Prior literature observes that information uncertainty exacerbates investor underreaction behavior. In this paper, I investigate whether, as professional investment intermediaries, sell‐side analysts suffer more behavioral biases in cases of greater information uncertainty. I show that greater information uncertainty predicts more positive (negative) forecast errors and subsequent forecast revisions following good (bad) news, which corroborates previous findings on the post‐analyst‐revision drift. The opposite effects of information uncertainty on forecast errors and subsequent forecast revisions following good versus bad news support the analyst underreaction hypothesis and are inconsistent with analyst forecast rationality or optimism suggested in prior literature.

This paper investigates two competing hypotheses for the accrual anomaly: investment/growth and persistence. Both investment/growth and persistence information in accruals are likely to vary cross-sectionally, depending on a firm's business model, a fact that generates different cross-sectional implications for the accrual anomaly. I find that the magnitude of the accrual anomaly monotonically increases with the investment information contained in accruals, as measured by the co-variation between accruals and employee growth. In industries/firms in which accruals co-vary with employee growth, accruals show strong predictive power for future stock returns. In industries/firms in which accruals show little correlations with employee growth, the accrual anomaly is much weaker. In contrast, the evidence from the cross-sectional analysis is inconsistent with the persistence argument. From the earnings perspective, the evidence on one-year-ahead earnings growth is inconclusive, but the results on longer-term earnings growth support the investment argument but not the persistence argument. Collectively, I conclude that these results support the view that the accrual anomaly is attributable to the fundamental investment information contained in accruals.

ABSTRACT There is substantial evidence of short‐term stock price continuation, which the prior literature often attributes to investor behavioral biases such as underreaction to new information. This paper investigates the role of information uncertainty in price continuation anomalies and cross‐sectional variations in stock returns. If short‐term price continuation is due to investor behavioral biases, we should observe greater price drift when there is greater information uncertainty. As a result, greater information uncertainty should produce relatively higher expected returns following good news and relatively lower expected returns following bad news. My evidence supports this hypothesis.

[This article solves the portfolio choice problem in a multi-asset jump-diffusion model. We decompose the optimal portfolio weight into components that correspond to a collection of fictitious economies, one of which is a standard diffusion economy, and the others of which are pure-jump economies. We derive a semi-closed-form solution for the optimal portfolio weight, and investigate its properties with or without ambiguity aversion. We find that an investor may not reduce her investment in risky assets when facing more frequent jumps, as suggested by a single-asset jump-diffusion model. Moreover, an investor who is extremely cautious about her estimates of higher moments of asset returns may still hold risky assets, contrary to the prediction of a pure-diffusion model with ambiguity aversion to the first moment.]

We take up Cochrane’s (2011) challenge to identify the firm characteristics that provide independent information about average U.S. monthly stock returns by simultaneously including 94 characteristics in Fama-MacBeth regressions that avoid overweighting microcaps and adjust for data-snooping bias. We find that while 12 characteristics are reliably independent determinants in non-microcap stocks from 1980 to 2014 as a whole, return predictability sharply fell in 2003 such that just two characteristics have been independent determinants since then. Outside of microcaps, the hedge returns to exploiting characteristics-based predictability also have been insignificantly different from zero since 2003.

This article solves the portfolio choice problem in a multi-asset jump-diffusion model. We decompose the optimal portfolio weight into components that correspond to a collection of fictitious economies, one of which is a standard diffusion economy, and the others of which are pure-jump economies. We derive a semi-closed-form solution for the optimal portfolio weight, and investigate its properties with or without ambiguity aversion. We find that an investor may not reduce her investment in risky assets when facing more frequent jumps, as suggested by a single-asset jump-diffusion model. Moreover, an investor who is extremely cautious about her estimates of higher moments of asset returns may still hold risky assets, contrary to the prediction of a pure-diffusion model with ambiguity aversion to the first moment. (JEL G11) It has been widely documented that stock returns exhibit both stochastic volatility and jumps (see, for example, Bakshi, Cao, and Chen 1997; Bates 2000; Eraker, Johannes, and Polson 2003). With jumps, an asset return model can explicitly allow for sudden but infrequent market movements of large magnitude, thus capturing both the “skewed ” and “fat-tailed ” features of stock

ABSTRACT Interpreting accruals as working capital investment, we hypothesize based on "q"-theory that firms optimally adjust their accruals in response to discount rate changes. A higher discount rate means less profitable investments and lower accruals, and a lower discount rate means more profitable investments and higher accruals. Our evidence supports this optimal investment hypothesis: (1) adding an investment factor into standard factor regressions substantially reduces the magnitude of the accrual anomaly, often to insignificant levels; (2) accruals covary negatively with discount rate estimates from the dividend discounting model, and for the most part, with estimates from the residual income model; (3) accruals with low accounting reliability covary more with capital investment than accruals with high accounting reliability; and (iv) expected returns to accruals-based trading strategies are time-varying, suggesting that the deterioration of the accrual effect in recent years might be temporary and likely to mean-revert in the near future. Copyright (c), University of Chicago on behalf of the Accounting Research Center, 2009.

Fundamental analysis is used in asset selection for equity portfolio management. In this paper, a generalized data envelopment analysis (DEA) model is developed to analyze a firm’s financial statements over time in order to determine a relative financial strength indicator (RFSI) that is predictive of firm’s stock price returns. RFSI is based on maximizing the correlation between the DEA-based score of financial strength and the stock market performance. This maximization involves a difficult binary nonlinear program that requires iterative re-configuration of parameters of financial statements as inputs and outputs. We utilize a two-step heuristic algorithm that combines random sampling and local search optimization. The proposed approach is tested with 230 firms from various US technology-industries to determine optimized RFSI indicators for stock selection. Then, those selected stocks are used within portfolio optimization models to demonstrate the usefulness of the scheme for portfolio risk management.

We investigate the joint hypothesis that (1) tax expense contains information about core profitability that is incremental to reported earnings and (2) that information is reflected in stock prices with a delay. We find that seasonally differenced quarterly tax expense, our proxy for tax expense surprise, is related positively to future returns. This anomaly is separate from previously documented pricing anomalies based on financial and tax variables. Additional investigation reveals that tax expense surprise is related positively to changes in future quarterly earnings and tax expense, and both those future changes are related positively to future returns. While the returns to investing in predictable future earnings changes has been documented before, these results suggest that predicting changes in future tax expense also generates incremental future returns.