Skip to main content
SpringerLink
Log in

A novel hybrid model based on recurrent neural networks for stock market timing

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Stock market timing is regarded as a challenging task of financial prediction. An accurate prediction of stock trend can yield great profits for investors. At present, recurrent neural networks (RNNs) have a good performance in stock market forecasting. However, there has been a relative lack of research in the stock market timing using RNNs. In this paper, a novel model named hybrid RNN model is proposed for stock market timing by incorporating multi-layer long short-term memory, multi-layer gated recurrent unit and one-layer ReLU layer. Moreover, based on five popular benchmark datasets from UCI Machine Learning Repository and six daily securities from Shanghai Stock Exchange, comparisons with 12 state-of-the-art models are conducted to verify the superiority of the proposed hybrid RNN model in terms of nine technical indicators. The findings from the experiment demonstrate that: (1) as opposed to 12 models, the average accuracy, MSE and AUC of hybrid RNN model (0.7406, 0.2592, 0.7368) are significantly better than other comparison models, and (2) the proposed hybrid RNN classification procedure can be considered as a feasible and effective tool for stock market timing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Ahmadi E, Jasemi M, Monplaisir L, Nabavi MA, Mahmoodi A, Jam PA (2018) New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic. Expert Syst Appl 94:21–31

    Article  Google Scholar 

  • Baek Y, Kim HY (2018) ModAugNet: a new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Syst Appl 113:457–480

    Article  Google Scholar 

  • Barak S, Dahooie JH, Tichy T (2015) Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick. Expert Syst Appl 42(23):9221–9235

    Article  Google Scholar 

  • Bebarta DK, Biswal B, Dash PK (2015) Polynomial based functional link artificial recurrent neural network adaptive system for predicting Indian stocks. Int J Comput Intell Syst 8(6):1004–1016

    Article  Google Scholar 

  • Bhunia AK, Konwer A, Bhunia AK, Bhowmick A, Roy PP, Pal U (2019) Script identification in natural scene image and video frames using an attention based convolutional-LSTM network. Pattern Recognit 85:172–184

    Article  Google Scholar 

  • Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econom 31(3):307–327

    Article  MathSciNet  MATH  Google Scholar 

  • Bollerslev T, Marrone J, Xu L, Zhou H (2014) Stock return predictability and variance risk premia: statistical inference and international evidence. J Financ Quant Anal 49(3):633–661

    Article  Google Scholar 

  • Bourbonnais R, Maftei MM (2012) ARFIMA process: tests and applications at a white noise process, a random walk process and the stock exchange index CAC 40. Econ Comput Econ Cybern Stud Res 46(1):5–16

    Google Scholar 

  • Bruni R (2017) Stock market index data and indicators for day trading as a binary classification problem. Data Brief 10:569–575

    Article  Google Scholar 

  • Cao J, Li Z, Li J (2019) Financial time series forecasting model based on CEEMDAN and LSTM. Phys A Stat Mech Appl 519:127–139

    Article  Google Scholar 

  • Chen WL, Yeo CK, Lau CT, Lee BS (2018) Leveraging social media news to predict stock index movement using RNN-boost. Data Knowl Eng 118:14–24

    Article  Google Scholar 

  • Cheng GF, Li X, Yan YH (2018) Using highway connections to enable deep small-footprint LSTM-RNNs for speech recognition. Chin J Electron 28(1):107–112

    Article  Google Scholar 

  • Chung H, Shin KS (2018) Genetic algorithm-optimized long short-term memory network for stock market prediction. Sustainability 10(10):3765–3783

    Article  Google Scholar 

  • Dou YL, Fang YH, Hu C, Zheng R, Yan FJ (2019) Gated branch neural network for mandatory lane changing suggestion at the on-ramps of highway. IET Intell Transp Syst 13(1):48–54

    Article  Google Scholar 

  • Gao TW, Chai YT (2018) Improving stock closing price prediction using recurrent neural network and technical indicators. Neural Comput 30(10):2833–2854

    Article  MathSciNet  Google Scholar 

  • Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451–2471

    Article  Google Scholar 

  • Gocken M, Ozcalici 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

    Article  Google Scholar 

  • Goykhman M, Teimouri A (2018) Machine learning in sentiment reconstruction of the simulated stock market. Phys A Stat Mech Appl 492:1729–1740

    Article  Google Scholar 

  • Graves A (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  • Henrique BM, Sobreiro VA, Kimura H (2019) Literature review: machine learning techniques applied to financial market prediction. Expert Syst Appl 124:226–251

    Article  Google Scholar 

  • Hernandez-Suarez A, Sanchez-Perez G, Toscano-Medina K, Perez-Meana H, Portillo-Portillo J, Sanchez V, Villalba LJG (2019) Using twitter data to monitor natural disaster social dynamics: a recurrent neural network approach with word embeddings and kernel density estimation. Sensors 19(7):1746–1768

    Article  Google Scholar 

  • Hu Y, Sun XY, Nie X, Li YZ, Liu L (2019) An enhanced LSTM for trend following of time series. IEEE Access 7:34020–34030

    Article  Google Scholar 

  • Hussain AJ, Knowles A, Lisboa PJG, El-Deredy W (2008) Financial time series prediction using polynomial pipelined neural networks. Expert Syst Appl 35(3):1186–1199

