Sentiment to Stocks: Rule-Based and Deep Learning Sentiment Analysis for LSTM-Driven Stock Prediction

Abstract

How to accurately predict stock prices is a persistent problem in the financial realm. According to behavioral economics, human actions in the stock market tend to be irrational, emotional, and easily misled. In such context, this papers approach involves adopting various sentiment analysis models in subsequent sections of the paper, which aims to synthesize a precise methodology for forecasting stock prices aligned with the available data, namely structured technical analysis, while considering and quantifying investor sentiment, namely unstructured fundamental analysis. This work points out that by adopting sentiment analysis using the VADER and FinBERT models separately, there are accuracy improvements in both integration of VADER or FinBERT and transactional data compared with merely doing stock price prediction by forecasting using historical data alone. This outcome resemble an valuable proposition of modeling and prediction on irrational human behavior. Nevertheless, this paper provides insights on future possible enhancements in this area, which analyzing additional emotional dimensions in textual data and recognizing the multifaceted nature of human emotions is needed.