The 28th Academic Luncheon of the Digital Technology and Finance

Abstract：

We leverage machine learning to introduce novel granular belief dispersion measures to distinguish two key sources of disagreement, i.e., interpretation heterogeneity (IH) and information asymmetry (IA). We find that IH and IA are pivotal components of disagreement, aligning with human decision-based dispersion. While IA pervasively negatively predicts returns, IH exhibits an asymmetric pricing pattern: IH positively predicts returns among underpriced stocks but negatively among overpriced stocks, consistent with the theoretical model of Atmaz(2018) in that disagreement amplifies expectation bias. Our results underscore that the structure of belief dispersion has important heterogeneous effects on market equilibrium. 

Speaker Introduction：

Yang Liu, Assistant Professor at the College of Finance and Statistics, Hunan University. He holds a PhD in Finance from Tsinghua University, a Bachelor's degree in Financial Engineering from Central University of Finance and Economics, and was a visiting scholar at the Olin Business School of Washington University in St. Louis. His research areas include empirical asset pricing, financial technology, and behavioral finance. His research findings have been published in The Review of Asset Pricing Studies, and multiple working papers have been selected for academic conferences such as the American Finance Association (AFA) and the China International Conference in Finance (CICF). As the project leader, he has undertaken one National Natural Science Foundation of China project and one Humanities and Social Sciences Foundation of the Ministry of Education project.