WOA-LSTM CSI 500 Forecast Model Based on Baidu Index

The trend of the stock market is not entirely determined by its own internal law, but also affected by investor attention. Combined with the investors’ attention represented by Baidu index data, this paper designs a prediction model of the CSI 500 index based on the whale optimization algorithm (WOA) and long short-term memory neural network (LSTM). Firstly, collect data, carry out the missing value and standardization treatment, and filter leading Baidu index keywords through the time difference correlation analysis method to reduce feature dimensions; secondly, the whale optimization algorithm is used to optimize the learning rate and the number of neurons in the hidden layer of the LSTM network. The experimental results show that the WOA-LSTM prediction model based on Baidu index data has significantly improved the prediction of the CSI 500 index compared with other algorithms and is suitable for quantitative trading.