media portrays actual picture of the target and informs the market about the deal characteristics. Consequently, deal proposals will be more scrutinized in these firms, which leads to a decrease in the probability of deal completion.

Thus, watchdog role of media, therefore, predicts that media connected firms would face difficulty in completing merger deals. Above discussion advances the following hypotheses:

H3a: Bids by media connected acquirers are more likely to close (Manipulation). H3b: Bids by media connected acquirers are less likely to close (Information Efficiency).

As acquirers observe that media connection facilitates higher bid announcement returns, lower takeover premium and faster deal execution, they may want to take advantage of the media connection and engage in more acquisitions. Manipulation hypothesis, therefore, predicts that media connected acquirers would be more acquisitive.

On the other hand, information efficiency hypothesis, predicts less manipulative

discretion for the media connected acquirers. Merger deal proposals will more thoroughly be analyzed in these firms and risky (and potentially value-destroying) acquisitions more avoided. In addition, absence of price run-up prior to bid announcement and disclosure of the synergistic gain by the watchdog media would further deter media connected firms from repeated acquisitions. Thus, the two-sided testable prediction is following:

H4a: Media connected acquirers are likely to be more acquisitive (Manipulation). H4b: Media connected acquirers are likely to be less acquisitive (Information Efficiency).

If an acquisition attempt is value enhancing for the shareholders, acquirer may not need to engage in media management. Managers are more likely to engage in media manipulation when they devise opportunistic takeovers. Manipulation hypothesis, thus, predicts that media connected acquirers would underperform in the long run relative to the non-media connected firms. Information efficiency hypothesis, in contrary, predicts that media connected acquirers would exhibit better post-merger long term performance.

Governance role of the media ensures that only value enhancing deals are completed.

Hence, there would be limited scope for the media connected acquirers to engage in value destroying mergers.

H5a: Post-merger long term performance are lower for media connected acquirers (Manipulation) H5b: Post-merger long term performance are higher for media connected acquirers (Information Efficiency)

3.1.1. Moderating Effect of Political Connection

Politicians and media are distinct, often opposing actors of the fabric of modern civil society. Given prior evidence that political connections matter for corporate policies, it is natural to examinine interactive effects of political and media connectedness.

Gropper et al. (2013) present evidence that political connections have important valuation

implications in banking industry. Ferris et al. (2016) demonstrate that acquirer’s political connectedness impact deal completion, bid announcement returns and post-merger performance. Their key argument is that politically connected acquirers find it convenient to overcome the regulatory barriers in the merger process. Moreover, Chaney et al.

(2011) show that politically connected firms have more opaque information environments, which is likely to motivate managers to engage in media manipulation.

Since political connection is an ingredient of merger outcomes, it would be interesting to investigate whether political connection and media connection complement or substitute each other.

H6a: The effect of media connections on bid announcement return, takeover premium and deal completion likelihood is greater for politically connected acquirers (Complement). H6b: The effect of media connections on bid announcement return, takeover premium and deal completion likelihood is lower for politically connected acquirers (Substitute).

3.1.2. Stock Deals

Acquirer’s price manipulation incentives are expected to be greater in stock-for- stock deals. In stock deals, target and acquirer negotiate a fixed number of acquirer shares in exchange for target shares. Therefore, acquirers are more likely to foster price manipulation through media connection prior the bid announcement. Moreover, as acquirers tend to manipulate price to achieve overvaluation, these overvalued firms may find stock deals more suitable than cash deals to create value for long-term shareholders by using their equity as currency (Savor and Lu, 2009). I therefore, conjecture that the effect of media connection on merger outcomes would be greater for stock deals than for cash deals.

H7: The effect of media connections on bid announcement return, takeover premium and deal completion likelihood is greater in stock deals.

3.1.3. Moderating Effect of Firm Recognition

Media companies are commercial entities and are likely to maximize profits by responding to readership appeals6. Similar logic could be applied for media coverage of acquisitions by large firms versus small firms. Large and well recognized firms attract greater readership than small and less recognized firms. Moreover, media is biased towards the advertisers as advertisement constitutes a significant portion of newspaper revenues (Reuter and Zitzewitz, 2006; Gurun and Butler, 2012). Larger firms are likely to spend more on advertising thereby influencing media companies to deliver biased coverage of these firms.

As small and less recognized firms are prone to both readership and advertising bias, media connection may facilitate transmission of the news of an upcoming merger for these type of firms. Larger and well known firms are less likely to require a favor from their friends in the media to broadcast deal related information. This leads to the following hypothesis regarding the moderating effect of firm recognition:

H8: The effect of media connections on bid announcement return, takeover premium and deal completion likelihood is greater for less recognized firms (smaller firms, firms with fewer advertising expenses).

3.1.4. Geographic Proximity to Large Metropolitan Area

Apart from familiarity, newsworthiness of an event may be also determined by the event’s geographic proximity to large readership areas. Adams (1986) studies news

6 An anecdotal example of readership bias is depicted by the substantially greater media coverage of the terrorist attack in Paris relative to the similar type of attack in Beirut. Carolyn Gregoire, in an article on Huffington Post, attributes such discrepancy in media coverage to less familiarity of Beirut to the American people. Stanford University psychologist Emma Seppala argues that “Most people in the US can’t even locate Lebanon on a map” and also “Research shows that we do feel more empathy for people that we feel are more similar to us”. 52

coverage of 35 natural disasters between January 1972 and June 1985 that each took at least 300 lives. He finds that other than the death toll, an important factor in determining media coverage of an international disaster is the event’s geographic proximity to the US.

If geographic proximity to US generates greater media coverage of a natural disaster, it could be also argued that firms in larger metro areas are more likely to receive greater media coverage. Demand for news about these firms is driven by the large pool of local investors. Therefore, firms headquartered in smaller metro areas would face difficulty in disseminating deal related information through media. Media connection may facilitate firms in smaller metro areas to transmit their acquisition related information to the market. Therefore, the effect of media connection would be greater for firms headquartered in small metro areas.

H9: The effect of media connections on bid announcement return, takeover premium and deal completion likelihood is greater for firms headquartered in smaller metro areas.

3.2. Media Connection and Innovation

Corporate investments in innovative projects face various challenges. Innovation projects usually require large investment over a long time-period and contain highly uncertain payoff, making it difficult for investors to evaluate a firm’s investments (Lev &

Zarowin, 1999). Therefore, firms that invest in innovative projects suffer from information asymmetry (Bhattacharya and Ritter, 1983), invest sub-optimally (Stein,

1989), and face undervaluation of investments (Froot et al., 1992). Additionally, investors are forced to look beyond the financial statements in order to assess the potential future benefits from innovation projects (Healy and Palepu, 2001).

From information efficiency perspective, media connection could have implications for firm innovations in two ways. First, social links with media could mitigate the difficulty faced by firms to convey information of innovation projects to investors. Media is a vital information intermediary in the financial market. Media connection may facilitate broadcasting of sufficient and timely information on innovation projects (That is, innovative firms make announcements about their imminent innovations, reveal experiment successes, and provide tentative product launching dates.

Besides the regular disclosure by firm on patents and R&D endeavors, journalists also write stories about the viability of the projects, industry trend in similar innovation projects, competitor’s attempts). As journalists face pressure through contract enforcement channel of social capital, their reporting ensures the accuracy and fairness of the news articles on innovative projects of the connected firms. Consequently, media connections should ameliorate information asymmetry and thus media connected firms would exhibit greater innovation efficiency.

Second, media connections could reduce financing constraints and through this channel increase innovation efficiency. Highly innovative firms suffer severely from limited external financing (Hall and Lerner, 2010). A wide array of studies show that media coverage affects asset prices mainly by disseminating information or by increasing visibility. Friends in the media could help improve disclosure of innovation projects and publicize firm to a broader set of investors, thereby facilitating access to finance and reduce financing costs. Bushman et al. (2016) show that media sentiment is associated with lower loan spreads in private lending. More relevant to our study, Di Giuli and Laux

(2016) find that directors’ media expertise is associated with an increased supply of debt and equity.

Manipulation hypothesis, however, contends that firms may strategically utilize media connection to camouflage their investment in suboptimal innovation projects.

Innovation projects are prone to agency problems since these projects are intangible in nature and highly uncertain in terms of pay-off (Kumar and Langberg, 2009; Hall and

Lerner, 2010). Agency problems inherent in innovative projects may entice managers to seek private benefits and invest in value decreasing innovation projects. Moreover, information asymmetry of R&D investment facilitates insiders with gain from insider trading (Aboody and Lev, 2000). Managers seeking private benefits through innovation projects may find it useful to exploit media connection in manufacturing false assurance to firms’ investors about the ongoing innovation projects. Manipulation hypothesis, thus, predicts that media connected firms would exhibit poor innovation efficiency compared to the non-media connected firms, who are unable to mask their investments in abysmal innovation projects.

H11a: Media connected firms exhibit higher innovation efficiency (Information Efficiency) H11b: Media connected firms exhibit lower innovation efficiency (Manipulation)

Next, changes in innovative efficiency attributable to media connections could have implications for firm performance. Hirshleifer et al. (2013) show that innovation efficiency strongly predicts future stock returns and operating performance. They argue that difficulty of evaluating the economic implications of patents stimulate investors’ under reaction to the information content of innovation efficiency. Firms with more

innovation efficiency, therefore, are likely to be undervalued and subsequently exhibit better stock returns and operating performance in future.

Information efficiency hypothesis predicts that by mitigating information asymmetry and by alleviating financing constraints, media connections would help firms accomplish value increasing innovation projects and reap long term benefits for the shareholders. Therefore, media connected firms would exhibit higher long-term performance and stock returns attributable to media connection driven innovation efficiency.

On the other hand, Manipulation hypothesis contends that managers are more likely to engage in media manipulation when their R&D investments perform abysmally.

Manipulation hypothesis, thus, predicts that media connected firms would underperform in the long run relative to the non-media connected firms. Above discussion leads to the following testable hypotheses:

H12a: Change in innovation efficiency attributable to media connection is positively associated with future stock returns and operating performance (Information Efficiency) H12b: Change in innovation efficiency attributable to media connection is negatively associated with future stock returns and operating performance (Manipulation)

4. Data and Methodology

4.1. Media Connections Measure

I estimate media connections measure by using the social network database from

BoardEx. I begin with identifying the firms that are part of the ‘Media and

directors identified in the previous step to obtain the connections of directors with the media directors. Then I count the number of current and prior connections of directors of a firm by each year and then aggregate the number of connections for each firm-year.

Finally, I construct the measure of media connection (Log_MC) by taking log of one plus the number of media connections for a firm-year.

4.2. Data and Sample Selection

I construct the merger sample by collecting acquisition data from SDC Platinum’s

Mergers and Acquisitions database. My initial sample contains 27,778 acquisitions made by US acquirers of public and private US targets from 2000 to 2016. I choose 2000 as the initial year since Boardex provides social network data starting from 1999. Then I merge this sample of mergers with the CRSP database to obtain stock returns data and

Compustat database to obtain firm fundamental data. Finally, I merge this sample with the acquirers’ media connections. I apply the following restriction criteria to obtain the final sample:

 The acquirer should acquire at least 50% of the target.

 Deal value should be at least 1 million USD.

 The acquirer should own 50% of the outstanding shares of the target six

months prior to the bid announcement and seeks to acquire 90% after the

deal.

 The relative size of the deal value to acquirer size is at least 1%.

Final sample contains 3,157 deal observations of 1,929 unique acquirers. 1,207 acquirers have at least one media connection and the average logged media connection

(Log_MC) is 3.36.

I construct a separate sample to test the hypothesis related to innovative efficiency. I collect the data on patent counts and patent citations from the National

Bureau of Economic Research (NBER) patent database as described in Kogan et al.

(2017) and shared in Noah Stoffman’s website7. The database contains data on the patents granted by the United States Patent and Trademark Office (USPTO) from 1976 to

2010. I begin sample construction with all the firms in CRP-Compustat universe starting from 2000 to 2016. I choose 2000 as the initial year since Boardex provides social network data starting from 1999. Then I merge this firms with the patent database using

GVKEY. Final sample contains 6,521 firm-year observations of 1,590 unique firms. I winsorize all variables at 1% level to eliminate the effect of outliers.

4.3. M&A Measures and Control Variables

I examine the effect of media connections on several merger related variables. I estimate CAR1 (CAR2) as the cumulative abnormal return over the [-1, +1] window around merger announcement date. Abnormal returns are the difference between daily returns and CRSP value (equal) weighted portfolio returns. Takeover premium (PREM) is estimated as the percentage change in offer price relative to target’s share price 42 days prior to the bid announcement, following Officer et al. (2003). I examine the post-merger long term performance by analyzing the acquirers’ operating performances in years t+1, t+2, and t+3, where t is the year of acquisition. I use return on assets (ROA), calculated as earnings before interest and taxes scaled by total assets, as a proxy for operating performance. I also use Tobin’s Q (Q), estimated as the market value of assets divided by the book value of assets, as a proxy for operating performance.

7 https://iu.app.box.com/v/patents 58

Multivariate regressions include several control variables that are found to be correlated with merger outcomes motivated by prior research. These controls include acquirer and target characteristics and also deal characteristics. Acquirer (target) characteristics include: SIZE_ACQ (SIZE_TGT) estimated as the log of the market value of the acquirer (target) at the end of the year before the announcement date, MB_ACQ

(MB_TGT) estimated as the market value of equity divided by book value, LIQ_ACQ

(LIQ_TGT) calculated as cash and equivalents scaled by total assets, ROA_ACQ

(ROA_TGT) measured as earnings before interest and taxes scaled by total assets.

Target’s stock price runup is also a standard control in merger literature. I estimate the runup in target’s stock price (RUNUP_TGT) during the 42 days prior to the merger announcement. I also control for the acquirer’s governance quality by including the acquirers’ board size (BRDSIZE_ACQ) as a control. Deal characteristics include dummy variables for tender offer (TENDER), hostile deals (HOSTILE), deals with competing bidders (COMPETE), non-diversifying deals (NONDIV). I also include the ratio of deal value to acquirer’s market value as relative size (DEAL_VAL) since relative size of the deal is positively correlated with the acquirer's CAR8.

In Table 1, Panel A shows the descriptive statistics of the variables. An average deal in my sample generate positive announcement returns (about 1%) and targets were sold at a moderate premium (about 11%). On average, acquirers are profitable with a

ROA of 2.0%, possess growth opportunity with market-to-book ratio of 2.9. Targets, on

8 Asquith, Bruner, and Mullins (1983), Jarrell and Poulsen (1989), and Servaes (1991) 59

average, are not profitable with a ROA of -3.1%, have growth opportunity with market- to-book ratio of 2.0. Also, acquirers are bigger in size and holds more cash than the targets.

Panel B reports the distribution of bids and acquisitions by industries. The most acquisitions occurred in the Business Equipment industry and the least in the Chemicals and Allied Products. In terms of media connections, firms in Telephone and Television and Transmission industry contain the most media connections. This is quite likely as media companies are also located within this industry.

Panel C provides year wise distribution of acquisitions. The highest number of bids and acquisitions occurred during the year 2000. A downward trend in acquisitions since 2000 is also observed in the table. The most recent time period of 2011 to 2016 experienced one of lowest number of acquisitions in the history.

4.4. Innovative Efficiency Measures and Control Variables

I calculate a total of four proxies for innovative efficiency: two based on number of patents and the other two based on number of citations. Following Hershleifer et al.

(2013), I define innovative efficiency IE1 as the ratio of the raw number of patents to

R&D capital (RDC). RDC is measured as the 5-year cumulative R&D expenses starting from fiscal year ending in year t-2 to year t-6 assuming an annual depreciation rate of

20%9. I estimate another measure of innovative efficiency IE2 as the ratio of the adjusted patents to RDC. Following Almeida et al. (2013), adjusted patents is calculated as the

9 Following Chan et al. (2001) and Lev et al. (2005), I estimate RDC using the following equation: 푅퐷퐶푖,푡 = 푅&퐷푖,푡−2 + 0.8 ∗ 푅&퐷푖,푡−3 + 0.6 ∗ 푅&퐷푖,푡−4 + 0.4 ∗ 푅&퐷푖,푡−5 + 0.2 ∗ 푅&퐷푖,푡−6

number of patents in each technological class by the cross-sectional average number of patents applied in the same year and assigned to the same technological class by the

USPTO.

I construct the two proxies based on patent citations by following similar methodology as in for those based on number of patents. As in Hershleifer et al. (2013), I measure innovative efficiency IE3 as the ratio of the raw number of citations to RDC.

Similar to IE2, I estimate IE4 as the ratio of adjusted citations to RDC. Adjusted citation is calculated by dividing the number of citations of a patent by the total number of citations received by all patents in that year in the same technological class as the patent.

Factors affecting innovative efficiency are controlled in multivariate regressions.

Following Seru (2014) I control for firm size estimated as sales (SALE), R&D expenditures scaled by total assets (RD), leverage scaled by total assets (LEV), firm profitability estimated as operating income before depreciation scaled by total assets

(EBITDA), operating cash estimated as cash scaled by total assets (CASH), asset tangibility measured as net property plant and equipment divided by total assets (PPE), growth opportunities calculated by the market-to-book ratio (MB), industry concentration estimated as the Herfindahl index (HI) and its square (HHI_SQ). I also control for the natural log of the ratio of total assets to the number of employees (Ln_KL) since Ln_KL controls for the association between capital intensity and firms’ innovation performance10.

5. Empirical Results

5.1. Mergers and Acquisitions

10 Almeida et al. (2013) Aghion, Van Reenen, and Zingales (2013) 61

5.1.1. Merger Announcement Returns

Social ties between acquirers and media personnel may provide the media with access to more accurate, relevant and timely information about the deal and acquirer’s future pay-offs, thereby reducing information asymmetry and consequently the extent of price surprise on merger announcement. In contrast, media connected acquirers would be successful in broadcasting good news and concealing bad news prior to merger announcements resulting in a greater mispricing of the acquirers’ shares and a greater bid announcement return. I test this conjecture by examining the relation between merger announcement returns and media connections in table 2.

I estimate merger announcement return CAR1 (CAR2) as the cumulative abnormal return over the [-1, +1] window around merger announcement date. Abnormal returns are the difference between daily returns and CRSP value (equal) weighted portfolio returns. Then I regress CAR on acquirers’ media connections and control for acquirer, target and deal characteristics. Acquirer and target characteristics include: size, growth opportunity, liquidity and profitability. I also include industry and/ or year fixed effects. Results in table 2 demonstrate that the coefficient of Log_MC is positive and significant across all specifications. On average, a 1 percentage point increase in acquirers’ media connection is associated with 1.23 (in column (4)) to 1.79 (in column

(1)) percentage point increase in acquirer’s CAR. Results are robust to inclusion of deal characteristics in addition to acquirer and target characteristics. Coefficient on most of the control variables exhibit expected sign. Acquirers’ bid announcement return is positively associated with acquirer profitability, liquidity, target liquidity, and negatively

related with acquirer size, growth opportunity, target size, profitability, and growth opportunity. In summary, results from table 2 provide support for the manipulation hypothesis. Acquirers’ social ties with media personnel would facilitate broadcasting of good news and concealing of bad news prior to merger announcements. Along with the timing of good/ bad news, media connected acquirers may also find it beneficial to manipulate the tone of their news articles prior to merger announcements. Such media management would foster greater mispricing of the acquirers’ shares and consequently result in greater bid announcement return.

5.1.2. Merger Takeover Premium

In table 3, I analyze the association between takeover premium and acquirers’ media connection. Media connection may positively affect takeover premium as information flow and monitoring mechanism would motivate media to reveal the true value of the target upon announcement. Transparency of information would make it difficult for the acquirers to undervalue targets and conceal synergic gains from acquisitions, thereby causing higher takeover premium. Potential acquirers, however, could use media connections strategically as a bargaining tool and disseminate negative information about targets in the press effectively undervaluing these firms and thus reducing observed premium.

Following Officer et al. (2003), I estimate takeover premium (PREM) as the percentage change in offer price relative to target’s share price 42 days prior to the bid announcement. I control for acquirer and target characteristics in columns (1) and (2), then add deal characteristics in columns (3) and (4). The explanatory variables are the same as in table 2. I also include industry and/ or year fixed effects.

I observe that the coefficient of Log_MC is negative and significant across all specifications. On average, a 1 percentage point increase in acquirers’ media connection is associated with 3.22 (in column (4)) to 3.89 (in column (1)) percentage point decrease in takeover premium. Results are robust to inclusion of deal characteristics in addition to acquirer and target characteristics. Coefficient on most of the control variables exhibit expected sign. Takeover premium is positively associated with acquirer size, profitability, growth opportunity, size, and negatively related with acquirer liquidity, target size, profitability, and growth opportunity. Overall, I find a negative relationship between takeover premium and acquirers’ media connection which supports Manipulation

Hypothesis.

5.1.3. Post-Merger Long Term Performance

In this section, I analyze the post-merger long term performance of the acquirer.

According to Information efficiency hypothesis, governance role of the media would ensure that only value enhancing deals are completed resulting in a positive relation between post-merger long term performance and media connection. However, managers are more likely to exploit media connection and engage in media manipulation when they tend to devise opportunistic takeovers. Manipulation hypothesis, thus, predicts that media connection would be negatively related with the long run performance.

Table 4 reports the results from the regression of post-merger long term performance on acquirers’ media connection and control variables. I use 1, 2 and 3 year ahead ex-post return on assets (ROA) in panel A and Tobin’s Q (Q) in panel B as

dependent variable. The explanatory variables are the same as in table 2. In panel A, I observe that coefficient of media connections (Log_MC) is negative and significant across all specifications. On average, a 1 percentage point increase in acquirers’ media connection is associated with 1.01 (in column (4)) to 1.49 (in column (1)) percentage point decrease in future ROA. In panel B also, the coefficient of Log_MC is negative and significant across most of the specifications. A 1 percentage point increase in acquirers’ media connection is associated with 10.23 (in column (1)) to 14.41 (in column (4)) percentage point decrease in future Q. Overall, these results are consistent with the manipulation hypothesis. According to the manipulation hypothesis, managers are more likely to engage in media manipulation as they tend to devise opportunistic takeovers.

5.1.4. Effect of Political Connections

In this section, I examine the interactive effects of political and media connectedness. Prior literature shows that political connection affects information environment (Chaney et al., 2011), firm valuation (Gropper et al., 2013), and deal completion, bid announcement returns and post-merger performance (Ferris et al., 2016).

Hence, it possible that political connection and media connection complement or substitute each other.

I present this analysis in table 5. Merger announcement period CAR is regressed on acquirers’ media connection by subsamples of political connection. I partition the sample based on acquirers’ political connection following Ferris et al. (2016). An acquirer is defined as politically connected if the firm has at least one director or a manager who has prior political experience. I find that the coefficient of media

connection is positive and significant at 5% level for politically connected acquirers while the coefficient of media connection is negative and insignificant for non-politically connected acquirers. This result is suggestive of the fact that media connection complements the effect of political connection in merger deals.

5.1.5. Stock Deals, Acquirer Recognition and Geographic Proximity

Next, I run cross-sectional analysis of merger announcement returns by splitting the sample based on deal consideration structure, acquirers’ recognition and acquirers’ geographic proximity to large metro areas. Table 6 demonstrates these results.

