Takeover protection and stock price crash risk: Evidence from state antitakeover laws

JBR-09235; No of Pages 8 Journal of Business Research xxx (2016) xxx–xxx

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Journal of Business Research

Takeover protection and stock price crash risk: Evidence from state antitakeover laws☆ Rahul Bhargava, Sheri Faircloth, Hongchao Zeng ⁎ Managerial Science Department, College of Business, University of Nevada, Reno, 1664 N Virginia St, Reno, NV 89557, United States

a r t i c l e

i n f o

Article history: Received 15 November 2015 Received in revised form 10 August 2016 Accepted 11 August 2016 Available online xxxx Keywords: Takeover protection Stock price crash risk State antitakeover laws Corporate governance Agency conflicts

a b s t r a c t We exploit the passage of state antitakeover laws to examine the relation between takeover protection and stock price crash risk. We find that firms incorporated in states that passed the laws are negatively associated with future stock price crash risk in the post-law periods, suggesting that takeover protection mitigates bad news hoarding activities. Further analysis shows that the mitigating effect is more pronounced when firms have severe information asymmetry or face strong product market competition. Together, our findings shed new light on the impact of takeover threats on managerial incentives to engage in bad news hoarding. © 2016 Elsevier Inc. All rights reserved.

1. Introduction In recent years, academic studies investigating the determinants of managerial bad news hoarding behavior and the consequent stock price crashes have drawn considerable interest. Among this growing literature, CEO overconfidence, option-based compensation, corporate tax avoidance, institutional investor stability, religiosity, corporate social responsibility (CSR), and accounting conservatism have been shown to be associated with future stock price crash risk (Kim, Wang, & Zhang, 2016; Kim, Li, & Zhang, 2011a, 2011b; Callen & Fang, 2013, 2015a; Kim, Li, & Li, 2014; Kim & Zhang, 2016). However, this literature contains few studies examining the relation between stock price crash risk and takeover protection, which can potentially affect managerial incentives to engage in bad news hoarding activities. This paper attempts to fill the void by exploring the impact of takeover protection on stock price crash risk. Empirical analyses using various indexes of antitakeover provisions as proxies for takeover protection are inevitably subject to endogeneity concerns because firm-level characteristics and policies are endogenously determined with firms' decisions to adopt antitakeover provisions (Gompers, Ishii, & Metrick, 2003; Bebchuck, Cohen, & Ferrell, 2009; Bebchuck, Cohen, & Wang, 2013). Similarly, studies relying on the takeover protection provided by the presence of state-level antitakeover statutes have limitations as well because firms' incorporation decisions are likely endogenous (Bebchuck & Cohen, 2003; Wald & Long, 2007; Zhao, Allen, & Hasan, 2013). Therefore, a clear causal effect

☆ We would like to thank Chunlin Liu, Greg Stone, Arun Upadhyay, Qun Wu, and the University of Nevada Reno for helpful comments. ⁎ Corresponding author. E-mail addresses: [email protected] (R. Bhargava), [email protected] (S. Faircloth), [email protected] (H. Zeng).

of takeover protection on stock price crash risk would be difficult to establish even if the empirical results suggest a correlation between these two variables. To address the endogeneity issue associated with takeover protection, we exploit exogenous variations in takeover protection induced by the passage of state antitakeover laws between 1985 and 1991. Following prior studies (Bertrand & Mullainathan, 2003; Qiu & Yu, 2009; Giroud & Mueller, 2010), we focus on business combination (BC) laws, which are considered the most stringent of the secondgeneration antitakeover laws (Bertrand & Mullainathan, 2003). Since these laws are adopted by states and are not likely driven by firmlevel variables, our identification strategy is unlikely to raise endogeneity concerns (Bertrand & Mullainathan, 2003). Stock price crashes are large negative outliers in the return distribution. Managers have incentives to conceal negative, firm-specific information from investors due to concerns about compensation and career prospects (Ball, 2009; Kothari, Shu, & Wysochi, 2009). However, there is a limit on the amount of bad news that managers are willing to absorb, and they tend to give up by releasing all accumulated bad news to the stock market at once when an abandonment level of bad news hoarding is reached, resulting in a stock price crash (Jin & Myers, 2006; Hutton, Marcus, & Tehranian, 2009). Prior studies suggest two opposing effects of takeover protection on stock price crash risk. On the one hand, the entrenchment view argues that takeover protection is positively associated with stock price crash risk. Managerial bad news hoarding reflects agency conflicts between managers and shareholders over disclosure preferences (Kothari et al., 2009). Takeover protection shields managers from the disciplining force of the market for corporate control and exacerbates agency conflicts between managers and shareholders (Gompers et al., 2003; Collins & Huang, 2011; Mathur, Singh, Thompson, & Nejadmalayeri, 2013). As a result, entrenched managers are more likely to participate in bad news hoarding activities in order

http://dx.doi.org/10.1016/j.jbusres.2016.08.021 0148-2963/© 2016 Elsevier Inc. All rights reserved.

Please cite this article as: Bhargava, R., et al., Takeover protection and stock price crash risk: Evidence from state antitakeover laws, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.08.021

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R. Bhargava et al. / Journal of Business Research xxx (2016) xxx–xxx

