Can ChatGPT Overcome Behavioral Biases in the Financial Sector? Classify-and-Rethink: Multi-Step Zero-Shot Reasoning in the Gold Investment (2411.13599v1)

Abstract: 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.

• The paper introduces the Classify-and-Rethink (CAR) framework that leverages prompt engineering and financial reasoning to mitigate behavioral biases in gold investment.
• The methodology classifies financial news into actionable categories and applies a rethinking process to adjust sentiment scores with a long-term perspective.
• Experimental results indicate that the CAR strategy achieves an 80.35% return with a Sharpe ratio of 1.071, outperforming traditional buy-and-hold strategies.

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

This paper investigates the application of ChatGPT, a LLM, in overcoming behavioral biases in the financial sector, specifically focusing on investment decision-making related to gold. The authors introduce a "Classify-and-Rethink" (CAR) strategy designed to enhance zero-shot reasoning performance of ChatGPT on such tasks, aligning its decision-making capabilities with financial reasoning, including overcoming cognitive biases like the framing effect.

Methodological Insights

The methodology is founded on prompt engineering, breaking traditional approaches by incorporating behavioral finance concepts. The CAR framework consists of classifying gold-related news into specific categories and then utilizing a rethinking process for adjusting sentiment scores based on a long-term perspective. This approach is distinctly innovative as it attempts to simulate, and subsequently correct, typical investor biases in financial decision-making.

• Classification: The model categorizes news into classes such as geopolitical events or central bank actions, extracting the most intrinsic market-relevant information.
• Rethinking: Subsequent reflection on the long-term impacts helps to realign the sentiment scoring process, theoretically overcoming short-term biases.

Experimental Evaluation

Experiments were conducted over a period from 2018 to 2023 using gold price data from the Shanghai Gold Exchange and news collected from a specified financial news source. The performance indicators used include both returns and Sharpe ratios, with the CAR strategy achieving superior results. Notably, the application of CAR yielded an 80.35% return with a Sharpe ratio of 1.071, outperforming both the "Classify" approach and traditional "Buy-and-Hold" strategies.

Additionally, the analysis of the scoring distribution demonstrates a shift toward a normal distribution under the CAR strategy, indicating a more financially grounded evaluation framework. This distribution suggests improved rationality in decision-making compared to baseline or simpler one-step prompting methods.

Theoretical and Practical Implications

This work significantly contributes to our understanding of how LLMs like ChatGPT can transcend traditional uses within the domain of NLP by providing a mechanism for addressing behavioral biases in financial decision-making. From a theoretical perspective, this paper bridges the gap between machine learning approaches in NLP and behavioral finance theories, offering a novel framework for simulating and ameliorating human-like cognitive biases in AI-driven decision systems.

Practically, this research provides a solid basis for future AI applications in finance, particularly for automated analysis and investment strategies. The CAR framework could serve as a prototype for other asset classes or financial instruments, prompting further exploration and refining of LLM capabilities within complex financial reasoning tasks.

Future Directions

The research opens several avenues for future inquiry. First, exploring the integration of more sophisticated financial datasets or alternative market data could enhance model performance. Additionally, extending the CAR framework to other domains within finance could test its versatility and efficiency in varied economic scenarios. Finally, evaluating the long-term adaptability of AI models in rapidly evolving market conditions will be crucial for understanding the sustainability of such approaches in real-world applications.

In conclusion, this paper offers a convincing demonstration of the potential of LLMs to mitigate cognitive biases, potentially transforming the landscape of AI in finance by embedding behavioral insights into automated reasoning systems.