Stock price prediction based on deep neural networks
- 25 Downloads
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.
KeywordsFinancial 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.
- 4.White H (1988) Economic prediction using neural networks: the case of IBM daily stock returns 451–458Google Scholar
- 10.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) Google Scholar
- 15.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 2327–2333Google Scholar
- 18.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 49:1–15Google Scholar
- 21.Graves A, Jaitly N (2014) Towards end-to-end speech recognition with recurrent neural networks. In: International conference on machine learning, pp 1764–1772Google Scholar
- 22.Greff K, Srivastava RK, Koutnik J et al (2016) LSTM: a search space Odyssey. IEEE Trans Neural Netw 28:1–11Google Scholar