to advance their personal objectives, leading to greater stock price crash risk. On the other hand, the managerial myopia view contends that takeover protection is negatively associated with stock price crash risk. Takeover pressures motivate managers to conceal negative news from investors for fear that the release of such news to the market may lead to poor stock price performance which draws the attention from corporate raiders and increases takeover probability. As takeover protection mitigates takeover pressures, managers are less incentivized to engage in managerial bad news hoarding activities. In our empirical analysis, we use the enactment of BC laws to capture an exogenous increase in takeover protection. Following prior studies (Jin & Myers, 2006; Hutton et al., 2009), we measure firm-level stock price crash risk by the number of days with positive firm-specific daily returns (COUNT), the negative skewness of firm-specific daily returns (NCSKEW), and the down-to-up volatility of firm-specific daily returns (DUVOL). Using the difference-in-differences methodology and a sample of U.S. public firms from 1980 to 1995, we find robust evidence that takeover protection is negatively associated with future stock price crash risk, consistent with the managerial myopia view that takeover protection mitigates managerial bad news hoarding activities. Our results are robust to controlling for a large number of firm characteristics that may affect future stock price crash risk. Further, we examine whether the mitigating effect of takeover protection on managerial bad news hoarding is affected by information asymmetry and product market competition. We find that the negative association between takeover protection and stock price crash risk is significant only when firms have high information asymmetry or face strong competitive threats in product markets. Since both information asymmetry and product market competition motivate managers to engage in managerial bad news hoarding activities, our findings suggest that takeover protection can be particularly important in firms that are subject to greater managerial myopic behaviors. Overall, the evidence in our study provides solid support for the managerial myopia view on takeover protection. Our study makes important contributions to two strands of literature. First, our study extends a growing body of literature on managerial bad news hoarding and future stock price crash risk. This literature begins with Jin and Myers (2006) who hypothesize that managerial bad news hoarding behavior leads to future stock price crashes. Following this study, more recent research has investigated various factors that are associated with managers' incentives to withhold negative information from the market and the resulting stock price risk. For example, while CEO overconfidence (Kim, Li, Li, 2014), corporate tax avoidance (Kim et al., 2011a), and CFO option-based compensation (Kim et al., 2011b), are positively associated with stock price crash risk, institutional investor stability (Callen & Fang, 2013), religiosity (Callen & Fang, 2015a), corporate social responsibility (CSR) (Kim, Li, Li, 2014), and accounting conservatism (Kim & Zhang, 2016) are negatively associated with stock price crash risk. We advance this literature by showing that takeover protection negatively impacts stock price crash risk through mitigating takeover pressures that motivate managers to hide bad news from investors. Our findings suggest that when designing risk management policies to decrease extreme negative returns in the stock market consideration should be given to the degree of takeover exposure faced by the firm. Second, our study contributes to the literature that documents the managerial myopic effects of takeover protection. Prior studies (Shleifer & Summers, 1988; Stein, 1988) predict that takeover pressures induce managers to make myopic decisions. According to this argument, managers who are protected from takeover threats should exhibit less myopic behavior, which in turn would benefit shareholders. Recent studies have documented evidence in support of the managerial myopia view with respect to corporate innovation (Chemmanur & Tian, 2013), accruals management and the likelihood of financial reporting fraud (Zhao & Chen, 2008), and voluntary disclosure (Zhao et al., 2013). We extend the analysis to managerial bad news hoarding and stock price

crash risk. To the best of our knowledge, this is the first study that examines the relation between takeover protection and firm-level stock price crash risk. The remainder of this paper is organized as follows. Section 2 discusses state antitakeover laws, reviews related literature, and develops hypotheses. Section 3 describes sample selection, discusses measures of stock price crash risk, and presents summary statistics. Section 4 presents the main results. Section 5 concludes. 2. State antitakeover laws, literature review, and hypothesis development 2.1. State antitakeover laws The U.S. corporate law system has a long history of passing legislation to regulate hostile takeovers. Virginia enacted the firstgeneration antitakeover statutes in 1968, and 36 other states followed suit in the 1970s. In 1982, a first-generation antitakeover statute, the Illinois antitakeover law, was declared unconstitutional by the Supreme Court. Consequently, many states responded by enacting second-generation antitakeover laws based on takeover defenses previously adopted as amendments to the charter of a corporation. There are five standard types of second-generation antitakeover statutes: control share acquisition, fair price, business combination (BC), poison pill, and constituency (Bebchuck & Cohen, 2003). These antitakeover statutes differ in their mechanisms to prevent a hostile bidder from taking control of the corporation. A control share acquisition statute requires the hostile bidder who has acquired or proposes to acquire a large block of shares to obtain a majority vote from the target firm's shareholders granting voting rights to the shares. Since the target management can delay this vote indefinitely, the bidder may not be able to gain control of the target firm. A fair price statute requires a bidder who has gained control to pay the remaining minority shareholders at a price equal to the original acquiring price. This prevents bidders from using a low-price buyout to pressure remaining shareholders into selling their shares. A BC statute imposes a moratorium on certain transactions such as mergers and asset sales for a period of up to five years. This moratorium hinders hostile bidders from gaining access to the target firm's assets for the purpose of paying down acquisition debt, thus making hostile takeovers more difficult and often impossible (Bertrand & Mullainathan, 2003). A poison pill statute entitles its holders to receive significant payment if a buyer acquires a large block of shares without the approval of the board of directors, making a hostile takeover prohibitively costly. Lastly, a constituency statute allows target managers to consider the interests of non-shareholders when defending against a takeover. Using an event study, Karpoff and Malatesta (1989) document a significant and negative average stock-price effect of secondgeneration state antitakeover statutes covered in the press from 1982 through 1987. However, they also show that there are large variations in the wealth effects of different types of secondgeneration antitakeover laws. While BC laws result in the greatest stock price drop, other laws such as control share acquisition and fair price laws result in negative but insignificant stock price changes. Bertrand and Mullainathan (2003) replicate the analysis and also find little effect associated with the announcements of other laws. The evidence supports the argument in Bertrand and Mullainathan (1999) that BC laws are the most stringent second-generation state antitakeover laws and induce the largest change in incumbent management's incentive structure. Therefore, we focus mainly on BC laws and use the passage of these laws to examine the relation between takeover protection and stock price crash risk. In the next section, we review prior literature on takeover protection and managerial behavior and develop hypotheses regarding the effects antitakeover laws may have on stock price crash risk.

