Managerial Characteristics and Corporate Cash Policy Keng-Yu Ho Department of Finance National Taiwan University Chia-Wei Yeh Department of Finance National Taiwan University December 3, 2014 Corresponding author: Keng-Yu Ho. E-mail: kengyuho@ntu.edu.tw. Address: No. 1, Sec. 4, Roosevelt Rd., Taipei City 10617, Taiwan (R.O.C.). Tel: 886-2-33661094. Fax: 886-2-33661094.
Managerial Characteristics and Corporate Cash Policy Abstract There has been a surge of research on corporate cash holdings in recent years. While most previous studies mainly focus on the relation between firm characteristics and corporate cash policy, few empirical studies explore the effects of managerial characteristics on corporate cash holdings. We contribute the related literature by considering the managerial characteristics, such as age, tenure, confidence, and education, and conduct empirical tests to investigate their effects on corporate cash policy. We find that firms managed by CEOs with longer tenure and by overconfident CEOs tend to have higher levels of cash holdings. However, firms managed by CEOs with MBA degree tend to preserve less cash. Our study shows mixed evidence on the relation between the age of managers and corporate cash holdings. Further analyses suggest that senior and longer-tenured CEOs are more likely to hold cash for precautionary motives, but overconfident CEOs do not preserve cash for such reason. In addition, CEOs with MBA degree seem to invest more when their firms have excess cash. Overall, this paper provides intriguing empirical evidence for the effects of managerial characteristics on corporate cash policy. Keywords: Cash holdings; Managerial characteristics; Speed of adjustment.
1 Introduction In recent years, there has been a surge of research on corporate cash holdings. Traditional economic theories provide different explanations on corporate cash holdings. The trade-off theory weighs the benefits and costs of holding cash, suggesting that firms set an optimal level of cash holdings to maximize their shareholders wealth. The agency theory motivated by the agency problem of free cash flow proposed by Jensen (1986) indicates the agency costs of holding cash, suggesting that managers may not act to increase shareholders wealth. The financing hierarchy theory of Myers and Majluf (1984) points out the information asymmetry between firms and investors makes equity financing costly, suggesting that firms have the motive to preserve internal funds when the information asymmetry is severe. These theories are extensively discussed and tested in the extant literature. The evidence that cash-rich firms are more likely to attempt acquisitions in Harford (1999) indicates the agency problem of cash holdings. His findings suggest that acquisitions by cash-rich firms are value destroying, providing a supportive evidence to the agency costs of free cash flow. Faulkender and Wang (2006) propose a framework for examining the marginal value of corporate cash holdings, and they find that the marginal value of cash declines with larger cash holdings. Following Faulkender and Wang (2006), Dittmar and Mahrt-Smith (2007) investigate the influence of corporate governance on the value of cash holdings. Their findings that poorly governed firms have lower value of cash support the agency costs of holding cash. Gao, Harford, and Li (2013) conduct empirical tests on the public and private U.S. firms, and their findings suggest that the agency problems affect substantially to corporate cash holdings. However, Harford, Mansi, and Maxwell (2008) find that poorly governed firms in the U.S. have smaller cash reserves than others. This finding seems to conflict with the agency motives of holding cash. They suggest that instead of hoarding the excess cash, managers of poorly governed firms tend to spend them quickly on acquisitions and capital expenditures. 1
Some studies investigate on corporate cash holdings from another perspective. Opler, Pinkowitz, Stulz, and Williamson (1999) propose a trade-off model of cash holdings and support it by empirical evidence. Their findings suggest that firms with strong growth opportunities and riskier cash flows hold more cash than other firms, but firms with greater access to the capital market tend to hold less cash. Bates, Kahle, and Stulz (2009) aim to explain for the increase of cash holdings for the U.S. firms from 1980 to 2006. Their findings support the precautionary role of cash holdings in explaining the increase but they find no consistent evidence that agency motives contribute to the increase. A substantial literature has discussed and debated on the determinants of cash holdings, and primarily the precautionary motives and the agency motives are cited to explain for the variation in corporate cash holdings. However, the extant research mainly focuses on the influence of firm characteristics on corporate cash policy. Few empirical studies explore the effects of managerial characteristics on corporate cash holdings. Some managers may not rationally preserve cash at the level that maximize shareholders benefits, hence it would be important to research on whether the managerial bias affects corporate cash holdings. Orens and Reheul (2013) integrate the upper echelons theory with the traditional theories to explain the managers influence on corporate cash holdings. They use a sample of privately held small and medium enterprises in Belgium to examine the additional power of CEO demographics in explaining the variation in corporate cash holdings. Their findings that older CEOs and CEOs without other-industry experience tend to preserve higher cash levels support the additional explanatory power of CEO characteristics. The purpose of this study is to contribute the related literature from a managerial characteristic perspective. Similar to Orens and Reheul (2013), we consider the managerial characteristics, such as age, tenure, confidence, and education, and conduct empirical tests to investigate their effects on corporate cash policy. However, our study based on a large dataset of the S&P 1,500 U.S. companies, provides more complete analyses than Orens and Reheul (2013). We investigate on not only the relation between managerial characteristics 2
