Büyük Dil Modelleri Rasyonel mi, Davranışsal mı? Yatırımcı Davranışlarına Dair Karşılaştırmalı Bir Analiz

Abstract

Bu çalışmada, farklı büyük dil modeli (Large Language Model-LLM) tabanlı yapay zekâ uygulamalarının davranışsal finans teorilerini anlama ve yatırımcı psikolojisi senaryolarını yorumlama kapasiteleri analiz edilmiştir. Analize dahil edilen beş uygulama (ChatGPT 4o, Deepseek, Gemini 2.0 Flash, QwenChat 2.5 Max, ve Copilot), 10 özgün senaryo üzerinden değerlendirilmiştir. Araştırma ile, LLM’lerin finansal karar alma süreçlerini anlamlandırmada rasyonel yatırımcı modelinin ötesine geçip geçemediğini ve davranışsal finans ilkelerine yönelik içgörüler sağlayıp sağlayamadığını ortaya koymak hedeflenmiştir. Sonuçlar, uygulamaların belirli durumlarda başarılı analizler sunduğunu, ancak veri kaynaklarının çeşitliliği, bağlamsal duyarlılık ve algoritmik yaklaşımlar açısından önemli farklılıklar gösterdiğini ortaya koymuştur. Ayrıca, yapay zekâların yatırımcı davranışlarına yönelik yorumlamalarında tutarlılık ve açıklanabilirlik açısından bazı sınırlamalar gözlemlenmiştir. Sonuç olarak, LLM tabanlı sistemlerin davranışsal finans yaklaşımlarını daha etkili bir şekilde benimseyebilmeleri için geliştirme süreçlerinde etik ilkeler, uzman katkısı ve veri kalitesine daha fazla odaklanılması gerekmektedir.

Keywords

• Davranışsal Finans
• Büyük Dil Modelleri (LLM)
• Finansal Karar Alma
• Üretken Yapay Zekâ

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

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