In panel A, I test the hypothesis that the effect of media connection on merger outcomes would be greater for stock swap deals than for cash deals since acquirers are more likely to adopt price manipulation through media connection prior the bid announcement in stock deals. Furthermore, as acquirers tend to manipulate price to achieve overvaluation, these overvalued firms may find stock deals more suitable than cash deals to create value for long-term shareholders by using their equity as currency

(Savor and Lu, 2009). Column (1) and (3) show results from the regression of CAR around merger announcement on media connection (Log_MC) and control variables for the subsample of stock deals, while column (2) and (4) report results for the subsample of cash deals. Consistent with hypothesis, I find that the coefficient of media connection is positive, significant and larger for the subsample of stock deals.

Next, I analyze the effect of acquirers’ recognition. Driven by commercial incentives, media companies are likely to maximize profits by responding to readership appeals and providing coverage to large and well recognized firms. Large firms are also

likely to generate better coverage due to advertisement bias (Reuter and Zitzewitz, 2006;

Gurun and Butler, 2012). Therefore, friends in the media could facilitate transmission of the news of an upcoming merger for small and less recognized firms. I test this conjecture in panel B of table 6. I partition the sample by the market capitalization of the acquirers.

Results show that the coefficient estimate of Log_MC is more than four times higher for small acquirers than that for the large acquirers. The effect of media connection on merger announcement return is greater for small acquirers which supports the manipulation hypothesis.

Geographic proximity to large readership areas also determines the newsworthiness of an event. Demand for news about firms in larger metro areas is driven by the large pool of local investors. Therefore, if the manipulation story is true then the effect of media connection would be greater for firms headquartered in small metro areas.

I use population size of a county as a proxy to separate large and small areas. In panel C, sample is partitioned by the population size of the county in which the acquirer is headquartered. Consistent with the manipulation hypothesis, I observe that the effect of media connection on merger announcement return is greater for acquirers’ located in small counties. The coefficient estimate of Log_MC is about 10 times higher for small county headquartered acquirers.

5.1.6. Deal Completion Likelihood and Acquisitiveness

Media connection may also determine the deal completion likelihood. Media plays an undisputed role in forming public opinion (DellaVigna and Kaplan, 2007;

Gerber et al., 2009) and such public opinion formed through media coverage is crucial to the success of an announced deal. Watchdog role of media predicts that deal proposals

would be more scrutinized in media connected firms, which leads to a decrease in the probability of deal completion. However, firms may also exploit media connection to devise opportunistic deals by creating stronger public opinion in favor of the deal which is also likely to influence the politicians and regulators to respond to public opinion

(Culpepper, 2010). Therefore, from manipulation perspective, acquirer’s social ties with media would contribute to the likelihood of deal completion.

I analyze the association between media connections and deal completion likelihood in table 8. I apply sequential logit regression model to estimate the probability of merger completion since merger completion is determined in two steps. A firm’s choice set is denoted by n ε {0, 1, 2}, where 0 = no bid, 1 = firms announcing a merger bid but failing to complete the bid, and 2 = firms announcing a merger bid and successfully completing the bid. The first node contains two alternatives: bidding (n = 1) or not bidding (n = 0). Conditional on first node, the second node consists of two alternatives: completing a bid (n = 2) or failure to complete a bid (n = 1). I include all the firms in compustat universe and create a dependent variable that is equal 0 for firms not announcing a bid in a year, 1 for firms announcing a bid, and 2 for firms actually completing a bid.

Table 7 reports the result of the sequential logit regressions. Columns (1) and (2) shows the result from the first stage, in which probability of bidding is determined.

Results suggest that an increase in media connection is positively associated with the probability of bidding. This is supportive of the acquisitiveness hypothesis. Columns (3) and (4) report second stage result, in which probability of bid completion is determined

conditioning on the probability of bidding. Coefficient estimate of Log_MC demonstrate that media connection increases the likelihood of completing a deal. This is supportive of the hypothesis that media connection facilitates successful completion of merger deals.

5.2. Innovative Efficiency

5.2.1. Media Connections and Innovative Efficiency

Now I analyze the consequences of media connections on innovative efficiency.

Innovative projects are difficult to evaluate for investors as such projects contain uncertain payoff and long term in nature (Lev & Zarowin, 1999). To assess the potential future benefits from innovation projects, investors rely on soft information beyond that provided in the financial statements (Healy and Palepu, 2001). Connections with the media outlets may develop efficient flow of information regarding the ongoing and future projects, thereby facilitating greater innovative efficiency. However, firms may also strategically utilize media connection to camouflage their investment in suboptimal innovation projects predicting a negative relation between media connection and innovation efficiency.

Table 8 reports results from the regression of innovative efficiency (IE) on media connections. Four different proxies of IE are used in columns (1) to (4). IE1 is estimated as the ratio of adjusted patent to R&D Capital (RDC). Adjusted patent is calculated by dividing each patent for each firm-year by the mean number of patents of all firms for that year in the same technology class as the patent. RDC is the 5 year cumulative R&D expenses starting from fiscal year ending in year t-2 to year t-6 assuming an annual

depreciation rate of 20%11. IE2 is estimated as the ratio of total number of patents to

RDC. IE3 is estimated as the ratio of adjusted citations to RDC. Adjusted citations is estimated by dividing number of citations of a patent by the total number of citations received by all patents in that year in the same technological class as the patent. IE4 is the ratio of total number of citations to RDC. In Panel (B), Log_IE1, Log_IE2, Log_IE3, and

Log_IE4 are the log transformations (1+IE) of IE1, IE2, IE3, and IE4, respectively. I include the control variables following prior literature on the determinants of innovative efficiency. I also include industry and year fixed effects.

Results show that the association between media connection and innovative efficiency is significantly negative at the 1% level across four models. On average, a 1 percentage point increase in media connection is associated with 5.10 (in column (4)) to

23.60 (in column (1)) percentage point decrease in innovative efficiency. Coefficient on most of the control variables exhibit expected sign. Innovative efficiency is positively associated with sales, R&D, capital expenditure, growth opportunity and negatively related with profitability (EBITDA), tangibility (PPE). Overall, results from table 8 provide support for the manipulation hypothesis that managers exploit media connection to conceal disclosure of value destroying investments in innovative projects.

5.2.2. Media Connections, Innovative Efficiency and Firm Performance

Changes in innovative efficiency attributable to media connections could have implications for firm performance. Media connections may mitigate information asymmetry and alleviate financing constraints, which would ultimately facilitate value

11 as in Chan, Lakonishok, and Sougiannis (2001) and Lev, Sarath, and Sougiannis (2005) 70

increasing innovative projects for firms. However, media connection is likely to cause underperformance in the long run when their R&D investments perform poorly.

In this section, I link media connections, innovative efficiency and firm performance by following a two stage process previously developed methodology

(Bhandari and Javakhadze, 2017; Bowen et al., 2008). At first stage, I run two separate regressions of innovative efficiency and save the predicted values for each of the regressions. In one model, innovative efficiency is regressed on media connections

(Log_MC) and all the control variables, and on the other one, innovative efficiency is regressed on all the control variables only. Then I estimate the change in innovative efficiency attributable to media connections by taking the difference in the predicted values of innovative efficiency from the two regressions. In second stage, I regress ex- post future firm performance on the change in innovative efficiency (IE_MC) and include the same controls as used in the first stage.

Table 9 shows result of this analysis. In panel (A), ex-post future firm performance is proxied by return on assets (ROA) in year t+1, t+2 and t+3. In panel (B),

Tobin’s Q (Q) in year t+1, t+2, t+3 are employed as proxies for future firm performance.

In both panels, columns (1) – (3) include the change in innovative efficiency (IE_MC1) estimated using the IE measure based on patent, and columns (4) – (6) include the change in innovative efficiency (IE_MC2) estimated using the IE measure based on citations.

Results show that the coefficient of change in innovative efficiency is negative and significant across all specifications. It suggests that the effect of media connection on innovative efficiency is harmful for future firm performance.

5.3. Endogeneity Concerns

The association between media connections and innovative efficiency could be subject to endogeneity in this study. Omitted variable bias could be a source of endogeneity concern in the relation between media connection and innovative efficiency.

Growth opportunities, for instance, is one such omitted variable. High growth firms are likely to have greater innovative efficiency. Such endogeneity concern due to omitted variable bias is mitigated in this study by conducting instrumental variable analysis.

I use the number of media firms located within the same county as the firm's headquarter as an instrument for media connection. Corporate executives' media connection is likely to be correlated with the number of media firms located within the community. However, it is not clear whether the presence of more media outlets in a locality may drive innovative efforts of the local companies. Table 10 shows the results of the two-stage LS regressions. I observe that F-test of excluding instruments is sufficiently greater than the cut-off value 10, which indicates that the instrument is relevant and do not suffer from weak instruments concerns. Furthermore, test of endogeneity shows that the media connections measure is indeed endogenously determined in the equation system. Results from the second stage regressions in columns

(2) and (4) suggest that predicted media connection negatively affects innovative efficiency.

6. Conclusions

Motivated by the debate about the media’s role in financial markets, I examine the effects corporate executives’ social ties with media personnel on a couple of investment

policies. Extent literature is inconclusive in determining whether media works as an important intermediary in the financial market through monitoring and disseminating information or whether media facilitates bias through manipulation of corporate news events. My dissertation contributes to this literature by examining corporate executives’ social ties with the media personnel in the context of corporate merger transactions and innovative efficiency. Given that acquirers can strategically use the media to manipulate the quantity and tone of coverage prior to bid announcements, social ties may benefit media connected acquirers. Also, media connection may facilitate strategic disclosure of innovative efforts.

Two competing hypotheses provide possible explanations for the effect of media connection on corporate merger activities. At one end, the Information Efficiency

Hypothesis suggests that media connections may help acquirers by facilitating an efficient flow of information and stronger corporate governance, thereby causing lower announcement return, lower takeover premium, lower likelihood of deal completion, less acquisitiveness, and better performance in the long run. In contrast, the Manipulation

Hypothesis proposes that social ties between acquirers and media personnel may distort the information flows to the market and create opportunities for effective manipulation.

As a result, media connected acquirers would exhibit greater announcement return, higher takeover premium, greater likelihood of deal completion, be more acquisitive, and exhibit long term underperformance.

Results are consistent with the Manipulation Hypothesis. Empirical analysis reveals that acquirers’ media connection increases merger announcements returns. Media connection allows acquirers to engage in media management prior to bid announcement

which result in greater abnormal return on bid announcements. Media connection also reduces acquirers’ takeover premiums, increases the likelihood of deal completion, and makes the acquirers more acquisitive. However, there is no statistically significant association between post-merger long term performance and media connection.

I also examine whether media connection affects efficiency of the investments in innovation projects and result in value creation in the long run. Consistent with manipulation hypothesis, I observe that media connection negatively affects innovative efficiency. The association between ex post long-term performance and change in innovative efficiency attributable to media connection is also negative and significant. It implies that the effect of media connection on innovative efficiency is harmful for future firm performance.

Table 3- 1: Descriptive Statistics

Panel A presents the descriptive statistics of the main variables. Distribution of bids, acquisitions, and descriptive statistics of media connections are reported across years in Panel B and across industries in Panel C. Variable definitions are provided in Appendix. Panel A: Descriptive Statistics Mean SD P25 P50 P75 CAR1 0.009 0.108 -0.039 0.004 0.049 CAR2 0.008 0.107 -0.042 0.003 0.047 PREM1 0.114 0.271 0.000 0.000 0.000 Log_MC 3.361 2.468 0.000 4.143 5.193 MB_ACQ 2.953 4.388 1.120 1.694 2.859 LIQ_ACQ 0.046 0.183 0.016 0.086 0.137 ROA_ACQ 0.020 0.219 -0.004 0.078 0.133 SIZE_ACQ 7.021 2.169 5.629 6.972 8.370 BRDSIZE_ACQ 8.609 2.646 7.000 8.000 10.000 MB_TGT 2.007 2.132 0.917 1.318 2.171 ROA_TGT -0.031 0.269 -0.066 0.048 0.107 SIZE_TGT 5.696 1.975 4.363 5.733 7.031 LIQ_TGT 0.009 0.231 -0.018 0.067 0.124

Panel B: Bids and Acquisitions by Industry FF No. of No. of Media Connection Industry Name Industry Bids Acquisitions Mean SD 1 Consumer Non-Durables 119 105 223.97 456.85 2 Consumer Durables 55 50 100.20 193.11 3 Manufacturing 265 236 168.52 418.24 Oil, Gas, and Coal Extraction and 4 Products 119 110 81.99 126.64 5 Chemicals and Allied Products 54 46 210.07 689.32 6 Business Equipment 1295 1207 311.76 1,087.15 Telephone and Television 7 Transmission 108 83 785.58 1,730.57 8 Utilities NA NA NA NA 9 Wholesale, Retail, and Some Services 185 159 155.48 253.61 Healthcare, Medical Equipment, and 10 Drugs 421 383 205.94 388.36 11 Money Finance NA NA NA NA 12 Other 536 466 185.03 393.99 Total 3,157 2,845

Panel C: Bids and Acquisitions by Year No. of No. of Media Connection Year Bids Acquisitions Mean SD 2000 417 372 100.41 328.23 2001 211 185 98.11 267.09 2002 146 137 237.93 995.29 2003 163 150 130.75 242.04 2004 176 162 169.16 304.35 2005 195 181 297.98 679.57 2006 198 181 408.51 1,521.13 2007 192 178 331.31 1,145.32 2008 137 113 348.69 1,479.47 2009 105 96 436.16 782.83 2010 120 107 254.76 394.24 2011 107 92 269.60 777.75 2012 107 99 361.99 955.41 2013 93 85 521.46 1,058.86 2014 134 124 417.10 918.75 2015 164 148 371.62 557.13 2016 110 100 504.70 1,439.61 Total 3,157 2,845

Table 3- 2: Merger Announcement Return

This table reports results from multivariate regression of cumulative abnormal return on media connections and control variables for a sample US acquisitions announced during the period of January 2000 to December 2016. Dependent variable is Cumulative Abnormal Return (CAR) calculated as the sum of daily abnormal returns over the [-1, +1] window around merger announcement date. Abnormal returns are calculated based on the daily returns of CRSP value and equal weighted portfolios in Panel A and Panel B, respectively. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the merger announcement. Control variables include acquirer, target, and deal characteristics. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Dependent Variable – CAR1 (1) (2) (3) (4) Log_MC 0.0179*** 0.0128** 0.0175*** 0.0123** (2.898) (2.135) (2.821) (2.098) MB_ACQ -0.0005 0.0012 -0.0005 0.0014 (-0.340) (0.870) (-0.284) (1.002) LIQ_ACQ 0.0245 0.0432 0.0282 0.0479 (0.422) (0.734) (0.497) (0.837) ROA_ACQ 0.1822*** 0.1546** 0.1828*** 0.1520** (3.025) (2.418) (3.137) (2.450) SIZE_ACQ -0.0049 -0.0044 -0.0048 -0.0046 (-0.902) (-0.770) (-0.775) (-0.702) BRDSIZE_ACQ -0.0033* -0.0021 -0.0029 -0.0018 (-1.749) (-1.166) (-1.529) (-0.950) RUNUP 0.0001 0.0001** 0.0001 0.0001** (1.505) (2.144) (1.631) (2.247) MB_TGT -0.0059** -0.0067** -0.0054* -0.0061** (-2.125) (-2.556) (-1.924) (-2.302) ROA_TGT -0.0278 -0.0269 -0.0239 -0.0213 (-0.691) (-0.689) (-0.585) (-0.541) SIZE_TGT -0.0071** -0.0117*** -0.0076** -0.0118*** (-2.284) (-3.501) (-2.072) (-3.146) LIQ_TGT 0.0205 0.0298 0.0108 0.0171 (0.459) (0.691) (0.240) (0.393) TENDER 0.0060 0.0068 (0.692) (0.759) HOSTILE -0.0351* -0.0242 (-1.929) (-1.376) COMPETE 0.0268 0.0326* (1.386) (1.700) NONDIV -0.0044 -0.0058 (-0.434) (-0.586) REL_VAL 0.0026 0.0006 (0.289) (0.059) Industry FE Yes Yes Yes Yes Year FE No Yes No Yes adj. R-sq 0.139 0.190 0.139 0.193 N 463 463 463 463

Panel B: Dependent Variable – CAR2 (1) (2) (3) (4) Log_MC 0.0174*** 0.0120** 0.0170*** 0.0116* (2.779) (1.995) (2.705) (1.964) MB_ACQ -0.0009 0.0008 -0.0008 0.0011 (-0.565) (0.597) (-0.505) (0.738) LIQ_ACQ 0.0201 0.0409 0.0234 0.0449 (0.350) (0.713) (0.416) (0.804) ROA_ACQ 0.1795*** 0.1484** 0.1803*** 0.1458** (3.009) (2.372) (3.121) (2.399) SIZE_ACQ -0.0045 -0.0040 -0.0044 -0.0043 (-0.821) (-0.709) (-0.707) (-0.666) BRDSIZE_ACQ -0.0035* -0.0022 -0.0031 -0.0018 (-1.854) (-1.184) (-1.631) (-0.968) MB_TGT 0.0001 0.0001* 0.0001 0.0001* (1.206) (1.825) (1.303) (1.899) MB_TGT -0.0059** -0.0068*** -0.0053* -0.0062** (-2.141) (-2.631) (-1.948) (-2.388) ROA_TGT -0.0300 -0.0307 -0.0262 -0.0250 (-0.717) (-0.765) (-0.614) (-0.616) SIZE_TGT -0.0066** -0.0115*** -0.0071* -0.0115*** (-2.106) (-3.438) (-1.927) (-3.083) LIQ_TGT 0.0197 0.0315 0.0102 0.0190 (0.428) (0.720) (0.219) (0.428) TENDER 0.0053 0.0065 (0.606) (0.728) HOSTILE -0.0374** -0.0243 (-1.980) (-1.380) COMPETE 0.0267 0.0327* (1.388) (1.717) NONDIV -0.0036 -0.0050 (-0.349) (-0.505) REL_VAL 0.0024 -0.0001 (0.261) (-0.007) Industry FE Yes Yes Yes Yes Year FE No Yes No Yes adj. R-sq 0.136 0.200 0.137 0.203 N 463 463 463 463

Table 3- 3: Merger Takeover Premium

This table reports results from multivariate regression of takeover premium on media connections and control variables for a sample US acquisitions announced during the period of January 2000 to December 2016. Dependent variable is Takeover Premium (PREM) calculated as the percentage change in offer price relative to target’s share price 42 days prior to the bid announcement. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the merger announcement. Control variables include acquirer, target, and deal characteristics. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Dependent Variable – PREM (1) (2) (3) (4) Log_MC -0.0389** -0.0312 -0.0399** -0.0322* (-2.080) (-1.614) (-2.114) (-1.658) MB_ACQ 0.0035 0.0032 0.0033 0.0030 (0.657) (0.596) (0.609) (0.545) LIQ_ACQ -0.2142 -0.2575 -0.1863 -0.2289 (-1.251) (-1.455) (-1.097) (-1.302) ROA_ACQ 0.0272 0.0839 0.0211 0.0800 (0.164) (0.507) (0.129) (0.486) SIZE_ACQ 0.0439*** 0.0409*** 0.0437*** 0.0406*** (3.416) (3.075) (3.227) (2.882) BRDSIZE_ACQ 0.0071 0.0054 0.0071 0.0055 (1.386) (1.012) (1.387) (1.039) RUNUP -0.0006*** -0.0006*** -0.0006*** -0.0006*** (-2.827) (-2.709) (-2.816) (-2.711) MB_TGT -0.0178*** -0.0167*** -0.0178*** -0.0167** (-2.839) (-2.597) (-2.764) (-2.528) ROA_TGT -0.3008** -0.2770* -0.3057** -0.2831* (-1.989) (-1.789) (-2.009) (-1.815) SIZE_TGT -0.0422*** -0.0366*** -0.0420*** -0.0361*** (-4.528) (-3.915) (-3.830) (-3.254) LIQ_TGT 0.3143** 0.2904* 0.3158** 0.2930* (2.065) (1.830) (2.053) (1.831) TENDER -0.0159 -0.0210 (-0.578) (-0.767) HOSTILE 0.0095 -0.0128 (0.176) (-0.234) COMPETE -0.0087 -0.0075 (-0.272) (-0.228) NONDIV -0.0258 -0.0258 (-0.966) (-0.967) REL_VAL 0.0012 0.0004 (0.082) (0.029) Industry FE Yes Yes Yes Yes Year FE No Yes No Yes adj. R-sq 0.092 0.096 0.088 0.093 N 938 938 938 938

Table 3- 4: Post-merger Long Term Performance

This table reports results from multivariate regression of post-merger long term performance on media connections and control variables for a sample US acquisitions announced during the period of January 2000 to December 2016. Dependent variable Return on Assets (ROA) and Tobin’s Q in Panel A and Panel B, respectively. ROA is calculated as earnings before interest and taxes scaled by total assets. Tobin’s Q is estimated as the market value of assets divided by the book value of assets. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the merger announcement. Control variables include acquirer, target, and deal characteristics. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Dependent Variable – ROA (1) (2) (3) (4) (5) (6) ROAt+1 ROAt+1 ROAt+2 ROAt+2 ROAt+3 ROAt+3 Log_MC -0.0103** -0.0101** -0.0149*** -0.0143** -0.0115*** -0.0111*** (-2.158) (-2.115) (-2.613) (-2.527) (-2.751) (-2.623) MB_ACQ -0.0041** -0.0039* -0.0148*** -0.0146*** -0.0074*** -0.0074*** (-1.966) (-1.850) (-3.418) (-3.367) (-3.082) (-3.059) LIQ_ACQ 0.5638*** 0.5589*** 0.5169*** 0.5092*** 0.4128*** 0.4084*** (10.851) (10.665) (8.422) (8.236) (7.051) (6.815) SIZE_ACQ 0.0123*** 0.0114** 0.0176*** 0.0181*** 0.0180*** 0.0184*** (2.642) (2.404) (4.009) (3.769) (4.410) (4.247) BRDSIZE_ACQ 0.0068*** 0.0067*** 0.0078*** 0.0077*** 0.0025 0.0025 (3.102) (3.094) (2.763) (2.749) (1.397) (1.367) RUNUP -0.0001* -0.0001 -0.0001* -0.0001 -0.0001** -0.0001* (-1.678) (-1.377) (-1.744) (-1.504) (-2.077) (-1.956) MB_TGT 0.0006 0.0004 -0.0005 -0.0003 0.0054** 0.0057** (0.238) (0.164) (-0.091) (-0.048) (1.985) (2.058) SIZE_TGT -0.0066** -0.0055** -0.0108*** -0.0115*** -0.0067** -0.0073** (-2.485) (-2.014) (-3.460) (-3.056) (-2.250) (-2.105) LIQ_TGT 0.0699*** 0.0681** 0.0481 0.0445 0.0389 0.0378 (2.592) (2.457) (1.490) (1.383) (1.219) (1.158) TENDER 0.0107 0.0118 0.0027 (1.503) (1.309) (0.333) HOSTILE -0.0033 0.0001 0.0120 (-0.275) (0.008) (0.707) COMPETE 0.0138* 0.0248** 0.0131 (1.749) (2.476) (1.386) NONDIV -0.0028 0.0003 0.0043 (-0.419) (0.038) (0.579) REL_VAL -0.0024 0.0021 -0.0002 (-0.674) (0.491) (-0.032) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes No No Yes Yes adj. R-sq 0.428 0.427 0.419 0.419 0.338 0.335 N 930 930 874 874 779 779

Panel B: Dependent Variable – Q (1) (2) (3) (4) (5) (6) Qt+1 Qt+1 Qt+2 Qt+2 Qt+3 Qt+3 Log_MC -0.1023** -0.1104** -0.1383*** -0.1441*** -0.0433 -0.0494 (-2.303) (-2.461) (-3.329) (-3.422) (-1.031) (-1.155) LIQ_ACQ 1.1257** 1.2166** 1.0221** 1.0809** 1.0526** 1.0899** (2.120) (2.301) (2.265) (2.365) (2.307) (2.355) ROA_ACQ 0.7989 0.7699 -0.1310 -0.1496 -0.0901 -0.1181 (1.584) (1.520) (-0.285) (-0.326) (-0.218) (-0.285) SIZE_ACQ 0.1338*** 0.1033*** 0.2234*** 0.2032*** 0.1549*** 0.1382*** (3.693) (2.808) (7.184) (6.354) (5.560) (4.648) BRDSIZE_ACQ -0.0175 -0.0164 -0.0317** -0.0306** -0.0209 -0.0202 (-1.163) (-1.086) (-2.340) (-2.261) (-1.590) (-1.522) RUNUP 0.0010** 0.0010* 0.0009 0.0008 0.0011** 0.0011** (1.988) (1.864) (1.462) (1.404) (2.343) (2.402) ROA_TGT -0.0199 -0.0346 0.1204 0.1115 -0.3070 -0.3003 (-0.061) (-0.105) (0.345) (0.317) (-0.742) (-0.723) SIZE_TGT 0.0271 0.0610* -0.0559** -0.0330 -0.0679*** -0.0492* (0.893) (1.781) (-2.339) (-1.154) (-2.868) (-1.706) LIQ_TGT -0.7981* -0.7846* -1.2852*** -1.2750*** -0.8265* -0.8285* (-1.878) (-1.830) (-2.919) (-2.877) (-1.885) (-1.870) TENDER -0.0375 -0.0263 0.0193 (-0.479) (-0.348) (0.258) HOSTILE -0.1555 -0.1256 -0.0650 (-0.842) (-0.911) (-0.465) COMPETE 0.0072 0.0014 -0.0476 (0.055) (0.013) (-0.394) NONDIV -0.1102 -0.0753 -0.0670 (-1.582) (-1.264) (-1.180) REL_VAL -0.1075*** -0.0707** -0.0512 (-2.901) (-2.001) (-1.244) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.255 0.256 0.289 0.288 0.279 0.277 N 939 939 882 882 787 787

Panel C: Dependent Variable – BHAR (1) (2) (3) (4) (5) (6) BHARt+1 BHARt+1 BHARt+2 BHARt+2 BHARt+3 BHARt+3 Log_MC 0.0039 0.0037 -0.0050 0.0009 0.0579 0.0663 (0.145) (0.134) (-0.129) (0.021) (1.152) (1.263) MB_ACQ -0.0661*** -0.0642*** -0.1596*** -0.1527*** -0.1400*** -0.1319*** (-2.796) (-2.705) (-4.946) (-4.622) (-3.758) (-3.475) LIQ_ACQ 0.9244*** 0.9074** 1.3598*** 1.3304*** 2.3198*** 2.2645*** (2.624) (2.566) (2.743) (2.717) (3.986) (3.939) SIZE_ACQ 0.0039 0.0002 0.0437* 0.0343 0.0153 0.0185 (0.202) (0.010) (1.655) (1.276) (0.446) (0.522) BRDSIZE_ACQ 0.0030 0.0021 0.0107 0.0103 0.0040 0.0017 (0.370) (0.261) (0.907) (0.877) (0.263) (0.114) RUNUP 0.0000 -0.0000 0.0005 0.0005 -0.0000 0.0001 (0.101) (-0.080) (1.046) (0.975) (-0.047) (0.104) ROA_TGT -0.3462 -0.3543 0.1304 0.1303 0.2275 0.2016 (-1.383) (-1.418) (0.284) (0.282) (0.418) (0.376) SIZE_TGT -0.0014 0.0061 -0.0250 -0.0164 -0.0208 -0.0196 (-0.118) (0.397) (-1.302) (-0.624) (-0.903) (-0.644) LIQ_TGT 0.3566 0.3728 -0.2894 -0.3033 -0.2856 -0.2991 (1.261) (1.303) (-0.598) (-0.623) (-0.452) (-0.480) TENDER -0.0209 0.0575 0.1508* (-0.505) (0.889) (1.785) HOSTILE -0.1518** -0.2279 -0.3699** (-2.183) (-1.339) (-2.073) COMPETE 0.0168 0.1835* -0.0350 (0.274) (1.945) (-0.326) NONDIV 0.0251 0.0118 0.0011 (0.734) (0.249) (0.018) REL_VAL -0.0289 -0.0190 0.0705 (-1.048) (-0.289) (0.936) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.056 0.050 0.140 0.142 0.139 0.141 N 404 404 364 364 332 332

Table 3- 5: Merger Announcement Return and Acquirers’ Political Connection

This table reports results from multivariate regression of cumulative abnormal return on media connections and control variables for a sample US acquisitions announced during the period of January 2000 to December 2016. Dependent variable is Cumulative Abnormal Return (CAR) calculated as the sum of daily abnormal returns over the [-1, +1] window around merger announcement date. In columns (1) and (2) abnormal returns are calculated based on the daily returns of CRSP value weighted portfolios, and in columns (3) and (4) abnormal returns are calculated based on the daily returns of CRSP equal weighted portfolios. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the merger announcement. Sample is partitioned by acquirer’s political connection. Political connection is defined as an indicator variable which takes value of 1 (PC) for firms that have at least a director or a manager who has prior political experience. Control variables include acquirer, target, and deal characteristics. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. (1) (2) (3) (4) Dependent Variable CAR1 CAR1 CAR2 CAR2 PC NPC PC NPC Log_MC 0.0176** -0.0111 0.0168** -0.0121 (2.279) (-1.121) (2.177) (-1.235) MB_ACQ 0.0023 0.0037* 0.0017 0.0036* (1.080) (1.720) (0.786) (1.671) LIQ_ACQ -0.0106 0.3745** -0.0208 0.3949*** (-0.158) (2.468) (-0.316) (2.654) ROA_ACQ 0.1555** 0.0173 0.1612** -0.0176 (2.128) (0.147) (2.236) (-0.151) SIZE_ACQ -0.0003 -0.0222* -0.0002 -0.0218* (-0.048) (-1.756) (-0.026) (-1.739) BRDSIZE_ACQ -0.0016 0.0005 -0.0015 -0.0003 (-0.744) (0.107) (-0.690) (-0.060) RUNUP 0.0001 0.0005 0.0001 0.0005 (1.260) (1.323) (1.074) (1.261) MB_TGT -0.0059 -0.0065* -0.0063 -0.0066 (-1.411) (-1.673) (-1.502) (-1.652) ROA_TGT 0.0119 -0.0448 0.0015 -0.0274 (0.219) (-0.545) (0.027) (-0.328) SIZE_TGT -0.0113** -0.0036 -0.0111** -0.0031 (-2.086) (-0.351) (-2.042) (-0.317) LIQ_TGT -0.0118 0.0248 -0.0027 0.0070 (-0.218) (0.268) (-0.048) (0.076) TENDER 0.0028 0.0160 0.0035 0.0143 (0.260) (0.727) (0.325) (0.646) HOSTILE -0.0153 -0.0974* -0.0156 -0.0948* (-0.678) (-1.907) (-0.713) (-1.877) COMPETE -0.0005 0.0901** -0.0008 0.0911** (-0.029) (2.038) (-0.050) (2.088) NONDIV 0.0018 -0.0345* 0.0023 -0.0350* (0.175) (-1.796) (0.226) (-1.890) REL_VAL -0.0046 -0.0194 -0.0048 -0.0213* (-0.194) (-1.597) (-0.203) (-1.760) Industry FE Yes Yes Yes Yes

Year FE Yes Yes Yes Yes adj. R-sq 0.162 0.372 0.174 0.386 N 311 152 311 152

Table 3- 6: Stock Deals, Acquirer Recognition and Geographic Proximity

This table reports results from multivariate regression of cumulative abnormal return on media connections and control variables for a sample US acquisitions announced during the period of January 2000 to December 2016. Dependent variable is Cumulative Abnormal Return (CAR) calculated as the sum of daily abnormal returns over the [-1, +1] window around merger announcement date. In columns (1) and (2) abnormal returns are calculated based on the daily returns of CRSP value weighted portfolios, and in columns (3) and (4) abnormal returns are calculated based on the daily returns of CRSP equal weighted portfolios. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the merger announcement. Sample is partitioned by stock and cash deals. Control variables include acquirer, target, and deal characteristics. In Panel A, sample is partitioned by the consideration structure of the deal i.e. Stock vs Cash. In Panel B, sample is partitioned by the acquirer’s market cap. In Panel C, sample is partitioned by the population size of the acquirer’s headquarter area. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Stock vs Cash Deals (1) (2) (3) (4) Dependent Variable CAR1 CAR1 CAR2 CAR2 Stock Cash Stock Cash Log_MC 0.0176** 0.0067 0.0160* 0.0061 (2.133) (0.771) (1.933) (0.690) MB_ACQ 0.0010 0.0015 0.0007 0.0003 (0.523) (0.375) (0.355) (0.066) LIQ_ACQ 0.0186 -0.0628 0.0119 -0.0461 (0.223) (-0.577) (0.146) (-0.422) ROA_ACQ 0.1546* 0.1074 0.1510* 0.0912 (1.763) (1.192) (1.765) (1.003) SIZE_ACQ -0.0043 -0.0092 -0.0042 -0.0087 (-0.335) (-1.495) (-0.329) (-1.379) BRDSIZE_ACQ -0.0026 -0.0013 -0.0025 -0.0015 (-0.904) (-0.496) (-0.871) (-0.573) RUNUP 0.0000 0.0001 0.0000 0.0001 (0.049) (1.563) (0.031) (1.400) MB_TGT -0.0045 0.0028 -0.0049 0.0032 (-1.238) (0.694) (-1.380) (0.767) ROA_TGT -0.0802 -0.0268 -0.0743 -0.0369 (-1.436) (-0.410) (-1.336) (-0.538) SIZE_TGT -0.0154** -0.0005 -0.0147** -0.0007 (-2.233) (-0.108) (-2.141) (-0.155) LIQ_TGT 0.0940 -0.0474 0.0850 -0.0388 (1.592) (-0.698) (1.432) (-0.552) TENDER 0.0093 -0.0153 0.0073 -0.0151 (0.479) (-1.343) (0.376) (-1.332) HOSTILE -0.0485* -0.0663*** -0.0473 -0.0646*** (-1.658) (-3.032) (-1.582) (-2.988) COMPETE 0.0696* 0.0088 0.0704* 0.0079 (1.813) (0.502) (1.836) (0.458) NONDIV -0.0075 0.0047 -0.0047 0.0031 (-0.434) (0.378) (-0.274) (0.249) REL_VAL -0.0075 0.0431** -0.0091 0.0458** (-0.624) (2.379) (-0.768) (2.419)

Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq 0.136 0.154 0.140 0.172 N 240 223 240 223

Panel B: Small vs Large Acquirers (1) (2) (3) (4) Dependent Variable CAR1 CAR1 CAR2 CAR2 Small Large Small Large Log_MC 0.0395** 0.0086 0.0387** 0.0076 (2.082) (1.493) (2.053) (1.331) MB_ACQ -0.0019 0.0018 -0.0023 0.0015 (-0.183) (1.372) (-0.222) (1.114) LIQ_ACQ 0.0039 0.0092 -0.0075 0.0104 (0.033) (0.106) (-0.064) (0.122) ROA_ACQ 0.2105 0.1015 0.2123 0.0940 (1.526) (1.327) (1.566) (1.235) SIZE_ACQ -0.0096 -0.0042 -0.0079 -0.0037 (-0.386) (-0.726) (-0.316) (-0.626) BRDSIZE_ACQ -0.0090* -0.0005 -0.0091* -0.0005 (-1.794) (-0.258) (-1.818) (-0.244) RUNUP -0.0001 0.0001*** -0.0002 0.0001*** (-0.729) (3.928) (-0.972) (4.120) MB_TGT -0.0128 -0.0050* -0.0136 -0.0049* (-1.221) (-1.669) (-1.312) (-1.702) ROA_TGT 0.0038 -0.0258 0.0039 -0.0198 (0.046) (-0.572) (0.047) (-0.437) SIZE_TGT -0.0028 -0.0063 -0.0018 -0.0067 (-0.193) (-1.476) (-0.121) (-1.581) LIQ_TGT -0.0048 -0.0191 -0.0128 -0.0195 (-0.046) (-0.385) (-0.119) (-0.391) TENDER 0.0592* -0.0111 0.0600* -0.0108 (1.922) (-1.241) (1.947) (-1.205) HOSTILE -0.0152 -0.0276 -0.0135 -0.0275 (-0.376) (-1.523) (-0.329) (-1.540) COMPETE 0.0986 0.0149 0.0980 0.0158 (1.627) (0.847) (1.638) (0.906) NONDIV 0.0121 -0.0053 0.0136 -0.0050 (0.394) (-0.578) (0.447) (-0.536) REL_VAL 0.0052 -0.0377** 0.0044 -0.0365** (0.237) (-2.372) (0.200) (-2.326) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq 0.120 0.214 0.130 0.225 N 140 323 140 323

Panel C: Acquirers based in Small vs Large Metro Areas (1) (2) (3) (4) Dependent Variable CAR1 CAR1 CAR2 CAR2 Small Large Small Large Metro Area Metro Area Metro Area Metro Area Log_MC 0.0218* 0.0024 0.0199* 0.0017 (1.882) (0.256) (1.719) (0.185) MB_ACQ 0.0042** -0.0033 0.0038* -0.0034 (2.195) (-0.966) (1.942) (-1.015) LIQ_ACQ 0.0069 0.1397 0.0288 0.1364 (0.074) (1.408) (0.313) (1.418) ROA_ACQ 0.1645* 0.1208 0.1397 0.1162 (1.814) (1.147) (1.561) (1.148) SIZE_ACQ -0.0127 -0.0029 -0.0123 -0.0029 (-1.628) (-0.266) (-1.580) (-0.271) BRDSIZE_ACQ 0.0012 -0.0011 0.0012 -0.0008 (0.453) (-0.384) (0.456) (-0.285) RUNUP 0.0001* 0.0001 0.0001 0.0002 (1.719) (1.347) (1.351) (1.497) MB_TGT -0.0078* -0.0003 -0.0079* -0.0003 (-1.777) (-0.064) (-1.814) (-0.069) ROA_TGT -0.0179 -0.0145 -0.0133 -0.0218 (-0.297) (-0.235) (-0.211) (-0.366) SIZE_TGT -0.0077 -0.0136** -0.0071 -0.0135** (-1.322) (-2.242) (-1.213) (-2.262) LIQ_TGT 0.0091 0.0140 0.0009 0.0206 (0.138) (0.203) (0.013) (0.309) TENDER 0.0258 -0.0051 0.0253 -0.0054 (1.587) (-0.376) (1.541) (-0.401) HOSTILE -0.0275 -0.0371 -0.0335 -0.0336 (-0.940) (-1.183) (-1.096) (-1.103) COMPETE 0.0367 0.0238 0.0356 0.0238 (1.261) (0.929) (1.220) (0.952) NONDIV 0.0128 -0.0340* 0.0129 -0.0338* (1.021) (-1.857) (1.037) (-1.878) REL_VAL 0.0007 0.0003 0.0004 -0.0003 (0.053) (0.022) (0.028) (-0.022) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq 0.288 0.144 0.301 0.156 N 212 251 212 251

Table 3- 7: Acquisitiveness and Deal Completion Likelihood

This table reports results from sequential logit regression of merger completion likelihood on media connections. A firm’s choice set is denoted by n ε {0, 1, 2}, where 0 = no bid, 1 = firms announcing a merger bid but failing to complete the bid, and 2 = firms announcing a merger bid and successfully completing the bid. The first node contains two alternatives: bidding (n = 1) or not bidding (n = 0). Conditional on first node, the second node consists of two alternatives: completing a bid (n = 2) or failure to complete a bid (n = 1). Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the merger announcement. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Node 1 Node 2 (Probability of Bidding) (Probability of Bid Completion) (1) (2) (3) (4) Log_MC 0.2683*** 0.3104*** 0.2031** 0.1994** (13.248) (14.849) (2.340) (2.273) MB_ACQ 0.0211*** 0.0174*** 0.0929* 0.0848* (8.286) (6.157) (1.923) (1.929) LIQ_ACQ -0.2648 -0.1788 0.3754 0.3542 (-1.622) (-1.004) (0.395) (0.366) ROA_ACQ 0.9412*** 0.8201*** 0.3372 0.3628 (5.842) (4.889) (0.416) (0.422) SIZE_ACQ 0.2129*** 0.1993*** -0.0753 -0.0789 (14.264) (13.125) (-1.288) (-1.324) LEV_ACQ -0.7172*** -0.7283*** -0.9640*** -1.0579*** (-6.536) (-6.417) (-2.699) (-2.876) BRDSIZE_ACQ -0.0847*** -0.0769*** -0.0090 -0.0082 (-7.468) (-7.051) (-0.231) (-0.209) Year FE No Yes No Yes N

Table 3- 8: Innovative Efficiency

This table reports results from multivariate regression of innovative efficiency (IE) on media connections and control variables for a sample US firms during the period of 1999 to 2010. IE1 is estimated as the ratio of Adjusted Patent (Pat_adj) to R&D Capital (RDC). Pat_adj is calculated by dividing each patent for each firm-year by the mean number of patents of all firms for that year in the same technology class as the patent. RDC is the 5 year cumulative R&D expenses starting from fiscal year ending in year t-2 to year t-6 assuming an annual depreciation rate of 20%. IE2 is estimated as the ratio of Number of Patents (NPats) to RDC. IE3 is estimated as the ratio of Adjusted Citations (Cite_adj) to RDC. Cite_adj is estimated by dividing number of citations of a patent by the total number of citations received by all patents in that year in the same technological class as the patent. IE4 is the ratio of Number of Citations (NCites) to RDC. In Panel (B), Log_IE1, Log_IE2, Log_IE3, and Log_IE4 are the log transformations (1+IE) of IE1, IE2, IE3, and IE4, respectively. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the merger announcement. Control variables include acquirer, target, and deal characteristics. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Dependent Variable – Innovative Efficiency (1) (2) (3) (4) IE1 IE2 IE3 IE4 Log_MC -0.0510*** -0.1613*** -0.2360*** -1.3188*** (-10.158) (-9.763) (-7.223) (-6.430) Ln_KL 0.0025 0.0678*** 0.1780*** 1.0774*** (0.440) (3.397) (4.410) (4.138) SALE 0.0000*** 0.0000*** 0.0000*** 0.0000*** (3.264) (2.993) (3.022) (2.653) RD 0.0000*** 0.0001*** 0.0001 0.0003 (5.029) (3.785) (1.581) (1.324) LEV 0.0035 -0.0078 -0.2349** -0.9781 (0.233) (-0.139) (-2.220) (-1.375) EBITDA -0.0251 -0.1492** -0.3570** -1.9198* (-1.284) (-2.097) (-2.250) (-1.834) CAPEX 0.5172*** 2.5422*** 3.6898*** 25.1024*** (3.945) (5.055) (4.057) (3.869) CASH 0.0049 -0.0134 -0.2727* -1.4756 (0.254) (-0.188) (-1.822) (-1.538) PPE -0.0626* -0.2720** -0.3692 -2.7790* (-1.784) (-2.379) (-1.582) (-1.851) MB 0.0020 0.0166*** 0.0576*** 0.4080*** (1.296) (2.810) (3.924) (4.072) HHI 0.0209 0.0661 0.0392 -1.4985 (0.392) (0.359) (0.114) (-0.684) HHI_SQ -0.0258 -0.1370 -0.2614 0.1450 (-0.445) (-0.711) (-0.746) (0.065) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq 0.167 0.176 0.115 0.133 N 5219 5464 4995 4995

Panel B: Dependent Variable – Log (1+Innovative Efficiency) (1) (2) (3) (4) Log_IE1 Log_IE2 Log_IE3 Log_IE4 Log_MC -0.0530*** -0.0916*** -0.0914*** -0.1500*** (-7.361) (-9.635) (-8.174) (-9.231) Ln_KL -0.0035 0.0261** 0.0591*** 0.1054*** (-0.488) (2.448) (4.398) (4.913) SALE 0.0000*** 0.0000*** 0.0000** 0.0000 (3.336) (2.789) (2.292) (0.658) RD 0.0000*** 0.0000*** 0.0000** 0.0001*** (4.678) (4.181) (2.069) (2.797) LEV 0.0196 -0.0013 -0.0950*** -0.1354** (0.924) (-0.042) (-2.593) (-2.081) EBITDA -0.0158 -0.0636* -0.1224** -0.1930** (-0.620) (-1.689) (-2.286) (-2.363) CAPEX 0.4695** 1.3086*** 1.5236*** 3.4927*** (2.280) (4.834) (5.324) (7.149) CASH -0.0069 -0.0084 -0.0840* -0.0673 (-0.345) (-0.241) (-1.775) (-0.844) PPE -0.0848 -0.1745*** -0.1903** -0.4882*** (-1.639) (-2.628) (-2.494) (-3.830) MB 0.0012 0.0083*** 0.0226*** 0.0401*** (0.733) (2.910) (4.561) (5.328) HHI -0.0383 -0.0580 -0.0306 -0.1609 (-0.648) (-0.609) (-0.258) (-0.813) HHI_SQ 0.0260 0.0305 -0.0258 0.0830 (0.420) (0.303) (-0.212) (0.398) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq 0.144 0.203 0.169 0.320 N 5219 5464 4995 4995