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Jiang XQ, Zhang LC (2019) Stock price fluctuation prediction method based on time series analysis. Discrete Contin Dyn Syst Ser S 12(4–5):915–927

    MathSciNet  MATH  Google Scholar 

  • Kim HY, Won CH (2018) Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst Appl 103:25–37

    Article  Google Scholar 

  • Kim JH, Shamsuddin A, Lim KP (2011) Stock return predictability and the adaptive markets hypothesis: evidence from century-long U.S. data. J Empir Finance 18(5):868–879

    Article  Google Scholar 

  • Liu C, Hou WY, Liu DY (2017a) Foreign exchange rates forecasting with convolutional neural network. Neural Process Lett 46(3):1095–1119

    Article  Google Scholar 

  • Liu BC, Fu CC, Bielefield A, Liu YQ (2017b) Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network. Energies 10(10):1453–1468

    Article  Google Scholar 

  • Long W, Lu ZC, Cui LX (2019) Deep learning-based feature engineering for stock price movement prediction. Knowl-Based Syst 164:163–173

    Article  Google Scholar 

  • Luo C, Jiang ZP, Zheng YJ (2019) A novel reconstructed training-set SVM with roulette cooperative coevolution for financial time series classification. Expert Syst Appl 123:283–298

    Article  Google Scholar 

  • Majhi B, Rout M, Baghel V (2014) On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices. J King Saud Univ Comput Inf Sci 26(3):319–331

    Google Scholar 

  • Minh DL, Sadeghi-Niaraki A, Huy ND, Min K, Moon H (2018) Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access 6:55392–55404

    Article  Google Scholar 

  • Mitra A, Bok LK, Chakraborti S (2019) An adaptive exponentially weighted moving average-type control chart to monitor the process mean. Eur J Oper Res 279(3):902–911

    Article  MathSciNet  MATH  Google Scholar 

  • Nakano M, Takahashi A, Takahashi S (2017) Generalized exponential moving average (EMA) model with particle filtering and anomaly detection. Expert Syst Appl 73(1):187–200

    Article  Google Scholar 

  • Paiva FD, Cardoso RTN, Hanaoka GP, Duarte WM (2019) Decision-making for financial trading: a fusion approach of machine learning and portfolio selection. Expert Syst Appl 115:635–655

    Article  Google Scholar 

  • Pal SS, Kar S (2019) Time series forecasting for stock market prediction through data discretization by fuzzistics and rule generation by rough set theory. Math Comput Simul 162:18–30

    Article  MathSciNet  Google Scholar 

  • Phan DHB, Sharma SS, Narayan PK (2015) Stock return forecasting: some new evidence. Int Rev Financ Anal 40:38–51

    Article  Google Scholar 

  • Rather AM, Agarwal A, Sastry VN (2015) Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst Appl 42(6):3234–3241

    Article  Google Scholar 

  • Rosillo R, Brugos JAL (2013) Technical analysis and the Spanish stock exchange: testing the RSI, MACD, momentum and stochastic rules using Spanish market companies. Appl Econ 45(12):1541–1550

    Article  Google Scholar 

  • Senol D, Ozturan M (2008) Stock price direction prediction using artificial neural network approach: the case of Turkey. J Artif Intell Res 1(2):92–107

    Google Scholar 

  • Tan YH, Chan CS (2019) Phrase-based image caption generator with hierarchical LSTM network. Neurocomputing 333:86–100

    Article  Google Scholar 

  • Tang HM, Dong PW, Shi Y (2019) A new approach of integrating piecewise linear representation and weighted support vector machine for forecasting stock turning points. Expert Syst Appl 78:685–696

    Google Scholar 

  • Wu YT, Yuan M, Dong SP, Lin L, Liu YQ (2018) Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing 275:167–179

    Article  Google Scholar 

  • Xiao JH, Zhu XH, Huang CX, Yang XG, Wen FH, Zhong MR (2019) A new approach for stock price analysis and prediction based on SSA and SVM. Int J Inf Technol Decis Mak 18(1):287–310

    Article  Google Scholar 

  • Xu QF, Zhuo XX, Jiang CX, Liu YE (2019) An artificial neural network for mixed frequency data. Expert Syst Appl 118:127–139

    Article  Google Scholar 

  • Yan HJ, Ouyang HB (2018) Financial time series prediction based on deep learning. Wirel Pers Commun 102(2):683–700

    Article  Google Scholar 

  • Zhang J, Teng YE, Chen W (2019) Support vector regression with modified firefly algorithm for stock price forecasting. Appl Intell 49(5):1658–1674

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the GreatWall Scholar Training Program of Beijing Municipality (CIT&TCD20190338), the Humanity and Social Science Foundation of Ministry of Education of China (No. 19YJAZH005), the Beijing Social Science Fund (No. 18YJB007).

Author information

Authors and Affiliations

  1. School of Management and Engineering, Capital University of Economics and Business, Beijing, China

    Yue Qiu, Hao-Yu Yang, Shan Lu & Wei Chen

Authors
  1. Yue Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao-Yu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Chen.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu, Y., Yang, HY., Lu, S. et al. A novel hybrid model based on recurrent neural networks for stock market timing. Soft Comput 24, 15273–15290 (2020). https://doi.org/10.1007/s00500-020-04862-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04862-3

Keywords

Search

Navigation