Winter-born CEOs and Corporate Policies

Essay I: The Season of Risk: CEO Season of Birth and Corporate Risk-taking

4. Main Results

4.4. Winter-born CEOs and Corporate Policies

Our analyses so far confirm our hypothesis that winter-born CEOs increase firm risk. In this section, we investigate the channels through which winter-born CEOs affect firm risk. In particular, we focus on the relationships between winter-bon CEOs and corporate policies.

4.4.1. Winter-born CEOs and Financial Policies

We employ three proxies for the riskiness of financial policies: capital structure, debt maturity structure, and working capital management. Our pooled regression models are in the following form:

𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑃𝑜𝑙𝑖𝑐𝑖𝑒𝑠_𝑖,𝑡+1= 𝛽₀+ 𝛽₁𝑊𝑖𝑛𝑡𝑒𝑟_𝑖,𝑡+ 𝛾𝑋_𝑖,𝑡+ 𝑌𝑒𝑎𝑟 𝐹𝐸 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸 + 𝜀_𝑖,𝑡

where 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑃𝑜𝑙𝑖𝑐𝑖𝑒𝑠_𝑖,𝑡+1 denotes the capital structure decision, debt maturity decision, working capital management of firm 𝑖 in year 𝑡 + 1. 𝑊𝑖𝑛𝑡𝑒𝑟_𝑖,𝑡 and 𝑋_𝑖,𝑡 stands for our main explanatory variable and a vector of control variables, respectively. As in the baseline regressions, , we include year and industry dummies.

The literature suggests that financial leverage is one of the possible channels through which CEOs could increase firm risk (Cronqvist, et al., 2012; Malmendier, Tate, and Yan, 2011). If a winter- born CEO has a natural inclination to take higher risk, he might increase firm risk through financing policy in two ways: capital structure and debt maturity decisions. First, he might increase firm risk by taking higher leverage because leverage would increase the financial risk of his firm. Second, he might drive up firm risk by issuing the higher proportion of short-term leverage because short-term debt

24

exposes his firm to greater risk of liquidation by lenders (Diamond, 1991). We test whether firms with winter-born CEOs exhibit significant differences in financial leverage decisions.

We begin our analyses with capital structure decisions. Motivated by Cassell et al. (2012), our key dependent variable is 𝐵𝐿𝑒𝑣_𝑖,𝑡+1, which is measured as total debt scaled by the book value of total assets of firm 𝑖 in year 𝑡 + 1, where total debt is calculated by subtracting the book value of equity from total assets. An alternative measure is 𝑀𝐿𝑒𝑣_𝑖,𝑡+1, which is measured as total debt scaled by the market value of total assets firm 𝑖 in year 𝑡 + 1, where the market value of total assets is calculated as the book value of total assets minus the book value of equity plus the market value of equity.

We rely on the vast empirical literature to control for variables that are known to have power to explain capital structure. Motivated by studies such as Chava and Pernanandam (2010), Cassell et al.

(2012) and Cain and Mckeon (2016), Coles et al. (2006), we control for firm size, return on assets, asset tangibility, sales growth, market-to-book ratio, firm age, CEO age, CEO compensation delta and vega.

We additionally include asset tangibility (𝑃𝑃𝐸_𝑡), measured as property, plant, and equipment scaled by total assets, to control for the firm’s assets available for collateral.

[Insert Table 12 Here]

Column (1) and (2) of Table 12 provides the regression results. We find that winter-born CEOs significantly increase both book leverage (𝛽₁= 0.010, t = 2.982) and market leverage (𝛽₁ = 0.008, t = 3.138). The impact of winter-born CEOs is also economically meaningful. Specifically, the existence of winter-born CEOs increases book leverage by 0.010 and market leverage by 0.008, which amounts to roughly 1.8 % of the mean of book leverage (mean=0.556) and 2.2% of the mean of market leverage (mean=0.356), respectively (see Table 3).

