Investor Sentiment Mining Based on Bi-LSTM Model and its Impact on Stock Price Bubbles

Abstract

Abstract We built a Bi-Directional long-term and short-term memory (Bi-LSTM) model to identify and classify the Chinese posting text of stocks on the Eastmoney website in China and constructed the daily index of Chinese investors’ sentiment. Furthermore, based on the GSADF method, we examine the stock price bubbles and study the impact of investor sentiment and stock price bubbles. We found investor sentiment has a positive effect on the existence of stock bubbles, as well as their intensity. This effect is more significant in small-scale, high-equity concentration, and non-state-owned enterprises. Investor sentiment has an impact on stock price bubbles through volatility, and stock price bubbles are often accompanied by higher premium risk. The conclusion is helpful to understand the mechanism of investor sentiment on stock bubbles from a micro perspective, and it also can be a reference in identifying stock bubbles from the viewpoint of investor sentiment.