A novel Gaussian process regression-based stock index interval forecasting model integrating optimal variables screening with bidirectional long short-term memory

Stock index forecasting has always been an interesting subject for investors and related scholars. Accurately stock index forecasting can provide some helpful suggestions for investors and keep financial markets stable. In this study, a new forecasting system, including point prediction and interval prediction, has been proposed to predict the stock index. For obtaining a better predictive effect, multiple influencing variables are also considered in the novel model. More specifically, in the point prediction models, this study applies gradient boosting decision tree (GBDT) to choose some variables related to the stock index by determining their contribution to accurate prediction. Next, an autoencoder (AE) is utilized to reduce the dimensionality of screened factors for the purpose of reducing the effect of noise and improving the efficiency of forecasting. These reconstructed features are all inputted into bidirectional long short-term memory (BiLSTM) to do point prediction. The interval prediction is based on point prediction results and Gaussian process regression (GPR), intended to quantitative uncertainty of the variables. This study chooses the Chinese stock index including the Shanghai Securities Composite Index (SSEC), Shenzhen Composite Index (SZI) and China Securities Index 300 (CSI300) to demonstrate the validity of the innovative hybrid model. Furthermore, this study also selects some other models for comparison. Evaluating the performance of the novel hybrid model, it could be considered as a valid way to do stock index forecasting.