Enhancing Stock Price Prediction through Attention-BiLSTM and Investor Sentiment Analysis

Abstract

The change of stock price is the focus of investors in the stock market, so stock price trend prediction has always been a hot topic in quantitative investment research. Traditional machine learning prediction model is difficult to deal with nonlinear, high frequency and high noise stock price time series, which makes the prediction accuracy of stock price trend low. In order to improve the forecasting accuracy, the temporal characteristics of stock price data are studied. A bidirectional long short-term memory neural network combining empirical mode decomposition (EMD), investor sentiment and attention mechanism is proposed to predict the rise and fall of stock prices. First, the empirical mode decomposition algorithm is used to extract the characteristics of stock price time series on different time scales, and the investor complex index of the text from the close of the last trading day to the opening of the next trading day is extracted by constructing the all-inclusive sentiment dictionary.The realization of a stock price trend prediction model based on Attention-BiLSTM involves combining the Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism. The BiLSTM processes data points from both past and future for better context understanding, while the attention mechanism selectively focuses on crucial information, improving the model's predictive accuracy in capturing and utilizing patterns in stock price movements. This sophisticated approach enhances the model's ability to forecast stock trends effectively.