ABSTRACT In this paper, we examine how CEO succession and succession planning affect perceptions of financial reporting risk among stakeholders who are responsible for and oversee firms' financial reporting (e.g., auditors, management, and audit committees). Management succession introduces uncertainty about firms' future operations, financial policies, and potential motivation for earnings management, which we predict elevates the perceived risk of financial reporting improprieties. Consistent with this prediction, we find that audit fees are higher for firms with new CEOs. Importantly, however, we note that careful CEO succession planning (i.e., promoting an “heir apparent”) attenuates perceptions of higher risk, as evidenced by a lack of an audit pricing adjustment. These results are robust to several alternative specifications and analyses designed to mitigate the concern that the association between audit fees and CEO succession and succession planning is driven by factors leading to the CEO change. We also show that audit fee increases dissipate over time as the new, non-heir CEO stays longer at the firm, reinforcing the inference that audit fees increase in response to the uncertainty surrounding a new CEO. Additionally, we do not find evidence of a deterioration in audit quality with new CEOs, independent of the succession plan. JEL Classifications: G30; M12; M41; M42.

ABSTRACT We classify all institutional investors that file Form 13-F over the period 1995–2013 as either “tax-sensitive” or “tax-insensitive” based on their trading behavior and portfolio characteristics. We examine tests of the effects of investor tax-sensitivity on portfolio rebalancing, price pressure, and fund performance, and compare our measure of tax-sensitive institutional investor ownership to three measures used in prior studies. We show that our measure of tax-sensitive investors dominates other measures in the portfolio rebalancing and price pressure tests. In the fund performance test, our measure of tax-sensitivity is the only one that finds that tax-sensitive investors have significantly lower returns on their portfolio stocks, which is a new result in the literature. JEL Classifications: G11; G20; H24.

ABSTRACT Propensity score matching (PSM) has become a popular technique for estimating average treatment effects (ATEs) in accounting research. In this study, we discuss the usefulness and limitations of PSM relative to more traditional multiple regression (MR) analysis. We discuss several PSM design choices and review the use of PSM in 86 articles in leading accounting journals from 2008–2014. We document a significant increase in the use of PSM from zero studies in 2008 to 26 studies in 2014. However, studies often oversell the capabilities of PSM, fail to disclose important design choices, and/or implement PSM in a theoretically inconsistent manner. We then empirically illustrate complications associated with PSM in three accounting research settings. We first demonstrate that when the treatment is not binary, PSM tends to confine analyses to a subsample of observations where the effect size is likely to be smallest. We also show that seemingly innocuous design choices greatly influence sample composition and estimates of the ATE. We conclude with suggestions for future research considering the use of matching methods. Data Availability: All data used are available from sources cited in the text.

ABSTRACT Developing models to detect financial statement fraud involves challenges related to (1) the rarity of fraud observations, (2) the relative abundance of explanatory variables identified in the prior literature, and (3) the broad underlying definition of fraud. Following the emerging data analytics literature, we introduce and systematically evaluate three data analytics preprocessing methods to address these challenges. Results from evaluating actual cases of financial statement fraud suggest that two of these methods improve fraud prediction performance by approximately 10 percent relative to the best current techniques. Improved fraud prediction can result in meaningful benefits, such as improving the ability of the SEC to detect fraudulent filings and improving audit firms' client portfolio decisions.