arXiv AI
Beyond Behavioral Accuracy: Evaluating Individual-Level Fidelity in LLM Investor Simulation
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arXiv AI
Sep 23
Are LLMs Good Financial User Simulators? Multi-view Investor Logic Alignment (MILA)
The study examines whether large language models (LLMs) can accurately simulate individual financial users by conducting a longitudinal paper‑trading experiment with 80 participants. Using a rolling next‑day prediction protocol, the researchers compared LLM predictions to a simple recent‑activity persistence baseline across multiple behavioral fidelity levels, from trade occurrence to asset selection and portfolio outcomes. Results show that no LLM consistently outperforms the baseline, with fidelity decreasing at finer behavioral granularity, and that recent trading history largely drives activity predictions while asset selection depends more on available evidence.
arXiv AI
Sep 15
Are LLMs Good Financial User Simulators? A Preliminary Study
Large language models (LLMs) are being tested as simulators of individual financial decision-making. In a controlled paper‑trading experiment with 120 volunteers, the study evaluated whether an LLM could predict a participant’s next‑day trading action, chosen security, and transaction size using only pre‑cutoff information. Results showed that including market context improved predictions of actions and tickers, but sizing remained challenging, and the models tended to over‑predict hold actions, under‑predict sells, and simplify multi‑security trades.
arXiv AI
Jun 3
BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces
arXiv:2606. 02798v1 Announce Type: new Abstract: Many decision-support settings require systems that adapt to individual users, but evaluation data for this problem remain limited.
arXiv AI
Aug 6
FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents
arXiv:2608. 04095v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unclear whether they can maintain and update an individualized user model over long horizons.
arXiv AI
Sep 28
The Price of Thought: Does Test-Time Reasoning Pay in LLM Trading?
The study investigates whether adding inference-time reasoning to large language models (LLMs) improves trading performance. Using a controlled experiment across DeepSeek, GPT, and Gemini models, the authors varied reasoning effort while keeping other variables constant and evaluated over a full year of U.S. equities under three input conditions. Results show that additional reasoning does not reliably increase net portfolio returns and can even lead to nonmonotonic performance and unstable outcomes.
arXiv AI
Sep 17
EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents
EvolveTrade is a self‑evolving framework that treats the system prompt of a tool‑using LLM trading agent as a text‑parameterized policy. After each update interval, a Policy Agent revises this policy using accumulated decision traces and portfolio feedback while keeping the backbone LLM fixed, allowing the agent to refine its information‑acquisition and portfolio‑construction procedures over time. Experiments across multiple market regimes and two LLM backbones show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed‑policy baselines, with behavioral analyses indicating increased code‑mediated analysis and regime‑relevant computations.