Please cite this article as: Bhargava, R., et al., Takeover protection and stock price crash risk: Evidence from state antitakeover laws, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.08.021

R. Bhargava et al. / Journal of Business Research xxx (2016) xxx–xxx

2.2. Antitakeover laws, managerial behavior, and stock price crash risk Antitakeover laws provide incumbent managers more power to block hostile takeovers, thus leading to increased takeover protection. Prior studies are divided on how takeover protection affects managerial behavior. A widely accepted view (the entrenchment view) considers the market for corporate control as a strong monitoring and disciplining mechanism that incentivizes managers to focus on value-enhancing activities (Jensen & Ruback, 1983). According to this view, takeover protection weakens corporate governance and induces managers to engage in value-destroying behaviors such as shirking managerial responsibilities, distorting investments toward manager-specific investments, and wasting corporate resources on organizational inefficiencies (Shleifer & Vishny, 1989; Jensen, 1988). Empirical evidence generally documents a dominating negative effect of takeover protection on different measures of firm performance (e.g., Gompers et al., 2003; Bebchuck & Cohen, 2005; Faleye, 2007; Bebchuck et al., 2009; Cohen & Wang, 2013). Many studies investigating the effects of takeover protection on various aspects of managerial behavior also find evidence consistent with the entrenchment view. For example, Masulis, Wang, and Xie (2007) document that acquirers with more antitakeover provisions are associated with significantly lower announcement abnormal returns. Yun (2009) examines the choice of corporate liquidity and finds that the passage of antitakeover legislation increases discretionary cash holdings relative to lines of credit, which are subject to bank monitoring. Francis, Hason, Kose, and Song (2011) study corporate dividend payout policy and show that dividend payout policies and propensities fall following the adoption of statelevel antitakeover laws. Additionally, Atanassov (2013) investigates corporate innovation and finds that the number of patents and citations per patent decline significantly for firms incorporated in states that pass antitakeover laws. One notable manifestation of the agency conflicts between managers and shareholders is the misalignment of managers' disclosure preferences with those of shareholders, which is more pronounced with respect to the disclosure of negative information due to managers' compensation and career concerns (Kothari et al., 2009). Regarding compensation concerns, the release of bad news results in a stock price decline which may lead to lower bonus payments and wealth loss for managers (Kothari et al., 2009). Regarding career concerns, since managers are held accountable by boards of directors and investors for current stock performance, declining stock prices following the disclosure of bad news could be associated with higher CEO turnover (Weisbach, 1988; Healy & Palepu, 2001). The potential effects of the release of bad news on managers' employment opportunities could also extend outside the firm. Fama and Jensen (1983) argue that the value of internal managers' outside directorships depends primarily on these managers' performance in their parent firms. Poor performance in the managers' parent firms can lead to their loss of outside directorships. Since takeover protection shields managers from the disciplining force of the market for corporate control, less monitored managers have stronger incentives to withhold or delay the release of bad news to the market, thus increasing future stock price crash risk. Consistent with this argument, Callen and Fang (2013) present evidence that stable institutional investors act as monitors and are negatively associated with future stock price crash risk by reducing managerial bad news hoarding activities. Kim and Zhang (2016) find that accounting conservatism, which is an alternative governance mechanism that curbs managerial incentives and abilities to overstate accounting numbers, is negatively associated with stock price crash risk. Meanwhile, takeover protection may entrench managers and exacerbate managerial self-dealing activities. Faleye (2007) shows that classified boards significantly reduce the likelihood of forced CEO turnover and lower the sensitivity of CEO compensation to firm performance. Entrenched managers are more incentivized to engage in managerial expropriation at the expense of shareholders. Straight-out expropriation ranges from directly capturing

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a portion of the company's cash flows to transfer pricing, i.e. managers set up their own companies and sell the product of the main company they run to their firms at below market prices (Shleifer & Vishny, 1997). More subtle forms of expropriation include consumption of perquisites and pursuing pet projects. In order to disguise expropriation activities, managers tend to withhold related information which is perceived by the market as negative from outside investors, leading to increased future stock price crash risk. Therefore, we conjecture that takeover protection may induce managers to engage in more bad news hoarding activities, and we test the following hypothesis: Hypothesis 1. Takeover protection is positively associated with future stock price crash risk, consistent with the entrenchment view. In contrast, a competing view (the managerial myopia view) argues that takeover protection mitigates managerial myopic behavior and is beneficial to shareholders. Shleifer and Summers (1988) suggest that incumbent managers facing high takeover threats have less power compared with shareholders and are thus less likely to take on innovative projects. Once managers are insulated from the pressure of the takeover market, they are more incentivized to invest in innovative activities. Similarly, Stein (1988) argues that investing in long-term innovative projects exacerbates asymmetric information which leads outside shareholders to undervalue the firm. Since undervaluation subjects firms to higher takeover probability, managers will invest less in innovative projects and more in routine projects that are easier to evaluate by the market. As such, takeover protection shields managers from the threat of hostile takeovers, and allows them to focus on long-term investments which otherwise would have been forgone due to information asymmetry. Consistent with the managerial myopia view, Chemmanur and Tian (2013) find that the number of firm-level antitakeover provisions is positively associated with corporate innovation. In addition to investments, takeover protection has been shown to affect other types of managerial behavior such as earnings management and voluntary disclosure. Zhao and Chen (2009) argue that takeover pressures provide incentives for managers to engage in earnings management. In the case of voluntary disclosure, takeover pressures also motivate managers to withhold disclosures in order to mitigate takeover threats because bidder uncertainty is higher for firms with less disclosure (Edlin & Stiglitz, 1995). As takeover protection mitigates takeover pressures faced by managers, they are less likely to manipulate earnings or withhold information from investors. Examining the relationship between state antitakeover laws and earnings quality, Zhao and Chen (2009) find that the passage of state antitakeover laws leads to lower magnitudes of abnormal accruals and higher levels of earnings informativeness. Further, Zhao et al. (2013) show that state antitakeover laws positively impact a firm's voluntary disclosure activity. Using similar arguments, we conjecture that managers bound by the disciplining force of the takeover market are more likely to engage in managerial bad news hoarding for job security reasons. The release of bad news often results in a decline in the stock price and poor stock price performance will likely be exploited by corporate raiders (Froot, Perold, & Stein, 1992). Palepu (1986) and Morck, Shleifer, and Vishny (1990) find that target CEO turnover is high in hostile takeovers. Thus, to avoid losing their current positions, managers exposed to takeover threats have incentives to withhold bad news from the market. However, once managers are insulated from the pressure of the takeover market, they would be less likely to myopically engage in managerial bad news hoarding. Therefore, we expect takeover protection to be associated with lower stock price crash risk through mitigating managerial bad news hoarding activities. This leads to our second hypothesis: Hypothesis 2. Takeover protection is negatively associated with future stock price crash risk, consistent with the managerial myopia view.

