An Optimal Weighted Combined Model Coupled with Feature Reconstruction and Deep Learning for Multivariate Stock Index Forecasting

Abstract

Stock index prediction plays an important role in the creation of better investment strategies. However, prediction can be difficult due to the random fluctuation of financial time series. In pursuit of improved stock index prediction, a hybrid prediction model is proposed in this paper, which contains two-step data pretreatment, double prediction models, and smart optimization. In the data pretreatment stage, in order to carry more information about the prediction target, multidimensional explanatory variables are selected by the Granger causality test, and to eliminate data redundancy, feature extraction is inserted with the help of principal component analysis; both of these can provide a higher-quality dataset. Bi-directional long short-term memory and bi-directional gated recurrent unit network, as the concurrent prediction models, can improve not only the precision, but also the robustness of results. In the last stage, the proposed model integrates the weight optimization of the cuckoo search of the two prediction results to take advantage of both. For the model performance test, four main global stock indices are used. The experimental results show that our model performs better than other benchmark models, which indicates the potential of the proposed model for wide application.

Improved BIGRU Model and Its Application in Stock Price Forecasting

“…Zhu [22] found that the VMD-BIGRU model outperformed GRUs and LSTM in predicting high returns when constructing a comprehensive financial investment decision system. Wang [23] tested a combination optimization model using BiLSTM and BIGRUs for simultaneous prediction on four stock groups, demonstrating superior performance compared to other benchmark models. Taguchi [24] simulated stock investments in a proposed portfolio strategy, and found that BIGRU had the lowest error, although its generalizability varied for different datasets and given tasks.…”

“…where X is the current solution position vector, X* is the current optimal solution position vector, t is the current number of iterations, and A and C are the coefficient vectors. The vectors A and C are given by Equations ( 22) and (23).…”