Do aircraft perquisites cause CEOs to withhold bad news?

North American Journal of Economics and Finance 35 (2016) 189–202

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North American Journal of Economics and Finance

Do aircraft perquisites cause CEOs to withhold bad news?夽 Chia-Wei Huang a,∗, Chih-Yen Lin b,1 a

College of Management, Yuan Ze University, 135 Yuan-Tung Road, Taoyuan 32003, Taiwan Department of Economics, College of Social Sciences, Fu Jen Catholic University, No. 510, Zhongzheng Road, Xinzhuang Dist., New Taipei City 24205, Taiwan b

a r t i c l e

i n f o

Article history: Available online 3 November 2015

Keywords: Executive compensation Perquisites Corporate jets Management forecasts

a b s t r a c t This paper explores the relation between management forecasts and expensive perquisites. We investigate Yermack’s (2006) conjecture that managers withhold bad news in order to receive expensive perquisites. We provide direct evidence supporting Yermack’s (2006) conjecture. The frequency and magnitude of bad news release is greater than that of good news after the chief executive officer (CEO) first discloses aircraft perks. In addition, managers with greater numbers of disclosed perks are more inclined to withhold bad news. Additional subsample analyses provide further support for managerial bad news withholding behavior. © 2015 Elsevier Inc. All rights reserved.

1. Introduction After recent well publicized examples of financial fraud, the researchers, media, and investors have paid increased attention to executive compensation (Kuo, Lin, Lien, Wang, & Yeh, 2014; Tai, Lai, & Lin, 2014), especially perquisites awards. In 2008, the median value of chief executive officer (CEO) perks

夽 We wish to thank Sheng-Syan Chen, Donald Lien (the editor of the special issue), Keng-Yu Ho, David Yermack, seminar participants at Asia University, and conference participants at the 2014 Business Finance and Regional Economic Development Conference, the 2014 Taiwan Econometric Society Annual Conference, and the 2015 TFA Annual Meeting Conference for helpful comments and suggestions. We also thank Professor Sheng-Syan Chen and David Yermack for kindly providing us with the data. ∗ Corresponding author. Tel.: +886 3 4638800x6366. 1

E-mail addresses: [email protected] (C.-W. Huang), [email protected] (C.-Y. Lin). Tel.: +886 3 4638800x6366; fax: +886 3 4630377.

http://dx.doi.org/10.1016/j.najef.2015.10.009 1062-9408/© 2015 Elsevier Inc. All rights reserved.

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of 309 companies in the Standard and Poor’s 500 rose nearly 7% even as overall CEO compensation fell 7% to $7.6 million.2 This trend suggests that perk awards are a growing form of CEO compensation in U.S. capital markets. Yermack (2006), investigating 237 Fortune 500 companies, finds that companies whose CEOs have personal aircraft perquisites generate inferior shareholder returns. Shareholders may treat aircraft perks as an exacerbation of the agency problem, and thus be unwilling to see that the firm grants its CEO such perks. This can motivate the CEO to neutralize shareholders’ dissatisfaction with his perk awards. To accomplish this, the CEO can alter the content and/or timing of normal information flows through disclosing optimistic earnings forecasts or withholding impending bad news. When the CEO has incentives to mislead investors, he is more likely to mislead them though withholding of bad news than through disclosure of good news. This is because investors cannot perfectly infer whether a firm is silent because it has no news to report or because it is strategically withholding bad news (Dye, 1985; Jung & Kwon, 1988; Verrecchia, 1983). Thus silences often result from ignorance, and ignorance can be used in court to defend the absence of a disclosure. In contrast, it is relatively difficult for the CEO to defend a misleading explicit assertion by claiming to have been ignorant. Ge and Lennox (2011) confirm the above prediction and find that acquirers are more likely to mislead investors through withholding impending bad news than through issuing optimistic earnings forecasts. Ertimur, Sletten, and Sunder (2014) find that IPO firms delay bad news disclosures until the earnings announcement in the lockup expiration quarter, in order to boost their stock selling prices. Therefore, we expect that withholding bad news is a more attractive option for a CEO who wants to keep the firm’s performance artificially high in anticipation of the reward of perks. Yermack (2006) finds that firms more frequently report quarterly earnings per share below the mean of analyst forecasts after the first award of the CEO aircraft perk. He conjectures that “managers withhold bad news from the marketplace and create an illusion of stronger performance in the years before they receive personal access to company planes. After this perk is secured, the flow of news to shareholders and analysts becomes less favorable than before. . .” (Yermack, 2006, p. 240). To investigate Yermack’s conjecture, we look at management earnings forecasts and empirically test whether the CEO withholds impending bad news before the disclosure of the aircraft perk. Kothari, Shu, and Wysocki (2009) hypothesize that the magnitude of the negative stock price reaction to bad news forecasts is greater than that of the positive stock price reaction to good news forecasts because managers accumulate and withhold bad news up to a certain threshold. If CEO withholds bad news before the first grant of the CEO aircraft perk, we would expect the stock market reactions to be larger for management forecasts of bad news than management forecasts of good news after the first disclosure of aircraft perks. We collect 3399 management forecasts from 192 Fortune 500 companies that disclosed CEO personal aircraft perks during the period from 1997 to 2006. We find evidence that the average stock market reaction is larger for management forecasts of bad news than for management forecasts of good news in the post-disclosure period, implying that the CEO withholds bad news prior to the first disclosure of aircraft perks. To ensure that the CEO withholding bad news is associated with the aircraft perks award, we further analyze the effect of the disclosed costs on the asymmetry in the disclosure of bad versus good news. Our evidence suggests that the CEO is more likely to withhold bad news when he receives more aircraft perk compensation. Researchers identify several factors that shape the content and/or timing of the issuance of management forecasts. Litigation risk can motivate managers to accelerate releases of bad news (Baginski, Hassell, & Kimbrough, 2002; Kasznik & Lev, 1995; Kothari et al., 2009; Skinner, 1994, 1997). High information asymmetry between managers and investors enables the CEO to hide bad news (Kothari et al., 2009). Laux’s (2008) model suggests that, when the CEO performs poorly (i.e., is of low ability), a