Please cite this article as: Bhargava, R., et al., Takeover protection and stock price crash risk: Evidence from state antitakeover laws, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.08.021

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3. Sample selection, measures of stock price crash risk, and descriptive statistics

on the “up” subsample. Finally we take a log transformation of the ratio to yield the following equation:

3.1. Sample selection

n X  o X DUVOL j;T ¼ log ðnu −1Þ R2 =ðnd −1Þ UP R2j;t DOWN j;t

We select all firm-year observations with available data in Compustat annual files during the sample period 1980–1995. We exclude firms in financial (SIC codes 6000–6999) and utility industries (SIC codes 4900–4999). We further delete firm-year observations from the sample using the following filters: 1) firm-years where total assets or sales are either missing or non-positive, 2) firm-years with missing states of incorporation, and 3) firm-years with missing data for the variables used in our empirical analysis. The final sample yields 56,018 firm-year observations corresponding to 8937 unique firms. 3.2. Measures of stock price crash risk Following previous literature on stock price crash risk (Chen, Hong, & Stein, 2001; Hutton et al., 2009), we construct three firm-specific stock price crash risk measures by estimating the firm-specific daily returns from an expanded market and industry index model regression: r j;t ¼ a j þ β1; j r m;t−1 þ β2; j ri;t−1 þ β3; j rm;t þ β4; j r i;t þ β5; j rm;tþ1 þ β6; j r i;tþ1 þ ɛ j;t

ð1Þ

where rj ,t is the return on stock j in day t, rm,t is the return on the CRSP value-weighted market index in day t, and ri ,t is the return on the valueweighted industry index in the two-digit SIC industries. We allow for nonsynchronous trading by including the lead and lag terms (rm , t − 1, ri , t − 1, rm , t + 1, and ri , t + 1) for the value-weighted market index (Dimson, 1979). We measure the firm-specific daily return for firm j in day t,Rj , t, as the natural log of one plus the residual return from Eq. (1). Our first measure of stock price crash risk is the difference between the number of downside frequencies and the number of upside frequencies (COUNT). Following the literature (Jin & Myers, 2006; Hutton et al., 2009), we choose 3.09 to generate frequencies of 0.1% in the normal distribution. A downside (upside) frequency occurs when the firm-specific daily return exceeds 3.09 standard deviations below (above) the mean return over the fiscal year. We then define COUNT as the number of downside frequencies minus the number of upside frequencies. A higher value of this measure suggests more crash risk since the stock is exposed to a higher frequency of crashes. We use the negative conditional return skewness (NCSKEW) as our second measure of firm-specific crash risk. We follow the literature (Chen et al., 2001) and calculate NCSKEW for a given firm in a fiscal year by taking the negative of the third moment of firm-specific daily returns and scaling it by the standard deviation of firm-specific daily returns raised to the third power: h i   3=2  NCSKEW j;T ¼ – nðn−1Þ3=2 ∑R3j;t = ðn−1Þðn−2Þ ∑R2j;t

ð2Þ

where n is the number of observations on daily returns for firm j during the fiscal year T. The numerator is the raw third moment of daily returns, which is normalized by the cubed standard deviation in the denominator to allow for comparisons across returns with different variances (Greene, 1993; Chen et al., 2001). We follow the literature and take the negative of the third moment so that an increase in NCSKEW corresponds to a more left-skewed distribution, and hence more crash risk. Our third measure of stock price crash risk is the down-to-up volatility (DUVOL). For each stock j over a fiscal year period, we first assign all the days with firm-specific daily returns above (below) the period mean to the “up” (“down”) day subsample. We then calculate the ratio of the standard deviation on the “down” subsample to the standard deviation

ð3Þ

where nu and nd are the number of up and down days over the fiscal year t, respectively. A higher value of this measure suggests that a stock is more “crash prone”, and thus corresponding to more crash risk. 3.3. Descriptive statistics Table 1 presents summary statistics for the variables used in our study. The mean values of the stock price crash risk measures, COUNT, NCSKEW, DUVOL, are −0.631, −0.181, −0.068, respectively, comparable to those reported in Chen et al. (2001) and Callen and Fang (2015b). The proportion of firms incorporated in a state that has adopted BC laws is 0.48, close to that reported in Atanassov (2013). The mean values and standard deviations of KURT (kurtosis of firm-specific daily returns), SIGMA (stock return volatility), and RET (past returns) are consistent with those reported in Callen and Fang (2015b). Moreover, the average firm in our sample has a market capitalization of $1301 million, a market-to-book ratio of 1.814, a leverage of 0.247, and a return on assets of −0.033. 4. Main empirical results 4.1. Baseline results To examine how takeover protection affects future stock price crash risk, we estimate the following regression model: CRASHRISK i;Tþ1 ¼ a0 þ a1 AfterBC i;T þ a2 NCSKEW i;T þ a3 KURT i;T þ a4 SIGMAi;T þ a5 RET i;T þ a6 SIZEi;T þ a7 MBi;T þ a8 LEV i;T þ a9 ROAi;T þ a10 DTURNi;T þ a11 ACCRi;T þ ∑Firm þ ∑Year þ ɛ i;T ð4Þ where the dependent variable CRASHRISKi , T + 1 is measured by COUNTi , T + 1, NCSKEWi , T + 1, or DUVOLi , T+ 1. Our primary variable of interest is AfterBCi ,T, a dummy variable that takes the value of one in the years after firm i's state of incorporation has passed BC laws, and zero otherwise. The coefficient on AfterBCi , T captures the effect of takeover protection on stock price crash risk. Following prior studies (Jin & Myers, 2006; Hutton et al., 2009), we control for various variables that are associated with crash risk. We include the negative skewness of firm-specific daily returns (NCSKEW) to control for potential serial correlation in the dependent variable. Callen and Fang (2015b) show that

Table 1 Descriptive statistics. This table presents descriptive statistics for the variables used in our analysis. The sample includes 56,018 firm-year observations for the period 1980 to 1995. Variable definitions are provided in Appendix A. Variables

Mean

Median

Std. dev.

