Stock closing price prediction based on ICEEMDAN-FA-BiLSTM–GM combined model

Abstract

The accuracy of stock price forecasting is of great significance in investment decision-making and risk management. However, the complexity and fluctuation of stock prices challenge the traditional forecasting methods to achieve the best accuracy. To improve the accuracy of stock price prediction, a sophisticated combination prediction method based on ICEEMDAN-FA-BiLSTM–GM has been proposed in this article. In this paper, a comprehensive and effective indicator system is constructed, covering 60 indicators such as traditional factors, market sentiment, macroeconomic indicators and company financial data, which affect stock prices. In the data preprocessing stage, in order to eliminate the influence of noise, the stock closing price series is first decomposed by using the ICEEMDAN method, which effectively divides them into high-frequency and low-frequency components according to their respective frequencies. Subsequently, LLE technique is used to narrow down the remaining indicators to obtain 9 narrowed features. Finally, each high-frequency subsequence is combined with all the dimensionality reduction features respectively to construct new indicator sets for input to the model. In the prediction stage, the hyperparameters of the prediction model for each subseries have been determined using the FA algorithm. The prediction has been carried out separately for the high-frequency and low-frequency components, employing the BiLSTM and GM prediction methods. Ultimately, the prediction results of each subseries have been superimposed to obtain the final stock price prediction value. In this paper, an empirical study was conducted using stock price data such as Shanghai composite index. The experimental results show that the established stock price prediction model based on ICEEMDAN-FA-BiLSTM–GM has obvious advantages in terms of prediction accuracy and stability compared with traditional methods and other combined prediction methods. This model can provide more accurate stock price prediction and promote the rationalization of investment decision and the accuracy of risk control.