Our robustness checks include performing identical regressions throwing out various portions
of the sample. For example, the results are largely the same if we exclude Nokia A and K
shares, the most traded stocks, from the sample. For the highly significant variables we focus
on in the paper, the non-Nokia coefficients are generally within 30 percent and frequently are
within 10 percent of those reported in Tables I and II. Also, we have performed the same
analysis using every other trading day and every fifth trading day to ensure that our test
statistics are not biased by first-order serial correlation. Although the test statistic reduction is
commensurate with the reduction in sample size, the coefficients are approximately the same,
and the test statistics that we focus on in the full regression are all highly significant in the
odd-day and even-day regressions.
3 These include ~1! 87 dummy variables for each stock ~but one!; to control for the tendency
of any group to sell or hold any one stock ~2! 25 dummy variables for each month analyzed ~but
one!; to control for calendar effects ~3! 35 dummy variables for the number of stocks held in the
portfolio ~one stock through 35 stocks, with greater than 35 being the omitted dummy!; to
control for cross-sectional differences in trading activeness across investors ~4! 15 birth-year
dummies; representing 5-year intervals to account for life-cycle effects ~5! market returns over
the same 11 past return intervals used for market-adjusted returns, ~found by Choe et al. ~1999!
to account for trading behavior!; and ~6! 11 cross-products between the market return variables
and a capital loss dummy to analyze if the disposition effect alters the reaction to past market
returns. Using these controls adds a level of comfort to our assertion that the interpretation of
the significant coefficients on our reported variables are not due to correlations with omitted
variables.