2 This information is available at http://www.manufacturing.net/news/2009/05/ceo-perks-rise-as-pay-falls. Perquisites generally are referred to as benefits outside of regular salary or wages. See http://www.merriam-webster.com/ dictionary/perquisite?show=0&t=1407389760.

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board with higher independence is more likely to fire the CEO. Since a CEO releases bad news regarding poor performance, this action of the CEO is more likely to be considered as signaling low ability by the board and the CEO will be more likely to be fired, especially under higher board independence. In order to secure his career, we predict that board independence can motivate the CEO to withhold bad news. Taken together, we expect that the asymmetry in the market’s reaction to good versus bad news increases for firms with lower litigation risk, higher information asymmetry, and greater board independence. In October 2000, the Securities and Exchange Commission (SEC) implemented the Regulation Fair Disclosure which prohibited firms from privately disclosing information to select audiences without simultaneously disclosing the same information to the public. This regulation can increase the cost of private information transfer from firms to favored audiences and potentially boost the market reaction to the issuance of management forecasts, thereby enhancing the incentive of managers to alter information flow (Ajinkya, Bhojraj, & Sengupta, 2005; Brockman, Khurana, & Martin, 2008). Therefore, we partition our sample into subsamples based on the extent of each factor that we discussed before, and then examine how these factors affect CEO withholding of bad news. Overall, our results are consistent with our predictions: firms with low litigation risk, high information asymmetry, and a high proportion of outside directors are more likely to withhold bad news. Moreover, this incentive is stronger in the post Regulation Fair Disclosure period. Yermack (2006) finds that firms exhibit stock underperformance after the year in which CEO personal aircraft use is first disclosed. Our study investigates the reason why firms offering aircraft perks have poor performance after perk disclosure. Specifically, we examine the association between the tendency to withhold bad news and subsequent abnormal stock returns. We find that after disclosure of aircraft perks, the market reacts more negatively to managers who withhold more information about bad news before perk disclosure. We focus on disclosed private plane use in an empirical investigation of perks for two reasons. First, based on the reporting requirement of the SEC, companies need only report perks above certain thresholds. CEO personal aircraft amounts are more likely to cross this perk reporting threshold than other perks. Accordingly, managers are more likely to strategically withhold bad news to justify their access to personal plane usage. We thus expect the motivation to withhold is more pronounced than with other perks. Second, “empirical tests related to plane use are likely to have higher statistical power than tests related to other perks” (Yermack, 2006, p. 212). Therefore, this study focuses on CEO plane use in order to identify the cause and effect relationship between management forecasts and perk consumption. Our study contributes to the literature in several ways. First, we complement the understanding of the use of management forecast for obtaining perk compensation. Many empirical studies analyze perks either from the determinants or from the consequences (Andrews, Linn, & Yi, 2009; Grinstein, Weinbaum, & Yehuda, 2011; Rajan & Wulf, 2006; Yermack, 2006). However, no study to date has empirically examined how managers justify their perks. Our study fills this gap by showing that the managerial tendency to withhold bad news is one of the important ways that managers obtain perks. Second, our findings also contribute to the management forecast literature on managerial compensation, which mainly emphasizes the equity-based compensation (Aboody & Kasznik, 2000). However, few studies explore nonmonetary (nonfinancial) compensation, such as perks. Finally, our study provides direct evidence to support Yermack’s (2006) conjecture and findings. The remainder of this paper is organized as follows. In Section 2, we describe the sample construction. In Section 3, we describe our methodology. In Section 4, we present the empirical results. We conclude in the final section.

