BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability

TL;DR

This paper develops a BERT-based sentiment index for Hong Kong stocks and demonstrates its effectiveness in predicting stock returns when combined with LSTM, surpassing traditional linear methods.

Contribution

It introduces a novel BERT-based sentiment analysis framework tailored for financial texts and integrates it with LSTM for improved stock return prediction.

Findings

BERT significantly outperforms traditional sentiment models.

Combining sentiment index with LSTM enhances stock return predictability.

The framework offers a comprehensive alternative to linear regression methods.

Abstract

Traditional sentiment construction in finance relies heavily on the dictionary-based approach, with a few exceptions using simple machine learning techniques such as Naive Bayes classifier. While the current literature has not yet invoked the rapid advancement in the natural language processing, we construct in this research a textual-based sentiment index using a well-known pre-trained model BERT developed by Google, especially for three actively trading individual stocks in Hong Kong market with at the same time the hot discussion on Weibo.com. On the one hand, we demonstrate a significant enhancement of applying BERT in financial sentiment analysis when compared with the existing models. On the other hand, by combining with the other two commonly-used methods when it comes to building the sentiment index in the financial literature, i.e., the option-implied and the market-implied approaches, we propose a more general and comprehensive framework for the financial sentiment analysis, and further provide convincing outcomes for the predictability of individual stock return by combining LSTM (with a feature of a nonlinear mapping). It is significantly distinct with the dominating econometric methods in sentiment influence analysis which are all of a nature of linear regression.