and corporate cash holdings, but also the speed of adjustment to firms target cash levels to explain whether the difference in level of cash holdings is due to precautionary motives. Our empirical results show that firms managed by CEOs with longer tenure and by overconfident CEOs tend to have higher levels of cash holdings. However, firms managed by CEOs with MBA degree tend to preserve less cash. The evidence on the relation between the age of managers and corporate cash holdings is mixed, where senior CEOs and junior CEOs both tend to hold higher levels of cash in comparison with average-aged CEOs. Our further analyses suggest that senior CEOs and longer-tenured CEOs are more likely to hold cash for precautionary motives but overconfident CEOs do not preserve cash for the same reason. In addition, CEOs with MBA degree seem to invest more when their firms have excess cash. The remainder of this paper is organized as follows. Section 2 develops the hypotheses to be tested. Section 3 describes the sample construction and the managerial characteristic measures, as well as providing summary statistics for the sample. Section 4 presents the empirical results. Section 5 concludes. 2 Hypothesis Development Being distinct from previous studies which mainly focus on the effects of firm characteristics on corporate cash policy, this paper aims to provide explanation for corporate cash holdings from the managerial characteristic perspective. We develop hypotheses related to the managerial characteristics, such as age, tenure, confidence and education, and consider the influences of these characteristics on their corporate cash holding decisions. Managers may behave differently during their lifetime. Older managers are expected to attach more value to the benefits of holding cash and to grant less value to the opportunity costs of holding cash. Hambrick and Mason (1984) suggest that older managers are more risk averse and conservative than younger managers because of their career and financial security 3
concerns. Hence, older managers tend to preserve more cash for their firms in order to hedge against future uncertainties. Moreover, older managers have shorter career horizon, and they may not conduct long-term investments which do not personally benefit them. Barker and Mueller (2002) find that the research and development (R&D) spending is greater at firms where CEOs are younger, indicating that older CEOs are less likely to spend money on longterm investments. Yim (2013) also suggests that CEOs incentives to pursue acquisitions will decline with their career horizon, which is supported by empire-building theories. Therefore, we infer that older managers are less concerned with the opportunity costs of not investing their cash. From the trade-off model perspective, older CEOs may set higher levels of cash holdings since they underestimate the costs of cash holdings. Based on these arguments, we suggest the following hypothesis: Hypothesis 1. The cash holdings are higher for firms managed by older CEOs. Managers also behave differently as their position tenure increase. Longer-tenured managers are expected to pay less attention to the opportunity costs of holding cash than shorter tenured managers. Hambrick, Geletkanycz, and Fredrickson (1993) suggest that CEOs with short position tenure are more likely to consider available alternatives, and are more open to change and experimentation than CEOs with long tenure. Following this argument, Orens and Reheul (2013) infer that shorter-tenured CEOs may identify more investment opportunities and are more aware of the opportunity costs of not investing firms cash. Hence, from the trade-off model perspective, shorter-tenured managers seem to set lower cash levels since they overestimate the costs of cash holdings and invest more. Orens and Reheul (2013) also suggest that longer-tenured CEOs incentives are more likely to be aligned with those of shareholders. Therefore, from the agency conflict perspective, longer-tenured CEOs may be allowed to use higher cash levels to their own discretion. Based on these arguments, we suggest the following hypothesis: Hypothesis 2. The cash holdings are higher for firms managed by longer-tenured CEOs. 4
Recently, an increasing number of empirical studies suggest that overconfident CEOs affect the financing decisions and the investment decisions of their firms. We posit that the financing decisions and the investment decisions taken by overconfident CEOs influence their corporate cash holdings, and firms managed by overconfident CEOs are expected to have higher cash levels. Malmendier and Tate (2005) suggest that overconfident managers tend to overestimate the future cash flows of their investment projects and view external funds as costly, hence they overinvestment when their firms have abundant internal funds, but curtail investment when they require external financing. Malmendier, Tate, and Yan (2011) suggest that overconfident managers believe their firms are undervalued and may view external financing as overpriced. Ben-David, Graham, and Harvey (2013) argue that overconfident managers tend to underestimate the volatility of their firms future cash flows because they overestimate their ability to predict the future. Their findings further suggest that firms with overconfident CFOs tend to follow more aggressive corporate policies such as investing more and using more debt financing. These recent studies on managerial overconfidence have documented overconfident managers preference for internal funds over costly external financing when