2. Sample The initial sample comprises 237 Fortune 500 firms constructed by Yermack (2006) from the period 1993 to 2002. To be included in the sample, a firm must be included in the 2002 Fortune 500 list and have non-missing executive compensation data in ExecuComp from 1996 to 2002. We then extend

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Table 1 Sample description. Panel A: Perk sample distribution by year Year

Number of observations

Percentage

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

7 10 6 14 17 16 25 25 44 28 192

3.65 5.21 3.13 7.29 8.85 8.33 13.02 13.02 22.92 14.58 100.00

Panel B: Firm characteristics Variables

Observations

Disclosed cost ($) Litigation (%) Information asymmetry Board independence (%)

147 192 192 188

Mean 76,008.85 5.01 3.70 73.12

Median 59,315.00 3.70 2.76 75.00

Sample firms are those which first disclosed CEO personal aircraft usage based on the sample of 237 Fortune 500 firms of Yermack (2006). Disclosed cost is the amount of money spent on CEO personal aircraft perks documented in the proxy statements. Litigation is the predicted probability of litigation using the coefficient estimates in Rogers and Stocken (2005). Information asymmetry is the factor analysis that extracts one underlying factor from five variables potentially measuring information asymmetry in Kothari et al. (2009): Market-to-book ratio, stock volatility, leverage ratio, high-tech industry membership, and regulatory status. Board independence is the fraction of number of board seats held by outsiders.

the sample to 2006 by manually collecting CEOs’ perquisite consumption from proxy statements filed in the SEC’s Electronic Data-Gathering, Analysis, and Retrieval system (EDGAR).3 We next merge these data with the First Call Company Issued Guidelines database that comprehensively covers management forecasts starting from January 1995. We include all forecasts for quarterly earnings and annual earnings.4 If multiple forecasts occurred in the same firm on the same day, we treat those forecasts as a single forecast event. However, to analyze the effect of these multiple forecasts on the CEO incentive to withholding bad news, we create an indicator variable to capture this situation. Next, we retrieve the following information from on-line databases covered by Wharton Research Data Services (WRDS). We obtain financial statement data from Standard and Poor’s Compustat, executive compensation data from Standard and Poor’s ExecuComp, stock prices and outstanding shares from the Center for Research in Stock Prices (CRSP), analysts’ forecasts from the Institutional Brokers Estimate System (I/B/E/S), and governance-related data from RiskMetrics (formerly IRRC). Finally, we restrict our sample to the period from 1997 to 2006 because this ensures sufficient data for observing management forecasts within two-year windows before and after the first disclosure year of the aircraft perk. This enables us to make a comprehensive comparison with Yermack (2006). Table 1 shows the sample characteristics of this study. Panel A of Table 1 reports the distribution of initial disclosure of CEO private aircraft perks categorized by calendar year. Consistent with Yermack (2006), after the terrorist attack on September 11, 2001, corporations seem to reward CEOs with private access to corporate planes more frequently. Panel B demonstrates the descriptive characteristics of

3 Although it appears our sampling method may introduce survivorship bias into the sample, all conclusions remain unchanged if we restrict our analyses to 2002 rather than 2006. 4 Cheng and Lo (2006) include all types of management forecasts: annual and quarterly forecasts of earnings, cash flows, or other performance measures (such as revenues). They note that more than 99% of the forecast days in their sample contain an earnings forecast.

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variables used in this study for firms reporting CEO aircraft usage. The firms have an average outside director proportion of 73.12%. 3. Methodology 3.1. The withholding bad news behavior prior to the first disclosure of aircraft perks We examine how CEOs strategically time information flows surrounding the first disclosure of aircraft perks by investigating the magnitude of stock market reactions for management forecasts before and after the two-year window for which CEO personal aircraft use is initially disclosed. We further classify the management forecast as good (bad) news if the news content of management’s forecast is positive (negative) (Kothari et al., 2009). The news content is the difference between management’s forecast of earnings per share (EPS) and analysts’ most recent consensus forecast, and divided by the absolute value of the analysts’ consensus forecast.5 Kothari et al. (2009) indicate that the stock market reactions are larger for management forecasts of bad news compared to management forecasts of good news, because bad news is withheld and accumulated by managers while good news is frequently leaked to the market. Therefore, we expect that the stock market reaction to bad news will be greater than the stock market reaction to good news after the year in which CEO personal aircraft use is initially disclosed. To test our prediction, we use all management forecasts issued in the two-year window after the year in which CEO personal aircraft use is initially disclosed and regress the three-day cumulative abnormal stock returns around management forecasts on the bad news dummy. The baseline regression equation is: Ret = ˛ + ˇ0 Bad + ˇ1 Multi + j + ıt + ε

(1)

where Ret is the three-day cumulative abnormal stock returns around the management forecast, measured as the sum of the excess returns over the CRSP value-weighted index returns around the window (−1, 1) relative to management forecast, with day 0 being the day of management forecast. For comparison of magnitude of stock market reactions between good news and bad news, Bad is a dummy variable that equals −1 for bad news and 0 otherwise.6 Multi is an indicator which is equal to 1 if multiple forecasts are issued by the same firm on the same day. All regressions include dummy variables  j and ıt to control for industry and year fixed effects, respectively. Industry dummies are based on the two-digit Standard Industrial Classification (SIC) code.7 To control the content (i.e., “magnitude”) of the management forecasts, we include the magnitude of the management forecasts in the regression. Specifically, we construct the following regression equation: Ret = ˛ + ˇ0 Bad + ˇ1 Multi + ˇ2 FR + j + ıt + ε

(2)

where FR (forecast revision) is the difference between management’s forecast and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast. 3.2. The effect of disclosed cost on management forecasts We investigate the effect of the disclosed cost of CEO aircraft perk on his withholding behavior. In other words, the incentive to withhold bad news is likely to increase in the disclosed costs of aircraft perks. We thus expect that the asymmetry in the market’s reaction to good versus bad news increases

5 For robustness, we also classify the management forecast as good (bad) news if the abnormal return around the management forecast is positive (negative). The abnormal return is calculated as the excess returns over the CRSP value-weighted index over the three-day window (−1, 1) around the management forecast (Brockman et al., 2008; Shroff et al., 2013). The results are qualitatively similar. 6 We obtain similar results when we use a bad news indicator that equals 1 for bad news with management forecast revision. 7 The results are similar when industry is defined as Fama and French’s 48-industry classification.

