Dr. Xuefeng Yang

Job Market Paper

Time-varying Probability Weighting in Non-Expected Utility Models and Investor Sentiment Toward Tails

This paper investigates the relationship between tail-overweighting parameters embedded in the probability weighting functions of non-expected utility models and an external sentiment measure, proxied by a principal-component-based index constructed from a comprehensive set of survey-based, market-based, and news-based sentiment indicators. The estimation of probability weighting is grounded in the premise that, on average, investors’ subjective probability estimates—distorted by behavioral biases and embodied in probability weighting functions—should be consistent with the distribution of realized returns. The subjective density function can be recovered by mapping risk-neutral probabilities into real-world probabilities, with the pricing kernel providing the structural link between the two measures. Deviations between the risk-neutral distribution (Q-measure) and the physical distribution (P-measure), especially in the tails, reflect investors’ risk preferences. The preferences can be captured through the assumed power utility function, which allows the reconciliation of the two distributions and facilitates the identification of probability weighting effects. Empirically, among the parametric weighting functions analyzed, the Prelec probability weighting function exhibits the strongest empirical alignment, with its curvature parameter α showing a notable 80 % correlation with PC1. The findings offer empirical evidence of a significant correlation between probability weighting parameters and an external sentiment measure in real-world, non-experimental data. This contributes to the broader behavioral asset pricing literature by shedding new light on the dynamic nature of probability weighting and its implications for resolving the pricing kernel puzzle.

My PhD Thesis

Work Experience

Beyond academia, I also bring entrepreneurial experience. I previously led an e-commerce venture and developed a practical business plan integrating an AI-based knowledge-management system into logistics and warehousing operations. The system provides a one-stop solution for cross-border e-commerce, including automated customer service, marketing copywriting, order fulfillment, and related functions. This blend of academic insight and practical implementation motivates my interest in interdisciplinary research at the intersection of behavioral science and AI.