conducting investment projects, we infer that they are more aware of the benefits of holding internal funds. Hence, from the trade-off model perspective, overconfident managers are more likely to set higher cash levels since they overestimate the benefits of cash holdings. Overconfident managers preference for internal cash may also be explained from the financing hierarchy perspective. Based on these arguments, we suggest the following hypothesis: Hypothesis 3. The cash holdings are higher for firms managed by overconfident CEOs. Besides CEO age, tenure and confidence, we also investigate the effect of the level of CEO education on corporate cash holdings. We especially focus on whether or not does CEO have formal professional education, the MBA degree in particular. We expect that MBAs are educated to develop innovative tendency and will be more open to new ideas and investment opportunities, which is in line with the argument in Orens and Reheul (2013). 5
Thus, CEOs with MBA degree will be better aware of the opportunity costs of not investing in profitable projects. Bertrand and Schoar (2003) find that managers with MBA degree seem to follow more aggressive strategies and are less conservative. Hence, from the trade-off model perspective, we may infer that CEOs with MBA degree tend to set lower levels of cash holdings since they overestimate the costs of cash holdings and are less concerned with the precautionary role of cash. Based on these arguments, we suggest the following hypothesis: Hypothesis 4. The cash holdings are lower for firms managed by CEOs with MBA degree. 3 Data 3.1 Sample Construction The sample consists of firms in the ExecuComp database from 1998 to 2012. 1 To be included in our sample, firms are required to report consistent CEO data with those obtained from the Capital IQ People Intelligence database. Specifically, both ExecuComp and Capital IQ should report the same CEO who is in charge of the sample firm in a specific fiscal year. Financial firms (SIC code 6000 6999) and utilities firms (SIC code 4900 4999) are excluded from the sample, because these firms are more regulated and have more different industrial traits. Our sample construction process yields a firm-year panel of 17,530 observations for 2,117 unique firms. The CEO profile data are retrieved from the Capital IQ People Intelligence, and the CEO compensation data for measuring managerial overconfidence are obtained from the ExecuComp database. We collect financial data, such as total assets, total debts, cash, cash flow, sales, capital expenditures, acquisition expenditures, R&D expenditures, dividend distribution, and stock repurchases, from the Compustat database. The RiskMetrics database 1 ExecuComp collects compensation data on top managers of the S&P 1,500 firms, which also includes data on CEO age and tenure. However, some start and end dates of CEO tenure may be misreported in ExecuComp. Capital IQ People Intelligence provides more complete data on CEO age and tenure, hence we use the CEO profile data obtained from Capital IQ instead. 6
provides corporate antitakeover provisions data for constructing the Gompers, Ishii, and Metrick (2003) G-index and the Bebchuk, Cohen, and Ferrell (2009) E-index. 3.2 Measuring Managerial Characteristics 3.2.1 CEO Age We identify CEOs as senior or junior based on their ages. CEOs are classified as junior if their ages are less than the 30th percentile (51 years old), and are classified as senior if their ages are greater than the 70th percentile (58 years old). The CEOs whose ages are between the 30th and 70th percentile are then classified as average-aged. Thus, we classify CEOs into three categories based on their ages. 3.2.2 CEO Tenure CEOs are identified as experienced or inexperienced based on their position tenure. Similar to the method for CEO age, CEOs are classified as inexperienced if their tenure are less than the 30th percentile (4 years), and are classified as experienced if their tenure are greater than the 70th percentile (9 years). The CEOs whose tenure are between the 30th and 70th percentile are then classified as average-tenured. Thus, we could also classify CEOs into three categories based on their position tenure. 3.2.3 CEO Overconfidence Since CEO overconfidence cannot be observed directly, the previous literature usually measures CEO overconfidence based on the actions taken by CEOs. Following Campbell, Gallmeyer, Johnson, Rutherford, and Stanley (2011) and Hirshleifer, Low, and Teoh (2012), we measure CEO overconfidence based on CEO s executive options holding decisions. Nonoverconfident CEOs may choose to exercise their exercisable executive options that are in the money. However, rather than exercising the exercisable in-the-money options, overconfi- 7
dent CEOs are more prone to hold them long, because they tend to overestimate the future profitability of their firms. The executive option moneyness is computed following Campbell et al. (2011). First, we estimate the average exercise price using a CEO s aggregated options data from ExecuComp. We compute the average realizable value as the estimated value of the unexercised exercisable options (ExecuComp item OPT UNEX EXER EST VAL) divided by the number of unexercised exercisable options (ExecuComp item OPT UNEX EXER NUM). We then subtract the average realizable value from the stock price at the fiscal year end (Compustat item PRCC F), and obtain the average exercise price of the exercisable options holding by a CEO. The average percent moneyness of the options holding by a CEO is then computed as the average realizable value divided by the average exercise price of the exercisable options. As in Hirshleifer et al. (2012), we classify CEOs as overconfident if they hold options that are greater than 67% in the money, and classify CEOs as non-overconfident if they do not hold in-the-money options that have moneyness greater than 67%. Futhermore, once CEOs are classified as overconfident, they retain the same classification forward in the sample period. Following this procedure, we are unable to classify some CEOs who do not have executive option data from ExecuComp. 3.2.4 CEO with MBA Degree The CEOs education records are available from Capital IQ People Intelligence. We simply classify CEOs as CEO with MBA degree if they have related records recorded in the database. CEOs who do not have any education record of MBA degree are then classified as CEO without MBA degree. 3.3 Summary Statistics Table 1 provides summary statistics for our sample. Panel A of Table 1 reports summary statistics of firm characteristics, containing cash and the control variables which are deter- 8