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as the disclosed cost increases. The regression equation is Ret = ˛ + ˇ0 Bad + ˇ1 High Disclosed Cost + ˇ2 Bad High Disclosed Cost + ˇ3 Multi + ˇ4 FR + j + ıt + ε

(3)

where High Disclosed Cost is a dummy variable which is equal to 1 for firms’ disclosed costs in the top half of the sample. We expect the coefficient on the interaction between Bad and High Disclosed Cost to be positive. 3.3. The effect of other factors on the incentives to withhold bad news In previous studies, many factors affect the CEO’s incentives to withhold bad news. The CEO has greater incentive to withhold bad news when firms exhibit lower litigation risk, high information asymmetry, and high board independence. To test these arguments, we partition our sample into two subsamples (based on the median of the factor) and re-estimate Eq. (3) for each subsample separately. The litigation risk measure is calculated using the coefficient estimates in Rogers and Stocken (2005). The explanatory variables used in their model are primarily stock return based variables such as market value, stock turnover, beta, and volatility (Rogers & Stocken, 2005, p. 1257). All the variables are computed at the end of the fiscal year prior to the first disclosure of aircraft perks. Information asymmetry is based on a factor analysis that extracts one underlying factor from five variables potentially measuring information asymmetry identified in Kothari et al. (2009): MB ratio, stock volatility, leverage ratio, high-tech industry membership, and regulatory status. MB is the market-to-book ratio, measured as the market value of equity divided by book value of equity, and stock volatility is the standard deviation of daily stock returns in a one-year period ending two months prior to the first disclosure of aircraft perks. Leverage ratio is the ratio of long-term debt to total assets. High-tech firms are firms falling within the SIC codes 2833–2836, 3570–3577, 3600–3674, 7371–7379 and 8731–8734. Regulated industries (other than financial institutions) are defined as SIC codes 4812–4813, 4833, 4841, 4811–4899, 4922–4924, 4931, and 4941. Board independence is the proportion of outside directors, which is measured as the number of seats on a corporate board held by outsiders. Finally, the SEC implemented the Regulation Fair Disclosure (FD) on October 23, 2000, which can increase the cost of private information transfer from firms to the public and potentially boost the market reaction to the release of management forecasts, thereby encouraging the CEO to withhold bad news. We thus segment our sample into two subperiods: The post-FD period begins from November 2000 (the first month after FD’s effective date) and the pre-FD period includes observations prior to October 2000. We re-estimate Eq. (3) for each subperiod separately. 3.4. The effect of withholding bad news on stock performance Yermack (2006) finds a significant decline in abnormal stock returns after the year in which CEO personal aircraft use is initially disclosed. We examine whether this decline is attributable to the CEO withholding bad news prior to the disclosure. We thus use all the management forecasts issued within the two-year window after the year of first disclosure of aircraft perks and perform the following regressions: BHR = ˛ + ˇ0 CAR Bad + ˇ1 MB + ˇ2 Leverage + ˇ3 Size + j + ıt + ε

(4)

BHAR = ˛ + ˇ0 CAR Bad + ˇ1 MB + ˇ2 Leverage + ˇ3 Size + j + ıt + ε

(5)

where BHR is measured as the two-year buy-and-hold return for the firm following the year of the first disclosure of aircraft perks. BHAR is BHR minus the buy-and-hold return of size and book-to-market matching firms. Matching firms must be Fortune 500 firms and not have had aircraft perks and must have a market value of equity within 80–120% of the aircraft perk firm. Among all firms meeting these criteria, we then select a matching firm based on the closest book-to-market ratio to the aircraft perk firm. CAR Bad is the average of the market’s reaction to bad news for a firm’s CEO issuing forecasts

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Table 2 Stock market reactions in years around the first aircraft perk disclosure. Year relative to CEO’s first disclosed personal aircraft use −2

−1

0

+1

+2

Good news Observations Mean (%)

205 2.802

231 2.657

271 2.454

257 2.578

297 2.321

Median (%)

2.540

1.914

2.415

2.627

2.162

Bad news Observations Mean (%)

368 2.707

437 2.760

413 2.305

463 5.539

457 5.247

Median (%)

1.202

1.276

1.156

4.783

4.651

0.102 (0.848) −0.638 [0.136]

−0.148 (0.746) −1.260** [0.039]

2.961*** (0.000) 2.156*** [0.000]

2.926*** (0.000) 2.490*** [0.000]

Difference tests (bad–good) −0.095 Mean (%) (0.870) −1.338** Median (%) [0.014]

0 to +1

0 to +2

0.125 (0.784) 0.211 [0.698]

−0.133 (0.749) −0.254 [0.608]

3.234*** (0.000) 3.627*** [0.000]

2.942*** (0.000) 3.495*** [0.000]

