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 novel model
BERT recently developed by Google, especially for three actively trading
individual stocks in Hong Kong market with hot discussion on Weibo.com. On the
one hand, we demonstrate a significant enhancement of applying BERT in
sentiment analysis when compared with existing models. On the other hand, by
combining with the other two existing methods commonly used on building the
sentiment index in the financial literature, i.e., option-implied and
market-implied approaches, we propose a more general and comprehensive
framework for financial sentiment analysis, and further provide convincing
outcomes for the predictability of individual stock return for the above three
stocks using LSTM (with a feature of a nonlinear mapping), in contrast to the
dominating econometric methods in sentiment influence analysis that are all of
a nature of linear regression.Comment: 10 pages, 1 figure, 5 tables, submitted to NeurIPS 2019, under revie