minants of corporate cash holdings, such as size, sales growth, cash flow, cash flow volatility, net working capital, leverage, capital expenditures, acquisition expenditures, and R&D expenditures. 2 These variables are all winsorized at the 1st and 99th percentile values. Since dividend and repurchase are indicator variables that take the value of one if a firm pays cash dividends or repurchases stocks, their mean represent the proportion of firms that initiate cash payouts through dividends or repurchases. G-index and E-index are corporate governance measures related to shareholder rights, that the higher index value implies a more entrenched manager and weaker shareholder rights for a firm. Since the antitakeover provisions data for constructing G-index and E-index are not provided every years, in order to maximize the number of observations, we fill in observations in missing years following Gompers et al. (2003) and Bebchuk et al. (2009). The summary statistics of CEO characteristics are reported in Panel B. The average (median) age of CEOs in the full sample is 55 (54) years old, and the average (median) position tenure of CEOs in the full sample is 8 (6) years. Rest of the variables in Panel B are indicator variables that take the value of one if a firm is managed by CEO with the certain characteristic, hence their mean represent the proportion of firms in our sample that are managed by CEOs with particular characteristics. The untabulated results show that the average (median) age of the junior CEOs is 46 (47) years old, the average (median) age of the average-aged CEOs is 55 (55) years old, and the average (median) age of the senior CEOs is 63 (62) years old. The results also show that the average (median) position tenure of the inexperienced CEOs is 2 (2) years, the average (median) tenure of the average-tenured CEOs is 6 (6) years, and the average (median) tenure of the experienced CEOs is 17 (15) years. [Insert Table 1 about here] In Table 2, we present cash ratios over time for the subsample of firms managed by CEOs with different characteristics. Panel A of Table 2 reports the mean and median cash 2 Total assets are adjusted to 2012 dollars when computing the firm size. 9
ratios over time for firms classified by the age of their CEO, Panel B reports the results for firms classified by the tenure of their CEO, Panel C reports the results for firms classified by whether their CEO is overconfident, and Panel D reports the results for firms classified by whether their CEO has MBA degree. [Insert Table 2 about here] The univariate results presented in Table 2 indicate the difference in levels of cash holdings between firms managed by CEOs with different characteristics. Panel A shows that the average cash ratios for firms managed by junior CEOs are on average greater than the average cash ratios for firms managed by average-aged CEOs, and the average cash ratios for firms managed by senior CEOs are slightly higher than the average cash ratios for firms managed by average-aged CEOs. This result reveals a nonmonotonic relation between corporate cash holdings and CEO ages. Panel B shows that the average cash ratios for firms managed by experienced CEOs are on average greater than the average cash ratios for firms managed by average-tenured CEOs, and the average cash ratios for firms managed by average-tenured CEOs are greater than the average cash ratios for firms managed by inexperienced CEOs. The result reveals a positive relation between corporate cash holdings and CEO tenure. Panel C shows that the average cash ratios for firms managed by overconfident CEOs are slightly higher than the average cash ratios for firms managed by non-overconfident CEOs. Panel D shows that the average cash ratios for firms managed by CEOs with MBA degree are on average less than the average cash ratios for firms managed by CEOs without MBA degree. 10
4 Empirical Results 4.1 Managerial Characteristics and Cash Holdings We conduct multivariate analyses to examine the relation between managerial characteristics and corporate cash holdings. We follow the multivariate regression model for cash holdings based on Gao et al. (2013) and modify it by introducing the CEO characteristic indicators as independent variables. Some previous studies suggest that cash holdings of firms may be affected by the incentives of their managers, and the agency conflict is more severe when firms are controlled by entrenched managers or have lower shareholder rights. Therefore, we control for the effect of shareholder rights on cash holdings by including related variables in our model as well. Table 3 and Table 4 present the regression results for the full sample of the following model specification: Cash i,t =α + βceo characteristics i,t + γfirm characteristics i,t + Year fixed effects + Industry fixed effects + ε i,t. (1) The dependent variable of the regression is corporate cash holdings, which is defined as the ratio of cash and cash equivalents to total assets. The independent variables are the CEO characteristic indicators and the firm characteristics that affect corporate cash holdings. We also control for the factors that vary over time but not across industries and the factors that vary across industries but not over time, by including year indicators and industry indicators in the regression. 3 The CEO characteristic indicators are key explanatory variables in our study that their coefficients interpret the difference in levels of cash holdings between firms managed by CEOs with different characteristics. 3 The industries are based on two-digit SIC code. [Insert Table 3 and Table 4 about here] 11
