An interpretable model for stock trading volume prediction based on interval belief rule base with adaptive rule

Abstract

Stock trading volume forecasting is important for maintaining stock market stability. In real stock market trading volume prediction systems, three problems exist: high system complexity, instability, and the need for interpretable prediction results. Thus, an interpretable model for stock trading volume prediction based on the interval belief rule base with adaptive rule (IBRB-AI) is proposed in this paper. First, an interpretable interval belief rule base (IBRB) prediction model is constructed, and an adaptive rule mechanism is defined for the model. Then, a projection covariance matrix adaptation evolution strategy (P-CMA-ES) algorithm with interpretability constraints is used to ensure the interpretability of the optimization process. The final mean square error of the model is 0.0037, and the R-squared value is 0.8812. Compared with black-box models such as the back propagation neural network (BPNN), the IBRB-AI model achieves the accuracy of black-box models, with a complexity reduction of 38.46% compared with the original IBRB, and the interpretability is also improved. The validity of the IBRB-AI model is proven through a case study of the Tesla stock market, and a general analysis is conducted through Apple, Inc. (AAPL).