The limits of excellence: Assessing fine-tuned ChatGPTs efficacy in stock price forecasting

Abstract

In this study, we explore the ability of ChatGPT to predict stock market trends based on stock news headlines and real stock market data. In order to evaluate the performance of the fine-tuned model, we first obtain the prediction results of GPT-3.5 Turbo on specific stocks future trends as a comparison. We fine-tuned GPT-3.5 Turbo and conducted related training, testing and result evaluation. The experiments implemented on the two datasets Bigdata2022 and Cikm illustrate that fine-tuning can help the model to produce expected structured output according to user requirements, based on its more sophisticated understanding of the text and data in this field. However, although the models performance is improved significantly, GPT-3.5 Turbo does not demonstrate better performance compared to other traditional large language models in terms of integrating time series data and news headline data for stock forecasting. The fine-tuned ChatGPT model is expected to achieve excellent results in the stock market forecasting tasks through more in-depth research and become one of the mainstream research models in this field.