Kernel multi-granularity double-quantitative rough set based on ensemble empirical mode decomposition: Application to stock price trends prediction

Abstract

As financial markets grow increasingly complex and dynamic, accurately predicting stock price trends becomes crucial for investors and financial analysts. Effectively identifying and selecting the most predictive attributes has become a challenge in stock trends prediction. To address this problem, this study proposes a new attribute reduction model. A rough set theory model is built by simplifying the prediction process and combining it with the long short-term memory network (LSTM) to enhance the accuracy of stock trends prediction. Firstly, the Ensemble Empirical Mode Decomposition (EEMD) is utilized to decompose the stock price data into a multi-granularity information system. Secondly, due to the numerical characteristics of stock data, a kernel function is applied to construct binary relationships. Thirdly, recognizing the noise inherent in stock data, the double-quantitative rough set theory is utilized to improve fault tolerance during the construction of decision attributes' lower and upper approximations. Moreover, calculate the correlation between conditional and decision attributes, and retain highly correlated conditional attributes for prediction. The kernel multi-granularity double-quantitative rough set based on the EEMD (EEMD-KMGDQRS) model proposed identifies the key factors behind stock data. Finally, the efficacy of the proposed model is validated by selecting 356 stocks from diverse industries in the Shanghai and Shenzhen stock markets as experimental samples. The results show that the proposed model improves the generalization of attribute reduction results through a fault tolerance mechanism by combining kernel function with multi-granularity double-quantitative rough set, thereby enhancing the accuracy of stock trends prediction in subsequent LSTM prediction processes.