Gaming the Float: How Managers Respond to EPS-based Incentives

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Column 2 presents the same specification as column 1, but uses an earnings volatility proxy adjusted for expectations based on prior earnings. Specifically, Net Income Volatility - Naive Forecast t−1 is measured as the standard deviation of the difference between actual earnings and a seasonally adjusted proxy for expected earnings. The expected earnings is earnings from 4 quarters prior, plus the difference between the earnings 4 quarters and 8 quarters before. Column 3 presents another specification using a third proxy for earnings volatility, Net Income Volatility - Analyst Forecast t−1 . This is the volatility of earnings adjusted for analysts’ forecasts over the last twelve quarters. Finally, column 4 presents the same specification as column 1, but includes a measure for the level of incentive compensation. This is measured as 1 − ( salary totalcompensation ) and is meant to capture both explicit EPS incentives (through bonuses) and implicit incentives including sensitivity to market reactions due to unexpected earnings (through grants and options). This measure is available only for firms in the ExecuComp database. [Insert Table 2 Here] The hypothesis described in Section 2 suggests that the number of shares outstanding should be related to the volatility of earnings relative to expectations as well as to the payoffs to managers, conditional on meeting earnings. We examine both of these aspects. The higher the volatility, the more likely a firm misses earnings by chance, while the lower the volatility, the higher the probability of meeting or beating earnings. In column 1, we see that the higher the level of Net Income Volatility t−1 , the higher the number of shares outstanding. This is strongly significant, both economically and statistically. A firm with earnings volatility one standard deviation above the mean has 0.21 standard deviations more shares outstanding. The association is consistent across columns 1-3 using different proxies for earnings volatility. In fact, when looking at volatility relative to analyst forecasts, the effect is even larger. One standard deviation higher earnings volatility is associated with 0.30 standard deviations more shares. We also examine payoffs to managers conditional on meeting or beating earnings. Under our hypothesis, we expect that the stronger the incentives for managers to avoid missing earnings forecasts, the more shares outstanding. In column 4, we proxy for these incentives with the fraction of CEO compensation that is performance based. Stock and option ownership should be associated with strong incentives to meet or beat earnings due to the strong negative stock market reaction to missing forecasts. Bonuses paid to managers are often paid based on meeting EPS targets (De Angelis and Grinstein (2011), Young and Yang (2011), Kim and Yang (2012)). The measure of total incentive compensation should capture both of these aspects of a manager’s incentive to meet earnings. Consistent with this, we see a small but significantly positive 16
elationship between incentive compensation and shares outstanding. Specifically, a one standard deviation larger incentive compensation is associated with 0.05 standard deviations more shares outstanding. Consistent with the nominal price puzzle (Weld et al. (2009)), we see that larger firms have more shares outstanding. A one standard deviation change in size is associated with 0.76 standard deviations more shares outstanding. We also find that a one standard deviation larger Amihud measure is associated with a small (0.11 of a standard deviation), but statistically significant lower number of shares outstanding. This is consistent with the evidence that liquidity tends to improve after a stock splits. In column 1, we see no evidence of Institutional Ownership relating to the share structure of the firm. However, in column 4, controlling for incentive compensation (which reduces the sample due to data requirements), we see that Institutional Ownership is negatively related to shares outstanding. This is consistent with Dyl and Elliott (2006) who find that smaller share price stocks have lower institutional ownership. While our evidence on the volatility of earnings and incentive compensation is consistent with our hypothesis, we find evidence that the level of net income is negatively associated with shares outstanding. This is not entirely surprising. After controlling for size, there is significantly less variation in net income. Moreover, these findings highlight the thorny fact that these firm characteristics are jointly determined and are likely related to other, unobserved characteristics. Therefore, fully understanding what is driving the the shares outstanding choice requires a different identification strategy. To the extent that firms have unobserved characteristics that determine share structure and managerial incentives, the simple pooled OLS estimates will be biased and inconsistent. The first step we take to mitigate these issues is to examine a simple first difference model. This specification will measure the relationship between changes in firm characteristics and changes in shares outstanding. To the extent that a firm’s level of shares is determined by some variety of unobserved firm characteristics, focusing on changes will allow us to identify the effect of firm observable characteristics that change over time. While the specification itself mitigates issues with unobserved time invariant heterogeneity, it does not rule out the possibility of reverse causality. In Table 3, we present three specifications, one for each of the earnings volatility proxies described in Table 2. Consistent with prior results, earnings volatility is an important determinant of shares outstanding. In this case, a one standard deviation change in earnings volatility is associated with between 0.03 and 0.07 standard deviations of changes in shares outstanding. This effect is statistically significant at the 1% level and is still economically large when considering it is an estimate of changes. [Insert Table 3 Here] 17
Page 1 and 2: Gaming the Float: How Managers Resp
Page 3 and 4: earnings will choose more shares ou
Page 5 and 6: targets. Managers change the target
Page 7 and 8: it does not increase as the firm’
Page 9 and 10: meeting that forecast. This leads t
Page 11 and 12: 2.2 Time series determinants of sha
Page 13 and 14: ealized but before they are reporte
Page 15: A and incentive compensation in Pan
Page 19 and 20: the view that increases in forecast
Page 21 and 22: In columns 2 and 3, we measure our
Page 23 and 24: atypical amount. In Table 9, we rep
Page 25 and 26: price levels observed in the data.
Page 27 and 28: management tools. 27
Page 29 and 30: De Angelis, D. and Y. Grinstein (20
Page 31 and 32: A Appendix We show that a drop in a
Page 33 and 34: Figure 1: Example of the relationsh
Page 35 and 36: Figure 3: Median Shares Outstanding
Page 37 and 38: Table 2: Determinants of Shares Out
Page 39 and 40: Table 4: Shares Outstanding: First
Page 41 and 42: Table 6: Changes in Shares Outstand
Page 43 and 44: Table 8: Changes in Shares and Acco

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