5th

95th

COUNTT + 1 NCSKEWT + 1 DUVOLT + 1 NCSKEWT AfterBCT KURTT SIGMAT RETT SIZET MBT LEVT ROAT DTURNT ACCRT

−0.631 −0.181 −0.068 −0.149 0.480 6.348 0.037 −0.001 4.165 1.814 0.247 −0.033 −0.001 0.087

−1.000 −0.154 −0.093 −0.152 0.000 2.821 0.030 −0.001 4.012 1.305 0.220 0.033 0.000 0.053

1.729 1.462 0.627 1.178 0.500 10.350 0.024 0.002 2.121 1.601 0.202 0.236 0.050 0.102

−4.000 −2.056 −1.004 −1.808 0.000 0.316 0.013 −0.005 0.873 0.769 0.000 −0.471 −0.075 0.004

2.000 1.725 0.938 1.664 1.000 24.871 0.083 0.002 7.980 4.591 0.628 0.143 0.076 0.291

Please cite this article as: Bhargava, R., et al., Takeover protection and stock price crash risk: Evidence from state antitakeover laws, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.08.021

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future stock price crash risk is negatively correlated with the negative skewness of firm-specific daily returns (NCSKEW). We control for the kurtosis of firm-specific daily returns (KURT) in year T since Jin and Myers (2006) argue that long tails in return distributions affect future stock price crash risk. Hutton et al. (2009) document a significant and negative effect of kurtosis on crash risk. We also control for stock return volatility (SIGMA), past returns (RET), firm size (SIZE), growth opportunities (MB), financial leverage (LEV), difference of opinion among investors (DTURN), operating performance (ROA), and earnings management (ACCR). Chen et al. (2001) find that stock return volatility, past stock returns, firm size, growth opportunities, and differences of opinion among investors are positively associated with future crash risk. Hutton et al. (2009) show that while financial leverage and operating performance are negatively associated with future stock price crash risk, management's manipulation of accruals leads to higher crash risk. Our estimation results of Eq. (4) are presented in Table 2, where future stock price crash risk is measured by COUNTT + 1, NCSKEWT + 1, and DUVOLT + 1 in columns (1) through (3), respectively. Across all columns, the coefficient estimates on AfterBCT are negative and significant at least at the 5% significance level, indicating that takeover protection is negatively associated with one-year-ahead stock price crash risk. On average, firms incorporated in states that have adopted the BC laws are associated with a 0.092 decrease in COUNTT + 1, a 0.061 decrease in NCSKEWT + 1, and a 0.044 decrease in DUVOLT + 1. Given that the mean values of COUNTT + 1, NCSKEWT + 1, and DUVOLT + 1 are −0.631, −0.181, and −0.068, respectively, the economic significance of this mitigating effect of takeover protection on future crash risk is nontrivial. Overall, our results are consistent with the managerial myopia view which suggests that takeover protection can mitigate

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managerial bad news hoarding by shielding firms from the takeover pressure in the market for corporate control. The coefficient estimates on other control variables (except for DTURN) are significant for at least one measure of stock price crash risk, and are generally consistent with those reported in prior studies. Specifically, we find that the negative skewness of firm-specific daily returns (NCSKEW) and the kurtosis of firm-specific daily returns (KURT) are negatively associated with crash risk, consistent with Hutton et al. (2009) and Jin and Myers (2006). Further, we show that stock return volatility (SIGMA), past returns (RET), firm size (SIZE), growth opportunities (MB), and earnings management (ACCR) are positively associated with crash risk, consistent with Chen et al. (2001) and Hutton et al. (2009). Though we document a positive association between financial leverage and crash risk, which is different from the negative relation between these two variables documented by Hutton et al. (2009), our results are similar to those reported in Callen and Fang (2015b), who also document significant positive associations between financial leverage and all specifications of future stock crash risk. 4.2. The effect of information asymmetry In this section, we investigate how information asymmetry affects the relation between takeover protection and stock price crash risk. Firms with severe information asymmetry may be subject to greater managerial myopic behavior. Due to information asymmetry, managers face short-term undervaluation pressures from the equity market because outside investors cannot properly evaluate managers' long-term investment (Chemmanur & Tian, 2013). In these circumstances, a bad news release likely results in even stronger negative reaction from the market (Kothari et al., 2009). As a result, managers of firms with high

Table 2 Takeover protection and stock price crash risk. This table presents the regression results of the relation between takeover protection and stock price crash risk. The sample includes 56,018 firm-year observations for the period 1980 to 1995. Variable definitions are provided in Appendix A. All regressions use White's heteroskedasticity-consistent standard errors, and include firm and year fixed effects. P-values are reported in parentheses. COUNTT + 1

NCSKEWT + 1

(1)

(2)

(3)

−0.092⁎⁎ (0.013) −0.044⁎⁎⁎

−0.061⁎⁎⁎ (0.000) −0.112⁎⁎⁎

−0.044⁎⁎⁎ (0.000) −0.036⁎⁎⁎

RETT

(0.000) −0.001⁎⁎ (0.033) 0.131⁎⁎ (0.012) 0.203⁎⁎⁎

(0.000) −0.001⁎⁎ (0.021) 0.177⁎⁎⁎ (0.000) 0.096⁎⁎⁎

SIZET

(0.002) 0.267⁎⁎⁎

(0.000) −0.002⁎⁎ (0.025) 0.091⁎⁎ (0.043) 0.001 (0.985) 0.229⁎⁎⁎

(0.000) 0.026⁎⁎⁎ (0.000) 0.350⁎⁎⁎

(0.000) 0.037⁎⁎⁎ (0.000) 0.422⁎⁎⁎

(0.000) 0.016⁎⁎⁎ (0.000) 0.336⁎⁎⁎

Constant

(0.000) 0.005 (0.925) 0.117 (0.106) 0.183⁎⁎⁎ (0.000) −2.218⁎⁎⁎

(0.000) −0.106⁎⁎⁎ (0.002) 0.019 (0.377) 0.029 (0.310) −0.801⁎⁎⁎

Firm fixed effects Year fixed effects Obs. Adjusted R2

(0.000) YES YES 56,018 0.085

(0.000) −0.044 (0.426) 0.073 (0.189) 0.100⁎ (0.079) −1.259⁎⁎⁎ (0.000) YES YES 56,018 0.133

AfterBCT NCSKEWT KURTT SIGMAT

MBT LEVT ROAT DTURNT ACCRT

DUVOLT + 1

(0.007) 0.168⁎⁎⁎

(0.000) YES YES 56,018 0.188

⁎ Statistical significance at the 10% level. ⁎⁎ Statistical significance at the 5% level. ⁎⁎⁎ Statistical significance at the 1% level.

