Prediction of Shanghai Stock Index Based on Investor Sentiment and CNN-LSTM Model

Abstract

In view of the breakthrough progress of the depth learning method in image and other fields, this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions. This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory (LSTM) and convolutional neural network (CNN). The model adopts an end-to-end network structure, using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure. The empirical part makes a comparative experimental analysis based on Shanghai stock index in China. By comparing the experimental prediction results and evaluation indicators, it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.