The stock market reaction is measured as the three-day cumulative abnormal stock returns around the management forecast. For ease of comparison, the reported statistics for the bad news are computed by the market reactions that are multiplied by −1. We classify the management forecast as good (bad) news if the news content of management’s forecast positive (negative). The news content is the difference between management’s forecast of EPS and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast. For comparisons between subgroups of good news and bad news, we use a two-sided t-test to test the difference in means, and the Kruskal–Wallis test for the difference between medians. Numbers in the parentheses (brackets) are the p-values for the mean (median) test. ** and *** denote statistical significance at 5% and 1%, respectively.

within a two-year period following the year of the first disclosure of aircraft perks.8 MB is measured as the market value of equity divided by book value of equity, at the fiscal year preceding the disclosure date of aircraft perks. Leverage is measured as long-term debt divided by total assets at the year prior to the first disclosure year. Size is the natural logarithm of market value of equity as of the fiscal year preceding the disclosure date of aircraft perks. 4. Empirical results 4.1. Univariate analyses Table 2 reports the market reactions of management forecasts for years −2 to +2 relative to the year of first disclosure of aircraft perks. For ease of comparison, the reported statistics for the bad news are computed by the market reactions that are multiplied by −1. In the post-disclosure period, the mean of the market reaction is 5.539% (5.247%) for bad news forecasts, compared with 2.578% (2.321%) for good news forecasts in the first (second) year following the year of first disclosure of aircraft perks. These differences are statistically significant at the 1% level. When we compare the median difference between good news and bad news, we find the Kruskal–Wallis tests are statistically significant at the 1% level in both the first and second years following the first disclosure year. However, the univariate results are not statistically significant for the years preceding the first disclosure year and first disclosure year itself.9 Our results in Table 2 show the stock market reacts asymmetrically to

8 The results are similar when CAR Bad is measured as the average of difference between the market’s reaction to bad and good news for a firm’s CEO issuing forecasts within a two-year period following the year of the first disclosure of aircraft perks. 9 In untabulated tests, we partition the management forecasts issued in the year of first disclosure of aircraft perks into preand post-disclosure subsamples based on the perk disclosure date, and compare the magnitude of the market reaction to good versus bad news for each subsample separately. We find no significant difference in either subsample.

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(Frequency)

bad and good news in the years after the CEO first discloses personal aircraft use. The magnitude of the bad news forecast is larger than that of the good news forecast. Fig. 1 depicts the frequency of news types in years around the first aircraft perk disclosure. If managers postpone bad news after they receive access to private jet perks, we expect to observe more bad news releases in subsequent periods. Fig. 1 supports our conjecture. We observe that more bad news is released after the initial aircraft perks. These results are consistent with the contention that the CEO misleads investors by withholding impending bad news before the year of first disclosure of aircraft perks. Interestingly, from Table 2, there are 257 (297) good news forecasts in the first (second) year following the first disclosure year that are larger than or equal to good news forecasts in the year preceding and the year of the first disclosure. Thus, the higher frequency of good news forecasts following the first disclosure year is contrary to the argument that CEOs issue more good news forecasts during the pre-disclosure period in order to justify their aircraft perks. We chart the average market reactions to good and bad news around the years of the first disclosure of aircraft perks in Fig. 2. The mean of market reactions to bad news is stable in the years preceding and the year of first disclosure, and the reaction is larger following the first disclosure year. Unlike

500 450 400 350 300 250 200 150 100 50 0

-2

0

-1 Good

1

2 (Year)

Bad

Fig. 1. Frequency of news types in years around the first aircraft perk disclosure. This figure shows the frequency of news types in years around the first aircraft perk disclosure. Sample consists of 192 CEOs of Fortune 500 companies which first disclose CEO aircraft perks. We classify the management forecast as good (bad) news if the news content of management’s forecast positive (negative). The news content is the difference between management’s forecast of EPS and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast.

8 6 4

(CAR %)

2 0 -2 -4 -6 -8 -2

-1

0

Good

1

2

(Year)

Bad

Fig. 2. Stock market reactions in years around the first aircraft perk disclosure. This figure presents the average stock market reactions to good and bad news in years around the first aircraft perk disclosure. Sample consists of 192 CEOs of Fortune 500 companies which first disclose CEO aircraft perks. CAR denotes the three-day cumulative abnormal returns around management forecasts. We classify the management forecast as good (bad) news if the news content of management’s forecast positive (negative). The news content is the difference between management’s forecast of EPS and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast.

