Due to a lack of an information history, IPO firms' information precision is not only generally low but also likely to be estimated initially with considerable error. I hypothesize and find that the deviation between expected and realized information precision is predictably associated with the magnitude and the persistence of long‐run abnormal returns after an IPO . Specifically, an upward (downward) revision of information precision results in positive (negative) abnormal returns over the period in which investors update their beliefs. In addition, the positive abnormal returns of firms with unexpectedly high realized information precision are less persistent than the negative abnormal returns of firms with unexpectedly low realized information precision, which can extend up to 18 months after the IPO . The findings imply that long‐term investors in IPO stocks do not necessarily behave irrationally, but that both positive and negative post‐ IPO abnormal performance is also consistent with rational investors gradually updating the perceived information precision parameter of these stocks.

This paper investigates how data requirements often encountered in archival accounting research can produce a data-restricted sample that is a non-random selection of observations from the reference sample to which the researcher wishes to generalize results. We illustrate the effects of non-random sampling on results of association tests in a setting with data on one variable of interest for all observations and frequently-missing data on another variable of interest. We develop and validate a resampling approach that uses only observations from the data-restricted sample to construct distribution-matched samples that approximate randomly-drawn samples from the reference sample. Our simulation tests provide evidence that distribution-matched samples yield generalizable results. We demonstrate the effects of non-random sampling in tests of the association between realized returns and five implied cost of equity metrics. In this setting, the reference sample has full information on realized returns, while on average only 16% of reference sample observations have data on cost of equity metrics. Consistent with prior research (e.g., Easton and Monahan The Accounting Review 80, 501–538, 2005), analysis using the unadjusted (non-random) cost of equity sample reveals weak or negative associations between realized returns and cost of equity metrics. In contrast, using distribution-matched samples, we find reliable evidence of the theoretically-predicted positive association. We also conceptually and empirically compare distribution-matching with multiple imputation and selection models, two other approaches to dealing with non-random samples.

We examine how the criteria for choosing estimation samples affect the ability to detect discretionary accruals, using several variants of the Jones (1991) model. Researchers commonly estimate accruals models in cross-section, and define the estimation sample as all firms in the same industry. We examine whether firm size performs at least as well as industry membership as the criterion for selecting estimation samples. For U.S. data, we find estimation samples based on similarity in lagged assets perform at least as well as estimation samples based on industry membership at detecting discretionary accruals, both in simulations with seeded accruals between 2% and 100% of total assets and in tests examining restatement data and AAER data. For non-U.S. data, we find industry-based estimation samples result in significant sample attrition and estimation samples based on lagged assets perform at least as well as estimation samples based on industry membership, both in simulations and in tests examining German restatement data, with substantially less sample attrition.

ABSTRACT This paper provides descriptive evidence on how stock market participants use Generative Artificial Intelligence (GenAI) to process investment‐related information. Using a data set of 1.7 million stock‐related queries from one of China's largest GenAI platforms during the first half of 2024, we document that user queries address a wide range of topics and tasks and vary systematically with usage intensity and financial sophistication. Query activity increases around corporate disclosure events, but these increases largely track contemporaneous media coverage. We also find evidence consistent with a substitution between the informativeness of voluntary managerial disclosures and investors' reliance on GenAI. Continued platform engagement more likely follows answers that are concise and contain directionally accurate trading signals. Over time, users' subsequent queries increasingly reflect the specificity and financial terminology present in earlier GenAI answers. At the market level, GenAI usage is associated with higher measures of informed trading and lower liquidity, while aggregated sentiment in GenAI‐generated answers correlates with same‐day abnormal returns, particularly when user feedback is positive. Overall, our findings offer insights into early‐stage GenAI adoption by retail investors and inform discussions on how GenAI shapes information processing in financial markets.

ABSTRACT For a broad sample of firms, we use structural equations modeling to construct latent variables for real-action aggressiveness and reporting policy aggressiveness. We estimate the association between the latent variables and the associations of each latent variable with shareholder payoffs (returns) and CEO payoffs (annual compensation to the CEO position). Results show the two types of aggressiveness are positively correlated but have different associations with the payoffs we consider. Greater policy-choice aggressiveness is associated with higher returns and compensation; the opposite is true for greater real-action aggressiveness. We find a positive association between policy-choice aggressiveness and restatement likelihood. Compared with nonrestatement firms, abnormal returns of restatement firms with aggressive policy choices are larger in the pre-restatement period and lower in the post-restatement period. Negative returns at the restatement announcement do not, on average, eliminate long-run (multi-year) positive returns of the pre-restatement period or of the period whose results are restated. JEL Classifications: M40; M41.

ABSTRACT Using path analysis, we investigate the direct and indirect links between three measures of earnings quality and the cost of equity. Our investigation is motivated by analytical models that specify both a direct link and an indirect link that is mediated by information asymmetry, but do not suggest which link would be more important empirically. We measure information asymmetry as both the adverse selection component of the bid-ask spread and the probability of informed trading (PIN). For a large sample of Value Line firms during 1993–2005, we find statistically reliable evidence of both a direct path from earnings quality to the cost of equity, and an indirect path that is mediated by information asymmetry, with the weight of the evidence favoring the direct path as the more important.

We examine the properties of a returns‐based representation of earnings quality, estimated from firm‐specific asset‐pricing regressions augmented by an earnings quality mimicking factor. The coefficient on the earnings quality factor (the “e‐loading”) captures the sensitivity of the firm's returns to earnings quality in a given year or quarter, analogous to beta as a measure of the sensitivity of returns to market movements. Relative to other proxies for earnings quality, e‐loadings can be calculated for larger samples of firms and can be estimated for shorter intervals at any point in time. Along all dimensions examined, we find that e‐loadings perform well in capturing notions of earnings quality.

We examine the properties of a returns-based representation of earnings quality, estimated from firm-specific asset-pricing regressions augmented by an earnings quality mimicking factor. The coefficient on the earnings quality factor (the “e-loading”) captures the sensitivity of the firm's returns to earnings quality in a given year or quarter, analogous to beta as a measure of the sensitivity of returns to market movements. Relative to other proxies for earnings quality, e-loadings can be calculated for larger samples of firms and can be estimated for shorter intervals at any point in time. Along all dimensions examined, we find that e-loadings perform well in capturing notions of earnings quality.