Table 3- 9: Firm Performance and Predicted Innovative Efficiency

This table reports results from multivariate regression of long term performance on excess innovative efficiency (IE) attributable to media connections. In Panel A, the dependent variable is Return on Assets (ROA), estimated as earnings before interest and taxes scaled by total assets. In Panel B, the dependent variable is Tobin’s Q (Q), estimated as earnings before interest and taxes scaled by total assets. In Panel (C), dependent variable is stocks’ excess returns from July of year t to June of year t+1, and control variables are for the fiscal year ending in year t-1. In all three panels, IE_MC1 (IE_MC2) is estimated as the difference between the predicted value of Log_IE1 (Log_IE3) in equation (1) and that in equation (2). All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Dependent Variable – ROA (1) (2) (3) (4) (5) (6) ROAt+1 ROAt+2 ROAt+3 ROAt+1 ROAt+2 ROAt+3 IE_MC1 -0.1538** -0.4899*** -0.6661*** (-2.221) (-4.452) (-5.732) IE_MC2 -0.0690** -0.2204*** -0.2999*** (-2.209) (-4.449) (-5.748) Ln_KL -0.0088 -0.0145** -0.0104* -0.0087 -0.0144** -0.0101 (-1.528) (-2.300) (-1.645) (-1.517) (-2.270) (-1.608) SALE 0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 (1.514) (-0.710) (-1.574) (1.510) (-0.718) (-1.586) RD 0.0000 0.0000*** 0.0000*** 0.0000 0.0000*** 0.0000*** (1.009) (4.389) (6.159) (0.990) (4.359) (6.123) LEV 0.0366* 0.0583** 0.0209 0.0367* 0.0585** 0.0212 (1.700) (1.995) (0.550) (1.703) (2.002) (0.556) EBITDA 0.9773*** 0.7980*** 0.6996*** 0.9774*** 0.7985*** 0.7002*** (27.413) (17.892) (15.917) (27.415) (17.898) (15.924) CAPEX -0.3291*** -0.0767 0.0135 -0.3305*** -0.0811 0.0075 (-3.225) (-0.649) (0.093) (-3.241) (-0.687) (0.052) CASH -0.0117 -0.0749** -0.1048*** -0.0117 -0.0750** -0.1050*** (-0.439) (-2.245) (-3.629) (-0.441) (-2.249) (-3.635) PPE -0.0073 -0.0484 -0.0829* -0.0071 -0.0476 -0.0820 (-0.292) (-1.198) (-1.657) (-0.283) (-1.181) (-1.640) MB 0.0016 -0.0049** -0.0056** 0.0016 -0.0049** -0.0056** (0.932) (-2.072) (-2.045) (0.937) (-2.062) (-2.033) HHI -0.0007 0.0019 -0.0494 -0.0011 0.0005 -0.0513 (-0.014) (0.034) (-0.970) (-0.024) (0.009) (-1.010) HHI_SQ 0.0017 0.0051 0.0575 0.0023 0.0071 0.0602 (0.036) (0.088) (1.126) (0.049) (0.123) (1.183) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.584 0.422 0.404 0.584 0.422 0.404 N 6146 6055 5910 6146 6055 5910

Panel B: Dependent Variable – Tobin’s Q (1) (2) (3) (4) (5) (6) Qt+1 Qt+2 Qt+3 Qt+1 Qt+2 Qt+3 IE_MC1 -1.7626** -1.8020*** -1.4451** (-2.300) (-2.854) (-2.376) IE_MC2 -0.7381** -0.7770*** -0.6263** (-2.137) (-2.730) (-2.284) Ln_KL -0.2525*** -0.2159*** -0.1633*** -0.2520*** -0.2153*** -0.1629*** (-6.913) (-6.855) (-5.455) (-6.900) (-6.838) (-5.439) SALE -0.0000 -0.0000*** -0.0000*** -0.0000 -0.0000** -0.0000*** (-0.776) (-2.582) (-2.756) (-0.764) (-2.575) (-2.752) RD 0.0003*** 0.0003*** 0.0002*** 0.0003*** 0.0003*** 0.0002*** (6.190) (6.999) (6.613) (6.173) (6.981) (6.597) LEV 0.2438 0.2935* 0.3193** 0.2445 0.2943** 0.3199** (1.319) (1.957) (2.256) (1.322) (1.962) (2.260) EBITDA -0.9209*** -1.3630*** -1.6139*** -0.9192*** -1.3612*** -1.6126*** (-4.280) (-7.636) (-9.258) (-4.271) (-7.623) (-9.247) CAPEX 5.6709*** 4.6154*** 4.4930*** 5.6562*** 4.6001*** 4.4810*** (6.394) (6.061) (6.366) (6.379) (6.043) (6.352) CASH 1.8176*** 1.2075*** 0.9953*** 1.8176*** 1.2075*** 0.9953*** (11.575) (8.780) (7.717) (11.575) (8.780) (7.717) PPE -0.9357*** -0.7544*** -0.8146*** -0.9341*** -0.7524*** -0.8129*** (-4.366) (-3.869) (-4.568) (-4.359) (-3.859) (-4.559) HHI -0.5759 -0.3255 -0.3525 -0.5816 -0.3314 -0.3573 (-1.376) (-0.927) (-1.054) (-1.390) (-0.944) (-1.069) HHI_SQ 0.6530 0.4147 0.3827 0.6613 0.4231 0.3896 (1.410) (1.061) (1.041) (1.428) (1.082) (1.060) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.221 0.228 0.238 0.221 0.227 0.238 N 6120 6020 5887 6120 6020 5887

Panel C: Dependent Variable – Excess Return (1) (2) (3) (4) IE_MC1 -0.3464*** -0.1359* (-4.289) (-1.650) IE_MC2 -0.1646*** -0.0650* (-4.399) (-1.706) Ln_KL 0.0176*** 0.0106*** 0.0177*** 0.0106*** (5.706) (3.341) (5.736) (3.356) SALE -0.0000 -0.0000 -0.0000 -0.0000 (-1.445) (-0.781) (-1.403) (-0.764) RD 0.0000*** 0.0000** 0.0000*** 0.0000** (2.837) (2.038) (2.855) (2.050) LEV 0.0151* 0.0067 0.0154** 0.0069 (1.943) (0.864) (1.986) (0.882) EBITDA -0.0169** -0.0179** -0.0164** -0.0177** (-2.031) (-2.116) (-1.967) (-2.087) CAPEX 0.0284 -0.0144 0.0259 -0.0154 (0.929) (-0.456) (0.849) (-0.489) CASH -0.0227*** -0.0179** -0.0225*** -0.0178** (-2.814) (-2.223) (-2.796) (-2.217) PPE 0.0515*** 0.0453** 0.0519*** 0.0454** (2.709) (2.364) (2.732) (2.373) HHI -0.0033 -0.0382 -0.0041 -0.0384 (-0.074) (-0.856) (-0.091) (-0.861) HHI_SQ -0.0084 0.0363 -0.0074 0.0366 (-0.190) (0.823) (-0.167) (0.831) Firm FE Yes Yes Yes Yes Year FE No Yes No Yes adj. R-sq -0.003 0.005 -0.003 0.005 N 73105 73105 73105 73105

Table 3- 10: Endogeneity Concern – 2-SLS Instrumental Variable Regression

This table reports results from 2-SLS IV regressions. In Stage 1, Media Connection (Log_MC) is instrumented by the log of 1 plus the number of media companies located in the same county as the firm’s headquarter (Log_MediaCo). In Stage 2, Innovative Efficiency (Log_IE1 and Log_IE3) is regressed on predicted media connection. Log_IE1, and Log_IE3 are the log transformations of IE1 and IE3, respectively. IE1 is estimated as the ratio of Adjusted Patent (Pat_adj) to R&D Capital (RDC). Pat_adj is calculated by dividing each patent for each firm-year by the mean number of patents of all firms for that year in the same technology class as the patent. RDC is the 5 year cumulative R&D expenses starting from fiscal year ending in year t-2 to year t-6 assuming an annual depreciation rate of 20%. IE3 is measured as the ratio of Adjusted Citations (Cite_adj) to RDC. Cite_adj is estimated by dividing number of citations of a patent by the total number of citations received by all patents in that year in the same technological class as the patent. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. (1) (2) (3) (4) st nd st nd 1 Stage 2 Stage 1 Stage 2 Stage Dependent Variable Log_MC Log_IE1 Log_MC Log_IE3 Log_MediaCo 0.111*** 0.113*** (5.351) (5.341) Log_MC -0.107*** -0.315*** (-3.249) (-3.626) Ln_KL 0.305*** 0.012 0.316*** 0.134*** (14.037) (0.971) (14.137) (4.058) SALE 0.000*** 0.000*** 0.000*** 0.000*** (10.726) (2.683) (10.514) (2.970) RD 0.001*** 0.000*** 0.001*** 0.000*** (21.793) (2.784) (21.297) (2.926) LEV 0.831*** 0.063* 0.840*** 0.083 (11.998) (1.921) (11.968) (1.052) EBITDA 0.474*** 0.012 0.506*** -0.013 (7.164) (0.405) (7.259) (-0.177) CAPEX 1.089** 0.526** 0.843* 1.691*** (2.530) (2.483) (1.842) (5.388) CASH -0.312*** -0.025 -0.316*** -0.171*** (-3.842) (-1.064) (-3.737) (-2.724) PPE 0.103 -0.082 0.155 -0.150* (0.768) (-1.547) (1.134) (-1.838) MB 0.023*** 0.002 0.025*** 0.029*** (4.429) (1.221) (4.430) (4.654) HHI 0.738*** -0.004 0.669** 0.096 (2.871) (-0.052) (2.522) (0.608) HHI_SQ -0.773** -0.013 -0.666** -0.161 (-2.460) (-0.160) (-2.053) (-0.939) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq -0.003 -0.109 N 5082 5082 4861 4861 F-test of excluding instruments 28.63 28.52 Test of Endogeneity statistic 3.487 9.090

[p-value] 0.0619 0.0026

CHAPTER IV: MEDIA CONNECTION AND CORPORATE FINANCING POLICIES

1. Introduction

I examine the effects of media connection on corporate financing policies in this essay. As part of the financing policy, I focus on financing through seasoned equity offerings (SEO) and repurchase of shares. Specific research questions include: Does media connection determine SEO likelihood, SEO announcement return and post-SEO long term performance? Does media connection affect repurchase announcement return, post-repurchase long term performance and payout choice?

A stylized fact regarding secondary equity offering (SEO) is that managers tend to sell overvalued shares12. While the trade-off and pecking-order theories fail to explain such phenomena, market timing theory seems to provide appealing explanation for valuation increase prior to SEOs. Media, being an active intermediary in the financial market, could impact managers’ strategy of market timing before SEO. Specifically, I explore whether media connection has any implication on the likelihood of equity issuance through SEO, SEO announcement return and post-SEO long term performance.

A number of studies provide support for the view that managers tend to issue stocks at overvalued price by attracting investors through tainted financial reporting practice. These practices include more voluntary disclosure (Lang and Lundholm, 2000), earnings management (Shivakumar, 2000), and increase discretionary accrual (Teoh et

12 Loughran and Ritter (1995), (1997), Asquith and Mullins (1986), Masulis and Korwar (1986), Baker and Wurgler (2002) 96

al., 1998) prior to SEO to hype stock price. Furthermore, Chemmanur and Yan (2009) show that firms not only engage in accounting manipulation but also increase their product market advertising in their SEO years compared to non-SEO years when they have no plan to issue new equity and also relative to matched sample. Cohen et al. (2016) find that firms about to issue equity and sell shares are significantly more likely to call on analysts with more optimistic views of the firm. In line with these papers, I argue that social ties with the media may also provide an explanation for the SEO likelihood, announcement return and post-SEO long term performance.

While a myriad of studies provide several explanations for repurchase, its popularity over dividends etc., evidence on managerial incentives to engage in manipulation prior to repurchase announcement is limited. These studies conclude that managers engage in downward earnings management prior to repurchase announcements

(Gong et al., 2008), managers release more bad (good) news prior to (following) increase the frequency and magnitude of bad (good) news announcements prior to (following) repurchasing shares (Brockman et al., 2008), and repurchases are used to offset EPS dilution when employee stock options are exercised (Bens et al., 2003). Since managers tend to buy back shares when their shares are undervalued, it would be interesting to study whether managers’ social ties with the media outlets affect the monitoring and information flow around share repurchases and subsequently the post-repurchase performance.

Based on the two competing hypothesis presented in chapter I, I derive testable hypotheses that may explain the effect of media connection on SEO and repurchase outcomes.

At one end, Information Efficiency hypothesis predicts that social ties between journalists and corporate executives may provide journalists with more accurate and timely access to inside information around the corporate financing events (SEOs and repurchases), thereby facilitating superior information flow from the firms to the investors and other market participants. In addition, contract enforcement channel of social capital would work as a governance mechanism, which is likely to foster more transparent coverage and efficient share price around these events. Combination of governance through contract enforcement and price efficiency through information flow would reduce the extent of mispricing and thus dampen managerial discretion to engage in market timing through SEOs and deter managers from devising opportunistic repurchases. Information efficiency hypothesis, therefore, predicts that media connection is negatively associated with the likelihood of conducting SEOs and repurchases.

Efficient information flow would also facilitate smaller price run-up prior to SEO and produce less surprise on announcement. In line with the same argument, information content of the repurchase announcements would be lower for media connected firms.

Therefore, information efficiency view posits that media connection would be positively

(negatively) related with SEO (repurchase) announcement returns. Moreover, since media connection alleviate adverse selection problem through the governance role and efficient reporting mechanism, it would be difficult for media connected managers to undertake opportunistic SEOs or repurchase programs. Media connection would thus result in better long-term performance for the SEO issuers and repurchasing firms.

Manipulation hypothesis, in contrast, contend that managers may exploit media connections to maneuver opportunistic financing events. Managers may exacerbate

mispricing prior to SEO (repurchase) announcements by disseminating more (less) favorable soft information through their friends in the media. While there is pervasive evidence of manipulation of hard information (e.g. earnings management) prior to SEOs and repurchase programs, managers may find it more convenient to manipulate soft information. In addition, media coverage could serve as an assurance to the investors that the event firms’ stocks are not mispriced. Social ties with the executives may bias the journalists to write stories that would appear authentic to the investors. In summary, manipulation hypothesis predicts that media connection would increase the likelihood of conducting SEOs and repurchases. The larger the mispricing, the greater would be the market adjustment on event announcements. Therefore, SEO (repurchase) announcement returns would be negatively (positively) associated with media connection. Furthermore, absence of governance and monitoring mechanism would facilitate successful equity offering by overvalued firms and share repurchases by undervalued firms, which would subsequently face reversal. Manipulation hypothesis thus conjectures that media connection is negatively (positively) associated with the long term operating performance of firms conducting SEO (repurchase).

I construct my sample by obtaining SEO issuance data from SDC Platinum, stock price data from Center for Research in Security Prices (CRSP) database and firm fundamental data from the Compustat database. Final sample contain 6,747 SEOs offered by 3,564 unique firms in the US for the period of 2000 to 2016. The sample of share repurchases is created by extracting data from SDC Platinum’s Mergers and Acquisitions database. Final sample of repurchase programs contain 12,268 repurchase announcements by 4,869 unique firms.

Results from the multivariate regressions reveal that media connection affects

SEO outcomes. Consistent with the Manipulation Hypothesis, media connection is likely to increase the probability of an SEO event, negatively associated with the announcement period CAR and positively associated with post SEO long term operating performance.

Further analysis of announcement returns reveal that the effect of media connection on

SEO announcement return is greater for younger firms and firms with high growth opportunity. Altogether, these results suggest that managers take advantage of media connection to engage in opportunistic SEOs.

Empirical analysis of repurchase programs show that media connection is a determinant of the repurchase outcomes. Media connection is associated with positive announcement return. In addition, the effect of media connection on repurchase announcement return is greater for firms in which top executives have greater than median option exercise value. These results provide strong support for the manipulation hypothesis as managerial incentive is likely to motivate media manipulation prior to SEO announcement. Tests of repurchase likelihood and repurchase amount demonstrate that media connection increases both the likelihood of repurchase and the amount repurchased. I also test the conjecture that repurchase is preferred over dividends when managers tend to utilize media connection to devise manipulative objectives. In line with manipulation hypothesis, I find that repurchase is the preferred over dividends as a mode of payout. However, results from the post repurchase long term performance do not provide any conclusive evidence.

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The chapter proceeds by reviewing relevant literature in section 2, explaining the hypothesis development in section 3, describing the data and methodology in section 4, analyzing empirical results in section 5, and providing concluding remarks in section 6.

2. Literature Review

2.1. Review of SEO Literature

In this sub-section, I review prior literature that provides pervasive evidence of pre-SEO price run-up, reversal on SEO announcement and post-SEO long term underperformance. According to Modigliani and Miller’s (1958) capital structure irrelevance theory, value of a firm is irrelevant to its capital structure and also value should not be affected by the type of financing, in a perfect market. Implication of this theorem on security issuance is that there should be no market reaction to the announcement of an SEO.

However, prior literature provides extensive evidence that common stocks of SEO issuers experience significant positive return prior to the announcement and a negative return on announcement. Asquith and Mullins (1986) examine equity offerings by utilities and industrial firms, and show that announcement of equity offerings create negative abnormal return. Masulis and Korwar (1986) investigate the SEO announcements on the Wall Street Journal Index and the Investment Dealer’s Digest during the period of 1963 to 1980. They report that announcement of SEO accompanies negative return. In line with these studies, Mikkelson and Partch (1986), Korajczyk et al.

(1991), Denis (1994), Jung et al. (1996), and Chemmanur and Jiao (2005) document negative SEO announcement returns.

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Following the early evidence of negative announcement returns, a number of studies demonstrate that SEO firms under perform in the long run too. Loughran and

Ritter (1995) use a sample of SEOs during 1970 to 1990 and find that SEO issuers underperform relative to non-issuers for five years after the offer date. Results indicate that investing in SEO issuing firms results in long term value destruction relative to investment in non-SEO issuing firms. Spiess and Affleck-Graves (1995) demonstrate similar results. They show that long term performance of SEO issuers during 1975 to

1989 were lower than a matched sample of firms within the same industry that did not issue equity. The evidence is consistent with the fact that companies tend to issue equity when their stock is overvalued. As the market does not adjust the stock value appropriately, the stock is still overvalued when the equity is actually offered.

Next, Loughran and Ritter (1997) link the stock price performance of SEO issuers to their operating performance and find that issuers experience better operating performance prior to the offering which deteriorates afterwards. Jung et al. (1996) find that SEO issuers experience positive abnormal returns several months prior to the issue.

Jegadeesh (2000) use several benchmarks including the equal and value weighted indexes to evaluate long term performance of SEO issuers. He finds that SEO issuers underperform all the benchmarks over the five years after issuance. Hovakimian et al.

(2001) find that firms with high past stock returns are likely to issue equity and retire debt, while firms with low past stock market performance are reluctant to issue equity.

Several theories explain the pre-SEO price run-up and post-SEO long term underperformance. Myers and Majluf (1984) highlights the adverse selection problem that arises due to the information asymmetry between managers and investors. Managers

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exploit their advantage of access to inside information by issuing overvalued shares and hence SEO announcement sends a negative signal to the market that the firm’s shares are overvalued. Lucas and McDonald (1990) also present a model based on adverse selection and conclude that equity issues convey negative signal to the market.

While trade off and pecking order theories have gained significant attention in explaining capital structure choice, these theories fail to explain the mispricing around

SEOs. According to the trade-off theory, large share price increase reduces leverage and consequently firms should lever up to maintain optimal capital structure. In practice,

SEOs instead of debt offerings follow large price run-ups, which amplifies the deleveraging due to the price run-up. Deleveraging creates untapped debt capacity and pecking order theory predicts that firms are unlikely to sell stocks in this scenario. Price run-ups imply increased future cash flows that could be used to support additional debt not equity. Therefore, issuing equity instead of debt after price run-ups undermine the pecking order theory.

Limitations of the trade off and pecking order theories make market timing as one of the most prominent theoretical explanations for SEOs. Market timing theory predicts that managers attempt to sell overpriced stocks when market conditions are favorable. In line with this theory, Baker and Wurgler (2002) conjecture that there is no optimal capital structure. Instead, the practice of market timing translates into the capital structure outcome. They use market-to-book ratio as a proxy for firm valuation and market timing opportunities perceived by managers. Results show that low (high) leverage firms tend to raise funds when their market valuations were high (low), and conversely highly levered firms tend to be those that raised funds when their valuations were low. DeAngelo et al.

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(2010) conclude that a firm’s market timing opportunities along with its corporate lifecycle stage are significant determinants of the likelihood of conducting an SEO.

Survey evidence by Graham and Harvey (2001) substantiate that managers themselves acknowledge market timing while considering equity offerings. Majority of the CFOs agree that the valuation of their stock is an important consideration and market prices are more important most other factors considered in decision to issue equity.

2.1.1. Manipulation around SEO

A line of literature demonstrate that managers engage in tainted financial reporting practice to attract investors and succeed in subsequent equity offerings. Firms do more voluntary disclosure, overstate earnings, increase discretionary accrual prior to

SEO to hype stock price. Lang and Lundholm (2000) examine voluntary disclosure of the

SEOs firms and find that these firms significantly increases disclosure six months prior to the offering. They also find that the effect of disclosure on pre-SEO price run-up is greater for the categories of disclosure over which firms have more discretion. Teoh et al.

(1998) studies the discretionary accruals around SEOs and hypothesize that SEO issuers have high income-increasing accounting adjustments pre-issue and poor earnings and stock return performance post-issue. They find that issuers who adjust discretionary current accruals to report higher net income prior to the offering have lower post-issue long-run abnormal stock returns and net income.

Further, Shivakumar (2000) analyzes earnings management around SEOs and finds that net income and accruals are high around equity offerings and pre-offering abnormal accruals predict subsequent declines in net income. He argues that earnings management is a rational response of issuers to anticipated market behavior at SEO

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announcement. Chemmanur and Yan (2009) show that firms not only engage in accounting manipulation but also increase their product market advertising in their SEO years relative to non-SEO years when they have no plan to issue new equity and also relative to matched sample. Cohen et al. (2016) find that firms about to issue equity and sell shares are significantly more likely to call on analysts with more optimistic views of the firm.

2.2. Review of Repurchase Literature

Prior literature documents various theories that explain announcement return and long term performance of repurchasing firms. Among these theories, the signaling theory and the free cash flow theory has gained most attention.

Signaling theory proposes that in presence of asymmetric information between insiders and shareholders, good quality firms signal their quality by a costly signal such as distributing cash to shareholders. Theoretical models of signaling theory include

Bhattacharya (1979), Vermaelen (1981), Miller and Rock (1985) and others. These models, in general, theorize that information asymmetry between insiders and shareholders may inspire the insiders to believe that the firm is undervalued. As a result, insiders would want to time the market by repurchasing shares which would send a positive signal to the investors that the stock is undervalued.

Empirical implication of the signaling theory is that repurchase announcement return would be positive and the repurchasing firms would exhibit better performance in the long term. Comment and Jarrell (1991) find positive abnormal returns around share repurchase announcement. Ikenberry et al. (1995) study the long term performance of the repurchasing firms. They report that undervaluation driven repurchasing firms exhibit

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significant abnormal returns during the post repurchase period for up to four years. Peyer and Vermaelen (2008) also find persistence in the drift followed by post-repurchase announcement. Manconi, Peyer, and Vermaelen (2013) find positive average CAR around open-market repurchase announcements using a sample of firms from 31 foreign countries. Among others, Dann (1981), Lakonishok and Vermaelen (1990), Nohel and

Tarhan (1998) find similar empirical evidence.

Free cash flow theory provides another explanation for repurchase and its value consequences. Jensen (1986) argues that firms with fewer investment opportunities are likely to spend their excess free cash flows on value destroying projects that reduce the shareholder wealth. He proposes that distributing cash to the shareholders would mitigate the overinvestment problem by reducing the excess cash from the managers. While cash could be distributed by several means (e.g. dividends, debt-for-equity swaps etc.), repurchases are more flexible and efficient. Free cash flow theory, therefore, predicts that repurchasing firms should experience a decline in their profitability and market reaction to share repurchase announcements should be stronger for firms that are more likely to overinvest.