We measure shareholder rights by G-index and E-index, and the regression results are presented in Table 3 and Table 4 respectively. Since the results in Table 3 are qualitatively the same as those shown in Table 4, we will focus on the results in Table 4 which based on a larger number of observations. 4 The model in Column (1) of Table 4 is served as a baseline, which contains none of the CEO characteristic indicators. The model in Column (2) of Table 4 explores the effect of CEO ages on cash holdings. We include only CEO age indicators as the main explanatory variables and control for other firm characteristics. The average-aged CEO indicator variable is omitted to avoid perfect multicollinearity when conducting the regression. As shown in Column (2), the coefficient on the senior CEO indicator variable is 0.009, which is statistically significant at 5% level. While the coefficient on the junior CEO indicator variable is 0.007, it is not statistically significant. This result suggests that firms managed by senior CEOs hold higher levels of cash than firms managed by average-aged CEOs do, and there is no significant difference in cash holdings between firms managed by junior CEOs and firms managed by average-aged CEOs. The result shown in Column (1) is consistent with the Hypothesis 1 we posited, that the levels of cash holdings are higher for firms managed by older CEOs. The model in Column (3) explores the effect of CEO tenure on cash holdings. The only CEO characteristic indicators we include are CEO tenure indicators, and the averagetenured CEO indicator variable is omitted. The result in Column (3) shows that coefficient on the experienced CEO indicator variable is 0.020, which is significant at 1% level. While the coefficient on the inexperienced CEO indicator variable is -0.001, it is not statistically significant. This result shows that firms managed by experienced CEOs hold more cash than firms managed by average-tenured CEOs do, and there is no significant difference in cash holdings between firms managed by inexperienced CEOs and firms managed by averagetenured CEOs. The result in Column (3) is consistent with the Hypothesis 2 we posited, 4 Due to the change of methodology for data collection in RiskMetrics, the historical G-index data may not be available for those firms established after 2007. Therefore, measuring shareholder rights by G-index will lead to a smaller sample size. 12
that the levels of cash holdings are higher for firms managed by longer tenured CEOs. In Column (4), the only CEO characteristic indicator included is the CEO overconfidence indicator. The coefficient on the overconfident CEO indicator variable is shown to be 0.017, which is significant at 1% level. This result suggests that firms managed by overconfident CEOs hold higher levels of cash in comparison with firms managed by non-overconfident CEOs, which is consistent with our Hypothesis 3. The only CEO characteristic indicator we include in the model in Column (5) is the MBA degree indicator. The coefficient on the CEO with MBA degree indicator variable of -0.015 is significant at 1% level. This result suggests that firms managed by CEOs with MBA degree hold less cash than firms managed by CEOs without MBA degree do, which is consistent with the Hypothesis 4. The model in Column (6) includes all the CEO characteristic indicators. The results of the coefficients on the CEO characteristic indicators are qualitatively the same as those obtained from the models in Column (3) to Column (5). As shown in Column (6), firms managed by experienced managers who have longer tenure in their current CEO position are inclined to hold higher cash reserves, and overconfident CEOs tend to hold more cash as well. The result also suggests that firms managed by CEOs with MBA degree have lower levels of cash reserves. The positive coefficient on the junior CEO indicator variable suggests that firms managed by junior CEOs seem to hold more cash, however, the significance is marginal. These findings are consistent with our hypotheses, except for the ambiguous relation between cash holdings and CEO ages. The coefficients on the control variables are also consistent with the previous findings in Bates et al. (2009) and Gao et al. (2013). Large firms and firms with more net working capital hold lower cash, because large firms have more property to liquidate when facing liquidity need, and the non-cash working capital can also be easily converted into cash. However, firms with greater cash flow volatility hold more cash for future need. If firms face more constraints on debt issuing, they will reduce their leverage and preserve more cash for future 13
