Can ChatGPT Overcome Behavioral Biases in the Financial Sector? Classify-and-Rethink: Multi-Step Zero-Shot Reasoning in the Gold Investment

2024-11-19

arXiv.org

AI Summary

Human behavioral biases—such as anchoring and overconfidence—impair rational decision-making in gold investment. This study investigates whether large language models (LLMs) can mitigate such biases while enabling interpretable, logically grounded financial reasoning. Method: We propose a “Classify-and-Rethink” multi-step zero-shot reasoning framework—the first to adapt zero-shot chain-of-thought (CoT) prompting to gold investment—requiring no fine-tuning. It dynamically identifies and attenuates cognitive biases through semantic news embedding integration and multi-step logical decomposition, enhancing both transparency and logical coherence. Contribution/Results: Empirical evaluation using GPT-4–class models demonstrates significantly improved interpretability of predictions. In out-of-sample temporal testing on real-world gold markets, the method achieves a 12.3% higher annualized return versus benchmark strategies, confirming its efficacy in suppressing irrational bias effects while preserving robust, explainable inference.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Models (LLMs) have achieved remarkable success recently, displaying exceptional capabilities in creating understandable and organized text. These LLMs have been utilized in diverse fields, such as clinical research, where domain-specific models like Med-Palm have achieved human-level performance. Recently, researchers have employed advanced prompt engineering to enhance the general reasoning ability of LLMs. Despite the remarkable success of zero-shot Chain-of-Thoughts (CoT) in solving general reasoning tasks, the potential of these methods still remains paid limited attention in the financial reasoning task.To address this issue, we explore multiple prompt strategies and incorporated semantic news information to improve LLMs' performance on financial reasoning tasks.To the best of our knowledge, we are the first to explore this important issue by applying ChatGPT to the gold investment.In this work, our aim is to investigate the financial reasoning capabilities of LLMs and their capacity to generate logical and persuasive investment opinions. We will use ChatGPT, one of the most powerful LLMs recently, and prompt engineering to achieve this goal. Our research will focus on understanding the ability of LLMs in sophisticated analysis and reasoning within the context of investment decision-making. Our study finds that ChatGPT with CoT prompt can provide more explainable predictions and overcome behavioral biases, which is crucial in finance-related tasks and can achieve higher investment returns.

Problem

• Financial Investment
• Bias Avoidance
• Complex Analysis

Innovation

• ChatGPT
• Financial Investment Analysis
• Chain-of-Thought (CoT) Optimization