Stephens and Weisbach (1998) show that level of cash flow is positively associated with the likelihood of repurchases, which supports the free cash flow theory.

They also find that repurchase activity is negatively correlated with prior stock returns, suggesting that firms repurchase stock when their stock process are perceived as undervalued. Dittmar (2000) analyzes US firms from 1977 to 1996 and reports that these firms repurchase stock to distribute excess capital. Lie (2000) reports a positive announcement return of 8% for a sample of 207 self-tender offers from 1981 through

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1994. He also finds that low Tobin’s Q firms generate higher announcement returns than high Tobin’s Q firms, which is consistent with the view that cash distributions through self-tender offers alleviate the overinvestment problem. Grullon and Michaely (2004) investigate firm performance and risk exposure for a sample of share repurchases during the period of 1980 to 2000. They find that repurchasing firms experience profitability and investment decline within three years after repurchases. Moreover, repurchasing firms’ costs of capital also decreases by about 16% in the three years from repurchase.

Brav et al. (2005) conduct a survey of the managers and findings reveal that managers tend to use share repurchases to reduce excess cash holdings. In addition, firms are more likely to repurchase shares when they do not have potential investment opportunities. Altogether, these evidences are supportive of the free cash flow hypothesis.

Positive abnormal return on repurchase announcement and subsequent changes in operating performance and systematic risk are consistent with the notion that accumulated free cash flows and declining growth opportunities motivate firms to repurchase shares.

Literature also demonstrates several hypothesis other than signaling or cash distribution to explain repurchase. These include leverage rebalancing hypothesis, takeover deterrent hypothesis, and employee stock option (ESO) hypothesis. Leverage rebalancing hypothesis predicts that a firm is likely to repurchase shares if its leverage ratio is lower than the target leverage ratio. Since share repurchase reduces the equity value of a firm, it increases the leverage ratio for the firm. Bagwell and Shoven (1988) and Hovakimian et al. (2001) and Lie (2002) provide empirical support for the leverage rebalancing hypothesis by showing that repurchasing firms’ leverage ratios are below the

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target level before repurchase and that on leverage ratios after repurchases are close to or above the predicted target level.

The ESO hypothesis predicts that firms with employee stock option plans are more likely to repurchase stocks since repurchase facilitates cash distribution without diluting the per-share value of stock. Preventing the stock price dilution is important when managers hold stock options. Among others, Lambert, Lanen, and Larcker (1989),

Jolls (1996), Weisbenner (2000), Fenn and Liang (2001), Kahle (2002) and Bens et al.

(2003) find empirical support in favor of the ESO hypothesis.

2.2.1. Manipulation around Repurchase

While a myriad of studies provide several explanations for repurchase, its popularity over dividends etc., a scant line of literature investigates managerial incentives to engage in manipulation prior to repurchase announcement. Bens et al. (2003) examine whether firms repurchases stocks as an instrument to prevent EPS dilution. They find that stock repurchases increase in years when options related EPS dilution increases, and annual earning is below the level required to sustain past EPS growth rates. Gong et al.

(2008) argue that managers who conduct repurchases for non-signaling purposes are likely to be motivated to reduce the repurchase price. They hypothesize that earnings management could be a plausible mechanism to depress stock prices prior to repurchase announcements. Results confirm their conjecture that managers engage in downward earnings management prior to repurchase announcements. They also report negative relation between pre repurchase abnormal accrual and post repurchase operating performance improvement, which substantiates pre-repurchase earnings management as a determinant of post-repurchase long term performance. Brockman et al. (2008) argue that

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managers may alter the information flow by releasing more good news after repurchase and more bad news before repurchase. Results show that managers increase the frequency and magnitude of bad (good) news announcements prior to (following) repurchasing shares. In addition, managers are more inclined to alter information flows as managerial ownership in the firm increases.

3. Hypothesis Development

In this section, I develop a set of testable hypothesis to analyze the effect of media connection on corporate financing policies. I use the two primary hypotheses – information efficiency hypothesis and manipulation hypothesis – developed in chapter II to construct the testable hypotheses for this essay.

3.1. Media Connection and SEO

Prior literature (e.g., DeAngelo et al. 2010; Graham and Harvey 2001) provides extensive evidence in favor of the market timing theory which predicts that managers tend to issue equities when their shares are overpriced. I propose that managerial discretion to engage in market timing through SEO could be impacted by media connections. The information efficiency and the manipulation hypotheses derived above

(chapter 3.1) make two distinct predictions with respect to the role of the media connectedness.

Information efficiency hypothesis is motivated by media’s information intermediary and monitoring role played in the financial market. Media coverage exerts significant impact on share prices by transmitting information to investors, by creating investor recognition, and by increasing visibility. I argue that social ties between journalists and corporate executives may provide journalists with more accurate and

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timely access to inside information around SEO announcement, thereby facilitating superior information flow from the SEO issuer to the investors and other market participants. In addition, contract enforcement channel of social capital would work as a governance mechanism, which is likely to foster more transparent coverage and efficient share price around SEO announcement. Combination of governance through contract enforcement and price efficiency through information flow would reduce the extent of mispricing and thus dampen managerial discretion to engage in market timing through

SEOs. Information efficiency hypothesis, therefore, predicts that media connected firms are less likely to conduct SEOs compared to non-media connected firms.

From manipulation perspective, media connections could be exploited to maneuver opportunistic SEOs. One source of manipulation could be in the form of false certification of SEO issuer’s stock pricing. Alti and Sulaeman (2012) argue that the market timing behavior of the SEO issuers is complemented by the presence of institutional investor demand since institutional investor demand acts as a certification of the SEO firm’s market valuation. Similarly, I argue that media coverage could serve as an assurance to the investors that the SEO firm’s stocks are not mispriced. Social ties with the SEO issuers may bias the journalists to write stories that would appear authentic to the investors. Since price runups aggravate valuation uncertainty, media coverage that validates the price runup would mitigate such concerns. Hence, media manipulation would provide biased certification of the issuer’s stock price.

Another type of manipulation could transpire through the maneuvering of soft information prior to SEO announcement. Soft information is qualitative in nature and interpretation of soft information is subjective, whereas hard information such as earnings

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numbers are mostly quantitative and interpretation is objective. Managers may therefore exacerbate mispricing prior to SEO announcements by disseminating more favorable soft information through their friends in the media. While there is pervasive evidence of manipulation of hard information (e.g. earnings management) prior to SEOs, I argue that managers may find it convenient to manipulate soft information. Overall, manipulation hypothesis predicts that media connection would exacerbate the extent of mispricing and influence managers to engage in market timing through SEOs resulting in greater likelihood of conduct SEOs compared to non-media connected firms. Consistent with this argumentation I postulate the following set of hypotheses:

H1a: Media connected firms are less likely to engage in SEOs (information efficiency). H1b: Media connected firms are more likely to engage in SEOs (manipulation).

Pre-SEO price run-up followed by a reversal around SEO announcements suggest that investors adjust their expectation about the SEO firm. If media connection ensures more transparent and efficient stock pricing of the connected SEO firm, then from information efficiency standpoint, SEO announcement return would be lower for media connected firms than that of non-media connected firms. On the other hand, from manipulation perspective, if the mispricing is large during pre-SEO price run-up, market adjustment on SEO announcement also would be larger for the media connected firms.

As a result, SEO announcement return would be more negative for the media connected firms.

H2a: SEO issuers with media connections would experience less negative return on SEO announcement (information efficiency). H2b: SEO issuers with media connections would experience more negative return on SEO announcement (manipulation).

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Extant literature documents pervasive evidence of long term underperformance by

SEO issuers. Information efficiency hypothesis conjectures that media connection would alleviate the adverse selection problem, which arises due to the information asymmetry between manager and investors, through the governance role and efficient reporting mechanism. It would be difficult for managers of media connected firms to undertake opportunistic SEO. As a result, media connection would ensure better long-term performance of the SEO issuers. However, from manipulation hypothesis, the more prices are manipulated through media, the more likely it is that SEO firms would experience long term underperformance. Absence of governance and monitoring mechanism would facilitate successful equity offering by overvalued firms, which would subsequently face reversal and translate into long term underperformance.

H3a: SEO issuers with media connections would exhibit better long-term performance (information efficiency). H3b: SEO issuers with media connections would exhibit worse long-term performance (manipulation).

3.1.1. Moderating Effect of Investor Sentiment

Stock prices are generally overpriced during high sentiment periods as uninformed traders become more optimistic and bid prices up from their fundamental values during these periods (Baker and Wurgler, 2006; Stambaugh et al., 2012). As overpricing is less likely during low sentiment period, manipulation hypothesis predicts that media connection would be more advantageous in creating price manipulation during low sentiment period. SEO issuers may not require assistance from friends in the media during high sentiment period since overvaluation is already articulated by optimistic investors. Therefore, media connection would exhibit greater effect on SEO related price manipulation and performance during low sentiment period.

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Information efficiency hypothesis, however, contends that the monitoring and dissemination role of media would offset some of the overpricing during the high sentiment period, thereby implying that the media connection would facilitate greater

‘watchdog’ role of media during high sentiment period. It leads to the following hypotheses:

H4a: The effect of media connections on pre-SEO price run-up, announcement return and post-SEO performance of SEO issuers is higher during low sentiment periods (Manipulation). H4b: The effect of media connections on pre-SEO price run-up, announcement return and post-SEO performance of SEO issuers is greater during high sentiment periods (Information Efficiency).

3.2. Media Connection and Share Repurchase

While extant literature identifies several factors that motivate managers to engage in repurchase, it is widely agreed that managers tend to buy back shares when their shares are undervalued. I propose that corporate executives’ social ties with the media may affect their market timing effort while conducting repurchase.

According to the Information efficiency hypothesis, media connection is likely to facilitate journalists with more accurate and timely access to inside information about the repurchasing firms’ existing projects, investment opportunities etc. In addition, contract enforcement mechanism is likely to foster more transparent and independent reporting of the connected firms’ endeavors. Combination of governance through objective reporting and price efficiency through information flow would reduce the information asymmetry between managers and investors. As signaling incentive diminishes with the extent of asymmetric information, managers of media connected firms would likely deter from devising opportunistic repurchases. Information efficiency hypothesis, therefore, predicts that media connected firms are less likely to conduct open market repurchase.

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Manipulation hypothesis, on the other hand, predicts that managers may exploit media connection to articulate opportunistic repurchases. At least two arguments validate this hypothesis. First, media connection may facilitate broadcasting of more bad news prior to the intended repurchase announcement in an attempt to reduce stock price.

Barclay and Smith (1988) conjecture that managers may alter the information flow to the market by accelerating bad news prior to repurchase and postponing good news after repurchase. Brockman et al. (2008) empirically show that managers increase both the frequency and magnitude of bad (good) news before (after) repurchasing shares. Second, media manipulation may provide biased certification of the repurchasing firm’s stock price. Social ties with the repurchasing firms may influence the journalists to write stories that would appear authentic to the investors. Media coverage that validates mispricing would mitigate valuation concern of the investors and thereby promote stock price manipulation prior to repurchase programs. Therefore, according to manipulation hypothesis, media connection would exacerbate the extent of mispricing and motivate managers to conduct more repurchases. Consistent with this argumentation I posit the following set of hypothesis:

H5a: Media connected firms are less likely to repurchase shares (information efficiency). H5b: Media connected firms are more likely to repurchase shares (manipulation).

Since the information efficiency hypothesis predicts lower level of information asymmetry for the connected firms, the information content of the repurchase announcement would be lower relative to the non-media connected firms who may face greater information asymmetry. Thus, repurchase announcement return for the media connected firms would be lower than that of non-media connected firms. On the other hand, price manipulation through media connection would aggravate the information

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asymmetry between manager and investors. As a result, repurchase announcement of media connected firms would contain greater information content than that of the non- media connected firms who are not likely to face price manipulation. Manipulation hypothesis, thus, predicts that repurchase announcement return for the media connected firms would be higher than that of non-media connected firms. Following this analysis, I postulate my next set of hypothesis:

H6a: Repurchasing firms with media connections would experience less positive return on announcement (information efficiency). H6b: Repurchasing firms with media connections would experience more positive return on announcement (manipulation).

Information efficiency through media connection may also affect the long-term performance of the repurchasing firms. As media connected firms have more efficient prices and low level of information asymmetry, neither signaling nor managerial private incentives could motivate these firms to conduct share repurchase. A plausible purpose of repurchase for these firms could be distribution of free cash flow, which predicts long term underperformance after repurchase (Grullon and Michaly, 2004). Therefore, according to the information efficiency hypothesis, long term performance of the media connected firms would be worse than the non-media connected firms.

Manipulation hypothesis predicts that long term stock return and operating performance of the media connected firms would be greater than that of the non-media connected firms. Signaling theory implies that when insiders perceive undervaluation of the firm, they send positive signal to the market by repurchasing shares. As a result, post repurchase long term return is negatively associated with pre-repurchase return

(Vermaelen, 1981; Comment and Jarrell, 1991; Ikenberry et al., 1995; Peyer and

Vermaelen, 2008). As media connection fosters greater price depression through

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manipulation prior to repurchase, long term return reversal also would be larger for media connected repurchasers. Moreover, as media connected firms exhibit greater mispricing and high level of information asymmetry, a plausible purpose of repurchase for these firms could be signaling of future operating performance (Bartov, 1991; Brav et al., 2005;

Lie, 2005)

H7a: Repurchasing firms with media connections would exhibit worse long-term stock return and operating performance (information efficiency). H7b: Repurchasing firms with media connections would exhibit better long-term stock return and operating performance (manipulation).

3.2.1. Employee Option Grant

Unlike dividends, repurchases do not dilute the per-share value of stock as repurchase reduces the number of outstanding shares proportionately. Consequently, stock options become more valuable after a repurchase than after a dividend to the managers as they hold the right to purchase their firm’s stock at a pre-specified price. A number of studies have advanced the employee stock option (ESO) hypothesis based on this argument and these studies document that firms with employee stock option plans are more likely to repurchase stocks13. As I argue that media manipulation motive increases with manager’s private incentives, the effect of media connection would have greater impact for firms with employee stock option plans. Therefore, I hypothesize:

H8: The effect of media connections on repurchase decision and value consequences would be greater for firms with Employee Stock Option Plans (ESOPs).

4. Data and Methodology

4.1. Media Connections Measure

13 Lambert et al. (1989), Jolls (1996), Weisbenner (2000), Fenn and Liang (2001), Kahle (2002) and Bens et al. (2003) 116

The measure for media connection is estimated by using the social network database from BoardEx. First, I identify the firms that are part of the ‘Media and

Communication’ industry in BoardEx. Then I categorize all the directors/ executives working in these media firms as ‘media directors’. BoardEx contains a director connection file that plots connections among directors formed through employment, education or social activities. I merge this director connection file with the media directors identified in the previous step to obtain a director’s connections with the media directors. After that, I count the number of current and prior connections of directors of a firm by each year and then aggregate the number of connections for each firm-year.

Finally, I construct the measure of media connection (Log_MC) by taking log of one plus the number of media connections for a firm-year.

4.2. Seasoned Equity Offerings Sample

4.2.1. Data and Sample Selection

My initial sample of SEOs contain 9,301 US common stock seasoned offerings between January 2000 and December 2016 reported in the SDC’s Global New Issues database. I choose 2000 as the initial year since Boardex provides social network data starting from 1999. I obtain stock price data from CRSP database and firm fundamental data from the Compustat database. After merging the SEO sample with CRSP and

Compustat databases, I obtain a sample of 6,747 SEOs by 3,564 firms. Finally, I merge this sample with the issuer’s media connections. Following prior research, I apply the following restrictions on my sample.

 Issue should offer some primary shares.

 Offer price should be between $3 and $400.

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 Issuers should be listed on NYSE or NASDAQ.

Final sample contains 4,114 SEOs by 2,575 unique issuers. 1,849 issuers have at least one media connection and the average logged media connection is 3.08.

4.2.2. Control Variables

Motivated by prior research, a number of control variables are included in multivariate regressions (Chemmanur et al., 2009; Lee and Masulis, 2009). These variables include both the issuer and deal characteristics. Issuer characteristics include:

SIZE estimated as the log of the market value at the end of the year before the SEO announcement date, MB as the market value of equity divided by book value, LEV as long term debt scaled by total assets, ROA as earnings before interest and taxes divided by total assets, INV as capital expenditure scaled by total assets, VOL as the standard deviation of monthly returns and TURNOVER as the stock turnover in the year prior to

SEO announcement. I also control for the issuer’s governance quality by including the board size (BRD_SIZE) as a control.

Issue characteristics include percentage of primary shares offered (%PRIMARY), offer proceeds scaled by the market capitalization prior to the offer (REL_PROC), SEO underpricing estimated as the offer day close price scaled by the offer price

(UNDERPRICING), indicator variables for stocks listed in NYSE at the time of the offer

(NYSE_D), Rule 415 shelf offer (SHELF_D).

4.2.3. Descriptive Statistics

In Table 1, Panel A shows the descriptive statistics of the variables. On average,

SEO issues exhibit negative announcement returns (-1.2%) and are heavily discounted

(60.2%). Issuers are moderately leveraged with a leverage ratio of 29%, possess growth

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opportunity with a market-to-book ratio of 2.4 and does not operate profitably as return on asset is -9.6%.

Table 1, panel B reports the distribution of offerings by industries. The most SEO offerings occur in the Money and Finance industry (about 26%) while the least occur in the Chemicals and Consumer Durables industry (about 1%). In terms of media connections, firms in the Consumer Durables industry contain the most media connections. Panel C shows the distribution of offerings across the years. While the most

SEOs occurred in 2009 with a total of 384 representing 8.7% of the sample, the least

SEOs occurred in 2008 with a total of 164 representing 3.7% of the sample.

4.3. Share Repurchase Sample

4.3.1. Data and Sample Selection

The sample of share repurchases is constructed by extracting data from SDC’s

Mergers and Acquisitions database. My initial sample consists of 11,778 US repurchases between January 2000 and December 2016. I merge this sample of repurchases with the

CRSP database to obtain stock returns data, Compustat database to get firm fundamental data, and ExecuComp database to collect employee stock option data. Then I merge the remaining sample of repurchases with media connections. Final sample contains 12,268 repurchase announcements by 4,869 unique firms. 2,503 firms have at least one media connection and the average logged media connection is 3.3. I winsorize all variables at

1% level to eliminate the effect of outliers.

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4.3.2. Control Variables

Based on the prior studies on share repurchases, I include a number of control variables in the regression models. These control variables are: SIZE measured as the log of the market value at the end of the year before the repurchase announcement date, MB as the market value of equity divided by book value, LEV as long term debt scaled by total assets, ROA as earnings before interest and taxes divided by total assets, CASH as the cash and equivalents scaled by total assets, CAPEX as capital expenditure divided by total assets, VOL as the standard deviation of monthly returns and TURNOVER as the stock turnover in the year prior to repurchase announcement. I also control for the percentage sought in the repurchase announcement (%SOUGHT), and the intended size of the repurchase scaled by the market capitalization at the end of the year prior to repurchase announcement (REPO_SIZE).

4.3.3. Descriptive Statistics

Panel A in table 7, illustrates descriptive statistics of data. On average, sample firm cash holdings are 14% of assets, capital expenditures are 5% of assets. These firms are, on average, moderately leveraged with 19% long term debt as percentage of assets, profitable with return on assets of 3.9%, possess potential growth opportunities with market-to-book ratio of 2.1.

The distribution of share repurchases across industries is presented in Panel B.

Money and Finance industry experienced the largest number of repurchase (3313), followed by Business Equipment industry (1830) during the sample period. Panel C shows the distribution of the share repurchases over the sample period. Number of share

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repurchases peaked in 2008 and decreased to the lowest in 2009, with 745 announcements in2015, and 513 in 2016. Though most number of announcements are in pre 2008 period, the total dollar value of the repurchase programs is larger post 2008 period.

5. Empirical Results

5.1. Seasoned Equity Offerings Sample

5.1.1. Likelihood of SEO

I begin empirical analysis on the effect of media connection on financing policies by examining likelihood of SEO. The probability of SEO offerings could be associated with issuers’ ties with the media outlets. Social ties between journalists and corporate executives may provide journalists with more accurate and timely access to inside information around SEO announcement, thereby facilitating superior information flow from the SEO issuer to the investors and other market participants. Combination of governance through contract enforcement and price efficiency through information flow would, therefore, reduce the extent of mispricing and inhibit managers from engaging in market timing through SEOs. However, media connections could be also exploited to maneuver opportunistic SEOs through false certification of SEO issuer’s stock price and maneuvering of soft information prior to SEO announcement. As a result, managers would be influenced to engage in market timing through SEOs resulting in a positive relation between media connection and SEO likelihood.

I examine the association between SEO likelihood and media connection in table

2. I take all the firms in compustat universe and create a dependent variable that is equal

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1 for firms announcing at least one SEO in a year, and 0 for all other firms. Columns (1) and (2) show results from OLS regressions and columns (3) and (4) show results from logit regressions. Control variables include: firm size (SIZE), firm age (AGE), market-to- book ratio (MB), leverage (LEV), capital expenditure (INV), profitability (ROA), R&D expenses (R&D), return volatility (VOL), share turnover (TURNOVER), prior return

(RETURN). I find that media connection is positive and significant at 1% level across all models. Results provide support to the manipulation hypothesis.

5.1.2. SEO Announcement Returns

Next, I investigate the consequences of media connection on SEO announcement returns. If connection with the media personnel ensures more transparent and efficient stock pricing of the SEO issuing firms, then SEO announcement return would be positively related to media connection. On the other hand, if the pre-SEO mispricing is large due to manipulation, market adjustment on SEO announcement also would be larger. As a result, SEO announcement return would be negatively related with media connection. I estimate cumulative abnormal return (CAR) as the 5 day (–2, +2) market- adjusted returns around the SEO announcement date. Market adjusted returns is calculated as the daily returns for a repurchase firm minus the daily returns of the CRSP value-weighted market index.

Table 3 shows results from the regression of CAR on issuer’s media connections and control variables. In addition to the control variables included in table 2, I also include issue characteristics such as percentage of primary shares offered (%PRIMARY), offer proceeds scaled by the market capitalization prior to the offer (REL_PROC), SEO

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underpricing estimated as ratio of the offer day close price to the offer price

(UNDERPRICING), indicator variables for stocks listed in NYSE at the time of the offer

(NYSE_D), and Rule 415 shelf offer (SHELF_D). Industry and/ or year fixed effects are also included in the models.

Results demonstrate that SEO announcement return is significantly negatively associated with media connection across all specifications. On average, a 1 percentage point increase in issuer’s media connection is associated with 6.7 (in column (1)) to 8.5

(in column (2)) percentage point decrease in announcement return. Results are robust to inclusion of issue characteristics in addition to issuer characteristics. Coefficient on most of the control variables exhibit expected sign. SEO announcement return is positively associated with issuer size, leverage, underpricing, and negatively related with profitability, growth opportunity. In summary, results from table 3 provide support for the manipulation hypothesis. Manipulation through media connection facilitates a larger shock to the SEO announcement return which generates a greater negative return on announcement.

