Stock Market Prediction Based on Financial News Text Mining and Investor Sentiment Recognition

Abstract

The stock market is usually regarded as the bellwether of the economy, which can reflect the economic operation of a country or region. As a significant part of the financial market, the equity market plays a critical role in the financial sector. Whether in academia or investment field, stock market forecasts always excite great interest. Financial news is an important source of information in the financial market, which reflects the mood swings of investors and often goes hand in hand with the market trend. However, due to the unstructured and professional characteristics of financial news, there are challenges in accurately quantifying their emotional tendencies. This research is based on Hidden Markov Model (HMM) to segment financial news text. The recognition and classification of news emotion is carried out by bidirectional long short-term memory (BI-LSTM) algorithm, and long short-term memory(LSTM) model is trained with text emotion index and stock market transaction data to realize the prediction of stock market. The results show that BI-LSTM algorithm performs better than the emotional dictionary algorithm in emotional recognition. And the emotional index of financial news text can enhance the accuracy of stock market prediction to a certain extent. Compared with using stock market technical index and news text vector only, the prediction accuracy can be improved by about 2%.