A novel parallel hybrid model for forecasting the stock price

In recent years, in addition to the growth in the number of investors in the stock market, there has been a growing interest in predicting stock prices. Accurate stock prices can effectively improve investment returns on the premise of reducing investment risks for stock investors. Therefore, this study presents a hybrid Bo-LSTM-SVR model to predict the next day's stock closing price. Firstly, the hyper-parameters of LSTM and SVR as well as the length of their respective sliding windows are optimized by the Bayesian optimization method, so as to obtain more accurate predicted values of the single models. The genetic algorithm is then adopted every day to decide the weight of the two single models, and finally, the combined predicted values are obtained. In order to ensure that the prediction of the proposed model is more accurate, this model and the other six models are applied to predict the closing prices of the Shanghai Composite Index on the next trading day. The results reveal that the model proposed in this study is the most accurate, with the smallest MAE and RMSE as well as the largest R(boolean AND)2. Compared with other models, the proposed model is more suitable for stock price prediction, which provides a dependable tool for investors to make stock investment decisions.