Please cite this article as: Bhargava, R., et al., Takeover protection and stock price crash risk: Evidence from state antitakeover laws, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.08.021

6

R. Bhargava et al. / Journal of Business Research xxx (2016) xxx–xxx

governance mechanism that disciplines managers and lowers agency costs (Shleifer & Vishny, 1997; Jensen, 1986). Giroud and Mueller (2010) document that weakened corporate governance leads to a significant drop in operating performance only in non-competitive industries. Alimov (2014) finds that acquirers exposed to greater increases in competitive pressure make better acquisitions by choosing targets with higher synergies. If managerial bad news hoarding reflects agency conflicts over disclosure preferences between managers and shareholders, we expect strong product market competition to be associated with decreased withholding of bad news and lower levels of stock price crash risk. However, since the previously documented mitigating effect of takeover protection on stock price crash risk is consistent with the managerial myopia view, we are unclear about the role of product market competition in the relation between takeover protection and stock price crash risk. On the other hand, competitive pressure in product markets may increase managerial tendency to withhold bad news from investors. In competitive product markets, firms have smaller profit margins and managers face greater pressure to maintain competitive advantages over their rivals. Ellis, Edward, and Thomas (2012) argue that managers face a tradeoff between the benefits of reducing information asymmetry with capital market participants and the costs of aiding rivals when deciding how much private information to disclose. The disclosure of private information, especially negative information, may be associated with higher proprietary costs that can weaken firms' competitive positions. Therefore, managers may have greater incentives to hide bad news from investors when they face greater product market threats. In this case, we expect the mitigating effect of takeover protection on stock price crash risk to be

information asymmetry have greater tendency to withhold bad news. Consistent with findings in this area, Kothari et al. (2009) show that the asymmetry in disclosure of bad news relative to good news increases in the level of information asymmetry and Hutton et al. (2009) document that opaque firms are more prone to future stock crashes. Therefore, we expect the mitigating effect of takeover protection to be stronger for firms with a higher level of information asymmetry. To test our hypothesis, we partition the sample into high and low information asymmetry subsamples based on the median values of firm age (Helwege & Liang, 1996). A firm-year observation belongs to the high information asymmetry subsample if its firm age is below or equal to the sample median. We rerun the regressions specified in Eq. (4) and present results in Table 3. For our hypothesis to be supported, we expect to find a coefficient estimate on AfterBCT that is larger in magnitude for the subsample of high information asymmetry. In columns (1) and (2) where the dependent variable is COUNTT + 1, the coefficient estimates on AfterBCT are statistically significant only for the high information asymmetry subsample, suggesting that the mitigating effect of takeover protection on future stock price crash risk is primarily driven by firms with a higher level of information asymmetry. In columns (3) to (6), we obtain similar results for alternative measures of future stock price risk. 4.3. The effect of product market competition We further investigate the impact of product market competition on the relation between takeover protection and stock price crash risk. On the one hand, product market competition is a strong corporate

Table 3 Subsample analysis on the effect of information asymmetry. This table presents the regression results of information asymmetry on the relation between takeover protection and stock price crash risk. The sample includes 56,018 firm-year observations for the period 1980 to 1995. Columns (1), (3), and (5) report the regression results for the subsample of high information asymmetry. Columns (2), (4), and (6) report the regression results for the subsample of low information asymmetry. Subsamples are obtained by portioning the sample using the median value of firm age. Variable definitions are provided in Appendix A. All regressions use White's heteroskedasticity-consistent standard errors, and include firm and year fixed effects. P-values are reported in parentheses. COUNTT + 1

NCSKEWT + 1

DUVOLT + 1

High IA

Low IA

High IA

Low IA

High IA

Low IA

(1)

(2)

(3)

(4)

(5)

(6)

−0.151⁎⁎ (0.021) −0.095⁎⁎⁎

−0.055⁎⁎⁎ (0.000) −0.026⁎⁎⁎

−0.009 (0.739) −0.058⁎⁎⁎

RETT

(0.000) −0.002⁎ (0.051) 0.135⁎⁎⁎ (0.000) 0.129⁎⁎⁎

(0.043) 0.336⁎⁎⁎

(0.000) 0.288⁎⁎⁎

(0.000) −0.004⁎ (0.058) 0.103 (0.214) 0.096 (0.109) 0.394⁎⁎⁎

(0.000) −0.009 (0.167) 0.726 (0.103) 0.084⁎⁎

SIZET

−0.112⁎⁎⁎ (0.000) −0.091⁎⁎⁎ (0.000) −0.002 (0.124) 0.116 (0.115) 0.081 (0.207) 0.207⁎⁎⁎

