Abstract We investigate how information choices affect equity returns and risk. Building on an existing theoretical model of information and investment choice, we estimate a learning index that reflects the expected benefits of learning about an asset. High learning index stocks have lower future returns and risk compared to low learning index stocks. Analysis of a conditional asset pricing model, long-run patterns in returns and volatilities, other measures of information flow, and the information environment surrounding earnings announcements reinforce our interpretation of the learning index. Our findings support the model’s predictions and illustrate a novel empirical measure of investor learning.

ABSTRACT Because of upward trends in research and development activity, accounting measures of financial distress have become less accurate. We document that (1) higher research and development spending increases the likelihood of misclassifying solvent firms, (2) adjusting for conservative accounting of research and development increases the number of correctly identified distressed firms, and (3) adjusted measures of distress alleviate previously documented anomalously low returns of large, high distress risk, low book‐to‐market firms. The results hold after updating stale parameters and under various tax assumptions. Our evidence raises concerns about interpretation of extant literature that relies on accounting measures of distress.

Pending changes in lease accounting standards will require firms to recognize obligations that have historically been kept off-balance-sheet (OBS). We examine the implications of this accounting treatment for a host of common risk and performance metrics. Conventional leverage, Z-Score, levered beta, return on capital and other asset utilization measures underestimate risk and overstate performance of firms relying heavily on OBS leasing. The distortion affects relative rankings as well as average levels and has increased over time. Proposed changes in reporting standards aim to mitigate future distortion, but necessitate adjustments for time-series comparisons. Under current reporting standards, investors, analysts, and researchers can estimate leased asset value and adjust accounting-based metrics to better reflect these fixed costs.

ABSTRACT Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974) . Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious.