Abstract
Neural network is widely used in stock price forecasting, but it lacks interpretability because of its “black box” characteristics. In this paper, L1-orthogonal regularization method is used in the GRU model. A decision tree, GRU-DT, was conducted to represent the prediction process of a neural network, and some rule screening algorithms were proposed to find out significant rules in the prediction. In the empirical study, the data of 10 different industries in China’s CSI 300 were selected for stock price trend prediction, and extracted rules were compared and analyzed. And the method of technical index discretization was used to make rules easy for decision-making. Empirical results show that the AUC of the model is stable between 0.72 and 0.74, and the value of F1 and Accuracy are stable between 0.68 and 0.70, indicating that discretized technical indicators can predict the short-term trend of stock price effectively. And the fidelity of GRU-DT to the GRU model reaches 0.99. The prediction rules of different industries have some commonness and individuality.
This work is supported by National Defense Science and Technology Innovation Special Zone Project (No. 18-163-11-ZT-002-045-04).
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Wu, W., Zhao, Y., Wang, Y., Wang, X. (2020). Stock Price Forecasting and Rule Extraction Based on L1-Orthogonal Regularized GRU Decision Tree Interpretation Model. In: Qin, P., Wang, H., Sun, G., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1258. Springer, Singapore. https://doi.org/10.1007/978-981-15-7984-4_23
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