−0.013 (0.807) −0.186⁎⁎⁎

(0.000) −0.004⁎ (0.052) 0.129 (0.134) 0.139⁎⁎

−0.069 (0.164) −0.047⁎⁎⁎ (0.000) −0.006⁎⁎⁎ (0.001) 0.523⁎⁎⁎ (0.000) 0.320⁎⁎⁎

(0.013) 0.139⁎⁎⁎

(0.000) 0.304⁎⁎⁎

(0.000) 0.011 (0.392) 0.369⁎⁎⁎

(0.000) 0.072⁎⁎⁎ (0.000) 0.371⁎⁎⁎

(0.000) 0.076⁎⁎⁎ (0.000) 0.478⁎⁎⁎

(0.000) 0.003 (0.744) 0.348⁎⁎⁎

(0.000) 0.045⁎⁎⁎ (0.000) 0.285⁎⁎⁎

(0.000) 0.016 (0.544) 0.389⁎⁎⁎

Constant

(0.001) −0.010 (0.890) 0.265 (0.205) 0.368⁎⁎⁎ (0.000) −1.973⁎⁎⁎

(0.000) −0.219⁎ (0.070) 0.313 (0.283) 0.198 (0.186) −2.635⁎⁎⁎

(0.000) −0.288⁎⁎⁎ (0.000) 0.156 (0.226) 0.012 (0.894) −1.499⁎⁎⁎

(0.000) −0.122 (0.102) 0.216 (0.191) 0.090 (0.307) −1.856⁎⁎⁎

(0.000) −0.224⁎⁎⁎ (0.000) 0.103 (0.124) 0.004 (0.927) −0.916⁎⁎⁎

(0.00) −0.188⁎⁎⁎ (0.000) 0.057 (0.468) 0.028 (0.609) −1.279⁎⁎⁎

Firm fixed effects Year fixed effects Obs. Adjusted R2

(0.000) YES YES 28,845 0.082

(0.000) YES YES 27,173 0.091

(0.000) YES YES 28,845 0.123

(0.000) YES YES 27,173 0.154

(0.000) YES YES 28,845 0.147

(0.000) YES YES 27,173 0.218

AfterBCT NCSKEWT KURTT SIGMAT

MBT LEVT ROAT DTURNT ACCRT

⁎ Statistical significance at the 10% level. ⁎⁎ Statistical significance at the 5% level. ⁎⁎⁎ Statistical significance at the 1% level.

Please cite this article as: Bhargava, R., et al., Takeover protection and stock price crash risk: Evidence from state antitakeover laws, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.08.021

R. Bhargava et al. / Journal of Business Research xxx (2016) xxx–xxx

more pronounced for firms that operate in more competitive markets. To examine the role of product market competition, we partition the sample into strong and weak product market competition subsamples using the median values of fitted Herfindahl-Hirschman Index (Hoberg & Phillips, 2010). A firm-year observation belongs to the strong product market competition subsample if its fitted HHI is below or equal to the sample median. We rerun the regressions specified in Eq. (4) and present results in Table 4. Across all columns, we report negative coefficient estimates on AfterBCT but the coefficients are statistically significant only in the subsample that faces strong product market competition. Overall, the evidence is consistent with the view that the mitigating effect of takeover protection on future stock price crash risk is more pronounced for firms that face greater product market competition.

7

Using the passage of state antitakeover laws to capture an exogenous shock in takeover protection, we provide evidence that is consistent with the managerial myopia view. Specifically, we show that takeover protection is negatively associated with all three measures of future stock price crash risk. In addition, we investigate the effect of information asymmetry and product market competition on the relation between takeover protection and stock price crash risk. We find that the association between takeover protection and crash risk is stronger when firms have high information asymmetry or face strong competitive pressure in product markets. Since both information asymmetry and product market competition may increase pressures for managers to engage in bad news hoarding, our findings suggest that the mitigating effect of takeover protection is more pronounced in firms that are subject to greater managerial myopic behavior. Appendix A. Variable definitions

5. Conclusion Crash risk measures In this paper, we study the relation between takeover protection and stock price crash risk by developing and testing two competing hypotheses. The management entrenchment view contends that takeover protection weakens corporate governance and thus induces managers to engage in more bad news hoarding activities which lead to greater stock price crash risk. In contrast, the managerial myopia view argues that takeover protection insulates managers from the pressure of the takeover market and as a result managers are less likely to myopically withhold bad news from investors.

COUNT is the number of downside frequencies minus the number of upside frequencies. A downside (upside) frequency occurs when the firm-specific daily return exceeds 3.09 standard deviations below (above) the mean return over the fiscal year. NCSKEW is the negative skewness of firm-specific daily returns over the fiscal year. DUVOL is the log of the ratio of the standard deviations of down-day to up-day firm-specific daily returns over the fiscal year.

Table 4 Subsample analysis on the effect of product market competition. This table presents the regression results of product market competition on the relation between takeover protection and stock price crash risk. The sample includes 49,879 firm-year observations for the period 1980 to 1995 with valid data on the fitted values of HHI. Columns (1), (3), and (5) report the regression results for the subsample facing strong product market competition. Columns (2), (4), and (6) report the regression results for the subsample facing weak product market competition. Subsamples are obtained by portioning the sample using the median fitted value of HHI (Hoberg & Phillips, 2010). Variable definitions are provided in Appendix A. All regressions use White's heteroskedasticity-consistent standard errors, and include firm and year fixed effects. P-values are reported in parentheses. COUNTT + 1

AfterBCT NCSKEWT KURTT SIGMAT RETT SIZET MBT LEVT ROAT DTURNT ACCRT Constant Firm fixed effects Year fixed effects Obs. Adjusted R2

NCSKEWT + 1

DUVOLT + 1

Strong PMC

Weak PMC

Strong PMC

Weak PMC

Strong PMC

Weak PMC

(1)

(2)

(3)

(4)

(5)

(6)

−0.180⁎⁎⁎ (0.003) −0.056⁎⁎⁎ (0.000) −0.003⁎⁎⁎ (0.000) 0.203⁎⁎ (0.029) 0.125 (0.164) 0.291⁎⁎⁎

−0.017 (0.749) −0.037⁎⁎⁎

−0.043⁎ (0.087) −0.110⁎⁎⁎

−0.025⁎⁎ (0.049) −0.026⁎⁎⁎

(0.000) −0.002 (0.834) 0.122 (0.893) 0.355⁎⁎⁎ (0.000) 0.274⁎⁎⁎

(0.000) −0.008 (0.647) 0.110 (0.135) 0.059 (0.362) 0.239⁎⁎⁎

−0.020 (0.696) −0.129⁎⁎⁎ (0.000) −0.006⁎⁎⁎ (0.000) 0.416 (0.585) 0.064 (0.407) 0.316⁎⁎⁎

