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We revisit La Porta's finding that returns on stocks with the most optimistic analyst long-term earnings growth forecasts are lower than those on stocks with the most pessimistic forecasts. We document the joint dynamics of fundamentals, expectations, and returns of these portfolios, and explain the facts using a model of belief formation based on the representativeness heuristic. Analysts forecast fundamentals from observed earnings growth, but overreact to news by exaggerating the probability of states that have become more likely. We find support for the model's predictions. A quantitative estimation of the model accounts for the key patterns in the data.

Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion . In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Studies 1–3). In fact, our results suggest that participants’ preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants’ preference for modifiable algorithms was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control—even a slight amount—over an imperfect algorithm’s forecast. Data, as supplemental material, are available at https://doi.org/10.1287/mnsc.2016.2643 . This paper was accepted by Yuval Rottenstreich, judgment and decision making .

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one-minute-ahead return forecasts using the entire cross-section of lagged returns as candidate predictors. The LASSO increases both out-of-sample fit and forecast-implied Sharpe ratios. This out-of-sample success comes from identifying predictors that are unexpected, short-lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identiy predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.

We examine the relationship between disclosure of nonfinancial information and analyst forecast accuracy using firm-level data from 31 countries. We use the issuance of stand-alone corporate social responsibility (CSR) reports to proxy for disclosure of nonfinancial information. We find that the issuance of stand-alone CSR reports is associated with lower analyst forecast error. This relationship is stronger in countries that are more stakeholder-oriented—i.e., in countries where CSR performance is more likely to affect firm financial performance. The relationship is also stronger for firms and countries with more opaque financial disclosure, suggesting that issuance of stand-alone CSR reports plays a role complementary to financial disclosure. These results hold after we control for various factors related to firm financial transparency and other potentially confounding institutional factors. Collectively, our findings have important implications for academics and practitioners in understanding the function of CSR disclosure in financial markets.

Social media has become a popular venue for individuals to share the results of their own analysis on financial securities. This paper investigates the extent to which investor opinions transmitted through social media predict future stock returns and earnings surprises. We conduct textual analysis of articles published on one of the most popular social media platforms for investors in the United States. We also consider the readers' perspective as inferred via commentaries written in response to these articles. We find that the views expressed in both articles and commentaries predict future stock returns and earnings surprises.

I study whether managers use real activities manipulation and accrual-based earnings management as substitutes in managing earnings. I find that managers trade off the two earnings management methods based on their relative costs and that managers adjust the level of accrual-based earnings management according to the level of real activities manipulation realized. Using an empirical model that incorporates the costs associated with the two earnings management methods and captures managers' sequential decisions, I document large-sample evidence consistent with managers using real activities manipulation and accrual-based earnings management as substitutes.

During 2005 to 2007, the SEC ordered a pilot program in which one-third of the Russell 3000 index were arbitrarily chosen as pilot stocks and exempted from shortsale price tests. Pilot firms' discretionary accruals and likelihood of marginally beating earnings targets decrease during this period, and revert to pre-experiment levels when the program ends. After the program starts, pilot firms are more likely to be caught for fraud initiated before the program, and their stock returns better incorporate earnings information. These results indicate that short selling, or its prospect, curbs earnings management, helps detect fraud, and improves price efficiency.

We investigate whether and when firms manage the tone of words in earnings press releases, and how investors react to tone management. We estimate abnormal positive tone, ABTONE, as a measure of tone management from residuals of a tone model that controls for firm quantitative fundamentals such as performance, risk, and complexity. We find that ABTONE predicts negative future earnings and cash flows, is positively associated with upward perception management events, such as, just meeting/beating thresholds, future earnings restatements, SEO, and M&A, and is negatively associated with a downward perception management event, stock option grants. ABTONE has a positive stock return effect at the earnings announcement and a delayed negative reaction in the one and two quarters afterward. Balance sheet constrained firms and older firms are more likely to employ tone management over accruals management. Overall, the evidence is consistent with managers using strategic tone management to mislead investors about firm fundamentals.

Corporate investment decisions require managers to forecast expected future cash flows from potential investments. Although these forecasts are a critical component of successful investing, they are not directly observable by external stakeholders. In this study, we investigate whether the quality of managers' externally reported earnings forecasts can be used to infer the quality of their corporate investment decisions. Relying on the intuition that managers draw on similar skills when generating external earnings forecasts and internal payoff forecasts for their investment decisions, we predict that managers with higher quality external earnings forecasts make better investment decisions. Consistent with our prediction, we find that forecasting quality is positively associated with the quality of both acquisition and capital expenditure decisions. Our evidence suggests that externally observed forecasting quality can be used to infer the quality of capital budgeting decisions within firms.

Investors, regulators, academics, and researchers all emphasize the importance of financial statement comparability. However, an empirical construct of comparability is typically not specified. In addition, little evidence exists on the benefits of comparability to users. This study attempts to fill these gaps by developing a measure of financial statement comparability. Empirically, this measure is positively related to analyst following and forecast accuracy, and negatively related to analysts' dispersion in earnings forecasts. These results suggest that financial statement comparability lowers the cost of acquiring information, and increases the overall quantity and quality of information available to analysts about the firm.