gender differential paper IJCRB

ijcrb.webs.com
JUNE 2011
INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS VOL 3, NO 2
1. Sufficient data are available from Compustat to calculate all required financial variables.
The above requirements resulted in a sample of 6,391 firm-years. Firm-years were deleted from
this sample if either the absolute value of their price-scaled cash flow, accrual or EVA
component changes were greater than 1.0 to avoid the excessive leverage of outliers on our
(linear) estimation. In addition, all firm-years with negative EPS were deleted based on findings
that loss firms have systematically different market responses to earnings than do profitable
firms (e.g., Hayn, 1995), and because analysts’ incentives and forecast accuracy are likely to
differ substantially for loss firms. Because of this deletion, our “good news” and “bad news”
partitions consist of firms with only profitable earnings increases and decreases. Our final sample
used to estimate equation (1) consists of 4,382 firm-years from 1991 to 2006, ranging from 232
to 362 firms per year.
To examine potential inefficiencies of analysts’ forecasts, the following additional requirements
were added:
2. The I/B/E/S CD-ROM contains at least three forecasts of earnings made in March of year t.
The requirement of at least three forecasts is necessary to calculate a standard deviation of the
forecasts.
Lastly, we eliminated firms with absolute price-scaled forecast errors greater than 1.0. In
addition, to ensure analysts were familiar with EVA we used forecasts made starting in 1999.
The above requirements limited our sample for the forecast error regression to 1,443 firm-years.
4. Results
Panel of Table 1 presents means of our analysis and control variables for the full sample,
partitioned according to sign of earnings change in year t−1. The mean cash flow change for
firm-years with earnings decreases in year t−1 (0.0165) is statistically indistinguishable from
firm-years with positive earnings changes (0.0098). The difference in the sign of the earnings
between these two sets of firms, therefore, seems to be driven by the accrual component as
evidenced by the negative accrual change in t−1 (mean=−0.0636) for firms with negative prior
earnings. The change in EVA adjustment in year t−1 is positive for both positive and negative
prior earnings firms (mean=0.0082 (0.0006) for firms with earnings changes below (above)
zero), indicating possibly that the computation of EVA cancels out some of the negative
information in the accrual component for firms with negative earnings changes in year t−1.
Panel B of Table 1 presents correlations for the independent variables used in the earnings
prediction tests. Based on the magnitude of these correlations, there is a potential for multi co
linearity in several regression models. However, all of our inferences are based upon means and
standard errors of annual coefficient estimates. Because we do not rely upon estimated OLS
standard errors in our statistical tests, the potential multi co linearity is not of immediate concern.
The most notable correlations are the positive correlation between EPS changes and EVA
changes (0.7188), which we expect given that they are competing performance proxies, and the
negative correlation between cash flow changes and accrual.
COPY RIGHT © 2011 Institute of Interdisciplinary Business Research 1911