Open Access

A Hybrid Model of Primary Ensemble Empirical Mode Decomposition and Quantum Neural Network in Financial Time Series Prediction

Caifeng Wang

School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, P. R. China

E-mail Address: cfwang@ustb.edu.cn

Corresponding author.

Search for more papers by this author

Yukun Yang

School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, P. R. China

Search for more papers by this author

Linlin Xu

Systems Design Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

Search for more papers by this author

, and

Alexander Wong

Systems Design Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

Search for more papers by this author

https://doi.org/10.1142/S0219477523400060Cited by:3 (Source: Crossref)

This article is part of the issue:

Special Issue: Financial Econometrics and Risk Management
Guest Editors: Zhi-Qiang Jiang, Gangjin Wang and Wei-Xing Zhou

Abstract

Financial time series are nonlinear, volatile and chaotic. Inspired by quantum computing, this paper proposed a new model, called primary ensemble empirical mode decomposition combined with quantum neural network (PEEMD-QNN) in predicting the stock index. PEEMD-QNN takes the advantages of the PEEMD which retains the main component of modal component and QNN. To demonstrate that our PEEMD-QNN model is robust, we used the new model to predict six major stock index time series in China at a specific time. Detailed experiments are implemented for both of the proposed prediction models, in which empirical mode decomposition combined with QNN (EMD-QNN), QNN and BP neural network are compared. The results demonstrate that the proposed PEEMD-QNN model has higher accuracy than BP neural network, QNN model and EMD-QNN model in stock market prediction.

Communicated by Wei-Xing Zhou

Keywords:

