Abstract
Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. In this paper, financial product price data are treated as a one-dimensional series generated by the projection of a chaotic system composed of multiple factors into the time dimension, and the price series is reconstructed using the time series phase-space reconstruction (PSR) method. A DNN-based prediction model is designed based on the PSR method and a long- and short-term memory networks (LSTMs) for DL and used to predict stock prices. The proposed and some other prediction models are used to predict multiple stock indices for different periods. A comparison of the results shows that the proposed prediction model has higher prediction accuracy.
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References
Zhang Li, Wang Fulin, Bing Xu, Chi Wenyu, Wang Qiongya, Sun Ting (2018) Prediction of stock prices based on LM-BP neural network and the estimation of overfitting point by RDCI. Neural Comput Appl 30(5):1425–1444
Oliveira ALI, Meira SRL (2006) Detecting novelties in time series through neural networks forecasting with robust confidence intervals. Neurocomputing 70(1):79–92
Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
White H (1988) Economic prediction using neural networks: the case of IBM daily stock returns 451–458
Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175
Vanstone B, Finnie G (2009) An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Syst Appl 36(3):6668–6680
Jasemi M, Kimiagari AM, Memariani A (2011) A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick. Expert Syst Appl 38(4):3884–3890
Ticknor JL (2013) A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst Appl 40(14):5501–5506
Wang J (2015) Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing 156:68–78
Xiong Z (2011) Research on RMB exchange rate forecasting model based on combining ARIMA with Neural networks. J Quant Tech Econ 28(06):64–76 (in Chinese)
Wu Q, Wang C, Tang Y (2013) Empirical research on volume-price relationship based on GARCH models and BP neural network. J Sichuan Univ Nat Sci Edn 50(04):703–708 (in Chinese)
Li X, Zhang Z (2014) Support vector machine method for financial time series prediction based on simultaneous error prediction. J Tianjin Univ Sci Technol 47(01):86–94 (in Chinese)
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
Shen F, Chao J, Zhao J (2015) Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing 167:243–253
Ding X, Zhang Y, Liu T, et al (2015) Deep learning for event-driven stock prediction. In Twenty-fourth international joint conference on artificial intelligence, pp 2327–2333
Zhao Y, Li J, Yu L (2017) A deep learning ensemble approach for crude oil price forecasting. Energy Econ 66:9–16
Krauss C, Do XA, Huck N (2017) Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S&P 500. Eur J Oper Res 259(2):689–702
Song Y, Lee JW, Lee J (2018) A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction. Appl Intell 49:1–15
Minh DL, Sadeghi-Niaraki A, Huy HD et al (2018) Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access 6:1–1
Göçken M, Özçalici M, Boru A, Dosdogru AT (2019) Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection. Neural Comput Appl 31(2):577–592
Graves A, Jaitly N (2014) Towards end-to-end speech recognition with recurrent neural networks. In: International conference on machine learning, pp 1764–1772
Greff K, Srivastava RK, Koutnik J et al (2016) LSTM: a search space Odyssey. IEEE Trans Neural Netw 28:1–11
Acknowledgements
This paper is supported by Natural Science Foundation of China. (No. 61673354), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan), the State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology (DMETKF2018020) and Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences (Wuhan).
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Yu, P., Yan, X. Stock price prediction based on deep neural networks. Neural Comput & Applic 32, 1609–1628 (2020). https://doi.org/10.1007/s00521-019-04212-x
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DOI: https://doi.org/10.1007/s00521-019-04212-x