ABSTRACT Previous research provides evidence that, for the clients of a large audit firm, audit clients with higher perceived business risk bear the expected costs of this risk with higher audit fees. We extend the literature, which focuses on the relation between litigation risk and audit fees, by examining alleged client misconduct that is not illegal but possibly increases business risk. In particular, we examine the relation between audit fees and business risk for audit clients doing business in developing countries where bribery of top government officials has been an accepted business practice. We hypothesize that bribery‐paying clients are riskier because of both client business risk and audit business risk. Using data collected from Securities and Exchange Commission filings and audit fee data in the 1970s, before the passage of the Foreign Corrupt Practices Act, we provide evidence that audit fees were higher for clients that disclosed paying bribes. This evidence is consistent with an audit market where auditors assess business risk at the client level, then pass their expected costs to the client in the form of higher audit fees.

We analyze the empirical power and specification of test statistics in event studies designed to detect long-run (one- to five-year) abnormal stock returns. We document that test statistics based on abnormal returns calculated using a reference portfolio, such as a market index, are misspecified (empirical rejection rates exceed theoretical rejection rates) and identify three reasons for this misspecification. We correct for the three identified sources of misspecification by matching sample firms to control firms of similar sizes and book-to-market ratios. This control firm approach yields well-specified test statistics in virtually all sampling situations considered.

This research evaluates methods used in event studies that employ accounting-based measures of operating performance. We examine the choice of an accounting-based performance measure, a statistical test, and a model of expected operating performance. We document the impact of these choices on the test statistics designed to detect abnormal operating performance. We find that commonly used research designs yield test statistics that are misspecified in cases where sample firms have performed either unusually well or poorly. In this sampling situation, the test statistics are only well specified when sample firms are matched to control firms of similar pre-event performance.

Fama and French (1992) document a significant relation between firm size, book-to-market ratios, and security returns for nonfinancial firms. Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results. We document that the relation between firm size, book-to-market ratios, and security returns is similar for financial and nonfinancial firms. In addition, we present evidence that survivorship bias does not significantly affect the estimated size or book-to-market premiums in returns. Our results indicate data-snooping and selection biases do not explain the size and book-to-market patterns in returns.

Fama and French (1992) document a significant relation between firm size, book-to-market ratios, and security returns for nonfinancial firms. Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results. The authors document that the relation between firm size, book-to-market ratios, and security returns is similar for financial and nonfinancial firms. In addition, they present evidence that survivorship bias does not significantly affect the estimated size or book-to-market premiums in returns. The authors' results indicate data-snooping and selection biases do not explain the size and book-to-market patterns in returns.

ABSTRACT Fama and French (1992) document a significant relation between firm size, book‐to‐market ratios, and security returns for nonfinancial firms. Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results. We document that the relation between firm size, book‐to‐market ratios, and security returns is similar for financial and nonfinancial firms. In addition, we present evidence that survivorship bias does not significantly affect the estimated size or book‐to‐market premiums in returns. Our results indicate data‐snooping and selection biases do not explain the size and book‐to‐market patterns in returns.

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 skewness‐adjusted 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.

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 skewness‐adjusted 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.

Prior research documents a negative aggregate earnings-returns relation. In contrast, we posit that the sign of the relation varies, depending upon the macroeconomic and financial market conditions that exist in the earnings announcement quarter. We argue that the existing macroeconomic and financial market conditions influence market participants’ frame of reference, which in turn affects whether they interpret aggregate earnings surprises to be informative about the expected inflation component of the discount rate, the market risk premium component of the discount rate, or aggregate future cash flows. Consistent with this, we find that the sign of the aggregate earnings-returns relation changes numerous times across our sample period. We also find that market participants interpret aggregate earnings to be informative about changes in expected inflation (market risk premium) when the sign of the aggregate earnings-returns relation is negative (positive). Finally, we identify macroeconomic and financial market conditions under which the aggregate earnings-returns relation is more (less) likely to be negative (positive).