We analyze tests for long-run abnormal returns and document that two approaches yield well-specified test statistics in random samples. The first uses a traditional event study framework and buy-and-hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewnessadjusted t-statistic or the empirically generated distribution of long-run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar-time portfolios and a time-series t-statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long-run abnormal returns is treacherous. COMMONLY USED METHODS TO TEST for long-run abnormal stock returns yield misspecified test statistics, as documented by Barber and Lyon ~1997a! and Kothari and Warner ~1997!. 1 Simulations reveal that empirical rejection levels routinely exceed theoretical rejection levels in these tests. In combination, these papers highlight three causes for this misspecification. First, the

A rapidly growing literature claims to reject the efficient market hypothesis by producing large estimates of long-term abnormal returns following major corporate events. The preferred methodology in this literature is to calculate average multi-year buy-and-hold abnormal returns and conduct inferences via a bootstrapping procedure. We show that this methodology is severely flawed because it assumes independence of multi-year abnormal returns for event firms, producing test statistics that are up to four times too large. After accounting for the positive cross-correlations of event firm abnormal returns we find virtually no evidence of reliable abnormal performance for our samples.

This paper addresses the following basic capital budgeting question: Suppose that crosssectional differences in stock returns can be predicted based on variables other than beta (e.g., book-to-market), and that this predictability reflects market irrationality rather than compensation for fundamental risk. In this setting, how should companies determine hurdle rates? I show how factors such as managerial tune horizons and financial constraints affect the optimal hurdle rate. Under some circumstances, beta can be useful as a capital budgeting tool, even if it is of no use in predicting stock returns.

Eric Sirri, Andrei Shleifer, and an anonymous referee for helpful comments. We acknowledge

Many studies have documented portfolio strategies that provide returns in excess of those expected, given the level of risk of the portfolio. Variables that seem to have predictive power for equity returns include the market capitalization of the firm’s equity and the ratio of the firm’s book equity to market equity (BE/ME). Firms with low market capitalization and high book-tomarket values seem to earn high returns. With respect to the book-to-market anomaly, it has been argued that the apparent superior performance is due to a subtle selection bias in a typical data source used to implement the tests of asset pricing models, the COMPUSTAT data. We use a sample of COMPUSTAT data that is free from this bias to investigate whether the previous evidence on the book-to-market anomaly is an artifact of this selection bias. The postulated selection bias does not seem to be important for samples restricted to NYSE/AMEX firms. There is some difference when NASDAQ firms are included in the standard COMPUSTAT sample. This may be due to a truly stronger BE/ME effect or to a more severe selection bias in that sample. Our data do not allow us to disentangle these two possible explanations.

We study the price effects of changes to the S&P 500 index and document an asymmetric price response: There is a permanent increase in the price of added firms but no permanent decline for deleted firms. These results are at odds with extant explanations of the effects of index changes that imply a symmetric price response to additions and deletions. A possible explanation for asymmetric price effects arises from changes in investor awareness. Results from our empirical tests support the thesis that changes in investor awareness contribute to the asymmetric price effects of S&P 500 index additions and deletions. The long-held assumption that stocks have perfect substitutes, and the perfect elasticity of demand that follows from it, is central to modern finance theory. If securities have (almost)

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The fragility of the CAPM has led to a resurgence of research that frequently uses trading strategies based on sorting procedures to uncover relations between firm characteristics (such as “value ” or “glamour”) and equity returns. We examine the propensity of these strategies to generate statistically and economically significant profits due to our familiarity with the data. Under plausible assumptions, data-snooping can account for up to 50 percent of the insample relations between firm characteristics and returns uncovered using single (one-way) sorts. The biases can be much larger if we simultaneously condition returns on two (or more) characteristics.

My objective in writing this survey is to provide an overview of the work that has been done in an important area of financial markets research—explaining the behavior of common stock returns. I have tried to make this survey as complete as possible, without getting bogged down in a lot of technical details. Since this area of research has been very active for the past several years, describing all of the work that has

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