This paper extends a recent study by Malatesta [14] on measuring abnormal performance using joint generalized least squares. For monthly data and a random sample of securities, Malatesta finds that there is little benefit in using more sophisticated econometric techniques to identify abnormal returns. The current study extends these results using a design that is more amenable to the benefits of the generalized methods and is consistent with actual event studies. Most notably, the study uses a sample of securities experiencing an actual event and tests both monthly and daily data. In addition, iterative techniques are compared to the ordinary least squares and estimated generalized least squares methods. The results of this study support the original conclusions of Malatesta, indicating no measurable gain in using any of the systems methods for event study applications.

The traditional Capital Asset Pricing Model (CAPM) provides a foundation for the estimation of systematic risk that has been applied extensively in studies of investment performance, market efficiency, predictive models, and capital budgeting, to name only a few. Lee [10] considered a special case of nonlinearities occurring in the estimation of systematic risk within the context of the investment horizon problem. His findings, based on a limited sample, provided significant methodological implications for the estimation process and have received wide readership through republication of the study in a readings text [6].

Relative to quantitative methods traditionally used in accounting and finance, textual analysis is substantially less precise. Thus, understanding the art is of equal importance to understanding the science. In this survey, we describe the nuances of the method and, as users of textual analysis, some of the tripwires in implementation. We also review the contemporary textual analysis literature and highlight areas of future research.

Form S-1 is the first SEC filing in the initial public offering (IPO) process. The tone of the S-1, in terms of its definitiveness in characterizing the firm’s business strategy and operations, should affect investors’ ability to value the IPO. We find that IPOs with high levels of uncertain text have higher first-day returns, absolute offer price revisions, and subsequent volatility. Our findings provide empirical evidence for the theoretical models of uncertainty, bookbuilding, and prospect theory.

This paper examines estimation issues associated with multivariate tests of asset pricing. Two issues are considered: (1) the constraint that the sample size (N) must be less than the time series (T), and (2) the relative effect on power of using the multivariate statistic versus a univariate counterpart. We find that an alternative statistic that allows for large N does not dominate the usual portfolio tests. More notably, we find that the power of a simple diagonal statistic usually dominates the multivariate statistic for cases considered in this study.

Defining and measuring readability in the context of financial disclosures becomes important with the increasing use of textual analysis and the Securities and Exchange Commission's plain English initiative. We propose defining readability as the effective communication of valuation‐relevant information. The Fog Index—the most commonly applied readability measure—is shown to be poorly specified in financial applications. Of Fog's two components, one is misspecified and the other is difficult to measure. We report that 10‐K document file size provides a simple readability proxy that outperforms the Fog Index, does not require document parsing, facilitates replication, and is correlated with alternative readability constructs.

The robustness of the multivariate test of Gibbons, Ross, and Shanken (1986) to nonnormalities in the residual covariance matrix is examined. After considering the relative performance of various tests of normality, simulation techniques are used to determine the effects of nonnormalities on the multivariate test. It is found that, where the sample nonnormalities are severe, the size and/or power of the test can be seriously misstated. However, it is also shown that these extreme sample values may overestimate the population parameters. Hence, we conclude that the multivariate test is reasonably robust with respect to typical levels of nonnormality.