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Table 3 Forecast revisions in years around the first aircraft perk disclosure. Year relative to CEO’s first disclosed personal aircraft use −2

−1

0

+1

+2

Good news Observations Mean (%)

205 12.126

231 11.889

271 11.517

257 11.839

297 10.590

Median (%)

4.294

4.545

4.082

3.846

2.985

Bad news Observations Mean (%)

368 11.988

437 13.556

413 12.762

463 18.455

457 19.277

Median (%)

4.969

4.598

4.688

7.182

7.299

1.667 (0.387) 0.052 [0.576]

1.246 (0.485) 0.606 [0.236]

6.616** (0.023) 3.336*** [0.000]

8.686** (0.020) 4.314*** [0.000]

Difference tests (bad–good) −0.138 Mean (%) (0.930) 0.675 Median (%) [0.232]

0 to +1

0 to +2

0.322 (0.913) −0.235 [0.547]

−0.926 (0.729) −1.097*** [0.004]

5.693** (0.025) 2.495*** [0.000]

6.514** (0.039) 2.612*** [0.000]

Forecast revision is the difference between management’s forecast and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast. For ease of comparison, the reported statistics for the bad news are computed by the forecast revisions that are multiplied by −1. We classify the management forecast as good (bad) news if the news content of management’s forecast positive (negative). The news content is the difference between management’s forecast of EPS and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast. For comparisons between subgroups of good news and bad news, we use a two-sided t-test to test the difference in means, and the Kruskal–Wallis test for the difference between medians. Numbers in the parentheses (brackets) are the p-values for the mean (median) test. ** and *** denote statistical significance at 5% and 1%, respectively.

bad news, the mean of market reactions to good news forecasts is quite stable over the whole period. Taken together, our results show that the frequency of news, the news type, and the stock market reaction for bad news are larger in magnitude than for good news. Although we find some evidence for managers withholding bad news prior to access to corporate plane perks and for the stock market reacting more negatively to such behavior, the results of larger market reaction to bad news in Table 2 and Fig. 2 may also suggest that investors use a magnifier to decipher the disclosure behavior of CEO aircraft usage, thus contributing to the more negative market reaction. It is possible that the asymmetry in stock market reactions for issuing good versus bad news is purely attributable to the greater sensitivity of investors to bad news after the CEO receives aircraft perks. If this is the case, then the average of forecast revisions for the years before and after first perk disclosure will not be significantly different. Therefore, we investigate forecast revisions for years −2 to +2 years after the year of first disclosure of private jet perks. Table 3 presents the statistics for management forecast revisions around the first CEO aircraft perk disclosure. For ease of comparison, the reported statistics for the bad news are computed by the forecast revisions that are multiplied by −1. This table shows the mean of forecast revision is 18.455% (19.277%) for bad news compared with 11.839% (10.590%) for good news in the first (second) year following the year of first disclosure of aircraft perks. This result is inconsistent with the argument that shareholders use a magnifier to decipher such strategic behavior. Rather, it demonstrates that managers tend to withhold bad news and that such behavior leads to more negative forecast revision. This results in a greater negative market reaction, as reported in Table 2, for the bad news forecast after the year of first disclosure of aircraft perks. We also chart the forecast revisions to good and bad news around the years of the first disclosure of aircraft perks in Fig. 3. The mean of forecast revisions for bad news is stable in the years preceding and the year of first disclosure, and the revision is larger following the first disclosure year. Unlike the results for bad news, the mean of forecast revisions for good news forecasts is quite stable over the

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25 20

(Forecast revision %)

15 10 5 0 -5 -10 -15 -20 -25 -2

-1

0

Good

1

2

(Year)

Bad

Fig. 3. Forecast revisions in years around the first aircraft perk disclosure. This figure presents the average forecast revisions to good and bad news in years around the first aircraft perk disclosure. Sample consists of 192 CEOs of Fortune 500 companies which first disclose CEO aircraft perks. Forecast revision is the difference between management’s forecast and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast. We classify the management forecast as good (bad) news if the news content of management’s forecast positive (negative). The news content is the difference between management’s forecast of EPS and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast.

Table 4 Stock market reactions to post-disclosure management forecasts. (1)

(2)

Intercept

−0.017 (−0.192)

−0.010 (−0.101)

Bad

0.081*** (24.422)

0.069*** (16.723)

Multi

−0.001 (−0.186)

−0.001 (−0.065) 0.054*** (4.051)

FR Industry effect Year effect Observations Adjusted R2

Yes Yes 1474 0.317

Yes Yes 1474 0.330

The ordinary least square regressions are employed to estimate the coefficients from Eq. (1) Ret = ˛ + ˇ0 Bad + ˇ1 Multi +  j + ıt + ε or Eq. (2) Ret = ˛ + ˇ0 Bad + ˇ1 Multi + ˇ2 FR +  j + ıt + ε. The dependent variable (Ret) is the three-day cumulative abnormal stock returns around the management forecast. For comparison of magnitude of stock market reactions between good news and bad news, Bad is a dummy variable that equals −1 for bad news and 0 otherwise. We classify the management forecast as good (bad) news if the news content of management’s forecast positive (negative). Multi is an indicator which is equal to 1 if multiple forecasts are issued by the same firm on the same day. FR is the difference between management’s forecast of EPS and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast. Numbers in the parentheses are the tstatistic. Standard errors are heteroskedasticity-consistent and allow for cluster at firm level. *** denotes statistical significance at 1%.

