We estimate Cumulative Prospect Theory parameters for fund managers using monthly data from nearly 200,000 funds across all asset classes, styles, and regions (1990-2022) via hierarchical Bayesian methods. Managers exhibit behavioral traits documented in experimental psychology, but with significantly different parameter values than laboratory studies. We find statistically significant differences across asset classes, fund categories, and fund sizes, while manager tenure has limited impact. Prospect-theory values computed under our market-estimated parameters explain 60% more cross-sectional variation in ex-post Sharpe ratio than conventional laboratory parameters and deliver superior fit for Morningstar ratings, demonstrating that parameter choice is economically material for valuing fund return distributions.
When a forecaster converts a probability estimate into a binary prediction, information about conviction is discarded even though the underlying belief has not changed. This paper measures the economic value of that discarded information. Using stock market data, we find that at equal accuracy, probabilistic forecasts are more profitable than binary ones, and forecasts of outperformance magnitude more profitable than directional forecasts alone. Both advantages concentrate in a specific accuracy range - roughly 53% to 65% - that appears independently in research on human forecasting well outside financial markets, suggesting a general property of human judgment. How a forecast is expressed can matter more than how accurate it is.
Purpose: To evaluate whether investor sentiment influences voluntary disclosure and earnings-management decisions of Chinese A-share firms. Methods: Using industry-adjusted Tobin's Q as a firm-level valuation pressure or sentiment proxy, based on 14,081 firm-years of data from Chinese A-share-listed companies between 2013 and 2023. The measurement of earnings management is centered on performance-related fit indicators. Results: When sentiment is high, profitable firms tend to report more positive earnings and issue disclosures more frequently, compared to loss-making firms. Conclusion: Overall, evidence indicates that the Chinese A-share market tends to exhibit behavioral fluctuations in response to investors' emotional sentiments.
In this study, we explore one explanation for the ambiguous association between measures of risk and future returns. Drawing on research in psychology, we hypothesize that during periods of high investor sentiment, investors overweight the probability of positive outcomes. Excess demand from overweighting positive outcomes leads to price premiums and subsequent underperformance, especially among the most volatile stocks. Using standard asset pricing methods, we find support for this hypothesis across several measures of risk. We provide additional evidence of our sentiment hypothesis in tests of returns around earnings announcements and using sentiment extracted from messages on social media.
Investors encounter earnings communication that is human prepared, artificial intelligence (AI) generated, formally reviewed, or unreviewed. A 2 & times; 2 randomized experiment separates AI authorship from governance review in a standardized earnings update. Investment-experienced participants allocated a hypothetical $10,000 among common stock, a corporate bond, and a risk-free alternative; stock allocation was the sole primary behavioral outcome. AI authorship reduced stock allocation when no formal review was disclosed, whereas governance review increased trust, perceived accountability, and stock allocation, particularly for AI-generated communication. Investors therefore respond not simply to AI use but to whether AI-mediated disclosure is visibly governed.