need as well, and therefore leverage has negative effect on cash reserve. Cash inflows have positive effects on the level of cash holdings, but cash outflows, such as capital expenditures, acquisition expenditures, and dividend payments, have negative effects on the level of cash holdings. Although R&D expenditures lead to cash outflows, firms with greater R&D are assumed to have greater growth opportunities and costs of financial distress, hence those firms will hold more cash. Furthermore, both of these tables reveal negative relation between G-index (E-index) and cash holdings, implying lower levels of cash holdings when firms have lower shareholder rights. These results are consistent with the findings in Harford et al. (2008), that firms with weaker corporate governance structures have smaller cash reserves. Being different from the perspective that firms with worse corporate governance will motivate managers to hold more cash reserves and pursue their own benefits, a possible explanation here is that firms with worse corporate governance are usually required to reduce their cash holdings to mitigate the agency problem of free cash flow. Shareholders and board of directors may force firms with excess cash to increase dividends. However, to avoid future payout commitments, poorly governed firms may choose to repurchase instead of paying dividends. Managers of poorly governed firms may also manage to spend excess cash quickly by overinvestment or pursuing private benefits to prevent shareholders or board of directors from being aware of the excess cash. All of these possible reasons will eventually lead to lower levels of cash holdings for firms with weaker shareholder rights. Overall, the results in Table 3 and Table 4 provide evidence for the difference in levels of cash holdings between firms managed by CEOs with different characteristics. If there exist target cash levels for firms, the target level of cash holdings may be different among firms because of the difference in managerial characteristics. We further investigate the relation between managerial characteristics and speed of adjustment to target cash level in the next subsection. 14
4.2 Speed of Adjustment to Target Cash Level The speed of adjustment to target cash level reflects the friction a firm may face when adjusting its cash level, which could be utilized to explain the precautionary motive for cash holdings. In this subsection, we examine the difference in speed of adjustment to target cash level between firms managed by CEOs with different characteristics, as well as their reactions when firms are cash-rich or cash-poor. We use the partial adjustment model resembles that in Fama and French (2002) and Lemmon, Roberts, and Zender (2008) to estimate the speed of adjustment of cash holdings, which is given by: Cash i,t Cash i,t 1 =α + λ 0 (Cash i,t Cash i,t 1 ) + ε i,t, (2) where Cash i,t is the target level of cash holdings for firm i at time t, and Cash i,t Cash i,t 1 measures the deviation of the firm s cash holdings from its target level, then the coefficient λ 0 measures the speed of adjustment to target cash level for the firm. 5 More specifically, the sign of λ 0 indicates that whether firms are adjusting their cash toward their target levels. The positive sign represents that firms are adjusting their cash toward their target levels, yet the negative sign represents that firms are deviating from their target levels. The absolute value of λ 0 is then representing the speed of adjustment toward their target levels or the speed they deviating from their target levels. Firms should especially adjust their cash holdings rapidly to their target levels when they are short of cash, but market imperfection may prevent firms from smoothly achieving that. If cash-poor firms face more friction and adjust their cash slowly, they may have the precautionary motives to hold more cash. We may also infer that firms with excess cash have the precautionary motives to preserve cash, if they are having slower speed of adjustment. We predict the target levels of cash holdings by estimating Equation (1), and define firms 5 Fama and French (2002) apply this approach to analyse the adjustment to target level of dividend payouts and the adjustment to target leverage for firms. Lemmon et al. (2008) conduct a similar procedure to estimate the adjustment to target capital structure for firms. 15
as cash-rich or cash-poor by measuring the deviation of firms cash holdings from their target levels. 6 In order to examine whether there is difference in speed of adjustment to target cash level between firms managed by managers with different characteristics, we further include the CEO characteristic indicators and the interaction terms in Equation (2), the partial adjustment model is modified as: Cash i,t Cash i,t 1 =α + βceo characteristics i,t + λ 0 (Cash i,t Cash i,t 1 ) + λceo characteristics i,t (Cash i,t Cash i,t 1 ) + ε i,t, (3) and the coefficient λ is then interpreting the difference in speed of adjustment which is due to managers with different characteristics. Table 5 presents the results for the full sample. First of all, the positive and significant coefficient on the deviation from target level (which is denoted by Cash ) in each column represents that firms adjust their cash levels toward target levels, but the less-than-one absolute values indicate that firms could not adjust their cash to target levels perfectly. The result of the negative coefficient on the interaction term between senior CEO indicator and the deviation from target cash level shown in Column (2) indicates that firms managed by senior CEOs have relatively slow speed of adjustment to target cash level. The result of the negative coefficient on the interaction term between experienced CEO indicator and the deviation from target cash level shown in Column (3) suggests that firms have slower speed of adjustment to target cash level when their managers have longer tenure. The result in Column (4) shows that there is no statistically significant difference in speed of adjustment between firms managed by overconfident managers and non-overconfident managers. The result in Column (5) also shows that there is no significant difference in speed of adjustment between firms managed by CEOs with MBA degree and CEOs without MBA degree. 6 Specifically, we sort the sample by the deviation of firms cash holdings from their target levels, and we define the firms in the lower 30 percent as cash-rich, and the firms in the higher 30 percent as cash-poor. 16
