Stock Market Sentiment Classification and Backtesting via Fine-tuned
  BERT
Jiashu Lou
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.