Language Expression and Economic Value: An Empirical Study on Stock Index Prediction Based on Multi-Feature Emotional Analysis of Financial Discourse

Abstract

As the stock market becomes increasingly diverse and complex, an accurate and feasible prediction of stock index has become an urgent demand for stock investors. As an important driver of changes in the stock market, financial discourse can guide investors' emotions, thus affecting the trading of stocks and the development of the stock market. Therefore, the prediction of stock index from the perspective of financial discourse emotion has gradually become a hotspot of research. Through the analysis of the existing literature, it is found that the models used in the current relevant research are not ideal, the prediction is not accurate, and there are problems such as single method, few selection indicators, narrow analysis area, etc. To solve the above problems, this study proposes an LSTM model integrating multiple feature emotional indexes, constructs the TextCNN emotional index and the metaphorical power index to quantitatively analyze the emotional expression and semantic use of news text, and then integrates the indexes into the LSTM neural network model to predict the Shanghai Stock Exchange Index.