We next analyze the effect of winter-born CEOs on debt maturity structure decision. Following Johnson (2003) and Brockman, Martin, and Unlu (2010), we use the ratios of short-term debt to total as proxies for debt maturity. ST1 is the ratio of debt maturing in one year to total debt, where debt maturing in one year is measured as debt in current liabilities. ST3 is the ratio of debt maturing in three years to total debt, where debt maturing in three years is measured as the sum of debt in current liabilities and debt maturing in the second and third year. Finally, ST5 is the proportion of debt maturing in five years to total debt, where debt maturing in five year is computed as the sum of debt in current liabilities and debt maturing in the second, third, fourth, and fifth year.

In the maturity structure regressions, we include a similar set of control variables as in Brockman, Martin, and Unlu (2010), Diamond (1991), Myers (1977), and Flannery (1986), such as firm size and its square term (𝐹𝑖𝑟𝑚 𝑆𝑖𝑧𝑒_𝑡²), long-term leverage (𝐿𝐿𝑒𝑣_𝑡), market-to-book ratio, CEO age, firm age, abnormal earnings (𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠_𝑡), and CEO compensation delta and vega. Note

25

that we include book leverage in the baseline regressions, while we control for leverage long-term leverage defined as long-term debt scaled by the market value of the firm in debt maturity regressions following the literature. Diamond (1991) argues that firms with high leverage tend to take higher long- term leverage to minimize default risk. Flannery (1986) argues that firms tend to raise more short-term debt in the existence of information asymmetry between them and the market as a way to signal their quality to the market. To capture this effect, we follow Barclay and Smith (1995) and control for a measure of abnormal earnings, which is computed as the ratio of the difference in next year’s (𝑡 + 1) and current year’s (𝑡) earnings per share to current year’s share price (𝑡).

Column (3), (4), and (5) report the results of regressions with 𝑆𝑇1_𝑡+1, 𝑆𝑇3_𝑡+1, and 𝑆𝑇5_𝑡+1 as dependent variables, respectively. Consistent with our prediction, we find meaningful relations between winter-born CEOs and the maturity structure of debt. Firms with winter-born CEOs have significantly higher short-term leverage measured by 𝑆𝑇1_𝑡+1 (𝛽₁ = 0.004, t = 2.321) and 𝑆𝑇3_𝑡+1 (𝛽₁ = 0.005, t = 2.124). In terms of economic magnitude, the existence of winter-born CEOs is associated with increase in 𝑆𝑇1_𝑡+1 by 0.004 and 𝑆𝑇3_𝑡+1 by 0.005, which corresponds to roughly 8.8% of mean 𝑆𝑇1_𝑡+1 (0.057) and 5.9% of mean 𝑆𝑇3𝑡+1, respectively (see Table 3). The impact of winter-born CEOs on 𝑆𝑇5𝑡+1 in Column (5) is statistically insignificant (𝛽₁ = 0.002, t = 0.746) and economically less meaningful (roughly 1.7% of mean 𝑆𝑇5_𝑡+1 (0.1188)) than the effect on other measures of debt maturity, suggesting that both statistical and economic significance are diminished from 𝑆𝑇1_𝑡+1 to 𝑆𝑇5_𝑡+1. The findings imply that winter-born CEOs are related to a higher proportion of short-term debt due in one to three years, but not related to a proportion of short-term debt with longer due, which further supports our prediction that winter-born CEOs take higher short-term leverage.

Finally, we analyze the association between winter-born CEOs and working capital. Working capital is a measure of asset liquidity, which represents the amount of liquid assets that a firm has at hand. Working capital is used to pay for expenses incurred from both expected and unexpected activities, such as starting business and meeting short-term duties and obligations, suggesting that higher working capital decreases firm risk. Thus, we predict that the winter-born CEO with risk-increasing incentives might reduce the level of working capital in his firm. In estimating the regression, the dependent variable is 𝑊𝐶_𝑡+1, which is computed as current assets minus current liabilities divided by total assets.¹⁵ We control for the same set of variables as in capital structure regressions in Column (1) and (2). Finally, Column (6) shows that firm with winter-born CEOs have less working capital. We find that winter-born

15 Following Cassel et al. (2012), we supplement missing observations of this measure with an alternative measure, computed as cash and short-term investment plus other current assets plus inventory plus current accounts receivable minus accounts payable minus other current liabilities minus debt in current liabilities minus income tax payable.

