Stock price prediction based on deep neural networks

  • Pengfei Yu
  • Xuesong YanEmail author
Deep Learning for Big Data Analytics


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.


Financial data prediction Neural networks Deep learning Phase-space reconstruction 



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).

Compliance with ethical standards

Conflict of interest

No conflicts of interest of this work.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratary of Intelligent Geo-Information ProcessingWuhanChina

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