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

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
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