Two essays in finance.
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How does past performance affect load mutual fund investor behavior ? I use a proprietary data set to provide evidence on how past performance affects the transaction decisions of a sample of load mutual fund investors by decomposing the overall effect into how past performance affects the probability investors will make a transaction and how past performance affects the size of the transactions made. I provide evidence on how mental accounting affects investors' willingness to redeem poor performers as discussed in Shefrin and Statman (1985). Investors are more likely to purchase past winners and make larger purchases of past winners, which is consistent with representativeness and the findings of Barber, Odean, and Zheng (2000), Sirri and Tufano (1998), and Fant and O'Neal (2000). However, I find no evidence of loss aversion when it comes to the probability of redeeming poor performers. My results show that when investors do redeem a poor performer they are more likely to liqudate the entire position. Finally, I find evidence consistent with Shefrin and Statman's (1985) hypothesis that investors are more willing to sell poor performers if the transaction is framed as a transfer rather than a sale.;All events induce variance: Analyzing abnormal returns when effects vary across firms. Widely used test statistics for non-zero mean abnormal returns in short-horizon event studies ignore cross-firm variation in event effects. We use a simple model of event effects and simulations patterned after Brown and Warner (1980, 1985) and Boehmer, Musumeci, and Poulsen (BMP, 1991) to highlight the resulting biases and the importance of using test procedures that appropriately allow for cross-sectional variation. We demonstrate analytically how cross-sectional variation produces “event-induced” variance increases and biases popular tests for non-zero mean abnormal returns. Our simulations provide evidence of that bias and of test power for several theoretically robust tests for non-zero means, including the standardized cross-sectional test statistic suggested by BMP, which we show equals the mean standardized prediction error divided by a heteroskedasticity-consistent standard error, and cross-sectional regression tests that condition on relevant regressors.