Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]
This work addresses modeling investor behavior in stock markets, but it is incremental as it applies existing RL methods to new data without broad conclusions.
The study tested whether reinforcement learning (Q-learning) could model the behavior of 46 players in a financial market game, finding that a simple RL model captured decision-making for a subset of players but did not show significant improvement over a myopic version, indicating players may use RL naively.
Decision making in uncertain and risky environments is a prominent area of research. Standard economic theories fail to fully explain human behaviour, while a potentially promising alternative may lie in the direction of Reinforcement Learning (RL) theory. We analyse data for 46 players extracted from a financial market online game and test whether Reinforcement Learning (Q-Learning) could capture these players behaviour using a risk measure based on financial modeling. Moreover we test an earlier hypothesis that players are "naïve" (short-sighted). Our results indicate that a simple Reinforcement Learning model which considers only the selling component of the task captures the decision-making process for a subset of players but this is not sufficient to draw any conclusion on the population. We also find that there is not a significant improvement of fitting of the players when using a full RL model against a myopic version, where only immediate reward is valued by the players. This indicates that players, if using a Reinforcement Learning approach, do so naïvely