Stock Prediction Based on Genetic Algorithm Feature Selection and Long Short-Term Memory Neural Network

In the financial market, there are a large number of indicators used to describe the change of stock price, which provides a good data basis for our stock price forecast. Different stocks are affected by different factors due to their different industry types and regions. Therefore, it is very important to find a multi factor combination suitable for a particular stock to predict the price of the stock. This paper proposes to use Genetic Algorithm(GA) for feature selection and develop an optimized Long Short-Term Memory(LSTM) neural network stock prediction model. Firstly, we use the GA to obtain a factors importance ranking. Then, the optimal combination of factors is obtained from this ranking with the method of trial and error. Finally, we use the combination of optimal factors and LSTM model for stock prediction. Thorough empirical studies based upon the China construction bank dataset and the CSI 300 stock dataset demonstrate that the GA-LSTM model can outperform all baseline models for time series prediction.