Are Large Language Models Rational or Behavioral? A Comparative Analysis of Investor Behavior Interpretation
Abstract
This study aims to evaluate the ability of Large Language Model (LLM)-based AI applications to understand and interpret the fundamental theories of behavioral finance. In this context, the responses of five current LLM applications (ChatGPT 4o, Deepseek, Gemini 2.0 Flash, QwenChat 2.5 Max, and Copilot) were comparatively analyzed based on ten distinct scenarios involving behavioral biases and investment decision-making. The findings reveal how each model responds to behavioral concepts such as conceptual depth, psychological insight, strategic recommendation level, and originality. The results indicate that while the applications demonstrate successful analyses in certain cases, they also differ significantly in terms of data source diversity, contextual sensitivity, and algorithmic approaches. In particular, notable discrepancies were observed in explainability, consistency, and theory-based interpretive capacity. Ultimately, the study concludes that LLM systems have the potential to assess investment decisions not only through a rational framework but also from a behavioral perspective. Accordingly, the research provides both theoretical and practical contributions to the development of AI-based financial decision support systems.
Keywords
Behavioral Finance, Large Language Models (LLMs), Financial Decision-Making, Generative Artificial Intelligence
Ethical Statement
This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.
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