5.1.3. Post-SEO Long Term Performance

Post-SEO long term performance also could be affected by the issuers’ media connection. Prior literature suggests that SEO issuers exhibit long term underperformance. Media connection could ensure better long-term performance of the

SEO issuers by alleviating the information asymmetry between manager and investors, through the governance role and efficient reporting mechanism. In contrast, absence of governance and monitoring mechanism would facilitate successful equity offering by

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overvalued firms, which would subsequently face reversal and translate into long term underperformance.

I analyze long term performance in table 4. Ex-post future firm performance is regressed on media connection and control variables. I use 1, 2 and 3 year ahead ex-post return on assets (ROA) in panel A, and Tobin’s Q (Q) in panel B as dependent variables.

ROA is estimated as earnings before interest and taxes scaled by total assets. Tobin’s Q is calculated as the market value of assets divided by the book value of assets. The explanatory variables are the same as in table 3. In panel A, I observe that coefficient of media connections (Log_MC) is negative and significant across all specifications. On average, a 1 percentage point increase in acquirers’ media connection is associated with

1.47 (in column (4)) to 2.16 (in column (2)) percentage point decrease in future ROA. In panel B also, the coefficient of Log_MC is negative and significant for 1 year ahead Q. A

1 percentage point increase in acquirers’ media connection is associated with 11.43 (in column (2)) to 13.67 (in column (1)) percentage point decrease in Q one year after the

SEO offerings. These results are consistent with the manipulation hypothesis, in general.

In line with manipulation hypothesis, SEO issuers’ social ties with media personnel impede the governance and monitoring mechanism of media. As a result, managers find it easier to devise opportunistic SEO that would ultimately face long term underperformance.

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5.1.4. Issuer Age, Growth Opportunity and Investor Sentiment

In this section, I run cross-sectional analysis of SEO announcement returns by partitioning the sample based on issuer age, growth opportunity and investor sentiment.

Results are shown in table 5.

In panel A, I test the effect of issuer’s age on the relation between media connection and SEO announcement return. Manipulation hypothesis predicts that the marginal effect of manipulation through media connection prior to SEO announcement would be greater for younger firms. Due to readership and advertisement bias, media outlets are more likely to disseminate the stories circulated by large and old firms without conducting proper due diligence. Younger firms may find it difficult to attract lime light prior to SEO and also may be subject to stricter scrutiny. Therefore, the effect of media manipulation should be greater for younger firms. I partition the sample by firm age and run regression of CAR on media connection in panel A of table 5. Results show that the coefficient of media connection is negative, significant and larger for the subsample of younger firms. On average, the coefficient estimate of Log_MC is more than three times larger for younger firms than that for the larger firms.

Then, I examine the effect of growth opportunity. The need for external financing and thus to sell shares at an inflated price would be higher for firms with good growth opportunities. Market to book ratio estimated as the market value of assets scaled by book value of assets is used as a proxy for growth opportunities. Media connection driven manipulation is likely to show up for the sample of high growth opportunity issuers. I test this conjecture in panel B of table 5. CAR is regressed on media connection by splitting

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sample based on growth opportunity. Consistent with the hypothesis, I find that the coefficient estimate of Log_MC is negative, significant and about three times higher for high growth opportunity issuers than that for the low growth opportunity issuers.

Periods of high investor sentiment could also drive manipulation through media connection. As overpricing is more likely during high sentiment period, manipulation hypothesis predicts that media connection would create even larger surprise element high sentiment period. Therefore, media connection would exhibit greater effect on SEO announcement return during high sentiment period. In panel C of table 5, I test this prediction by splitting sample based on the investor sentiment index of Baker and

Wurgler (2006). Results demonstrate that the effect of media connection on SEO announcement return is higher during high sentiment period.

5.1.5. Endogeneity Concerns

The relation between equity issuance decision and media connection is subject to endogeneity concerns. Omitted variable bias is one such concern. The effect of media connection on SEO issuance likelihood may be driven by some unobservable firm specific factors. I address this concern by running an instrumental variable based 2SLS regression. Number of media firms located within the same county as the firm's headquarter us used as an instrument for media connection. Corporate executives' media connection is likely to be correlated with the number of media firms located within the community. However, it is not clear whether the presence of more media outlets in a locality may affect the equity issuance decision of the local companies.

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Table 6 shows the results of the two-stage least squared regressions. In first stage,

I regress media connection on number of media firms (column (1) and (3)) and include the control variables employed in table 6. In second stage, predicted media connection from stage one is used as the main explanatory variable, and the same controls used in the first stage are included. I also control for industry and/ or year fixed effects. F-test of excluding instruments is sufficiently greater than the cut-off value 10, which indicates that the instrument is relevant and do not suffer from weak instruments concerns.

Moreover, test of endogeneity shows that the media connections measure is indeed endogenously determined in the equation system. Results from the second stage regressions in columns (2) and (4) suggest that predicted media connection positively affects the likelihood of SEO.

5.2. Share Repurchases Sample

5.2.1. Repurchase Announcement Returns

I examine the implications of media connections on repurchase announcement returns in this section. As media connection reduces information asymmetry, the information content of repurchase announcements would be lower for media connected firms. Efficient stock pricing through accurate media reporting therefore predicts that announcement return would be negatively related to media connection. On the other hand, price manipulation through media connection would aggravate the information asymmetry between manager and investors resulting in a greater announcement return for media connected firms. I use standard event study methodology to examine stock price responses to repurchase announcements. I regress 5 day (–2, +2) market-adjusted returns around the repurchase announcement date on media connections and control variables.

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Abnormal return is calculated as the difference between the actual return and the return of the CRSP value-weighted market index.

Results in table 8 report that repurchase announcement return is significantly positively related with media connection across all models. On average, a 1 percentage point increase in media connection is associated with 2.0 (in column (1)) to 3.1 (in column (4)) percentage point increase in announcement return. Coefficient on most of the control variables exhibit expected sign. Repurchase announcement return is positively associated with leverage, capital expenditure and return volatility, and negatively related with growth opportunity, profitability, cash holdings, past return and share turnover.

Overall, these results are consistent with the manipulation hypothesis.

5.2.2. Post-Repurchase Long Term Performance

In this section, I analyze the post-repurchase long term performance. As media connection facilitates efficient prices and low level of information asymmetry, neither signaling nor managerial private incentives could motivate these firms to conduct share repurchase. Hence, distribution of free cash flow is a plausible reason for repurchase for media connected firms which predicts a negative association between long term performance and media connection. On the other hand, media connection may foster greater price depression through manipulation prior to repurchase. Long term return reversal also would be larger for media connected repurchasers. Moreover, as media connected firms exhibit greater mispricing and high level of information asymmetry, a plausible purpose of repurchase for these firms could be signaling of future operating performance (Bartov, 1991; Brav et al., 2005; Lie, 2005) 128

Table 9 shows results from the regression of ex-post future firm performance on media connection and control variables. Dependent variables are 1, 2 and 3 year ahead ex-post return on assets (ROA) in panel A, and Tobin’s Q (Q) in panel B. I use the same independent variables as in table 8 . In panel A, I observe that coefficient of media connections (Log_MC) is positive and significant in two out of six models. On average, a

1 percentage point increase in acquirers’ media connection is associated with 0.41 percent increase in two-year ahead ROA and 0.47 percent increase in three-year ahead

ROA. In panel B, however, the coefficient of Log_MC is negative and significant for future Q. A 1 percentage point increase in acquirers’ media connection is associated with

1.45 (in column (2)) to 4.23 (in column (1)) percentage point decrease in Q after the repurchase program. These results are contradictory to each other. Manipulation hypothesis predicts a negative association while information efficiency hypothesis conjectures a positive association with operating performance. Therefore, it could not be concluded from this analysis whether media connection facilitates value increasing or value destroying repurchases.

5.2.3. Employee Stock Option Plans and Firm Age

To further investigate the effect of media connection on repurchase, I run cross- sectional analysis of repurchase announcement return by partitioning the sample based on option exercise value and firm age. Table 10 shows the results.

Prior literature provide ample evidence that firms with employee stock option plans are more likely to repurchase stocks. Stock options become more valuable after a repurchase since repurchases do not dilute the per-share value of stock. Therefore, driven

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by manipulative incentives, the effect of media connection would be greater for firms with employee stock option plans. I test this conjecture in panel A of table 10. I partition the sample by the high-low value of option exercise value and run regression of CAR on media connections. Results show that the coefficient of media connection is positive and significant for the subsample of high option exercise value firms while the coefficient is negative and insignificant for the subsample of low option exercise value firms. On average, a 1 percentage point increase in Log_MC is associated with 0.8 percentage increase in repurchase announcement return for firms with high option exercise value.

These results provide strong support for the manipulation hypothesis as managerial incentive is likely to motivate media manipulation prior to SEO announcement.

Next, I examine whether manipulation is more likely in younger or older firms.

Media connection driven manipulation is likely to show up for the sample of younger firms. I test this conjecture in panel B of table 10. CAR is regressed on media connection by splitting sample based on firm age. Consistent with the hypothesis, I find that the coefficient estimate of Log_MC is positive, significant and about two times higher for young firms than that for old firms.

5.2.4. Repurchase Likelihood, Amount and Payout Choice

The likelihood of repurchase, actual amount repurchased and the payout choice between repurchase and dividend is examined in this section. According to information efficiency hypothesis, media connected firms are likely to deter from opportunistic repurchase as governance role of media and price efficiency through information flow would diminish the signaling incentive. In contrary, manipulation hypothesis predicts that

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managers may exploit media connection by exacerbating mispricing and providing false certification of price to articulate opportunistic repurchases.

I test the association between likelihood of repurchase and media connection by regressing an indicator variable (REPO_D) that equals 1 for repurchase announcing firms and 0 for all other firms. In table 11, columns (1) and (2) show results from logit regression of REPO_D on media connection and a set of control variables. Control variables are: firm size (SIZE), market-to-book ratio (MB), profitability (ROA), cash holdings (CASH), capital expenditure (CAPEX), leverage (LEV), P/E ratio (PE), R&D expenses (R&D), prior return (RETURN), and return volatility (VOL). I also control for year and industry fixed effects. Results suggest that the probability that a firm will repurchase stock is positively related to media connection. A 1% point increase in media connection increases the likelihood of a share repurchase by 1.41% relative to the unconditional mean. Furthermore, I find that larger firms, value firms, profitable firms, firms with more cash holdings, less capital expenditure, less investments in R&D are more likely to repurchase shares. Firms with higher past return and lower return volatility are also more likely to undertake a share repurchase program.

I estimate the actual amount of repurchase (REPO_AMT) as the sum of purchase of common and preferred stocks minus any reduction in the redemption value of the net number of preferred stocks outstanding14. Columns (3) and (4) in table 11 show results from tobit regression of REPO_AMT on media connection and a set of control variables.

14 Following Grullon and Michaely (2002) 131

Control variables are the same as used in columns (1) and (2). All models control for year and industry fixed effects. Results suggest that the amount of shares repurchased is positively associated with media connection. A 1% point increase in media connection increases the repurchase amount by 7.81%. Most of the control variables exhibit signs consistent with the models in columns (1) and (2).

While share repurchase is associated with the executives’ pay-performance sensitivity, dividends are not. Therefore, repurchase should be preferred over dividends if managers tend to utilize media connection to devise manipulative intents. I test this conjecture in panel B of table 11. I repeat the same tests as conducted in panel A, but limit the sample to firms that payout in either forms: repurchase and dividends. Results suggest that the probability that a firm will repurchase stock is positively related to media connection. A 1% point increase in media connection increases the likelihood of a share repurchase by 1.33% relative to the unconditional mean. Also, a 1% point increase in media connection increases the repurchase amount by 2.15%.

5.3. Endogeneity Concerns

Endogeneity concerns could arise in the relation between repurchase decision and media connection. Some unobservable firm specific factors may bias the effect of media connection on repurchase likelihood. Instrument variable based 2SLS regression is used to tackle this concern. I use the number of media firms located within the same county as the firm's headquarter as an instrument for media connection.

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Table 12 reports results of the two-stage least squared regressions. In first stage, I regress media connection on number of media firms (column (1) and (3)) and include the control variables employed in table 11. In second stage, I use the predicted media connection estimated in stage one as the main explanatory variable. The same controls variables as used in the first stage are included in second stage too. I also control for industry and/ or year fixed effects. F-test of excluding instruments is sufficiently greater than the cut-off value 10, which indicates that the instrument is relevant and do not suffer from weak instruments concerns. Moreover, test of endogeneity shows that the media connections measure is indeed endogenously determined in the equation system. Results from the second stage regressions in columns (2) and (4) indicate that predicted media connection positively affects the repurchase amount.

6. Conclusions

This chapter examines the association between media connection and corporate financing policies. Though a few studies explore the role of media coverage and sentiment on different corporate policies no study yet has investigated whether social ties with media outlets affect corporate financing policies, particularly, SEOs and share repurchases.

According to Information Efficiency hypothesis, governance through contract enforcement coupled with price efficiency through information flow would reduce the extent of mispricing and thus dampen managerial discretion to engage in market timing through SEOs and deter managers from devising opportunistic repurchases. Manipulation hypothesis, however, contend that managers may exploit media connections to maneuver opportunistic financing events. Managers may exacerbate mispricing prior to SEO

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(repurchase) announcements by disseminating more (less) favorable soft information through their friends in the media. Based on these two competing hypotheses, I investigate the effect of corporate executives’ media connections on SEO likelihood, announcement return and post-SEO long term performance for a sample of SEOs during the period 2000 to 2016. I also I study the effect of media connections on repurchase announcement return, post-repurchase long term performance and payout choice for a sample of repurchase programs during the same period.

Most of the empirical analysis provide support for the Manipulation Hypothesis.

Results show that media connection is likely to increase the probability of an SEO event, reduces announcement period CAR and improvers the post SEO long term operating performance. In addition, the effect of media connection on SEO announcement return is greater for younger firms and firms with high growth opportunity. Altogether, these results suggest that managers take advantage of media connection to engage in opportunistic SEOs.

Media connection also determines the repurchase outcomes. Results from multivariate regressions show that media connection is associated with positive announcement return and the effect is greater for firms with high option exercise value.

This is consistent with the manipulation hypothesis as managerial incentive is likely to motivate media manipulation prior to repurchase announcement. Tests of repurchase likelihood and repurchase amount demonstrate that media connection increases both the likelihood of repurchase and the amount repurchased. I also find that repurchase is the preferred over dividends as a mode of payout. However, results from the post repurchase long term performance do not provide any conclusive evidence.

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Table 4- 1: Descriptive Statistics – SEO Sample

Panel A presents the descriptive statistics of the main variables. Distribution of SEO issues and total issuance amounts and descriptive statistics of media connections are reported across industries in Panel B and across years in Panel C. Variable definitions are provided in Appendix. Panel A: Descriptive Statistics Mean SD P25 P50 P75 CAR1 -0.012 0.085 -0.053 -0.012 0.024 CAR2 -0.008 0.098 -0.056 -0.011 0.032 Log_MC 3.077 2.077 0.000 3.850 4.663 DISCOUNT1 0.314 1.429 -0.589 -0.043 0.640 DISCOUNT2 0.602 1.250 0.000 0.000 0.640 SIZE 5.601 0.909 5.041 5.762 6.332 MB 2.438 3.018 0.911 1.329 2.795 LEV 0.288 0.265 0.044 0.240 0.460 INV 0.051 0.086 0.002 0.019 0.058 ROA -0.096 0.312 -0.160 0.023 0.070 BRD_SIZE 8.466 2.674 7.000 8.000 10.000 VOL 0.153 0.110 0.072 0.125 0.201 TURNOVER 1.973 1.930 0.764 1.379 2.464 %PRIMARY 91.843 20.847 100.000 100.000 100.000 PROCEED 0.227 0.152 0.129 0.188 0.274 UNDERPRICING 0.033 0.069 0.000 0.016 0.056 NYSE_D 0.382 0.486 0.000 0.000 1.000 SHELF_D 0.466 0.499 0.000 0.000 1.000 Log_PRICE 2.402 2.029 1.728 2.845 3.648 CAR>0 0.003 0.081 0.000 0.000 0.000 CAR<0 -0.018 0.050 -0.025 0.000 0.000 TICK 0.916 0.277 1.000 1.000 1.000 CLUSTER 0.330 0.470 0.000 0.000 1.000 NADAQ_D 0.544 0.498 0.000 1.000 1.000

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Panel B: Number of SEOs and Issuance Amounts by Industry FF No. of Issuance Media Connection Industry Name Industry SEOs Amount Mean SD 1 Consumer Non-Durables 54 9,720 2.89 1.99 2 Consumer Durables 37 8,018 4.11 1.91 3 Manufacturing 186 26,778 3.48 1.69 Oil, Gas, and Coal Extraction and 4 Products 257 54,156 3.13 1.59 5 Chemicals and Allied Products 26 4,932 3.96 1.17 6 Business Equipment 545 97,392 2.93 2.25 7 Telephone and Television Transmission 76 21,822 2.58 2.64 8 Utilities 182 50,017 4.06 1.34 9 Wholesale, Retail, and Some Services 164 18,512 3.01 1.70 Healthcare, Medical Equipment, and 10 Drugs 805 78,795 3.80 1.71 11 Money Finance 1,138 261,455 3.65 1.68 12 Other 957 174,427 3.48 1.82 Total 4,114 806,023

Panel C: Number of SEOs and Issuance Amounts by Year No. of Media Connection Year Issuance Amount SEOs Mean SD 2000 271 56,912 2.05 1.95 2001 257 36,383 2.38 1.94 2002 222 35,746 2.89 2.08 2003 294 40,744 3.03 1.90 2004 243 25,432 3.00 1.93 2005 216 31,146 3.17 1.83 2006 229 36,162 3.39 1.81 2007 178 52,841 3.62 1.94 2008 164 55,517 3.92 1.88 2009 387 88,919 3.51 2.01 2010 217 40,013 3.45 1.85 2011 206 25,854 3.18 1.97 2012 237 40,552 3.29 2.01 2013 265 46,117 3.25 2.13 2014 294 48,760 3.46 2.01 2015 209 37,630 3.93 1.65 2016 225 51,260 3.87 1.72 Total 4,114 806,023

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Table 4- 2: SEO Likelihood

This table reports results from regression of SEO Likelihood on media connections and control variables for a sample SEOs announced during the period of January 2000 to December 2016. Dependent variable is an indicator that takes value of 1 for SEO issuers, 0 otherwise. Columns (1) and (2) show results from OLS regressions and columns (3) and (4) show results from Logit regressions. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the SEO announcement. Control variables issuer and issue characteristics. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. (1) (2) (3) (4) SEO_D SEO_D SEO_D SEO_D Log_MC 0.0042*** 0.0044*** 0.1053*** 0.1179*** (15.878) (15.496) (13.692) (12.934) SIZE 0.0006* 0.0002 0.0136 0.0040 (1.921) (0.704) (1.633) (0.430) MB 0.0016*** 0.0016*** 0.0102*** 0.0122*** (3.525) (3.446) (3.413) (4.085) LEV 0.0186*** 0.0059* 0.3117*** 0.1425*** (6.166) (1.926) (7.782) (3.147) INV -0.0615*** 0.0009 -1.2786*** 0.3878 (-4.449) (0.066) (-3.237) (1.228) ROA -0.0032 -0.0034 -0.0256 -0.0436 (-1.063) (-1.152) (-0.638) (-1.073) R&D 0.0061 -0.0088 0.2000** -0.0796 (0.999) (-1.401) (2.280) (-0.852) VOL 0.0080 0.0238** 0.1401 0.4628** (0.824) (2.449) (0.768) (2.518) TURNOVER -0.0016*** -0.0014*** -0.0279*** -0.0243*** (-4.823) (-4.321) (-3.250) (-2.807) RETURN 0.0113*** 0.0110*** 0.2279*** 0.2242*** (8.719) (8.353) (11.045) (10.379) AGE -0.0184*** -0.0170*** -0.3559*** -0.3228*** (-14.671) (-13.158) (-15.926) (-14.265) IPO_D -0.9619*** -0.9531*** (-639.425) (-507.221) Industry FE No Yes No Yes Year FE Yes Yes Yes Yes adj. R-sq 0.488 0.493 N 49992 49523 49078 47855

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Table 4- 3: SEO Announcement Return

This table reports results from multivariate regression of cumulative abnormal return on media connections and control variables for a sample of SEOs announced during the period of January 2000 to December 2016. Dependent variable is Cumulative Abnormal Return (CAR) calculated as the sum of daily abnormal returns over the [-2, +2] window around SEO announcement date. Abnormal returns are calculated based on the daily returns of CRSP value and equal weighted portfolios in Panel A and Panel B, respectively. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the SEO announcement. Control variables include issuer and issue characteristics. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Dependent Variable – CAR1 (1) (2) (3) (4) Log_MC -0.0083*** -0.0095*** -0.0080*** -0.0081*** (-2.771) (-3.135) (-2.600) (-2.616) RUNUP -0.0113*** -0.0104*** -0.0115*** -0.0110*** (-4.426) (-3.951) (-4.335) (-4.051) SIZE 0.0127*** 0.0128*** 0.0123*** 0.0121*** (3.671) (3.015) (3.515) (2.839) MB -0.0018 -0.0013 -0.0023 -0.0019 (-0.930) (-0.638) (-1.156) (-0.962) LEV 0.0153 0.0150 0.0184 0.0162 (1.290) (1.254) (1.575) (1.358) INV -0.0065 -0.0036 -0.0180 -0.0150 (-0.123) (-0.068) (-0.340) (-0.282) ROA -0.0353** -0.0310* -0.0389** -0.0356** (-2.155) (-1.877) (-2.345) (-2.146) BRD_SIZE -0.0000 -0.0003 -0.0002 -0.0004 (-0.042) (-0.290) (-0.201) (-0.371) VOL 0.0489* 0.0486* 0.0520* 0.0526* (1.692) (1.660) (1.712) (1.695) TURNOVER 0.0006 0.0000 0.0005 0.0002 (0.260) (0.013) (0.242) (0.085) %PRIMARY 0.0001 0.0001 (1.081) (1.101) PROCEED 0.0034 0.0012 (0.160) (0.058) UNDERPRICING 0.0797* 0.0816* (1.934) (1.929) NYSE_D 0.0029 0.0020 (0.459) (0.314) SHELF_D 0.0205*** 0.0196*** (4.060) (3.754) Industry FE Yes Yes Yes Yes Year FE No No Yes Yes adj. R-sq 0.028 0.044 0.030 0.041 N 1541 1528 1541 1528

