MIS Speaker's Series: Xiao Liu

Sunset over McClelland Hall


1 to 2 p.m., Dec. 1, 2023


Xiao Liu, Assistant Professor of Information Systems, W. P. Carey School of Business, Arizona State University. 

Title: Post-Earnings-Announcement Drift Prediction: Leveraging Post-event Investor Responses with Multi-task Learning 

Abstract: Post-Earnings-Announcement Drift (PEAD) refers to the phenomenon in which a company’s stock price tends to drift persistently when the information released during the earnings announcement event deviates from expectations. Predicting PEAD is of great interest to both investors and researchers, as the magnitude of PEAD is economically significant. Despite decades of studying how to predict PEAD, researchers have thus far refrain from utilizing post-event investor responses, a critical intermediary in the PEAD mechanism, due to the limitations of the single-task learning (STL) framework. This study proposes design innovations for PEAD prediction based on multi-task learning (MTL), leveraging the post-event investor responses as auxiliary tasks. We also propose a new adaptive task weighting method, GradPerp, which assigns large weights to auxiliary tasks that can provide diverse training signals. We employed a multi-level, multi-query Transformer to enable cross-task learning and the effective utilization of the structured features and the lengthy earnings call transcripts. Evaluation of the model from 2010 to 2021 demonstrates that the proposed design innovations not only outperform benchmark models in terms of prediction accuracy, but also generate a daily risk-adjusted return (alpha) two times larger than the traditional earnings-surprise-based models. This study contributes to the stream of IS literature at the intersection of artificial intelligence (AI), finance, and design science research. Our work provides valuable decision support modules and managerial implications for investment and other financial decision makers. 

Bio: Xiao Liu (xiao.liu.10@asu.edu) is an Assistant Professor in the Department of Information Systems at Arizona State University. She received her PhD in Management Information Systems from the Eller College of Management at the University of Arizona. Dr. Liu’s research interests include data science and predictive analytics in healthcare, education, and fintech. Her work has appeared in several academic journals and peer-reviewed conferences, such as MIS Quarterly, Journal of Management Information Systems, Journal of Medical Internet Research, Journal of the American Medical Informatics Association, and the Proceedings of International Conference in Information Systems, among others. 


Seokjun Youn