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Traditional sentiment construction in finance relies heavily on thedictionary-based approach, with a few exceptions using simple machine learningtechniques such as Naive Bayes classifier. While the current literature has notyet invoked the rapid advancement in the natural language processing, weconstruct in this research a textual-based sentiment index using a well-knownpre-trained model BERT developed by Google, especially for three activelytrading individual stocks in Hong Kong market with at the same time the hotdiscussion on Weibo.com. On the one hand, we demonstrate a significantenhancement of applying BERT in financial sentiment analysis when compared withthe existing models. On the other hand, by combining with the other twocommonly-used methods when it comes to building the sentiment index in thefinancial literature, i.e., the option-implied and the market-impliedapproaches, we propose a more general and comprehensive framework for thefinancial sentiment analysis, and further provide convincing outcomes for thepredictability of individual stock return by combining LSTM (with a feature ofa nonlinear mapping). It is significantly distinct with the dominatingeconometric methods in sentiment influence analysis which are all of a natureof linear regression.

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