[Insert Table 5 about here] Table 6 presents the results of cash-rich firms. The results in Table 6 also show that firms are adjusting their cash toward target levels. As shown in Column (2), the coefficient on the interaction term between senior CEO indicator and the deviation from target cash level is negative, however, the significance is marginal. This result provides some evidence that senior CEOs are more likely to have precautionary motives to hold more cash for future need. The result in Column (3) shows that firms are adjusting their cash toward target levels with slower speed of adjustment when their managers have longer tenure. We may infer that experienced CEOs are holding cash for precautionary motives. An alternative possibility that the firms managed by experienced CEOs have slower speed of adjustment is due to their reluctance to change. Those experienced CEOs are more likely to take conservative strategies, and this may result in piling of cash if there is no suitable investment opportunity. The result in Column (4) shows that firms are adjusting their cash toward target levels, however, there is no statistically significant difference in speed of adjustment between firms managed by overconfident managers and non-overconfident managers. Although our previous findings suggest that overconfident CEOs tend to preserve more cash, we may infer that they are less likely to hold cash for precautionary motives. The result in Column (5) of shows that CEOs with MBA degree spend their cash slightly faster to reach the target levels when firms are cash-rich. This provides some evidence that CEOs with MBA degree are more aware of the opportunity costs of holding cash and hence they are more likely to conduct investment projects when their firms have excess cash. The results shown in Table 7 indicate that there is no significant difference in speed of adjustment to target cash levels for cash-poor firms. [Insert Table 6 and Table 7 about here] 17
5 Conclusion Few empirical studies on corporate cash policy explore the influence of managerial characteristics. In this paper, we investigate the relation between CEO characteristics and corporate cash holdings. Overall, we provide important and intriguing empirical evidence for the effects of managerial characteristics on corporate cash policy. Our findings show that experienced CEOs who have longer tenure tend to hold more cash than inexperienced CEOs who have shorter tenure. We may infer that experienced CEOs are holding cash for precautionary motives from the results of the speed of adjustment. The possible explanation is that experienced CEOs are more conservative and are more reluctant to change in comparison with inexperienced CEOs, hence they are less aware of the opportunity costs of not investing their firms cash. We also find that overconfident CEOs are more inclined to hold higher levels of cash than non-overconfident CEOs. However, our empirical results show no statistically significant difference in speed of adjustment between firms managed by overconfident managers and non-overconfident managers, suggesting that overconfident CEOs do not preserve cash for precautionary motives. Furthermore, CEOs with MBA degree are shown to hold less cash than CEOs without MBA degree and adjust cash toward target levels more rapidly when their firms have excess cash. It appears that CEOs with MBA degree are more aggressive in making corporate investment decisions. We find mixed evidence on the relation between CEO ages and corporate cash holdings in different model specifications. Our empirical results appear to suggest that both senior CEOs and junior CEOs tend to preserve more cash in their firms in comparison with averageaged CEOs. While the results of the speed of adjustment to target levels of cash holdings may partially explain for senior CEOs precautionary motives to preserve cash, the evidence that higher levels of cash holdings for junior CEOs remains unexplainable from our limiting findings. We suggest that the incentives of junior CEOs to engage in empire-building may be a possible explanation, which needs to be explored through further analyses in the future. 18
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Table 1. Summary Statistics This table presents the summary statistics for the sample. The sample consists of 17,530 firm-year observations from 2,117 firms covering the period from 1998 to 2012. Panel A provides summary statistics of firm characteristics. Cash is the cash and cash equivalents scaled by total assets. Size is the logarithm of total assets. Sales growth is the rate of change in sales. Cash flow is the operating cash flow scaled by total assets, where the operating cash flow is computed as earnings after interest, dividends, and taxes but before depreciation. Cash flow volatility is the standard deviation of industry-median-adjusted quarterly operating cash flows over the previous two years, where industries are classified using two-digit SIC code. Net working capital is the difference between current non-cash asset and current liabilities, scaled by total assets. Leverage is the ratio of total debts to total assets. Capital expenditure is the capital expenditures scaled by total assets. Acquisition is the acquisition expenditures scaled by total assets. R&D is the R&D expenditures scaled by total assets, and is set to zero when R&D expenditures is missing. Dividend is an indicator variable that equals one if a firm pays cash dividends. Repurchase is an indicator variable that equals one if a firm repurchases stocks. G-index is the Gompers, Ishii, and Metrick (2003) index of shareholder rights. E-index is the Bebchuk, Cohen, and Ferrell (2009) index of shareholder rights. Panel B provides summary statistics of CEO characteristics. CEO age is the age of CEO in years. CEO tenure is the number of years the CEO has held the CEO title at the current firm. Other variables in Panel B are indicator variables that equal one if a firm is managed by CEO with specific characteristic. Refer to Section 3.2 for details