Paul Hribar

Investor Sophistication and the Mispricing of Accruals

This paper examines the role of institutional investors in the pricing of accruals. Using Bushee;s (1998) classification of institutional investors, we show that firms with a high level of institutional ownership and a minimum threshold level of active institutional traders have stock prices that more accurately reflect the persistence of accruals. This result holds after controlling for differences in the persistence of accruals between firms with high and low institutional ownership, and after controlling for other characteristics that are correlated with institutional ownership and future returns. Additionally, firms with low institutional ownership are smaller, less profitable, and have lower share turnover, suggesting that limits to arbitrage impede institutional investors from exploiting the seemingly large abnormal returns for these firms.

Two-Stage Regression Analysis and Biased Estimates in Accounting Research: An Application of the Frisch-Waugh-Lovell Theorem

We analyze a procedure common in empirical accounting and finance research where researchers use ordinary least squares to decompose a dependent variable into its predicted and residual components and use the residuals as the dependent variable in a second regression. This two‐step procedure is used to examine determinants of constructs such as discretionary accruals, real activities management, discretionary book‐tax differences, and abnormal investment. We show that the typical implementation of this procedure generates biased coefficients and standard errors that can lead to incorrect inferences, with both Type I and Type II errors. We further show that the magnitude of the bias in coefficients and standard errors is a function of the correlations between model regressors. We illustrate the potential magnitude of the bias in accounting research in four commonly used settings. Our results indicate significant bias in many of these settings. We offer three solutions to avoid the bias.

An Examination of the Relative Abilities of Earnings and Cash Flows to ExplainReturns and Market Values

We examine why, as a summary statistic, earnings is better than cash flows at explaining contemporaneous returns despite being a worse predictor of future operating cash flows. Several studies compare the ability of earnings and operating cash flows to predict valuation-related outcome variables including, stock returns, market values, and future operating cash flows. Although past results are mixed, recent studies suggest earnings are a better summary predictor of returns while operating cash flows better predict future operating cash flows. In this study, we replicate these two findings using a constant sample and consistent variable definitions, and examine several possible explanations for why earnings outperform cash flows in explaining contemporaneous returns and market values. Our results suggest that earnings outperform cash flows in explaining variation in both future free cash flows and discount rates. When directly comparing the two, we find that earnings superior ability to explain variation in discount rates is more responsible than its ability to explain variation in future free cash flows. We provide evidence that the mechanism by which earnings explain more variation in discount rates than cash flows is accounting conservatism or timely loss recognition.

Coefficient Bias When Using Residuals as the Dependent Variable

We analyze a procedure common in empirical accounting and finance research where researchers use ordinary least squares to decompose a dependent variable into its predicted and residual components and use the residuals as the dependent variable in a second regression. This two‐step procedure is used to examine determinants of constructs such as discretionary accruals, real activities management, discretionary book‐tax differences, and abnormal investment. We show that the typical implementation of this procedure generates biased coefficients and standard errors that can lead to incorrect inferences, with both Type I and Type II errors. We further show that the magnitude of the bias in coefficients and standard errors is a function of the correlations between model regressors. We illustrate the potential magnitude of the bias in accounting research in four commonly used settings. Our results indicate significant bias in many of these settings. We offer three solutions to avoid the bias.