An interpretable intuitionistic fuzzy inference model for stock prediction. (1st March 2023)

• Record Type: Journal Article
• Authors: Wang, Weiming; Lin, Weiwei; Wen, Yiming; Lai, Xiaozheng; Peng, Peng; Zhang, Yi; Li, Keqin
• Is Part Of: Expert systems with applications. Volume 213:Part A(2023)
• Publication Date: 2023-03-01
• Subjects: Fuzzy neural network -- Interpretable network -- Intuitionistic fuzzy set -- Neuro fuzzy system -- Stock market prediction; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118908
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Stock price prediction and modeling demonstrate high economic value in the financial market. Due to the non-linearity and volatility of stock prices and the unique nature of financial transactions, it is essential for the prediction method to ensure high prediction performance and interpretability. However, existing methods fail to achieve both the two goals simultaneously. To fill this gap, this paper presents an interpretable intuitionistic fuzzy inference model, dubbed as IIFI. While retaining the prediction accuracy, the interpretable module in IIFI can automatically calculate the feature contribution based on the intuitionistic fuzzy set, which provides high interpretability of the model. Also, most of the existing training algorithms, such as LightGBM, XGBoost, DNN, Stacking, etc, can be embedded in the inference module of our proposed model and achieve better prediction results. The back-test experiment on China's A-share market shows that IIFI achieves superior performance — the stock profitability can be increased by more than 20% over the baseline methods. Meanwhile, interpretable results show that IIFI can effectively distinguish between important and redundant features via rating corresponding scores to each feature. As a byproduct of our interpretable methods, the scores over features can be used to further optimize the investment strategy. Highlights: An interpretable fuzzy inference model for stock prediction is proposed. The proposed model is orthogonal to most of the existing training algorithm. The contribution of features can be automatically calculated by the model. The model can render interpretable information for stock prediction result. The model achieves both high prediction performance and interpretability.