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whole period. This phenomenon indicates that managers withhold bad news until they receive access to private jet perks, thus contributing to the bigger forecast revision than with good news. 4.2. Regression results The baseline regression results for Eqs. (1) and (2) are reported in Table 4. Column (1) shows the results estimated using Eq. (1), while column (2) demonstrates the results estimated using Eq. (2). Column (1) of Table 4 shows that the dummy variable Bad is statistically significant at the 1% level. This result indicates that the market reaction to the management forecast of bad news is larger than to a management forecast of good news, which is an asymmetry. To control for the content of management forecast, we include the variable FR in Eq. (2). Results are reported in column (2), and are qualitatively similar to the results in column (1). We test the effect of disclosed cost of CEO personal aircraft use on the withholding behavior in Table 5. Column (1) presents the results of Eq. (3) without controlling for management forecast content (i.e., forecast revision), while column (2) adds control variables. In column (1), the dummy variable Bad is positive and statistically significant at the 1% level, which indicates that the stock market reacts more strongly to bad news than to good news. More importantly, the interaction term between Bad and High Disclosed Cost is positive and statistically significant at the 1% level, which shows such asymmetric reactions to bad news are greater with higher disclosed perk cost. The results of adding the forecast revision to column (2) are qualitatively similar to those in column (1). Taken together, the results in Table 5 show that CEOs are more inclined to withhold bad news when they expect the asymmetry in the market’s reaction to good versus bad news to increase with the disclosed cost. Table 5 The effect of disclosed cost on post-disclosure management forecasts. (1)

(2)

Intercept

0.145*** (5.459)

0.154*** (6.096)

Bad

0.062*** (10.379)

0.053*** (8.090)

High disclosed cost

−0.014* (−1.920)

−0.011 (−1.405)

High disclosed cost × bad

0.026*** (3.249)

0.026*** (3.316)

Multi

−0.003 (−0.660)

−0.003 (−0.705) 0.044*** (2.671)

FR Industry effect Year effect Observations Adjusted R2

Yes Yes 1063 0.366

Yes Yes 1063 0.376

The ordinary least square regressions are employed to estimate the coefficients from the equation Ret = ˛ + ˇ0 Bad + ˇ1 High Disclosed Cost + ˇ2 Bad High Disclosed Cost + ˇ3 Multi + ˇ4 FR +  j + ıt + ε. The dependent variable (Ret) is the three-day cumulative abnormal stock returns around the management forecast. For comparison of magnitude of stock market reactions between good news and bad news, Bad is a dummy variable that equals −1 for bad news and 0 otherwise. We classify the management forecast as good (bad) news if the news content of management’s forecast positive (negative). High Disclosed Cost is an indicator which is equal to 1 for firms’ disclosed costs in the highest half of sample. Multi is an indicator which is equal to 1 if multiple forecasts are issued by the same firm on the same day. FR is the difference between management’s forecast of EPS and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast. Numbers in the parentheses are the t-statistic. Standard errors are heteroskedasticity-consistent and allow for cluster at firm level. * and *** denote statistical significance at 10% and 1%, respectively.

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4.3. Subsample analyses In this section, we test whether CEOs under certain circumstances are more likely to withhold bad news in Table 6. We define a dummy variable High (Low) to denote whether a firm with the factor in question lies above (below) the whole sample median. All columns report the results of Eq. (3) for the subsamples. In the analyses of litigation risk subsamples, consistent with Kothari et al. (2009), managers are inclined to withhold bad news when litigation costs are low. In both subsamples, the Bad dummy is both positive and significant. However, the interaction term between Bad and High Disclosed Cost is only significantly positive at the 1% level in the low litigation risk subsample. This result suggests a strong asymmetry reaction to bad news versus good news with high disclosed cost under low litigation risk. In the analyses of information asymmetry subsamples, we find the interaction term between Bad and High Disclosed Cost is only significantly positive at the 1% level in the high information asymmetry subsample. Consistent with Kothari et al. (2009), managers are inclined to withhold bad news in high asymmetry circumstances. The result suggests a strong asymmetry reaction to bad news versus good news with high disclosed cost under high information asymmetry. Thus, managers with high disclosed cost are more inclined to withhold bad news for fear of disturbing investors under high information asymmetry.

Table 6 Subsample analyses. Litigation risk

Information asymmetry

Board independence

Regulation fair disclosure

High

Low

High

Low

High

Low

Pre

Post

Intercept

0.023 (0.778)

0.128*** (6.113)

0.035 (1.142)

0.164*** (8.481)

−0.022 (−1.125)

0.112** (2.548)

−0.144* (−1.909)

0.160*** (9.323)

Bad

0.059*** (5.130)

0.044*** (5.282)

0.063*** (6.224)

0.041*** (4.391)

0.045*** (6.161)

0.071*** (5.729)

0.146* (1.773)

0.050*** (8.192)

High disclosed cost

−0.011 (−0.814)

0.006 (0.551)

−0.028** (−1.979)

−0.004 (−0.381)

−0.010 (−1.035)

0.009 (0.459)

−0.206* (−1.794)

−0.009 (−1.154)

High disclosed cost × bad

0.009 (0.722)

0.036*** (3.114)

0.034*** (2.811)

0.018 (1.632)

0.051*** (4.786)

−0.004 (−0.347)

−0.140 (−1.158)

0.027*** (3.530)

Multi

−0.001 (−0.091)

−0.006 (−1.058)

−0.004 (−0.583)

−0.002 (−0.411)

−0.004 (−0.690)

−0.007 (−1.012)

−0.015 (−0.436)

−0.002 (−0.514)

FR

0.030 (1.282)

0.075*** (2.617)

0.036 (1.397)

0.068*** (3.066)

