From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets
arXiv:2605.28359v1 Announce Type: new Abstract: Evaluating whether large language model (LLM) agents can profit in capital markets is increasingly framed as end-to-end trading: place an agent in a historical market, let it trade, and measure portfolio returns. This setup is vulnerable to two evaluation failures. First, long backtests often overlap with the knowledge cutoffs of frontier LLMs, allowing memorized tickers, dates, prices, and market narratives to substitute for investment reasoning. Second, raw returns are a noisy proxy for stock-selection ability, since positive performance may co
The rapid advancement of LLMs has naturally led to explorations of their application in complex financial domains, making robust evaluation methods critically necessary.
This research highlights fundamental flaws in current evaluations of LLM agents in financial markets, crucial for understanding true AI capabilities and preventing misallocation of resources.
The focus for evaluating AI trading agents will likely shift from simple backtest returns to more sophisticated, memory-controlled benchmarks that account for information leakage.
- · AI evaluation methodology developers
- · Sophisticated quantitative trading firms
- · Academic researchers in AI and finance
- · Ethical AI development
- · Over-optimistic AI trading solution providers
- · Investors relying on flawed LLM performance metrics
- · Simple backtesting methodologies
- · Generative AI models with poor memory control
Financial firms will increasingly scrutinize LLM agent performance claims, demanding more rigorous, memory-controlled evaluation frameworks.
The development of LLM agents will pivot towards architectures specifically designed to mitigate memorization, focusing instead on genuine reasoning and adaptive learning.
Improved evaluation standards could lead to a more realistic and sustainable integration of AI into financial markets, potentially reducing systemic risks from poorly understood AI capabilities.
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Read at arXiv cs.AI