3. THE EFFECT OF EPS ON CAPITAL STRUCTURE
3.1 DETERMINANTS OF ANNUAL CHANGES IN CAPITAL STRUCTURE
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statistics to those reported in previous empirical studies of capital structure, such as Leary and Roberts (2014).
[Table 1 to be inserted here]
3. The Effect of EPS on Capital Structure
3.1 Determinants of annual changes in capital structure
I begin studying the net effect of EPS on the annual change in leverage by estimating the following regression,
Δ𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡 = 𝛼 + 𝛽𝐸𝑃𝑆𝑖𝑡−1+ 𝜸𝑿𝒊𝒕−𝟏+ 𝛿𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1+ 𝜂𝑖 + 𝜈𝑡+ 𝜀𝑖𝑡, (1) where i indexes firms; t indexes years; X is a set of 1-year lagged control variables; η is a firm fixed effect (Lemmon, Roberts, and Zender, 2008); ν is a year fixed effect;
and ε is a random error term assumed to be possibly heteroskedastic within firms.
There are different definitions of leverage used in the literature. For the purpose of this study, I use the definitions of market and book leverage ratios, which are consistent with previous studies such as Titman and Wessels (1988), Frank and Goyal (2009), Lemmon et al. (2008), and Leary and Roberts (2014). Book leverage is defined as book debt to total assets. Market leverage is defined as book debt divided by the sum of total debt and the market value of equity. Both are symbolized as Leverage.
𝐸𝑃𝑆𝑖𝑡−1, the firm i’s 1-year lagged EPS, is the key regressor in equation (1).
Although several studies find that diluted EPS are more strongly associated with stock prices and managers’ decisions than basic EPS, I use both measures of EPS to test
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(repurchasing outstanding shares). Thus higher (lower) EPS would decrease (increase) the debt ratios.The lagged leverage, 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1, is included because leverage is bounded between zero and one. When leverage is near one of these boundaries, the change in leverage can only go in one direction, regardless of the values of the other variables.
Lagged leverage therefore enters with a negative sign. The control variables, 𝑿, are referred to in previous academic literature and are well shown as important variables affecting debt ratios.4 They are market-to-book ratio, firm size, fixed asset proportion, R&D expenses, industry median debt ratio, cash flow volatility, and profitability. The definitions of all of the variables and the conjectures on the relations between the control variables and leverage are detailed in the Appendix A and Appendix B respectively.
The results from estimating equation (1) using book and market leverage are presented in Table 2. I present the results focusing on basic EPS in columns (1) to (3) and diluted EPS in columns (4) to (6). Column (1) and column (4) present the results of a model consisting solely of EPS. The coefficients on the EPS are significantly
3 Basic EPS is calculated as earnings available to common shareholders divided by weighted-average common shares outstanding, which does not incorporate the effect of ‘‘potentially dilutive’’ securities such as warrants, convertible debt, and employee stock options. Diluted EPS, on the other hand, uses the treasury stock method to account for the effects of potentially dilutive securities. The denominator of this ratio increases as newly granted employee stock options move into the money and existing employee stock options move further into the money. Thus, diluted EPS expands on basic EPS by including the shares of convertibles or warrants outstanding in the outstanding shares number.
Although several studies find that diluted EPS is more highly associated with stock prices and managers’ decision than basic EPS, we follow Jennings, LeClere, and Thompson (1997), Core, Guay, and Kothari (2002), and Bens et al. (2003) and consider both measures to reduce significant impact of the different measures on the results.
4 For studies adopting these control variables, see, among others, Rajan and Zingales (1995), Baker and Wurgler (2002), Lemmon et al. (2008), Frank and Goyal (2009), and Leary and Roberts (2014).
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negatively associated with both market and book leverage, indicating that the effect of low (high) EPS is to increase (decrease) leverage. These results are consistent with the idea that when EPS is low, firms tend to choose debt rather than equity when they need external funds, or even buy back outstanding shares to boost EPS. The other columns show the results incorporating control variables into the specification. The coefficient estimates of EPS are largely consistent with previous evidence, in terms of sign and statistical significance. Specifically, the coefficients on EPS remain highly significant and even reveal positive changes in economic significance when considering the control variables.
[Table 2 to be inserted here]
The coefficient estimates of the control variables are largely consistent with those reported in the literature (Rajan and Zingales, 1995; Baker and Wurgler, 2002;
Lemmon et al., 2008; Frank and Goyal, 2009; Leary and Roberts 2014). Higher market-to-book ratio, cash flow volatility, and profitability tend to reduce leverage, whereas larger firms and those with a higher fixed asset proportion tend to increase leverage. Industry median debt ratio has positive effect on a specific firm’s leverage, which is consistent with the evidence that firms’ financing decisions are responses to the financing decisions of peer firms (Leary and Roberts, 2014).
So far I have documented the negative relationship between EPS and leverage changes, but this negative relationship does not indicate whether EPS affects capital structures through equity changes, as the EPS story implies. In addition, EPS may be thought of as an indicator of a company's profitability, and thus the relationship between EPS and leverage change I have found is only the result of profitability.
Profitability should have a negative impact on net equity issues according to the traditional theory of capital structure. Under the trade-off theory, more profitable
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firms use more debt (less equity) to shield taxable income and to control the agency cost of free cash flow (Jensen, 1986). Under the pecking order theory, holding investment fixed, more profitable firms keep the level of internal capital higher, and thus have less need to finance new projects with external capital, thus reducing new equity issues. I use the following regression to examine the effect of EPS on net divided by total assets. I include EPS and Profitability (measured as operating income before depreciation divided by total assets) in the same equation. If EPS is merely a measure of profitability, I expect the sign of the coefficients on EPS and profitability to be equal and negative based on both the trade-off theory and the pecking order theory. Table 3 presents the results of estimating equation (2).
[Table 3 to be inserted here]
Table 3 shows that the coefficient on EPS is significantly positive, whereas the coefficient on profitability is significantly negative. The results show the distinguishable effects of EPS and profitability on net equity issues. Firms’ managers are less concerned about EPS dilution when EPS is relatively high, in which situation they are willing to issue new shares. By contrast, when EPS is low, managers seem less inclined to increase the outstanding shares, which would further depress the reported EPS. They may even buy back outstanding shares to increase EPS (Bens et al., 2003; Hribar et al., 2006; Oded and Michel, 2008; Almeida et al., 2015). Most importantly, I find that EPS affects capital structure through net equity issues.
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Profitability is significantly negatively related to net equity issues, which is consistent with both the trade-off theory and the pecking order theory, as well as the empirical results found in Baker and Wurgler (2002).