The paper proposes an improvement to large language model (LLM) portfolio agents by developing a method to credit experiences with their actual impact, rather than relying on market moves. The proposed agent, MemTrial, drafts each decision with multiple combinations of experiences and credits each experience with its Banzhaf value, a measure of its average impact. This approach improves the agent's performance on several benchmarks, particularly when its values do not hold.