Prediction of stock index of two-scale long short-term memory model based on multiscale nonlinear integration

Abstract

Abstract Although the prediction of stock prices and analyses of their returns and risks have always played integral roles in the stock market, accurate predictions are notoriously difficult to make, and mistakes may be devastatingly costly. This study attempts to resolve this difficulty by proposing and applying a two-stage long short-term memory (LSTM) model based on multi-scale nonlinear integration that considers a diverse array of factors. Initially, variational mode decomposition (VMD) is used to decompose an employed stock index to identify the different characteristics of the stock index sequence. Then, an LSTM model based on the multi-factor nonlinear integration of overnight information is established in a second stage. Finally, the joint VMD-LSTM model is used to predict the stock index. To validate the model, the Shanghai Composite, Nikkei 225, and Hong Kong Hang Seng indices were analyzed. Experiments show that, by comparison, the prediction effect of the mixed model is better than that of a single LSTM. For example, RMSE, MAE and MAPE of the mixed model of the Shanghai Composite Index are 4.22, 4.25 and 0.2 lower than the single model respectively. The RMSE, MAE and MAPE of the mixed model of the Nikkei 225 Index are 47.74, 37.21 and 0.17 lower than the single model respectively, and the RMSE, MAE and MAPE of the mixed model of the Hong Kong Hang Seng Index are 37.88, 25.06 and 0.08 lower than the single model respectively.