Abstract: We develop a theoretical framework to study competition between AI-powered and human investors with heterogeneous sophistication. Human investors possess superior private information but are limited by bounded strategic reasoning, modeled through a cognitive-hierarchy structure. AI investors, in contrast, learn and trade through reinforcement learning that autonomously optimizes trading profits over time. We show that human investors can consistently outperform sophisticated AI investors because AI sophistication is constrained by the data it learns from, which reflects the behavior of the average rather than the most advanced human trader. Three forces limit AI profitability: (i) the advantage of human private information, (ii) the price-stabilizing actions of the most sophisticated human traders, and (iii) the growing price impact of AI trading as its market share expands. Together, these mechanisms reveal the limits of algorithmic superiority and provide a foundation for understanding AI–human competition in financial markets.
Martin Szydlowski, Hong Kong University of Science & Technology
Abstract: We study pricing dynamics and risk-sharing in a market with rational investors and a Q-learning trader. The Q-learner’s trading generates a feedback loop in prices: their demand for the risky security depends on their perceived benefit from trading, which in turn, depends on realized returns. We show that this loop generates state-dependent stochastic volatility, predictable returns, and novel price dynamics which depend on the mass and learning rate of the Q-learner. When rational investors have strong risk-sharing motives for trading, we show that Q-learners can (i) earn trading profits and (ii) improve average investor utility, even though they increase the volatility of prices.
Discussant: Francesco Sangiorgi, Frankfurt School of Finance and Management
Abstract: We use frontier advancements in Artificial Intelligence and machine learning to extract and classify the part of key economic agents’ behaviors that are predictable from past behaviors. Even the agents themselves might view these as novel (innovative) decisions; however, we show in strong contrast that a large percentage of these actions and behaviors can be predicted - and thus mimicked - in the absence of these individuals. In particular, we show that 71% of mutual fund managers’ trade directions can be predicted in the absence of the agent making a single trade. For some managers, this increases to nearly all of their trades in a given quarter. Further, we find that manager behavior is more predictable and replicable for managers who have a longer history of trading and are in less competitive categories. The larger the ownership stake of the manager in the fund, the less predictable their behavior. Lastly, we show strong performance implications: less predictable managers strongly outperform their peers, while the most predictable managers significantly underperform. Even within each manager's portfolio, those stock positions that are more difficult to predict strongly outperform those that are easier to predict. Aggregating across the universe of fund managers each quarter, stocks whose position changes are least predictable additionally significantly outperform stocks whose position changes are most predictable across the universe. Our framework allows researchers to delineate and classify the portion of financial agents’ action sets which are predictable from those which are novel responses to stimuli -- open to being evaluated for value creation or destruction.
Discussant: Maxime Bonelli, London Business School