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Panel B: Dependent Variable – CAR2 (1) (2) (3) (4) Log_MC -0.0088*** -0.0100*** -0.0089*** -0.0090*** (-2.954) (-3.338) (-2.903) (-2.922) RUNUP -0.0119*** -0.0110*** -0.0120*** -0.0114*** (-4.748) (-4.253) (-4.607) (-4.315) SIZE 0.0128*** 0.0125*** 0.0127*** 0.0122*** (3.749) (2.991) (3.714) (2.912) MB -0.0016 -0.0010 -0.0022 -0.0019 (-0.834) (-0.536) (-1.147) (-0.954) LEV 0.0148 0.0145 0.0186 0.0164 (1.261) (1.237) (1.616) (1.396) INV -0.0003 0.0031 -0.0099 -0.0064 (-0.007) (0.060) (-0.195) (-0.125) ROA -0.0351** -0.0308* -0.0388** -0.0355** (-2.188) (-1.902) (-2.393) (-2.190) BRD_SIZE -0.0002 -0.0005 -0.0003 -0.0005 (-0.174) (-0.435) (-0.234) (-0.417) VOL 0.0390 0.0390 0.0469 0.0478 (1.372) (1.355) (1.575) (1.571) TURNOVER 0.0007 0.0002 0.0006 0.0002 (0.316) (0.075) (0.249) (0.100) %PRIMARY 0.0001 0.0001 (0.943) (1.035) PROCEED -0.0003 -0.0027 (-0.016) (-0.132) UNDERPRICING 0.0787* 0.0816* (1.930) (1.954) NYSE_D 0.0033 0.0028 (0.525) (0.445) SHELF_D 0.0210*** 0.0195*** (4.220) (3.788) Industry FE Yes Yes Yes Yes Year FE No No Yes Yes adj. R-sq 0.032 0.049 0.035 0.047 N 1541 1528 1541 1528

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Table 4- 4: Post-SEO Long Term Performance

This table reports results from multivariate regression of post-SEO long term performance on media connections and control variables for a sample SEOs announced during the period of January 2000 to December 2016. Dependent variable Return on Assets (ROA), Tobin’s Q (Q), and Buy and Hold Abnormal Return (BHAR) in Panel A, Panel B, and Panel C, respectively. ROA is calculated as earnings before interest and taxes scaled by total assets. Tobin’s Q is estimated as the market value of assets divided by the book value of assets. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the SEO announcement. Control variables include issuer and issue characteristics. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Dependent Variable – ROA (1) (2) (3) (4) (5) (6) ROAt+1 ROAt+1 ROAt+2 ROAt+2 ROAt+3 ROAt+3 Log_MC -0.0203*** -0.0212*** -0.0165** -0.0165** -0.0153** -0.0165** (-3.189) (-3.365) (-2.316) (-2.336) (-2.136) (-2.301) RUNUP 0.0223*** 0.0221*** 0.0084 0.0072 0.0084 0.0083 (4.506) (4.611) (1.387) (1.191) (1.492) (1.473) SIZE 0.0655*** 0.0676*** 0.0595*** 0.0634*** 0.0560*** 0.0576*** (8.335) (7.501) (7.093) (6.467) (6.162) (5.392) MB -0.0264*** -0.0255*** -0.0293*** -0.0290*** -0.0233*** -0.0228*** (-5.888) (-5.843) (-5.245) (-5.163) (-3.789) (-3.672) LEV 0.0082 0.0254 0.0749*** 0.0911*** 0.1082*** 0.1201*** (0.277) (0.866) (2.977) (3.516) (4.138) (4.443) INV -0.0260 -0.0219 -0.0344 -0.0232 -0.1751* -0.1609 (-0.282) (-0.238) (-0.306) (-0.203) (-1.665) (-1.509) BRD_SIZE -0.0034 -0.0028 -0.0026 -0.0016 -0.0038 -0.0039 (-1.430) (-1.186) (-0.923) (-0.589) (-1.228) (-1.229) VOL -0.1772** -0.1869*** -0.0977 -0.1256* -0.0524 -0.0606 (-2.481) (-2.610) (-1.321) (-1.677) (-0.597) (-0.660) TURNOVER -0.0119*** -0.0104** -0.0071 -0.0052 -0.0061 -0.0050 (-2.822) (-2.453) (-1.448) (-1.057) (-1.283) (-1.059) %PRIMARY -0.0009*** -0.0013*** -0.0007*** (-4.223) (-4.997) (-2.676) PROCEED 0.0771** 0.0667* 0.0527 (2.217) (1.808) (1.252) UNDERPRICING -0.0382 0.2091* 0.0627 (-0.381) (1.881) (0.481) NYSE_D 0.0041 0.0033 0.0230* (0.363) (0.243) (1.790) SHELF_D -0.0378*** -0.0387*** -0.0433*** (-3.164) (-2.807) (-2.993) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.552 0.560 0.513 0.525 0.469 0.472 N 1555 1542 1474 1464 1315 1305

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Panel B: Dependent Variable – Q (1) (2) (3) (4) (5) (6) Qt+1 Qt+1 Qt+2 Qt+3 Qt+3 Qt+3 Log_MC -0.1400** -0.1191** -0.0297 -0.0241 -0.0040 0.0027 (-2.405) (-2.039) (-0.666) (-0.537) (-0.088) (0.061) RUNUP 0.0184 0.0092 -0.1290*** -0.1292*** -0.0761* -0.0769* (0.313) (0.160) (-3.341) (-3.397) (-1.935) (-1.961) SIZE 0.3645*** 0.3529*** 0.1709*** 0.1265** 0.1354*** 0.0859 (6.755) (5.619) (3.398) (2.097) (2.919) (1.567) LEV -0.5014** -0.5115** 0.0289 0.1507 0.0400 0.1406 (-2.284) (-2.400) (0.151) (0.793) (0.226) (0.791) INV -0.2089 -0.2915 -0.2570 -0.3645 0.0727 -0.0133 (-0.323) (-0.461) (-0.410) (-0.585) (0.130) (-0.024) ROA -1.8790*** -1.7694*** -1.5759*** -1.6354*** -0.9238*** -0.9599*** (-7.073) (-7.038) (-5.752) (-5.807) (-3.666) (-3.713) BRD_SIZE -0.0710*** -0.0656*** -0.0485*** -0.0443** -0.0442** -0.0407** (-3.583) (-3.271) (-2.731) (-2.492) (-2.459) (-2.265) VOL 1.3722** 1.1868* 0.5892 0.4264 0.1023 -0.0190 (2.278) (1.932) (1.164) (0.836) (0.207) (-0.038) TURNOVER -0.0834*** -0.0711** -0.0857*** -0.0766** -0.0432 -0.0361 (-2.887) (-2.472) (-2.841) (-2.495) (-1.411) (-1.175) %PRIMARY -0.0036* -0.0016 -0.0009 (-1.835) (-0.840) (-0.520) PROCEED -0.7536** -0.9376*** -0.7971*** (-2.281) (-3.774) (-3.588) UNDERPRICING 2.2444*** 0.3772 -0.1991 (3.170) (0.546) (-0.273) NYSE_D -0.3429*** -0.2135*** -0.1326* (-4.122) (-2.591) (-1.673) SHELF_D 0.0344 -0.2290*** -0.0968 (0.334) (-2.600) (-1.087) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.452 0.457 0.452 0.461 0.409 0.410 N 1561 1548 1476 1466 1319 1309

141

Panel C: Dependent Variable – Buy and Hold Abnormal Return (BHAR) (1) (2) (3) (4) (5) (6) BHARt+1 BHARt+1 BHARt+2 BHARt+3 BHARt+3 BHARt+3 Log_MC 0.0220 0.0140 0.0367 0.0459 0.0540 0.0752 (0.770) (0.492) (0.830) (0.999) (0.907) (1.187) RUNUP -0.0339 -0.0348 0.0352 0.0367 0.1011 0.1088 (-1.226) (-1.224) (0.749) (0.776) (1.224) (1.333) SIZE -0.0598* -0.0401 -0.0838 -0.1085 -0.0720 -0.1299 (-1.772) (-1.016) (-1.462) (-1.617) (-0.728) (-1.134) MB -0.0192 -0.0204 -0.0539 -0.0504 -0.1396 -0.1461 (-0.908) (-0.942) (-0.995) (-0.906) (-1.037) (-1.077) LEV 0.2382** 0.2430** 0.3745** 0.3661** 0.5873** 0.5707** (2.434) (2.443) (2.379) (2.323) (2.173) (2.135) INV -0.0749 -0.0699 -0.2113 -0.2227 0.0175 0.0228 (-0.236) (-0.221) (-0.344) (-0.353) (0.019) (0.024) ROA 0.2952 0.2901 0.6626** 0.6650** 0.9771 1.0142 (1.354) (1.314) (2.295) (2.257) (1.147) (1.154) BRD_SIZE 0.0113 0.0103 0.0120 0.0114 0.0118 0.0080 (1.080) (0.968) (0.660) (0.620) (0.405) (0.271) VOL -0.3010 -0.2582 -0.4084 -0.4355 -1.1638 -1.2480 (-0.765) (-0.644) (-0.670) (-0.699) (-1.238) (-1.304) TURNOVER 0.0154 0.0137 0.0176 0.0188 -0.0179 -0.0132 (0.853) (0.756) (0.637) (0.662) (-0.402) (-0.288) %PRIMARY -0.0006 0.0015 0.0018 (-0.439) (0.679) (0.536) PROCEED 0.1840 -0.1010 -0.2739 (1.232) (-0.403) (-0.703) UNDERPRICING -0.1574 -0.0451 -0.3945 (-0.433) (-0.077) (-0.433) SHELF_D 0.0012 0.0937 0.1824* (0.028) (1.305) (1.719) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.099 0.093 0.043 0.038 0.066 0.067 N 396 392 367 364 326 323

142

Table 4- 5: Issuer Age, Growth Opportunity and Investor Sentiment

This table reports results from multivariate regression of cumulative abnormal return on media connections and control variables for a sample SEO announced during the period of January 2000 to December 2016. Dependent variable is Cumulative Abnormal Return (CAR) calculated as the sum of daily abnormal returns over the [-2, +2] window around SEO announcement date. In columns (1) and (2) abnormal returns are calculated based on the daily returns of CRSP value weighted portfolios, and in columns (3) and (4) abnormal returns are calculated based on the daily returns of CRSP equal weighted portfolios. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the SEO announcement. Sample is partitioned by issuers’ age in Panel (A), by growth opportunity in Panel (B), and investor sentiment in Panel (C). Control variables include issuer and issue characteristics. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Old vs Young Firms (1) (2) (3) (4) Dependent Variable CAR1 CAR1 CAR2 CAR2 Old Young Old Young Log_MC -0.0036 -0.0123** -0.0045 -0.0130** (-0.883) (-2.377) (-1.100) (-2.569) RUNUP -0.0118*** -0.0100** -0.0121*** -0.0106** (-3.378) (-2.372) (-3.569) (-2.561) SIZE 0.0003 0.0231*** 0.0000 0.0236*** (0.047) (3.894) (0.005) (4.059) MB -0.0004 -0.0036 -0.0005 -0.0034 (-0.148) (-1.373) (-0.168) (-1.310) LEV 0.0314** 0.0068 0.0316** 0.0067 (2.112) (0.344) (2.167) (0.345) INV 0.0767 -0.0421 0.0752 -0.0229 (1.258) (-0.582) (1.242) (-0.333) ROA -0.0282 -0.0061 -0.0284 -0.0060 (-1.583) (-0.293) (-1.613) (-0.297) BRD_SIZE 0.0015 -0.0018 0.0014 -0.0018 (1.046) (-0.826) (0.978) (-0.844) VOL 0.0485 0.0593 0.0443 0.0522 (1.141) (1.258) (1.059) (1.127) TURNOVER 0.0008 -0.0004 0.0006 0.0002 (0.288) (-0.082) (0.227) (0.037) %PRIMARY -0.0001 0.0005** -0.0001 0.0005*** (-0.407) (2.569) (-0.502) (2.607) PROCEED 0.0075 -0.0096 0.0011 -0.0108 (0.278) (-0.293) (0.040) (-0.341) UNDERPRICING 0.1086* 0.0689 0.1012* 0.0762 (1.882) (1.082) (1.796) (1.218) NYSE_D 0.0041 0.0020 0.0060 0.0013 (0.468) (0.200) (0.695) (0.130) SHELF_D 0.0335*** 0.0017 0.0321*** 0.0026 (5.034) (0.191) (4.849) (0.307) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes 143

adj. R-sq 0.072 0.024 0.076 0.035 N 817 715 817 715

Panel B: High vs Low Growth Opportunities (1) (2) (3) (4) Dependent Variable CAR1 CAR1 CAR2 CAR2 High Low High Low GO GO GO GO Log_MC -0.0119** -0.0036 -0.0126** -0.0045 (-1.982) (-0.952) (-2.146) (-1.211) RUNUP -0.0162*** -0.0045 -0.0163*** -0.0056 (-4.145) (-1.230) (-4.251) (-1.568) SIZE 0.0168*** 0.0083 0.0166*** 0.0090* (2.622) (1.536) (2.639) (1.687) MB -0.0009 -0.0236 -0.0008 -0.0224 (-0.384) (-1.455) (-0.370) (-1.395) LEV 0.0417*** -0.0221 0.0415*** -0.0222 (2.630) (-1.285) (2.667) (-1.313) INV -0.0816 0.0589 -0.0696 0.0588 (-1.015) (1.030) (-0.902) (1.039) ROA -0.0080 -0.0114 -0.0087 -0.0100 (-0.506) (-0.374) (-0.565) (-0.331) BRD_SIZE -0.0013 -0.0001 -0.0014 -0.0002 (-0.554) (-0.108) (-0.595) (-0.123) VOL 0.0695 0.0633 0.0613 0.0627 (1.593) (1.336) (1.427) (1.348) TURNOVER -0.0000 0.0017 0.0003 0.0014 (-0.005) (0.463) (0.112) (0.382) %PRIMARY 0.0003* -0.0000 0.0003 -0.0001 (1.656) (-0.232) (1.617) (-0.351) PROCEED -0.0365 0.0051 -0.0370 0.0009 (-0.778) (0.221) (-0.811) (0.039) UNDERPRICING 0.1446** 0.0311 0.1453** 0.0293 (2.414) (0.520) (2.455) (0.497) NYSE_D 0.0149 -0.0058 0.0166 -0.0052 (1.112) (-0.764) (1.259) (-0.689) SHELF_D 0.0138 0.0206*** 0.0151* 0.0196*** (1.571) (3.181) (1.730) (3.067) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq 0.056 0.036 0.064 0.039 N 757 775 757 775

144

Panel C: SEOs During High vs Low Sentiment Periods (1) (2) (3) (4) Dependent Variable CAR1 CAR1 CAR2 CAR2 High Low High Low Sentiment Sentiment Sentiment Sentiment Log_MC -0.0093** -0.0050 -0.0095** -0.0066 (-2.126) (-1.107) (-2.208) (-1.459) RUNUP -0.0091** -0.0066* -0.0102*** -0.0069* (-2.279) (-1.803) (-2.627) (-1.947) SIZE 0.0168*** 0.0121** 0.0167*** 0.0125** (2.932) (2.156) (2.982) (2.247) MB -0.0003 -0.0005 -0.0001 -0.0006 (-0.096) (-0.192) (-0.037) (-0.238) LEV 0.0082 0.0326* 0.0077 0.0337* (0.538) (1.860) (0.506) (1.962) INV -0.0419 0.0955 -0.0226 0.0940 (-0.565) (1.291) (-0.321) (1.277) ROA 0.0099 -0.0077 0.0107 -0.0089 (0.434) (-0.479) (0.497) (-0.560) BRD_SIZE -0.0022 -0.0005 -0.0021 -0.0005 (-1.135) (-0.289) (-1.121) (-0.314) VOL 0.0251 0.0983** 0.0118 0.0986** (0.565) (2.323) (0.270) (2.387) TURNOVER -0.0039 -0.0026 -0.0035 -0.0027 (-1.187) (-0.903) (-1.063) (-0.961) %PRIMARY 0.0003* 0.0003 0.0003 0.0004 (1.814) (1.535) (1.633) (1.632) PROCEED 0.0139 0.0074 0.0106 0.0030 (0.472) (0.248) (0.370) (0.103) UNDERPRICING -0.0282 0.0528 -0.0180 0.0472 (-0.511) (1.012) (-0.333) (0.920) NYSE_D 0.0049 0.0044 0.0056 0.0051 (0.547) (0.478) (0.631) (0.568) SHELF_D 0.0130* 0.0271*** 0.0139* 0.0264*** (1.736) (3.464) (1.875) (3.441) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq 0.024 0.024 0.032 0.027 N 691 753 691 753

145

Table 4- 6: Endogeneity Concern – 2-SLS Instrumental Variable Regression

(1) (3) (2) (4) 1st Stage 2nd Stage 1st Stage 2nd Stage Dependent Variable Log_MC SEO_D Log_MC SEO_D Log_MediaCo 0.104*** 0.0791*** (17.90) (14.69) Log_MC 0.0283*** 0.0386*** (2.65) (2.60) SIZE 0.418*** -0.00971** 0.413*** -0.0142** (137.63) (-2.14) (135.02) (-2.30) MB -0.0523*** 0.00313*** -0.0604*** 0.00399*** (-13.64) (3.65) (-15.24) (3.56) LEV 0.0509*** 0.0194*** 0.305*** -0.00664 (2.79) (4.86) (15.20) (-1.08) INV -0.0424 -0.0867*** -0.252** 0.000913 (-0.39) (-4.09) (-2.16) (0.04) ROA -0.133*** -0.00237 -0.211*** 0.00232 (-4.67) (-0.47) (-7.80) (0.40) R&D 0.740*** -0.0125 0.494*** -0.0299*** (14.60) (-1.08) (10.64) (-2.64) VOL 1.485*** -0.0410* 0.991*** -0.0229 (21.59) (-1.88) (14.74) (-1.10) TURNOVER -0.00125 -0.00167*** 0.00159 -0.00163*** (-0.46) (-2.94) (0.59) (-2.84) RETURN -0.177*** 0.0170*** -0.150*** 0.0176*** (-19.76) (6.50) (-17.21) (6.06) AGE -0.0931*** -0.0210*** -0.0604*** -0.0200*** (-14.04) (-10.25) (-9.17) (-9.88) IPO_D 0.0874*** -0.958*** -0.0241 -0.944*** (3.17) (-432.77) (-0.95) (-362.68) Industry FE No No Yes Yes Year FE Yes Yes Yes Yes N 35264 35264 34935 34935 adj. R-sq 0.477 0.459 F-test of excluding instruments 320.40 215.90 Test of Endogeneity statistic 8.405 6.570 [p-value] 0.0037 0.0104

146

Table 4- 7: Descriptive Statistics – Repurchase Sample

Panel A presents the descriptive statistics of the main variables. Distribution of repurchases and total amounts, and descriptive statistics of media connections are reported across industries in Panel B and across years in Panel C. Variable definitions are provided in Appendix. Panel A: Descriptive Statistics Mean SD P25 P50 P75 CAR1 0.020 0.086 -0.018 0.015 0.057 CAR1 0.020 0.092 -0.023 0.015 0.061 Log_MC 3.301 2.491 0.000 4.143 5.268 SIZE 6.824 1.975 5.411 6.828 8.194 MB 2.099 1.337 1.251 1.701 2.444 CASH 0.144 0.105 0.093 0.140 0.193 CAPEX 0.049 0.048 0.018 0.034 0.063 LEV 0.190 0.187 0.007 0.157 0.302 RETURN 1.057 0.432 0.793 1.021 1.259 VOL 0.114 0.067 0.068 0.096 0.138 TURNOVER 1.794 1.433 0.780 1.435 2.363 %SOUGHT 9.716 8.836 5.000 7.200 10.600 REPO_SIZE 0.096 0.113 0.038 0.064 0.108

Panel B: Number of Repurchases and Amounts by Industry Media FF No. of Repurchase Industry Name Connection Industry Repurchases Amount Mean SD 1 Consumer Non-Durables 454 303,300 4.04 2.43 2 Consumer Durables 209 56,919 3.78 2.07 3 Manufacturing 880 529,996 3.76 2.09 Oil, Gas, and Coal Extraction and 4 Products 222 450,542 3.50 2.07 5 Chemicals and Allied Products 219 170,209 4.29 2.01 6 Business Equipment 1,830 1,280,631 4.36 2.05 Telephone and Television 7 Transmission 190 310,356 5.11 2.61 8 Utilities 94 39,987 3.84 2.08 9 Wholesale, Retail, and Some Services 1,181 822,883 4.11 1.98 Healthcare, Medical Equipment, and 10 Drugs 656 615,934 3.94 1.96 11 Money Finance 3,313 1,337,147 2.83 2.30 12 Other 3,020 1,284,125 3.98 2.17 Total 12,268 7,202,028

147

Panel C: Number of Repurchases and Amounts by Year No. of Repurchase Media Connection Year Repurchases Amount Mean SD 1999 1,468 269,999 NA NA 2000 777 258,418 1.90 2.18 2001 639 269,420 2.34 2.32 2002 445 158,903 2.75 2.31 2003 452 178,857 2.74 2.41 2004 546 333,769 3.52 2.29 2005 631 421,127 3.67 2.35 2006 604 520,127 3.66 2.45 2007 922 708,351 3.83 2.31 2008 1,012 371,222 3.80 2.24 2009 410 173,991 3.53 2.32 2010 546 422,004 4.12 2.20 2011 784 504,389 4.11 2.19 2012 569 632,857 4.25 2.29 2013 523 642,650 4.35 2.20 2014 682 410,550 4.35 2.04 2015 745 579,122 4.31 2.14 2016 513 346,274 4.32 2.05 Total 12,268 7,202,028

148

Table 4- 8: Repurchase Announcement Return

This table reports results from multivariate regression of cumulative abnormal return on media connections and control variables for a sample of repurchases announced during the period of January 2000 to December 2016. Dependent variable is Cumulative Abnormal Return (CAR) calculated as the sum of daily abnormal returns over the [-2, +2] window around repurchase announcement date. Abnormal returns are calculated based on the daily returns of CRSP value and equal weighted portfolios in Panel A and Panel B, respectively. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the repurchase announcement. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Dependent Variable – CAR1 (1) (2) (3) (4) Log_MC 0.0020*** 0.0028*** 0.0026*** 0.0031** (3.157) (3.133) (2.956) (2.328) SIZE -0.0033*** -0.0037*** -0.0034*** -0.0039*** (-5.193) (-3.763) (-4.808) (-3.429) MB -0.0021* -0.0035* -0.0020 -0.0035* (-1.712) (-1.725) (-1.629) (-1.665) ROA -0.0150 -0.0117 -0.0162 -0.0110 (-0.995) (-0.469) (-1.061) (-0.436) CASH -0.0037 -0.0023 -0.0033 -0.0026 (-0.496) (-0.181) (-0.438) (-0.205) CAPEX 0.0351 0.0528 0.0312 0.0466 (1.287) (1.216) (1.139) (1.061) LEV 0.0036 0.0045 0.0031 0.0037 (0.684) (0.471) (0.584) (0.390) RETURN -0.0190*** -0.0250*** -0.0180*** -0.0264*** (-7.172) (-5.322) (-6.543) (-5.419) VOL 0.1365*** 0.1940*** 0.1304*** 0.1904*** (5.936) (5.662) (5.123) (5.000) TURNOVER -0.0023*** -0.0037** -0.0022** -0.0038** (-2.624) (-2.143) (-2.387) (-2.076) %SOUGHT 0.0003 0.0003 (1.235) (1.229) REPO_SIZE 0.0238 0.0246 (1.111) (1.147) Industry FE Yes Yes Yes Yes Year FE No No Yes Yes adj. R-sq 0.038 0.068 0.038 0.065 N 8527 3230 8527 3230

