Conference Agenda
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Session Overview |
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E6: Experimental Finance
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11:00am - 11:30am
Ranking concerns or reference points: The impact of communicating expected payoffs in experimental studies Universität Duisburg-Essen, Germany Online platforms such as CloudResearch, Amazon MTurk, or Prolific require researchers to communicate average expected payoffs to participants prior to experiments. We show that knowledge of expected payoffs introduce confounding effects in experimental studies on risk-taking. We use the literature that shows that individuals with lower ranks take higher risks as a playground. Our experimental design disentangles this ranking effect from a reference point effect introduced by the average expected payoff. Holding the rank constant, risk-taking is 18.23% higher below the reference point on average. Holding the reference point distance constant, ranks have no significant effect on risk-taking. These results are robust to nonsocial settings and alternative risk-taking measures. 11:30am - 12:00pm
Will AI replace or enhance human intelligence in investment management? 1University of Texas at Dallas, United States of America; 2Wilfrid Laurier University, Canada Using unique data from LinkedIn profiles, we measure the adoption of AI technologies by mutual fund managers. Compared to low-AI funds, high-AI funds generate superior returns and incur lower expenses. AI outperformance is particularly strong among discretionary funds, which rely on human judgment. The greater the AI adoption, the more pronounced the time-varying skill of fund managers across different market conditions. The stock-picking abilities of high-AI funds improve with the availability of big data, such as satellite imagery of parking lots. The local availability of AI skills is a key determinant of cross-sectional variation in mutual fund AI investment. Our findings are robust to using geographic variation in AI supply as an instrument for AI utilization by mutual funds. 12:00pm - 12:30pm
Making GenAI smarter: Evidence from a portfolio allocation experiment 1Dresden University of Technology; 2Catholic University Eichstaett-Ingolstadt We evaluate the performance implications of providing various types of domain-specific information to large language models (LLMs) in a simple portfolio allocation task. We compare the recommendations of seven state-of-the-art LLMs in various experimental conditions against a benchmark of professional financial advisors. Our main result is that the provision of domain-specific information does not unambiguously improve recommendations. We find that LLM recommendations underperform recommendations by human financial advisors in the baseline condition. Providing firm-specific information improves historical performance in LLM portfolios and closes the gap to human advisors. Performance improvements are achieved through higher exposure to market risk and not through an increase in mean-variance efficiency within the risky portfolio share. Risk-averse investors are recommended substantially riskier portfolios when firm-specific information is provided. | ||