Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making
This work addresses the challenge of making financial AI systems more robust by incorporating human-like biases, though it appears incremental as it builds on existing RL methods without achieving clear improvements.
The study tackled the problem of financial markets being influenced by human cognitive biases by integrating biases like overconfidence and loss aversion into reinforcement learning frameworks for trading, but it reported inconclusive or negative results without providing concrete performance numbers.
Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the impact of psychological factors. This study integrates cognitive biases into RL frameworks for financial trading, hypothesizing that such models can exhibit human-like trading behavior and achieve better risk-adjusted returns than standard RL agents. We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes and evaluate their performance in simulated and real-world trading environments. Despite its inconclusive or negative results, this study provides insights into the challenges of incorporating human-like biases into RL, offering valuable lessons for developing robust financial AI systems.