Large Language Models in Financial Decision-Making: A Methodological Framework for Evaluating AI Trading Strategies

By Theo Nicolas Sitjar Large Language Models (LLMs) offer new possibilities for financial decision-making, but evaluating their effectiveness in trading requires systematic approaches. This paper describes a practical framework for assessing LLM performance in stock market scenarios. Our method follows a 5-step process: data preparation, prompt engineering, LLM inference, backtesting, and statistical analysis. We include memory mechanisms and standard risk metrics to evaluate trading strategies comprehensively. Through testing against fifteen traditional quantitative baseline strategies, we examine both the potential benefits...