(0.000) 0.179⁎⁎⁎

−0.014 (0.566) −0.041⁎⁎⁎ (0.000) −0.002⁎⁎⁎ (0.000) 0.430 (0.726) 0.051 (0.438) 0.228⁎⁎⁎

(0.000) 0.013 (0.344) 0.305⁎⁎⁎

(0.003) 0.045⁎⁎⁎ (0.000) 0.462⁎⁎⁎

(0.000) 0.046⁎⁎⁎ (0.000) 0.474⁎⁎⁎

(0.000) 0.008 (0.446) 0.413⁎⁎⁎

(0.000) 0.020⁎⁎⁎ (0.003) 0.335⁎⁎⁎

(0.000) −0.002 (0.802) 0.393⁎⁎⁎

(0.004) 0.007 (0.934) 0.089 (0.210) 0.148 (0.129) −2.086⁎⁎⁎ (0.000) YES YES 24,768 0.086

(0.000) −0.027 (0.293) 0.316⁎ (0.057) 0.261⁎⁎ (0.037) −2.299⁎⁎⁎

(0.000) −0.002 (0.978) 0.278⁎ (0.011) 0.147 (0.126) −1.424⁎⁎⁎

(0.000) −0.157⁎⁎⁎ (0.000) 0.069 (0.170) 0.050 (0.330) −0.924⁎⁎⁎

(0.000) −0.081⁎ (0.098) 0.017 (0.446) 0.015 (0.779) −1.217⁎⁎⁎

(0.000) YES YES 25,111 0.091

(0.000) YES YES 24,768 0.134

(0.000) −0.104 (0.223) 0.039 (0.438) 0.023 (0.782) −1.821⁎⁎⁎ (0.000) YES YES 25,111 0.160

(0.000) YES YES 24,768 0.185

(0.000) YES YES 25,111 0.219

(0.000) −0.004 (0.551) 0.286⁎⁎⁎ (0.000) 0.169⁎⁎⁎

⁎ Statistical significance at the 10% level. ⁎⁎ Statistical significance at the 5% level. ⁎⁎⁎ Statistical significance at the 1% level.

Please cite this article as: Bhargava, R., et al., Takeover protection and stock price crash risk: Evidence from state antitakeover laws, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.08.021

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R. Bhargava et al. / Journal of Business Research xxx (2016) xxx–xxx

We measure the firm-specific daily return as the natural log of one plus the residual return from an expanded market and industry index model regression: r j;t ¼ a j þ β1; j r m;t−1 þ β2; j ri;t−1 þ β3; j r m;t þ β4; j r i;t þ β5; j r m;tþ1 þ β6; j r i;tþ1 þ ɛ j;t where rj ,t is the return on stock j in day t, rm,t is the return on the CRSP value-weighted market index in day t, and ri ,t is the return on the valueweighted industry index in the two-digit SIC industries. Takeover protection measure AfterBC is a dummy variable equal to one in the years after firm i's state of incorporation has passed BC laws, and zero otherwise. Control variables KURT is defined as the kurtosis of firm-specific daily returns in year t. SIGMA is defined as the standard deviation of firm-specific daily returns in year t. RET is defined as the average firm-specific daily returns in year t. SIZE is defined as the log of the market value of equity at the end of year t. MB is defined as the market value of equity divided by the book value of equity at the end of year t. LEV is defined as the book value of all liabilities divided by total assets at the end of year t. ROA is defined as income before extraordinary items divided by total assets at the end of year t. DTURN is defined as the average monthly share turnover over year t minus the average monthly share turnover over the previous year t − 1. ACCR is defined as the absolute discretionary accruals estimated using the modified Jones model (Dechow, Sloan, & Sweeney, 1995). References Alimov, A. (2014). Does product market competition discipline managers? Evidence from exogenous trade shock and corporate acquisitions. Working paper. available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2380514. Atanassov, J. (2013). Do hostile takeover stifle innovation? Evidence from antitakeover legislation and corporate patenting. The Journal of Finance, 68, 1097–1131. Ball, R. (2009). Market and political/regulatory perspectives on the recent accounting scandals. Journal of Accounting Research, 44, 207–242. Bebchuck, L., & Cohen, A. (2003). Firms' decisions where to incorporate. Journal of Law and Economics, 46, 383–425. Bebchuck, L., & Cohen, A. (2005). The costs of entrenched boards. Journal of Financial Economics, 78, 409–433. Bebchuck, L., Cohen, A., & Ferrell, A. (2009). What matters in corporate governance? Review of Financial Studies, 22, 783–827. Bebchuck, L., Cohen, A., & Wang, C. Y. (2013). Learning and the disappearing association between governance and returns. Journal of Financial Economics, 108, 323–348. Bertrand, M., & Mullainathan, S. (1999). Is there discretion in wage setting? A test using takeover legislation. The Rand Journal of Economics, 30, 535–554. Bertrand, M., & Mullainathan, S. (2003). Enjoying the quiet life? Corporate governance and managerial preferences. Journal of Political Economy, 111, 1043–1075. Callen, J. L., & Fang, X. (2013). Institutional investor stability and crash risk: Monitoring versus short-termism? Journal of Banking & Finance, 37, 3047–3063. Callen, J. L., & Fang, X. (2015a). Religion and stock price crash risk. Journal of Financial and Quantitative Analysis, 50, 169–195. Callen, J. L., & Fang, X. (2015b). Short interest and stock price crash risk. Journal of Banking & Finance, 60, 181–194. Chemmanur, T., & Tian, X. (2013). Do antitakeover provisions spur corporate innovation? Working paper. available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id= 1572219. Chen, J., Hong, H., & Stein, J. C. (2001). Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices. Journal of Financial Economics, 61, 345–381. Cohen, A., & Wang, C. Y. (2013). How do staggered boards affect shareholder value? Evidence from a natural experiment. Journal of Financial Economics, 110, 627–641. Collins, D., & Huang, H. (2011). Management entrenchment and the cost of equity capital. Journal of Business Research, 64, 356–362.

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Please cite this article as: Bhargava, R., et al., Takeover protection and stock price crash risk: Evidence from state antitakeover laws, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.08.021