The Impact of Generative Artificial Intelligence on Individual Manual Investment Decisions: Empirical Evidence from Mutual Funds
COM1 Level 2
SR2, COM1-02-04
Abstract:
The rapid ascent of generative artificial intelligence (GAI) has led individual investors to increasingly use GAI-based consulting tools such as GAI-based investment consultants (GAICs). Despite warnings from some institutions about the risks of solely relying on GAICs for investment advice, there is limited empirical research on their impact on individual investors' market investments. To fill this gap, we collaborated with Ant Fortune, Ant Group's investment team, analyzing data related to the rollout of their GAIC, Zhi Xiaobao. Our analyses offer the first empirical evidence that GAICs positively influence investment decisions, redemption activities, and overall investment profit. Interestingly, our findings challenge the belief that novice investors benefit most from AI tools, showing that experienced investors benefit more significantly from GAICs by utilizing their financial acumen. Additionally, we found that the platform's influence on investment profit is more pronounced for risk-seeking investors, suggesting GAICs enhance their market decision-making efficacy. However, novice and risk-averse investors, despite being more active in redemption behaviors, do not attain equivalent profit compared to their experienced and risk-seeking counterparts. This underscores the importance of financial literacy in harnessing GAICs' economic benefits. In summary, while GAICs improve decision-making for experienced and risk-tolerant investors, they offer limited advantages for novices and risk-averse individuals. Our research provides essential insights for platform managers considering GAIC applications and guides policymakers in enhancing the use of GAICs, especially for vulnerable investor segments. This highlights the need for tailored financial education to fully leverage the potential of these advanced AI tools in diverse investment contexts.
Bio:
Dr. Siliang (Jack) Tong joined the Division of Information Technology and Operation Management at Nanyang Business School, Nanyang Technological University on 01/02/2021. He earned his Doctoral degree in Marketing from the Fox School of Business, Temple University in 2020. Siliang is an empirical modeler who is interested in the substantive areas of artificial intelligence, mobile app marketing, and sharing economy. He is very active in research as his works were awarded as the best paper and finalist in multiple occasions such as PACIS, AMA conference and ISMS Marketing Science conference. His research is published in Strategic Management Journal, Marketing Science, Journal of Marketing and the Journal of the Academy of Marketing Science. Before joining the academia, he had 6-year management experience in digital marketing and web analytics, and he obtained his MBA degree from the University of Wisconsin-Madison in 2016