【Topic】
A Machine Learning Approach to Mitigating Investor Irrationality in Social Trading
【Abstract】
Social trading allows layman investors (followers) to evaluate and copy expert traders’ trades. The key challenge in social trading is how followers should identify a portfolio of traders to follow. This paper proposes a machine learning (ML) approach to augment followers’ decisions in following traders. We show how ML algorithms can help mitigate irrationality present in human decisions such as herding and overreliance on source credibility. By mitigating these human decision irrationality, ML can produce a more effective portfolio.
【Speaker】
Zhiqiang Zheng
(the Ashbel Smith Professor of Information Systems and Finance at the Jindal School of Management, University of Texas at Dallas)
Zhiqiang (Eric) Zheng received his PhD from the Wharton School of Business, University of Pennsylvania. His current research interests focus on FinTech, Blockchain and Digital Asset Management. He is the founding director of the Center for Fintech and Digital Asset Management at UTD. He has served as a senior editor for Information Systems Research and is the author of the textbook “Blockchain Principle, Technology and Application”.