0.040** (2.326)

0.047 (1.572)

−0.059 (−0.688)

0.061*** (4.463)

Industry effect Year effect Observations Adjusted R2

Yes Yes 500 0.365

Yes Yes 563 0.396

Yes Yes 509 0.410

Yes Yes 554 0.342

Yes Yes 504 0.450

Yes Yes 536 0.356

Yes Yes 58 0.191

Yes Yes 1005 0.39

The ordinary least square regressions are employed to estimate the coefficients from the equation Ret = ˛ + ˇ0 Bad + ˇ1 High Disclosed Cost + ˇ2 Bad High Disclosed Cost + ˇ3 Multi + ˇ4 FR +  j + ıt + ε. The dependent variable (Ret) is the three-day cumulative abnormal stock returns around the management forecast. Litigation risk is the predicted probability of litigation using the coefficient estimates in Rogers and Stocken (2005). Information asymmetry is the product from the factor analysis using variables from Kothari et al. (2009). Board independence is the fraction of board seats held by outsiders. High (Low) is a dummy variable that equals one if a firm with the factor in the question lies above (below) the sample median. Pre (Post) stands for the period before (after) the enactment of Regulation Fair Disclosure. For comparison of magnitude of stock market reactions between good news and bad news, Bad is a dummy variable that equals −1 for bad news and 0 otherwise. We classify the management forecast as good (bad) news if the news content of management’s forecast positive (negative). High Disclosed Cost is an indicator which is equal to 1 for firms’ disclosed costs in the highest half of sample. Multi is an indicator which is equal to 1 if multiple forecasts are issued by the same firm on the same day. FR is the difference between management’s forecast of EPS and analysts’ most recent consensus forecast, divided by the absolute value of the analysts’ consensus forecast. Numbers in the parentheses are the t-statistic. Standard errors are heteroskedasticity-consistent and allow for cluster at firm level. *, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

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In the subsample analyses of board independence, we find the interaction term between Bad and High Disclosed Cost is only significantly positive at the 1% level in the high board independence subsample. This result suggests a strong asymmetric reaction to bad news with high disclosed cost when board independence is high. In the subsample analyses based on the passage of the Regulation Fair Disclosure, our results suggest a strong asymmetric reaction to bad news with high disclosed cost after the passage of the Regulation Fair Disclosure. The interaction term between Bad and High Disclosed Cost is only significantly positive at the 1% level in the post regulation subsample. Thus, managers with high disclosed cost are more inclined to withhold bad news for fear of disturbing investors after the implementation of the Regulation Fair Disclosure.

4.4. Annual buy-and-hold abnormal returns and withholding bad news Yermack (2006) shows corporations with CEO personal aircraft perks underperform more than 400 basis points per year. To explain his findings, we conjecture that CEOs withhold bad news to obtain their aircraft perks and that such bad news withholding behavior results in negative abnormal returns after disclosure of aircraft perks. We test whether the significant decline in stock returns can be attributed to the CEO withholding bad news prior to the disclosure of aircraft perks in Table 7. Columns (1) and (2) present the results of Eq. (4), while columns (3) and (4) present the results of Eq. (5). In all columns, the coefficients on CAR Bad are significantly positive at the 5% level or better. These results suggest a strong linkage between the decline in corporate stock performance and managerial withholding of bad news. The results are also qualitatively similar if we use other matching procedures to find matching firms or use the calendar time abnormal return approach.

Table 7 Association between the withholding of bad news and stock performance. BHR

BHAR

(1)

(2)

(3)

(4)

Intercept

−0.235 (−1.516)

3.279 (0.841)

0.081 (0.260)

−2.074 (−0.375)

CAR Bad

1.890*** (3.039)

1.834*** (2.779)

1.996** (2.224)

1.948** (2.138)

MB

−0.008 (−0.186)

−0.017 (−0.326)

Leverage

−0.434 (−0.945)

−0.181 (−0.283)

Size

−1.031 (−0.876)

0.671 (0.400)

Industry effect Year effect Observations Adjusted R2

Yes Yes 192 0.137

Yes Yes 192 0.131

Yes Yes 192 0.092

Yes Yes 192 0.109

The ordinary least square regressions are employed to estimate the coefficients from Eq. (4) BHR = ˛ + ˇ0 CAR Bad + ˇ1 MB + ˇ2 Leverage + ˇ3 Size + j + ıt + ε or Eq. (5) BHAR = ˛ + ˇ0 CAR Bad + ˇ1 MB + ˇ2 Leverage + ˇ3 Size + j + ıt + ε. The dependent variable is either two-year buy-and-hold return (BHR) or two-year buy-and-hold abnormal return (BHAR). CAR Bad is the average market reaction to the bad news. MB is measured as the market value of equity divided by book value of equity, at the fiscal year preceding the disclosure date of aircraft perks. Leverage is measured as long-term debt divided by total assets as of the fiscal year preceding the disclosure date of aircraft perks. Size is the natural logarithm of market value of equity as of the fiscal year preceding the disclosure date of aircraft perks. Numbers in the parentheses are the t-statistic. Standard errors are heteroskedasticity-consistent and allow for cluster at firm level. ** and *** denote statistical significance at 5% and 1%, respectively.

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