References

1. K. J. Kim and I. Han, Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index, Expert Syst. Appl. 19(2) (2000) 125–132. Crossref, Web of Science, Google Scholar
2. W. W. Chow and M. K. Fung, Volatility of stock price as predicted by patent data: An MGARCH perspective, J. Empir. Finance 15(1) (2008) 64–79. Crossref, Web of Science, Google Scholar
3. A. A. Ariyo, A. O. Adewumi and C. K. Ayo, Stock price prediction using the ARIMA model, in 2014 UKSim-AMSS 16th Int. Conf. Computer Modelling and Simulation (IEEE, 2014), pp. 106–112. Crossref, Google Scholar
4. E. Hoseinzade, S. Haratizadeh and A. Khoeini, U-cnn pred: A universal cnn-based predictor for stock markets, Expert Syst. Appl. 129 (2019) 273–285. Crossref, Web of Science, Google Scholar
5. H. Y. Kim and C. H. Won, Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models, Expert Syst. Appl. 103 (2018) 25–37. Crossref, Web of Science, Google Scholar
6. A. H. Moghaddam, M. H. Moghaddam and M. Esfandyari, Stock market index prediction using artificial neural network, J. Econ. Finance Adm. Sci. 21(41) (2016) 89–93. Crossref, Google Scholar
7. R. Dash and P. Dash, Efficient stock price prediction using a self evolving recurrent neuro-fuzzy inference system optimized through a modified differential harmony search technique, Expert Syst. Appl. 52 (2016) 75–90. Crossref, Web of Science, Google Scholar
8. A. Esfahanipour and P. Mardani, An ANFIS model for stock price prediction: The case of Tehran stock exchange, in 2011 Int. Symp. Innovations in Intelligent Systems and Applications (IEEE, 2011), pp. 44–49. Crossref, Google Scholar
9. M. Göçken, M. Özçalıcı, A. Boru and A. T. Dosdoğru, Integrating metaheuristics and artificial neural networks for improved stock price prediction, Expert Syst. Appl. 44 (2016) 320–331. Crossref, Web of Science, Google Scholar
10. B. B. Nair, S. G. Sai, A. N. Naveen, A. Lakshmi, G. S. Venkatesh and V. P. Mohandas, A GA-artificial neural network hybrid system for financial time series forecasting, in Int. Conf. Advances in Information Technology and Mobile Communication (Springer, Berlin, Heidelberg, 2011), pp. 499–506. Crossref, Google Scholar
11. H. Gunduz, Y. Yaslan and Z. Cataltepe, Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations, Knowledge-Based Syst. 137 (2017) 138–148. Crossref, Web of Science, Google Scholar
12. M. Hagenau, M. Liebmann and D. Neumann, Automated news reading: Stock price prediction based on financial news using context-capturing features, Decis. Support Syst. 55(3) (2013) 685–697. Crossref, Web of Science, Google Scholar
13. F. E. Tay and L. Cao, Application of support vector machines in financial time series forecasting, Omega 29(4) (2001) 309–317. Crossref, Web of Science, Google Scholar
14. Y. Shynkevich, T. M. McGinnity, S. A. Coleman, A. Belatreche and Y. Li, Forecasting price movements using technical indicators: Investigating the impact of varying input window length, Neurocomputing 264 (2017) 71–88. Crossref, Web of Science, Google Scholar
15. D. E. Rumelhart, G. E. Hinton and R. J. Williams, Learning representations by back-propagating errors, Nature 323(6088) (1986) 533–536. Crossref, Web of Science, Google Scholar
16. D. Reby, S. Lek, I. Dimopoulos, J. Joachim, J. Lauga and S. Aulagnier, Artificial neural networks as a classification method in the behavioural sciences, Behav. Process. 40(1) (1997) 35–43. Crossref, Web of Science, Google Scholar
17. W. Long, Z. Lu and L. Cui, Deep learning-based feature engineering for stock price movement prediction, Knowledge-Based Syst. 164 (2019) 163–173. Crossref, Web of Science, Google Scholar
18. Z. Yu, L. Qin, Y. Chen and M. D. Parmar, Stock price forecasting based on LLE-BP neural network model, Physica A. Stat. Mech. Appl. 553 (2020) 124197. Crossref, Web of Science, Google Scholar
19. H. Liu and Z. Long, An improved deep learning model for predicting stock market price time series, Digital Signal Process. 102 (2020) 102741. Crossref, Web of Science, Google Scholar
20. J. Cao and J. Wang, Exploration of stock index change prediction model based on the combination of principal component analysis and artificial neural network, Soft Comput. 24(11) (2020) 7851–7860. Crossref, Web of Science, Google Scholar
21. J. Z. Wang, J. J. Wang, Z. G. Zhang and S. P. Guo, Forecasting stock indices with back propagation neural network, Expert Syst. Appl. 38(11) (2011) 14346–14355. Crossref, Web of Science, Google Scholar
22. M. Khashei and M. Bijari, Fuzzy artificial neural network (p, d, q) model for incomplete financial time series forecasting, J. Intell. Fuzzy Syst. 26(2) (2014) 831–845. Crossref, Web of Science, Google Scholar
23. S. Mehdi, Stock price forecasting, J. Mon. Econ. 9 (2014) 108–126. Google Scholar
24. R. P. Mahajan, Stock price prediction using quantum neural network, J. Global Res. Comput. Sci. 1(4) (2010) 58–64. Google Scholar
25. R. Schützhold, Pattern recognition on a quantum computer, Phys. Rev. A 67(6) (2003) 062311. Crossref, Web of Science, Google Scholar
26. M. Schuld, I. Sinayskiy and F. Petruccione, Prediction by linear regression on a quantum computer, Phys. Rev. A 94(2) (2016) 022342. Crossref, Web of Science, Google Scholar
27. P. W. Shor, Algorithms for quantum computation: Discrete logarithms and factoring, in Proc. 35th Annual Symp. Foundations of Computer Science (IEEE, 1994), pp. 124–134. Crossref, Google Scholar
28. Y. Shang, X. Lu and R. Lu, Computing power of Turing machines in the framework of unsharp quantum logic, Theor. Comput. Sci. 598 (2015) 2–14. Crossref, Web of Science, Google Scholar
29. G. Castagnoli, Highlighting the mechanism of the quantum speedup by time-symmetric and relational quantum mechanics, Found. Phys. 46(3) (2016) 360–381. Crossref, Web of Science, Google Scholar
30. E. Aimeur, G. Brassard and S. Gambs, Quantum speed-up for unsupervised learning, Mach. Learn. 90(2) (2013) 261–287. Crossref, Web of Science, Google Scholar
31. E. C. Behrman, J. Niemel, J. E. Steck and S. R. Skinner, A quantum dot neural network, in Proceedings of the 4th Workshop on Physics of Computation (PhysComp96), IEEE Computer Society Press (Boston University, November 1996>), pp. 22–24. Google Scholar
32. N. Wiebe, C. Granade and D. G. Cory, Quantum bootstrapping via compressed quantum Hamiltonian learning, New J. Phys. 17(2) (2015) 022005. Crossref, Web of Science, Google Scholar
33. E. F. Fama, Market Efficiency, Long-Term Returns, and Behavioral Finance (University of Chicago Press, 2021), pp. 174–200. Google Scholar
34. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng and H. H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London. Ser. A. Math. Phys. Eng. Sci. 454 (1998) 903–995. Crossref, Web of Science, Google Scholar
35. J. Wang and J. Wang, Forecasting stochastic neural network based on financial empirical mode decomposition, Neural Netw. 90 (2017) 8–20. Crossref, Web of Science, Google Scholar
36. J. Cao, Z. Li and J. Li, Financial time series forecasting model based on CEEMDAN and LSTM, Phys. A. Stat. Mech. Appl. 519 (2019) 127–139. Crossref, Web of Science, Google Scholar
37. H. Liu, C. Chen, H. Q. Tian and Y. F. Li, A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks, Renew. Energy 48 (2012) 545–556. Crossref, Web of Science, Google Scholar
38. W. Chen, M. Jiang, W. G. Zhang and Z. Chen, A novel graph convolutional feature based convolutional neural network for stock trend prediction, Inf. Sci. 556 (2021) 67–94. Crossref, Web of Science, Google Scholar
39. E. Paquet and F. Soleymani, QuantumLeap: Hybrid quantum neural network for financial predictions, Expert Syst. Appl. 195 (2022) 116583. Crossref, Web of Science, Google Scholar
40. G. Liu and W. Ma, A quantum artificial neural network for stock closing price prediction, Inf. Sci. 598 (2022) 75–85. Crossref, Web of Science, Google Scholar

Vol. 22, No. 04

Metrics

Downloaded 311 times

History

Received 30 November 2022

Accepted 13 March 2023

Published: 30 March 2023

Information

This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords

PDF download

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

A Hybrid Model of Primary Ensemble Empirical Mode Decomposition and Quantum Neural Network in Financial Time Series Prediction

Abstract

Recommended