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

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While Table 2 and Table 3 establish a relationship between earnings volatility and shares outstanding in both the cross section and time series, they do not establish a clear causal link due to the presence of unobserved firm characteristics that likely change through time. In order to examine this question more carefully and test the explicit hypotheses outlined above, we use exogenous variation in the volatility of earnings around the expectation to establish a causal link between incentives and share structure. In Table 4 we use an instrumental variable approach for the first difference model described above. We instrument for the volatility of earnings (proxied for in Table 2 using the volatility of earnings relative to analyst forecasts) by using exogenous drops in analyst coverage. Our identification strategy is similar to those used in Hong and Kacperczyk (2010), Kelly and Ljungqvist (2012), and Derrien and Kecskés (2012) who argue that the consolidation of analyst coverage following brokerage house mergers leads to exogenous drops in overall coverage. When brokerage houses merge, the combined houses will often have more than one analyst covering a stock. As brokerages reduce coverage to eliminate this duplication of effort, the total number of analysts following certain stocks drops; however, this drop in coverage is exogenous to the firm’s fundamentals. [Insert Table 4 Here] We use a slight variation in the brokerage house strategy employed by Degeorge et al. (2012). We use drops in analyst coverage brought on by analysts that leave the IBES database permanently in that period. As shown in Degeorge et al. (2012), these drops match the distribution of those from brokerage mergers. After a drop in analyst coverage, the consensus forecasts will be noisier because the consensus is formed over a smaller sampling of analysts - the variance of the sample mean is decreasing in n. Therefore, it is less likely that realized EPS will meet forecasts solely as a result of the drop. In Table 4, we show results from the second stage of a 2SLS regression using the drop in a analyst coverage as an instrument for Net Income Volatility-Analyst Forecast t−1 . Our instrument is strong. In the first stage regression, the t-statistic on the instrument is > 5 and the F-statistic is > 25, both well above the benchmarks established in Stock et al. (2002). Using the instrumental variable approach, we see still see a strong causal relationship between changes in the volatility of forecast errors and shares outstanding. Specifically, a one standard deviation increase in Net Income Volatility-Analyst Forecast t−1 is associated with a 0.53-0.58 standard deviation increase in the number of shares outstanding. This is an economically large effect, as one standard deviation is measured over the entire cross sectional distribution of shares outstanding. Given the exogenous nature of the instrument, we can now document evidence consistent with 18
the view that increases in forecast volatility cause managers to increase the number of shares outstanding. Comparing these coefficients to Table 3 we see a much larger effect. This difference provides strong evidence of the endogenous relationship between these variables and shows the importance of the exogenous shock in terms of measuring magnitudes. We also see a clearer relationship with respect to other incentives and share structures. Changes in Market to Book are negatively related to shares outstanding. A one standard deviation increase in Market to Book is associated with 0.03 standard deviation decrease in shares outstanding, but this effect is insignificant when we control for incentive compensation. We also find a positive effect of 0.08 standard deviations due to a change in Market Cap. These two results are consistent with De Angelis and Grinstein (2011) who show that small, high growth firms compensate managers less on accounting based criteria. We also observe that as Net Income grows, shares outstanding increase. Finally, in column 2 of Table 4 there is a small but significant positive relationship between increases in incentive compensation and the change in shares outstanding. Overall, we find that changes in forecast error volatility cause an increase in shares and that net income growth and incentive compensation increases are also associated with an increase in shares. This evidence supports our hypothesis H1A described in Section 2. In Table 5, we explore in more detail the mechanism by which managers increase their shares outstanding. In order to compare across columns, we use standardized independent variables and unstandardized measures for changes in shares outstanding. This allows us to interpret the coefficients as the number of shares increased (or decreased) due to a one standard deviation change in a given independent variable. We do this to compare across different sources of share increases which have different cross sectional distributions. Column 1 reports the effect of changes in characteristics on increases in shares from sources other than stock splits. While the point estimate for changes in Net Income Volatility as instrumented by analyst coverage drops shows an increase in shares outstanding from a combination of issuances, treasury shares, and stock awards, the effect is not statistically significant. In contrast, column 2 shows a large and significant increase in shares due to an increase in Net Income Volatility (as instrumented by analyst coverage) as a result of stock splits. These results suggest that the channel managers use to increase the range of Net Income that will round to analyst expectations is through stock splits, not through issuances or use of treasury stock. This evidence is consistent with hypothesis H4. To provide further evidence that managers engage in target size management, we explore a unique feature of the relationship between shares, net income, and target size. The size of the earnings target is a function of the magnitude of the EPS forecast, not the sign. Therefore, earnings growth has opposite effects, depending 19
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 and 16: A and incentive compensation in Pan
Page 17: elationship between incentive compe
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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