149

Panel B: Dependent Variable – CAR2 (1) (2) (3) (4) Log_MC 0.0021*** 0.0029*** 0.0025*** 0.0029** (3.348) (3.249) (2.815) (2.206) SIZE -0.0032*** -0.0036*** -0.0032*** -0.0037*** (-5.029) (-3.661) (-4.521) (-3.285) MB -0.0015 -0.0026 -0.0016 -0.0027 (-1.211) (-1.267) (-1.310) (-1.311) ROA -0.0207 -0.0166 -0.0192 -0.0139 (-1.378) (-0.673) (-1.262) (-0.555) CASH -0.0041 -0.0030 -0.0035 -0.0032 (-0.554) (-0.238) (-0.480) (-0.252) CAPEX 0.0308 0.0485 0.0273 0.0431 (1.141) (1.133) (1.006) (0.994) LEV 0.0022 0.0001 0.0020 0.0000 (0.415) (0.010) (0.374) (0.004) RETURN -0.0184*** -0.0244*** -0.0176*** -0.0259*** (-6.990) (-5.228) (-6.445) (-5.332) VOL 0.1299*** 0.1864*** 0.1340*** 0.1913*** (5.676) (5.432) (5.281) (5.009) TURNOVER -0.0021** -0.0033* -0.0021** -0.0034* (-2.381) (-1.896) (-2.311) (-1.919) %SOUGHT 0.0003 0.0003 (1.143) (1.120) REPO_SIZE 0.0234 0.0248 (1.097) (1.162) Industry FE Yes Yes Yes Yes Year FE No No Yes Yes adj. R-sq 0.035 0.062 0.034 0.059 N 8527 3230 8527 3230

150

Table 4- 9: Post-Repurchase Long Term Performance

This table reports results from multivariate regression of post-repurchase long term performance on media connections and control variables for a sample repurchases announced during the period of January 2000 to December 2016. Dependent variable Return on Assets (ROA), Tobin’s Q (Q), and Buy and Hold Abnormal Return (BHAR) in Panel A, Panel B, and Panel C, respectively. ROA is calculated as earnings before interest and taxes scaled by total assets. Tobin’s Q is estimated as the market value of assets divided by the book value of assets. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the repurchase announcement. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Dependent Variable – ROA (1) (2) (3) (4) (5) (6) ROAt+1 ROAt+1 ROAt+2 ROAt+2 ROAt+3 ROAt+3 Log_MC 0.0003 0.0011 0.0013 0.0041*** 0.0012 0.0047*** (0.376) (0.854) (1.311) (2.696) (1.062) (2.648) SIZE 0.0020*** 0.0013 0.0028*** 0.0023* 0.0039*** 0.0016 (2.840) (1.134) (3.672) (1.782) (4.468) (1.118) MB 0.0425*** 0.0449*** 0.0385*** 0.0400*** 0.0347*** 0.0381*** (34.501) (18.971) (28.696) (14.704) (23.690) (13.206) CASH -0.0715*** -0.0892*** -0.0655*** -0.0742*** -0.0679*** -0.0851*** (-8.186) (-5.696) (-6.658) (-4.164) (-6.433) (-4.323) CAPEX 0.0597** 0.0927** -0.0082 0.0333 -0.0134 0.0155 (2.189) (2.052) (-0.253) (0.623) (-0.363) (0.249) LEV -0.0028 -0.0104 0.0002 -0.0088 -0.0012 -0.0161 (-0.515) (-0.980) (0.027) (-0.708) (-0.181) (-1.178) RETURN 0.0561*** 0.0616*** 0.0448*** 0.0528*** 0.0351*** 0.0363*** (18.021) (11.355) (12.311) (8.399) (9.743) (5.615) VOL -0.3341*** -0.2914*** -0.3702*** -0.2986*** -0.3168*** -0.2942*** (-13.207) (-7.679) (-12.988) (-7.094) (-10.845) (-6.444) TURNOVER 0.0034*** 0.0000 -0.0008 -0.0082*** -0.0020* -0.0086*** (3.369) (0.002) (-0.679) (-3.420) (-1.694) (-3.306) %SOUGHT -0.0005* -0.0005 -0.0007** (-1.665) (-1.618) (-2.048) REPO_SIZE 0.0671*** 0.0780*** 0.0914*** (2.771) (2.911) (3.472) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.474 0.466 0.399 0.381 0.348 0.334 N 8531 3221 7980 3009 7126 2724

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Panel B: Dependent Variable – Q (1) (2) (3) (4) (5) (6) Qt+1 Qt+1 Qt+2 Qt+2 Qt+3 Qt+3 Log_MC -0.0287*** -0.0145 -0.0423*** -0.0338** -0.0405*** -0.0228 (-3.147) (-1.244) (-4.177) (-2.427) (-3.651) (-1.581) SIZE 0.0963*** 0.1001*** 0.0994*** 0.1068*** 0.0938*** 0.0950*** (13.439) (10.211) (12.635) (9.386) (11.091) (8.023) MB 5.2809*** 4.5759*** 4.7269*** 4.1334*** 4.2259*** 4.2591*** (29.504) (16.074) (25.717) (13.759) (21.223) (13.234) CASH 1.7177*** 1.4730*** 1.6878*** 1.5832*** 1.6234*** 1.5100*** (21.766) (10.789) (19.237) (10.784) (16.783) (9.150) CAPEX 2.1745*** 2.3507*** 1.7931*** 1.9613*** 1.5539*** 1.6104*** (8.109) (5.402) (5.901) (3.891) (4.302) (2.580) LEV 0.2386*** 0.2802*** 0.2660*** 0.3028*** 0.3003*** 0.3494*** (4.286) (2.749) (4.465) (2.716) (4.354) (2.795) RETURN 0.3867*** 0.3276*** 0.2260*** 0.1185** 0.1764*** 0.0671 (12.787) (6.601) (7.611) (2.507) (5.580) (1.347) VOL 1.2455*** 1.4409*** 0.6729*** 0.5531 0.6926** 0.9959** (5.293) (3.928) (2.865) (1.575) (2.459) (2.314) TURNOVER -0.0368*** -0.0428** -0.0436*** -0.0394** -0.0437*** -0.0581*** (-3.837) (-2.493) (-4.174) (-2.049) (-3.733) (-2.775) %SOUGHT -0.0073*** -0.0091*** -0.0091*** (-2.834) (-3.023) (-2.776) REPO_SIZE 0.3509* 0.6786*** 0.6918** (1.794) (2.617) (2.516) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.542 0.545 0.495 0.499 0.462 0.496 N 8509 3219 7954 3007 7087 2718

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Panel C: Dependent Variable – Buy and Hold Abnormal Return (BHAR) (1) (2) (3) (4) (5) (6) BHARt+1 BHARt+1 BHARt+2 BHARt+2 BHARt+3 BHARt+3 Log_MC 0.0028 0.0213** 0.0128 0.0420** 0.0251* 0.0627** (0.489) (2.033) (1.350) (2.426) (1.787) (2.416) SIZE -0.0055 -0.0069 -0.0133 -0.0148 -0.0354*** -0.0510** (-1.090) (-0.705) (-1.619) (-0.975) (-2.859) (-2.218) MB -0.0195** -0.0261 -0.0208 -0.0294 -0.0394* -0.0260 (-2.216) (-1.430) (-1.335) (-0.945) (-1.902) (-0.636) ROA 0.1897 0.3915 0.0269 0.2794 -0.0252 0.2302 (1.479) (1.491) (0.125) (0.648) (-0.087) (0.434) CASH 0.0478 0.0107 0.1460 0.1086 0.1659 0.0428 (0.834) (0.101) (1.631) (0.600) (1.206) (0.144) CAPEX -0.0780 0.0641 -0.4091 -0.0262 -0.3141 0.8437 (-0.431) (0.184) (-1.423) (-0.048) (-0.726) (1.010) LEV -0.0212 -0.0909 -0.0090 -0.1393 0.0317 -0.0684 (-0.626) (-1.437) (-0.164) (-1.388) (0.393) (-0.464) RETURN 0.0173 -0.0164 0.0223 0.0262 0.0318 -0.0167 (0.881) (-0.426) (0.717) (0.417) (0.701) (-0.201) VOL 0.3320* 0.6870** 0.5693* 0.8300* 0.4104 0.5733 (1.661) (2.011) (1.911) (1.671) (0.968) (0.808) TURNOVER -0.0077 -0.0183 -0.0082 -0.0165 -0.0293* -0.0573** (-1.080) (-1.285) (-0.719) (-0.779) (-1.854) (-2.038) %SOUGHT -0.0021 -0.0009 -0.0015 (-1.529) (-0.378) (-0.550) REPO_SIZE 0.1871 0.1429 0.2426 (1.354) (0.629) (0.821) Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes adj. R-sq 0.050 0.070 0.054 0.081 0.053 0.080 N 3641 1189 3188 1078 2785 996

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Table 4- 10: Employee Stock Option Plans and Firm Age

This table reports results from multivariate regression of cumulative abnormal return on media connections and control variables for a sample of repurchases announced during the period of January 2000 to December 2016. Dependent variable is Cumulative Abnormal Return (CAR) calculated as the sum of daily abnormal returns over the [-1, +1] window around repurchase announcement date. In columns (1) and (2) abnormal returns are calculated based on the daily returns of CRSP value weighted portfolios, and in columns (3) and (4) abnormal returns are calculated based on the daily returns of CRSP equal weighted portfolios. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the repurchase announcement. Sample is partitioned by executives’ option exercise value in Panel A, by repurchasing firms’ age in Panel B. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: High vs Low Option Exercise Value (1) (2) (3) (4) Dependent Variable CAR1 CAR1 CAR2 CAR2 High Low High Low Option Value Option Value Option Value Option Value Log_MC 0.0079*** -0.0009 0.0074** -0.0008 (2.644) (-0.311) (2.445) (-0.253) SIZE -0.0059** -0.0016 -0.0058** -0.0023 (-2.208) (-0.600) (-2.109) (-0.842) MB -0.0022 -0.0068 -0.0009 -0.0067 (-0.653) (-1.568) (-0.273) (-1.539) ROA 0.0153 0.0148 0.0115 0.0109 (0.293) (0.293) (0.212) (0.218) CASH 0.0053 0.0142 0.0020 0.0083 (0.205) (0.607) (0.077) (0.355) CAPEX 0.0526 0.0154 0.0095 0.0136 (0.633) (0.181) (0.109) (0.161) LEV 0.0003 -0.0098 -0.0090 -0.0161 (0.019) (-0.547) (-0.577) (-0.898) RETURN -0.0177* -0.0217** -0.0180* -0.0201** (-1.819) (-2.463) (-1.843) (-2.301) VOL -0.0437 0.2150*** -0.0258 0.1948** (-0.457) (2.655) (-0.265) (2.408) TURNOVER 0.0013 -0.0038 0.0010 -0.0031 (0.407) (-1.201) (0.317) (-0.969) %SOUGHT -0.0008 0.0014*** -0.0008* 0.0014*** (-1.603) (2.692) (-1.700) (2.790) REPO_SIZE 0.0408 -0.0824* 0.0453 -0.0954** (1.352) (-1.911) (1.420) (-2.220) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq 0.025 0.021 0.019 0.012 N 706 857 706 857

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Panel B: Old vs Young Firms (1) (2) (3) (4) Dependent Variable CAR1 CAR1 CAR2 CAR2 Old Young Old Young Log_MC 0.0024 0.0038* 0.0021 0.0038* (1.371) (1.816) (1.194) (1.832) SIZE -0.0023* -0.0066*** -0.0022 -0.0064*** (-1.722) (-3.017) (-1.576) (-2.897) MB -0.0019 -0.0039 -0.0014 -0.0030 (-0.699) (-1.298) (-0.510) (-1.000) ROA -0.0175 -0.0090 -0.0130 -0.0173 (-0.509) (-0.257) (-0.392) (-0.499) CASH -0.0033 -0.0093 -0.0040 -0.0111 (-0.216) (-0.487) (-0.258) (-0.581) CAPEX 0.0376 0.0548 0.0370 0.0479 (0.764) (0.735) (0.760) (0.651) LEV 0.0008 0.0071 -0.0009 0.0019 (0.058) (0.511) (-0.073) (0.141) RETURN -0.0251*** -0.0268*** -0.0244*** -0.0262*** (-3.678) (-3.901) (-3.571) (-3.824) VOL 0.1551*** 0.1833*** 0.1445** 0.1895*** (2.700) (3.547) (2.522) (3.638) TURNOVER -0.0048** -0.0025 -0.0045** -0.0021 (-2.187) (-0.822) (-2.065) (-0.693) %SOUGHT 0.0009*** -0.0000 0.0008** 0.0000 (2.638) (-0.034) (2.327) (0.048) REPO_SIZE -0.0220 0.0534* -0.0210 0.0520 (-0.896) (1.655) (-0.820) (1.634) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes adj. R-sq 0.053 0.077 0.041 0.075 N 1666 1564 1666 1564

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Table 4- 11: Repurchase Likelihood and Amount

This table reports results from regression of repurchase likelihood and amount on media connections and control variables for a sample of repurchases announced during the period of January 2000 to December 2016. In Panel (A), all firms are included and in Panel (B) firms that pay dividend or announces repurchase are included. In both panels, Columns (1) and (2) show results from logit regression of REPO_D and columns (3) and (4) show results from tobit regressions of REPO_AMT. REPO_D is an indicator that takes value of 1 for repurchase announcing firms, 0 otherwise. REPO_AMT is the actual repurchase amount. Media connection (Log_MC) is estimated as the log of one plus number of media connections of the acquirer in the year prior to the repurchase announcement. All variables are defined in details in Appendix. t-stats based on robust standard errors adjusted for heteroskedasticity in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%. Panel A: Repurchase Likelihood – Full Sample (1) (2) (3) (4) REPO_D1 REPO_D2 REPO_AMT1 REPO_AMT2 Log_MC 0.3036*** 0.1231*** 70.6748*** 7.8137*** (38.378) (30.351) (4.593) (14.572) SIZE 0.0902*** 0.1800*** 431.1518*** 50.4023*** (12.317) (41.588) (27.534) (74.472) MB -0.1294*** -0.0281*** -69.2117*** -2.9492*** (-10.123) (-5.667) (-4.131) (-11.356) ROA 3.6320*** 0.6538*** 97.6556 -25.4537*** (22.758) (9.477) (0.563) (-11.815) CASH 0.5792*** 0.3847*** -17.1460 -23.3281*** (7.005) (8.074) (-0.160) (-6.596) CAPEX -1.0698*** -0.9700*** -1676.3886*** -180.4074*** (-3.455) (-6.561) (-4.788) (-18.242) LEV -0.6931*** -0.5173*** -143.0013* 0.8853 (-9.737) (-13.514) (-1.768) (0.313) PE 0.0011*** -0.0002 -0.6588** -0.0891*** (4.074) (-1.087) (-2.443) (-7.581) R&D -0.1773 -0.6825*** 1067.3565** -2.0477 (-0.601) (-5.404) (2.332) (-0.392) RETURN 0.1694*** -0.1597*** -85.2340*** -26.5285*** (6.349) (-10.835) (-3.176) (-27.911) VOL -2.7099*** -2.6529*** -130.1354 20.7479*** (-11.384) (-23.767) (-0.540) (3.437) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes N 91620 91620 8097 85013

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Panel B: Repurchase Likelihood – Payer Sample (1) (2) (3) (4) REPO_D1 REPO_D2 REPO_AMT1 REPO_AMT1 Log_MC 0.3592*** 0.1679*** 67.3836* 2.1480*** (16.999) (11.677) (1.752) (5.428) SIZE 0.0752*** -0.2044*** 321.9040*** 4.1366*** (4.228) (-14.618) (10.212) (8.082) MB -0.2631*** -0.0130 -114.9586*** -0.3273** (-5.829) (-1.372) (-2.846) (-2.265) ROA 3.4847*** -0.0087 112.0448 -1.9213* (6.550) (-0.129) (0.267) (-1.680) CASH 1.0931*** 0.9762*** -6.4255 7.0557** (4.857) (7.080) (-0.024) (2.381) CAPEX -0.8388 -0.2225 -1249.5791* -17.8255*** (-0.999) (-0.538) (-1.676) (-3.006) LEV -0.1796 0.2356*** -171.0560 3.8772** (-1.083) (2.869) (-1.054) (2.328) PE 0.0014** -0.0030*** -0.7715 -0.0203 (2.079) (-4.921) (-1.073) (-1.018) R&D -1.0264 -0.6184** 1492.6704 -10.5000*** (-0.923) (-2.534) (1.026) (-3.363) RETURN 0.2731*** -0.0082 -145.3503** -1.7443* (3.863) (-0.201) (-2.138) (-1.875) VOL -2.2725*** 2.8297*** 748.3385 34.9452*** (-3.483) (10.994) (1.281) (4.532) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes N 21016 21258 1149 19249

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Table 4- 12: Endogeneity Concern – 2-SLS Instrumental Variable Regression

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APPENDIX

This Appendix provides the variable description and the sources of data. Variable Name Description and Source Log_MC Natural log of one plus the number of media connections of a firm. Number of media connections is estimated as the number of current and prior connections of directors/ executives of a firm with media directors. Media directors are the directors/ executives of the firms in the ‘media and communications’ industry. (Boardex)

Merger Related Variables CAR1 (CAR2) Cumulative abnormal return over the [-1, +1] window around merger announcement date. Abnormal returns are calculated based on the daily returns of CRSP value (equal) weighted portfolios. (CRSP) PREM Percentage change in offer price relative to target’s share price 42 days prior to the bid announcement. (SDC, CRSP) RUNUP Target’s stock price run-up during the 42 days prior to the merger announcement. (CRSP) TENDER Dummy variable equal to one if the deal is a tender offer, zero otherwise. (SDC) HOSTILE Dummy variable equal to one if the deal is hostile, zero otherwise. (SDC) COMPETE Dummy variable equal to one if the deal is a tender offer, zero otherwise. (SDC) NONDIV Dummy variable equal to one if the acquirer and target are from the same 2 digit SIC code industry, zero otherwise. (SDC) REL_VAL Deal value scaled by the acquirer’s market cap. (SDC)

Innovative Efficiency Related Variables NPats Raw number of patents granted in a firm-year. NCites Raw number of citations. Pat_adj Adjusted patents estimated as the number of patents for each firm- year divided by the mean number of patents of all firms for that year in the same technology class as the patent. Cite_adj Adjusted citations measured as the number of citations of a patent divided by the total number of citations received by all patents in that year in the same technological class as the patent.

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RDC R&D capital calculated as 5 year cumulative R&D expenses starting from fiscal year ending in year t-2 to year t-6 assuming an annual depreciation rate of 20%. (Compustat) IE1 Ratio of adjusted patent (Pat_adj) to R&D capital (RDC). IE2 Ratio of number of patents (NPats) to R&D capital (RDC). IE3 Ratio of adjusted citations (Cite_adj) to R&D capital (RDC). IE4 Ratio of number of citations (NCites) to R&D capital (RDC).

SEO Related Variables CAR1 (CAR2) Cumulative abnormal return over the [-2, +2] window around SEO announcement date. Abnormal returns are calculated based on the daily returns of CRSP value (equal) weighted portfolios. (CRSP) RUNUP Issuer’s stock price run-up during the 42 days prior to the merger announcement. (CRSP) DISCOUNT Percentage change in offer price relative to pre-offer day close price. (SDC) %PRIMARY Percentage of primary shares offered. (SDC) PROCEED Offer proceeds scaled by the market capitalization prior to the offer. (SDC) UNDERPRICING Offer day close price scaled by the offer price NYSE_D Dummy variable equal to one if the issuer is listed in NYSE at the time of the offer, zero otherwise. (SDC) SHELF_D Dummy variable equal to one for Rule 415 shelf offer, zero otherwise. (SDC) CAR>0 (CAR<0) Cumulative Abnormal Return if CAR is positive (negative), and zero otherwise. (CRSP) TICK Dummy variable equal to one if the pre offer day close price is not a multiple of $0.25, zero otherwise. (SDC) CLUSTER Dummy variable equal to one if the offer price is an integer dollar value, zero otherwise. (SDC) NADAQ_D Dummy variable equal to one if the issuer is listed in NASDAQ at the time of the offer, zero otherwise. (SDC) IPO_D Dummy variable equal to one if the firm has been public for less than two years, zero otherwise. (SDC)

Repurchase Related Variables CAR1 (CAR2) Cumulative abnormal return over the [-2, +2] window around repurchase announcement date. Abnormal returns are calculated based on the daily returns of CRSP value (equal) weighted portfolios. (CRSP) %SOUGHT Percentage sought in the repurchase announcement. (SDC) REPO_SIZE Intended size of the repurchase scaled by the market capitalization at the end of the prior fiscal year. (SDC) REPO_D Dummy variable equal to 1 if a firm has made at least one repurchase announcement, 0 otherwise. (SDC)

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REPO_AMT1 Intended repurchase amount. (SDC) REPO_AMT2 Actual repurchase amount estimated as the purchase of common and preferred stocks minus decrease in the value of the net number of preferred stocks outstanding. (Compustat)

Firm Characteristics MB Market value of equity plus book value of debt and preferred stocks scaled by book value of assets. (Compustat) LIQ Operating activities net cash flow scaled by book value of assets. (Compustat) ROA Earnings before interest and taxes scaled by total assets. (Compustat) SIZE Natural log of market value of assets. (Compustat) AGE Natural log of the number of years since the first appearance on Compustat. (Compustat) Ln_KL Natural log of the ratio of total assets to the number of employees. (Compustat) SALE Sales volume. (Compustat) RD R&D expenditures scaled by total assets. (Compustat) LEV Long term debt scaled by total assets. (Compustat) EBITDA Operating income before depreciation scaled by total assets. (Compustat) CAPEX Capital expenditures scaled by total assets. (Compustat) CASH Cash and marketable securities scaled by total assets. (Compustat) PPE Net property plant and equipment scaled by total assets. (Compustat) HHI Herfindahl index. (Compustat) BRDSIZE Number of board members. (Boardex) RETURN Stock return during the prior fiscal year using monthly returns. (CRSP) VOL Standard deviation of monthly stock returns during the prior fiscal year. (CRSP) TURNOVER Average monthly share turnover during the prior fiscal year. Monthly share turnover is calculated as the trading volume scaled by number of shares outstanding. (CRSP)

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PROCEED Offer proceeds scaled by the market capitalization prior to the offer. (SDC) UNDERPRICING Offer day close price scaled by the offer price NYSE_D Dummy variable equal to one if the issuer is listed in NYSE at the time of the offer, zero otherwise. (SDC) SHELF_D Dummy variable equal to one for Rule 415 shelf offer, zero otherwise. (SDC) CAR>0 (CAR<0) Cumulative Abnormal Return if CAR is positive (negative), and zero otherwise. (CRSP) TICK Dummy variable equal to one if the pre offer day close price is not a multiple of $0.25, zero otherwise. (SDC) CLUSTER Dummy variable equal to one if the offer price is an integer dollar value, zero otherwise. (SDC) NADAQ_D Dummy variable equal to one if the issuer is listed in NASDAQ at the time of the offer, zero otherwise. (SDC) IPO_D Dummy variable equal to one if the firm has been public for less than two years, zero otherwise. (SDC)

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