CNN-LSTM Stock Price Prediction Model Based On Bayesian Optimization

With the development of the financial market,people are increasingly paying attention to the trends of the stock market.Stock price forecasting can not only bring huge profits to investors,but also serve as an effective risk management tool.With the advent of the artificial intelligence era,new technologies such as machine learning and deep learning have rapidly developed in various industries,greatly improving business efficiency and user experience.Machine learning and deep learning technologies can quickly and effectively discover stock volatility patterns by analyzing massive amounts of data,thereby achieving functions such as predicting market trends and providing personalized services,bringing significant economic benefits to enterprises and individuals.This article applies deep learning technology to stock price prediction and constructs a new BO-CNN-LSTM combination model.Firstly,in terms of data processing,basic and technical indicators are selected to form the stock feature system,and the data after eliminating redundant information between each indicator through principal component analysis(PCA)is used as the initial data.Secondly,the stock price prediction model is built by combining the advantages of CNN and LSTM models.At the same time,in view of the common overfitting phenomenon and the low efficiency of manual trial and error,Bayesian optimization(BO)algorithm is used to optimize the model hyperparameter,and the prediction performance of genetic algorithm(GA)and particle swarm optimization(PSO)algorithm,which are widely used at present,is discussed.Finally,use the constructed BO-CNN-LSTM model to predict the representative closing price of the Shanghai and Shenzhen 300 Index The results indicate that the BO-CNN-LSTM model has better predictive performance compared to the benchmark model At the same time,compared with GA and PSO algorithms,the BO algorithm is more efficient in the search of neural network hyperparameter,the training time of the model is shorter,and the prediction effect of the model is similar,which indicates that the BO-CNN-LSTM model constructed in this paper has certain advantages in the accuracy and time consumption of stock price prediction.