on CEO characteristic measures. All continuous variables except for G-index and E-index are winsorized at the 1st and 99th percentile values. Variable Mean Median StdDev Min. Max. Obs. Panel A: Firm characteristics Cash 0.169 0.102 0.180 0.001 0.776 17,459 Size 7.402 7.271 1.577 3.820 11.541 17,461 Sales growth 0.084 0.051 0.256 0.526 1.357 17,400 Cash flow 0.091 0.095 0.092 0.350 0.318 17,440 Cash flow volatility 0.013 0.009 0.014 0.002 0.087 16,981 Net working capital 0.064 0.061 0.147 0.370 0.433 17,070 Leverage 0.216 0.193 0.197 0.000 0.952 17,461 Capital expenditure 0.052 0.035 0.051 0.002 0.285 17,443 Acquisition 0.028 0.000 0.063 0.002 0.351 17,443 R&D 0.034 0.003 0.057 0.000 0.296 17,451 Dividend 0.479 0.000 0.500 0.000 1.000 17,467 Repurchase 0.556 1.000 0.497 0.000 1.000 17,467 G-index 9.138 9.000 2.540 1.000 18.000 12,318 E-index 2.541 3.000 1.304 0.000 6.000 12,805 Panel B: CEO characteristics CEO age 54.505 54.000 7.173 38.000 74.000 17,436 CEO tenure 7.906 6.000 6.820 1.000 34.000 16,623 Senior CEO 0.288 0.000 0.453 0.000 1.000 17,436 Average-aged CEO 0.418 0.000 0.493 0.000 1.000 17,436 Junior CEO 0.294 0.000 0.455 0.000 1.000 17,436 Experienced CEO 0.282 0.000 0.450 0.000 1.000 16,623 Average-tenured CEO 0.422 0.000 0.494 0.000 1.000 16,623 Inexperienced CEO 0.296 0.000 0.456 0.000 1.000 16,623 Overconfident CEO 0.624 1.000 0.485 0.000 1.000 16,064 Non-overconfident CEO 0.376 0.000 0.485 0.000 1.000 16,064 CEO with MBA degree 0.276 0.000 0.447 0.000 1.000 17,512 CEO without MBA degree 0.724 1.000 0.447 0.000 1.000 17,512 22
Table 2. Mean and Median Cash Ratios by Managerial Characteristics This table presents the mean and median cash ratios over time from 1998 to 2012 for firms managed by CEOs with different characteristics. Cash ratio is defined as the ratio of cash and cash equivalents to total assets. Panel A presents the mean and median cash ratios over time for firms classified by the age of their CEO. Panel B presents the results for firms classified by the tenure of their CEO. Panel C presents the results for firms classified by whether their CEO is overconfident. Panel D presents the results for firms classified by whether their CEO has MBA degree. Panel A: Classify firms by the age of their CEO Junior CEO Average-aged CEO Senior CEO Number Mean Median Number Mean Median Number Mean Median Year of firms cash cash of firms cash cash of firms cash cash 1998 114 0.211 0.095 122 0.136 0.055 70 0.119 0.052 1999 297 0.198 0.083 348 0.128 0.048 174 0.113 0.035 2000 352 0.188 0.088 401 0.126 0.042 214 0.114 0.046 2001 353 0.197 0.103 444 0.139 0.054 253 0.130 0.066 2002 355 0.208 0.133 470 0.153 0.078 303 0.136 0.071 2003 393 0.225 0.161 488 0.167 0.093 347 0.161 0.101 2004 387 0.213 0.160 488 0.165 0.099 375 0.167 0.100 2005 371 0.201 0.159 515 0.158 0.098 356 0.175 0.097 2006 393 0.203 0.148 525 0.151 0.088 383 0.157 0.080 2007 441 0.204 0.129 586 0.153 0.083 403 0.150 0.081 2008 400 0.191 0.130 595 0.144 0.087 417 0.149 0.089 2009 366 0.215 0.161 602 0.178 0.126 418 0.177 0.122 2010 326 0.218 0.176 578 0.172 0.120 436 0.177 0.121 2011 293 0.196 0.152 559 0.155 0.103 427 0.170 0.114 2012 261 0.189 0.139 537 0.157 0.109 429 0.159 0.105 Total 5, 102 0.204 0.137 7, 258 0.154 0.090 5, 005 0.156 0.093 Panel B: Classify firms by the tenure of their CEO Inexperienced CEO Average-tenured CEO Experienced CEO Number Mean Median Number Mean Median Number Mean Median Year of firms cash cash of firms cash cash of firms cash cash 1998 91 0.155 0.060 92 0.161 0.049 71 0.177 0.100 1999 221 0.120 0.040 266 0.146 0.052 219 0.191 0.083 2000 317 0.128 0.043 303 0.147 0.048 240 0.178 0.085 2001 384 0.145 0.053 334 0.153 0.064 242 0.188 0.104 2002 380 0.149 0.077 405 0.168 0.086 274 0.194 0.121 2003 349 0.173 0.109 518 0.181 0.105 301 0.203 0.126 2004 336 0.165 0.105 525 0.175 0.103 336 0.202 0.135 2005 350 0.172 0.131 544 0.163 0.088 306 0.201 0.123 2006 383 0.171 0.108 549 0.155 0.086 333 0.184 0.109 2007 414 0.165 0.095 593 0.159 0.089 383 0.178 0.096 2008 405 0.163 0.106 599 0.146 0.095 370 0.168 0.095 2009 367 0.181 0.127 592 0.181 0.124 392 0.198 0.142 2010 305 0.183 0.140 594 0.175 0.121 407 0.193 0.135 2011 282 0.150 0.103 558 0.161 0.110 415 0.190 0.130 2012 307 0.158 0.104 501 0.150 0.106 399 0.185 0.126 Total 4, 891 0.160 0.096 6, 973 0.163 0.096 4, 688 0.189 0.118 23
Table 2. (Continued) Panel C: Classify firms by whether their CEO is overconfident Non-overconfident CEO Overconfident CEO Number Mean Median Number Mean Median Year of firms cash cash of firms cash cash 1998 101 0.126 0.055 186 0.159 0.069 1999 237 0.079 0.030 509 0.166 0.068 2000 311 0.093 0.028 594 0.168 0.067 2001 370 0.115 0.039 615 0.176 0.082 2002 454 0.137 0.071 627 0.185 0.099 2003 451 0.152 0.093 716 0.201 0.125 2004 404 0.159 0.097 766 0.187 0.117 2005 412 0.163 0.105 745 0.180 0.105 2006 420 0.171 0.104 791 0.165 0.088 2007 453 0.172 0.103 831 0.163 0.089 2008 518 0.164 0.108 750 0.153 0.085 2009 549 0.184 0.129 712 0.190 0.132 2010 490 0.176 0.120 739 0.190 0.141 2011 442 0.156 0.107 708 0.179 0.133 2012 430 0.164 0.109 673 0.167 0.120 Total 6, 042 0.153 0.093 9, 962 0.176 0.105 Panel D: Classify firms by whether their CEO has MBA degree CEO without MBA degree CEO with MBA degree Number Mean Median Number Mean Median Year of firms cash cash of firms cash cash 1998 249 0.161 0.062 66 0.144 0.063 1999 648 0.150 0.052 182 0.150 0.053 2000 762 0.148 0.053 218 0.138 0.048 2001 800 0.159 0.071 259 0.148 0.061 2002 852 0.171 0.091 287 0.154 0.084 2003 907 0.185 0.118 328 0.183 0.105 2004 911 0.181 0.113 344 0.179 0.111 2005 891 0.178 0.105 352 0.171 0.114 2006 934 0.172 0.094 370 0.159 0.108 2007 1, 020 0.173 0.097 412 0.153 0.089 2008 1, 009 0.162 0.098 404 0.150 0.092 2009 978 0.191 0.137 410 0.178 0.123 2010 930 0.191 0.141 410 0.171 0.120 2011 883 0.172 0.119 397 0.164 0.107 2012 841 0.167 0.114 387 0.159 0.106 Total 12, 615 0.172 0.103 4, 826 0.162 0.098 24