可预测性
标题
股票市场
情绪分析
计算机科学
投资决策
金融经济学
计量经济学
库存(枪支)
经济
行为经济学
人工智能
地理
数学
业务
财务
统计
广告
考古
背景(考古学)
作者
Alejandro Lopez-Lira,Yuehua Tang
出处
期刊:Social Science Research Network
[Social Science Electronic Publishing]
日期:2023-01-01
被引量:205
摘要
We examine the potential of ChatGPT, a large language model, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and document a positive correlation between these "ChatGPT scores" and subsequent daily stock market returns. Further, ChatGPT outperforms traditional sentiment analysis methods. Our results suggest that incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies.
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