The event study is an important research tool in economics and finance. The goal of an event study is to measure the effects of an economic event on the value of firms. Event study methods exploit the fact that, given rationality in the marketplace, the effects of an event will be reflected immediately in security prices. Thus the impact can be measured by examining security prices surrounding the event. In this paper event study methods are described including some of the potential complications. An example is included to illustrate the approach.

A number of studies have presented evidence rejecting the validity of the Sharpe-Lintner capital asset pricing model (CAPM). Possible alternatives include risk-based models, such as multifactor asset pricing models, or nonrisk-based models which address biases in empirical methodology, the existence of market frictions, or the presence of irrational investors. Distinguishing between the alternatives is important for applications such as cost of capital estimation. This paper develops a framework which shows that, ex ante, CAPM deviations due to missing risk factors will be very difficult to detect empirically, whereas deviations resulting from nonrisk-based sources are easily detectable. The results suggest that multifactor pricing models alone do not entirely resolve CAPM deviations.

This paper evaluates the power of multivariate tests of the Capital Asset Pricing Model. The results indicate that when employing an unspecified alternative hypothesis, the ability of the tests to distinguish between the CAPM and other pricing models is poor. An upper bound is derived for the distance the alternative distribution of the test statistic can be from the null distribution when the deviations from the CAPM are due to missing factors. This upper bound explains the low power of the tests.

When a risk factor is missing from an asset pricing model, the resulting mispricing is embedded within the residual covariance matrix. Exploiting this phenomenon leads to expected return estimates that are more stable and precise than estimates delivered by standard methods. Portfolio selection can also be improved. At an extreme, optimal portfolio weights are proportional to expected returns when no factors are observable. We find that such portfolios perform well in simulations and in out-of-sample comparisons.

[This article examines intraday transaction data for S&P 500 stock index futures prices and the intraday quotes for the underlying index. The data indicate that the futures price changes are uncorrelated and that the variability of these price changes exceeds the variability of price changes in the S&P 500 index. This excess variability of the futures over the index remains even after controlling for the nonsynchronous prices in the index quotes, which induces autocorrelation in the index changes. We advance and examine empirically two hypotheses regarding the difference between the futures price and its theoretical value: that this "mispricing" increases on average with maturity, and that it is path-dependent. Evidence supporting these hypotheses is presented.]

[Tests of financial asset pricing models may yield misleading inferences when properties of the data are used to construct the test statistics. In particular, such tests are often based on returns to portfolios of common stock, where portfolios are constructed by sorting on some empirically motivated characteristic of the securities such as market value of equity. Analytical calculations, Monte Carlo simulations, and two empirical examples show that the effects of this type of data snooping can be substantial.]

[If returns on some stocks systematically lead or lag those of others, a portfolio strategy that sells "winners" and buys "losers" can produce positive expected returns, even if no stock's returns are negatively autocorrelated as virtually all models of overreaction imply. Using a particular contrarian strategy we show that, despite negative autocorrelation in individual stock returns, weekly portfolio returns are strongly positively autocorrelated and are the result of important cross-autocorrelations. We find that the returns of large stocks lead those of smaller stocks, and we present evidence against overreaction as the only source of contrarian profits.]

[In this article we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sample period (1962-1985) and for all subperiods for a variety of aggregate returns indexes and size-sorted portfolios. Although the rejections are due largely to the behavior of small stocks, they cannot be attributed completely to the effects of infrequent trading or time-varying volatilities. Moreover, the rejection of the random walk for weekly returns does not support a mean-reverting model of asset prices.]

This paper develops tests of unconditional mean-variance efficiency under weak distributional assumptions using a generalized method of moments framework. These tests are potentially more robust than commonly employed tests which rely on the assumption that asset returns are normally distributed and temporarily i.i.d. Using returns for size-based portfolios from 1926 to 1988, the authors show that the conclusion concerning the mean-variance efficiency of market indexes can be sensitive to the test considered.

When a risk factor is missing from an asset pricing model, the resulting mispricing is embedded within the residual covariance matrix. Exploiting this phenomenon leads to expected return estimates that are more stable and precise than estimates delivered by standard methods. Portfolio selection can also be improved. At an extreme, optimal portfolio weights are proportional to expected returns when no factors are observable. We find that such portfolios perform well in simulations and in out-of-sample comparisons.