26

CEOs significantly decrease working capital (𝛽₁ = -0.008, t = -2.591). The economic magnitude amounts to approximately 15.4 percent of mean working capital in Table 3 (mean = 0.052).

4.4.2. Winter-Born CEOs and Investment Policies

We now turn to the aspects of corporate investment policies associated with riskiness, namely, R&D expenditure, investment rate in tangible capital. Investment decisions are important channels through which managers increase the volatility of the firm’s earnings and stock returns. The literature provides evidence that managers with risk-increasing incentives tend to make riskier investment decisions (Coles et al., 2006; Chava and Purnanandam, 2010; Cain and Mckeon, 2016; Cassell et al., 2012; Sunder, Sunder, and Zhang, 2017). In this section, we test whether winter-born CEOs adopt riskier investment policies. We consider two measures of the riskiness of corporate investment policies: R&D expenditure and investment rate in tangible assets. Our regression models are in the following form:

𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑃𝑜𝑙𝑖𝑐𝑖𝑒𝑠_𝑖,𝑡+1= 𝛽₀+ 𝛽₁𝑊𝑖𝑛𝑡𝑒𝑟_𝑖,𝑡+ 𝛾𝑋_𝑖,𝑡+ 𝑌𝑒𝑎𝑟 𝐹𝐸 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸 + 𝜀_𝑖,𝑡

where 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑃𝑜𝑙𝑖𝑐𝑖𝑒𝑠_𝑖,𝑡+1 denotes R&D expenditure and investment rate in tangible capital of firm 𝑖 in year 𝑡 + 1, whereas the main explanatory variable is the existence of winter-born CEO in year 𝑡. 𝑋_𝑖,𝑡 denotes a vector of control variables. As in the baseline regressions, we include year and industry dummies. In estimating all the regression models, we include control variables that have been found to be related to the riskiness of corporate investment policies, such as firm size, book leverage, return on assets, sales growth, cash surplus (𝐶𝑎𝑠ℎ 𝑆𝑢𝑟𝑝𝑙𝑢𝑠_𝑡), CEO age, and CEO compensation delta and vega.

Richardson (2006) shows that free cash follow is positively associated with over-investment. Therefore, we follow Coles et al. (2006) and Cassell et al. (2012) and include surplus cash ratio, calculated as cash from assets-in-place scaled by total assets, where cash from assets-in-place is defined as net cash flow from operating activities plus R&D expense minus depreciation and amortization, divided by total assets.

[Insert Table 13 Here]

We first examine the effect of winter-born CEOs on R&D intensity. Our proxy for R&D intensity is computed as R&D expense divided by total assets of firm 𝑖 in year 𝑡 + 1.¹⁶¹⁷ Column (1) of Table 13 provides the regression results. The coefficient estimate shows that firms with winter-born CEOs exhibit 0.001 greater R&D intensity relative to firms without winter-born CEOs (𝛽₁= 0.001, t = 1.970). In terms of economic significance, the magnitude of coefficient corresponds to approximately 3.4% of mean R&D intensity in Table 3.

16 We reach a qualitatively similar result if we use the ratio of R&D expense to sales as a proxy for R&D investment.

17 We treat all missing values for R&D as zero.

27

We next investigate the effect of winter-born CEOs on investment rate in tangible assets.

Following Hilary and Hui (2009), we use investment rate (𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡_𝑡+1), which is defined as capital expenditures divided by net property, plant, and equipment of firm 𝑖 in year 𝑡 + 1. Column (2) of Table 13 presents the results. The coefficient estimate shows that winter-born CEOs increase investment rate in tangible capital by 0.004 (t = 1.853), which corresponds to approximately 2.05% of mean investment rate in Table 3.

Overall, the results establish an important implication. Winter-born CEOs play an important role in determining a wide range of riskier corporate policies. Specifically, winter-born CEOs increase the risk of the firms through both financial policies, such as capital structure, debt maturity structure, and working capital, and investment policies, such as investment in R&D activities and tangible assets.

The effect on winter born CEOs on corporate policies is well beyond the effects of first-order determinants of these policies already established in the literature.