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.
Keywords
This work is supported by National Defense Science and Technology Innovation Special Zone Project (No. 18-163-11-ZT-002-045-04).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, C.: Prediction of stock index futures price based on BP neural network. Master Thesis of Qingdao University, Qingdao (2012)
Yu, Z., Qin, L., Zhao Z., Wen, W.: Stock price prediction based on principal component analysis and generalized regression neural network. Stat. Decis. Making 34(18), 168–171 (2008)
Malkiel, B.G., Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Financ. 25, 383–417 (1970)
Bodie, Z., Kane, A., Marcus, A.J.: Investments, 10th edn. McGraw-Hill Education, New York (2014)
Liu, Z., Wang, Y.: An empirical study on the forecasting effectiveness of price-based technical indicators in bull and bear cycles of China’s Shanghai stock market. In: Proceeding of 12th International Conference on Management of e-Commerce and e-Government (ICMECG 2018), pp. 412–417 (2018)
Chen, X., Sun A.: Effectiveness test of CAPM in Chinese stock market. J. Peking Univ. (philosophy and social sciences), 28–37 (2000)
Wang, J.-H., Leu, J.-Y.: Stock market trend prediction using arima-based neural networks. In: IEEE International Conference on Neural Networks, vol. 4, pp. 2160–2165. IEEE (1996)
White H.F.: Economic prediction using neural networks: the case of IBM daily stock returns. Earth Surf. Process. Land. 2, 451–458 (1988)
Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M.: Stock market prediction system with modular neural networks. In: 1990 IJCNN International Joint Conference on Neural Networks, vol. 1, pp. 1–6 (1990)
Yoon, Y., Swales, G.: Predicting stock price performance: a neural network approach. In: Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences, Kauai, HI, USA, vol. 4, pp. 156–162 (1991)
Nelson, D.M.Q., Pereira, A.C.M., de Oliveira, R.A.: Stock market’s price movement prediction with LSTM neural networks. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, pp. 1419–1426 (2017)
Gao, S.E., Lin, B.S., Wang, C.: Share price trend prediction using CRNN with LSTM structure. In: 2018 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan, pp. 10–13 (2018)
Althelaya, K.A., El-Alfy, E.M., Mohammed, S.: Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU). In: 2018 21st Saudi Computer Society National Computer Conference (NCC), Riyadh, pp. 1–7 (2018)
Bach, S., Binder, A., Montavon, G., Klauschen, F., Muller, K.-R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PloS ONE, 10(7), e0130140 (2015)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Statist. 29(5), 1189–1232 (2001)
Zilke, J.R., Loza Mencía, E., Janssen, F.: DeepRED – rule extraction from deep neural networks. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 457–473. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46307-0_29
Lakkaraju, H., et al.: Interpretable & explorable approximations of black box models. arXiv preprint, arXiv:1707.01154 (2017)
Puri, N., et al.: Magix: model agnostic globally interpretable explanations. arXiv preprint, arXiv:1706.07160 (2017)
Wu, M., Hughes, M.C., Parbhoo, S., et al.: Beyond sparsity: tree regularization of deep models for interpretability. In: Proceeding of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), pp. 1670–1678 (2018)
Schaaf, N., Huber, M.F.: Enhancing decision tree based interpretation of deep neural networks through L1-orthogonal regularization. arXiv preprint, arXiv:1904.05394 (2019)
Feuerriegel, S., Gordon, J.: News-based forecasts of macroeconomic indicators: a semantic path model for interpretable predictions. Eur. J. Oper. Res. 272(1), 162–175 (2019)
Rajab, S., Sharma, V.: An interpretable neuro-fuzzy approach to stock price forecasting. Soft Comput. J. 23(3), 921–936 (2019). https://doi.org/10.1007/s00500-017-2800-7
Wu, W., et al.: Preliminary study on interpreting stock price forecasting based on tree regularization of GRU. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds.) ICPCSEE 2019. CCIS, vol. 1059, pp. 476–487. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-0121-0_37
Patel, J., Shah, S., Thakkar, P., et al.: Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Syst. Appl. J. 42(1), 259–268 (2015)
Hong, J.H.: Research on stock price trend prediction based on GBDT model. Jinan university (2017)
Granger, C.W.J., Pesaran, M.H.: Economic and statistical measures of forecast accuracy. J. Forecast. 19(7), 537–560 (1999). Cambridge Working Papers in Economics
Varma, S., Simon, R.: Bias in error estimation when using cross-validation for model selection. BMC Bioinform. 7(1), 91–100 (2006). https://doi.org/10.1186/1471-2105-7-91
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-15-7984-4_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7983-7
Online ISBN: 978-981-15-7984-4
eBook Packages: Computer ScienceComputer Science (R0)