Stock Market Sentiment Classification and Backtesting via Fine-tuned BERT

TL;DR

This paper develops a fine-tuned BERT model to classify stock market sentiment from user comments, integrating it into a regression-based trading strategy that significantly improves returns.

Contribution

It introduces a sentiment classification approach using fine-tuned BERT for stock trading and demonstrates its effectiveness in enhancing trading performance.

Findings

Sentiment classification accuracy improved with fine-tuning.

Incorporating emotional factors increased return rate by 73.8%.

The model outperformed baseline and original Alpha191 models.

Abstract

With the rapid development of big data and computing devices, low-latency automatic trading platforms based on real-time information acquisition have become the main components of the stock trading market, so the topic of quantitative trading has received widespread attention. And for non-strongly efficient trading markets, human emotions and expectations always dominate market trends and trading decisions. Therefore, this paper starts from the theory of emotion, taking East Money as an example, crawling user comment titles data from its corresponding stock bar and performing data cleaning. Subsequently, a natural language processing model BERT was constructed, and the BERT model was fine-tuned using existing annotated data sets. The experimental results show that the fine-tuned model has different degrees of performance improvement compared to the original model and the baseline model. Subsequently, based on the above model, the user comment data crawled is labeled with emotional polarity, and the obtained label information is combined with the Alpha191 model to participate in regression, and significant regression results are obtained. Subsequently, the regression model is used to predict the average price change for the next five days, and use it as a signal to guide automatic trading. The experimental results show that the incorporation of emotional factors increased the return rate by 73.8\% compared to the baseline during the trading period, and by 32.41\% compared to the original alpha191 model. Finally, we discuss the advantages and disadvantages of incorporating emotional factors into quantitative trading, and give possible directions for further research in the future.