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Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets

Author

Listed:
  • Wun-Hua Chen
  • Jen-Ying Shih
  • Soushan Wu

Abstract

Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention. However, most researches are for the US and European markets, with only a few for Asian markets. This research applies Support-Vector Machines (SVMs) and Back Propagation (BP) neural networks for six Asian stock markets and our experimental results showed the superiority of both models, compared to the early researches.

Suggested Citation

  • Wun-Hua Chen & Jen-Ying Shih & Soushan Wu, 2006. "Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(1), pages 49-67.
    • Handle: RePEc:ids:ijelfi:v:1:y:2006:i:1:p:49-67
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    Citations

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    Cited by:

    1. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
    2. Mishra, Sasmita & Padhy, Sudarsan, 2019. "An efficient portfolio construction model using stock price predicted by support vector regression," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    3. ?enol Emir & Hasan Din?er & Mehpare Timor, 2012. "A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 106-122, August.
    4. Pincak, R., 2013. "The string prediction models as invariants of time series in the forex market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6414-6426.
    5. Bundzel, Marek & Kasanický, Tomáš & Pinčák, Richard, 2016. "Using string invariants for prediction searching for optimal parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 680-688.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," IREA Working Papers 201507, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
    7. Katarzyna Kryńska & Robert Ślepaczuk, 2022. "Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index," Working Papers 2022-25, Faculty of Economic Sciences, University of Warsaw.
    8. Duan, Wen-Qi & Stanley, H. Eugene, 2011. "Cross-correlation and the predictability of financial return series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 290-296.
    9. Alina Barbulescu & Cristian Stefan Dumitriu, 2021. "Artificial Intelligence Models for Financial Time Series," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 685-690, August.
    10. Saeed Hajibabaei & Nematollah Hajibabaei & Seyed Mohammad Hoseini & Somaye Hajibabaei & Sajad Hajibabaei, 2014. "Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 18(2), pages 105-130, Spring.
    11. Marek Bundzel & Tomas Kasanicky & Richard Pincak, 2016. "Using String Invariants for Prediction Searching for Optimal Parameters," Papers 1606.06003, arXiv.org.
    12. Sharmin Islam & Md. Shakil Sikder & Md. Farhad Hossain & Partha Chakraborty, 2021. "Predicting the daily closing price of selected shares on the Dhaka Stock Exchange using machine learning techniques," SN Business & Economics, Springer, vol. 1(4), pages 1-16, April.
    13. Hakob GRIGORYAN, 2015. "Stock Market Prediction using Artificial Neural Networks. Case Study of TAL1T, Nasdaq OMX Baltic Stock," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(2), pages 14-23, October.
    14. Jasleen Kaur & Khushdeep Dharni, 2022. "Assessing efficacy of association rules for predicting global stock indices," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 49(3), pages 329-339, September.
    15. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
    16. Nawaf Almaskati, 2022. "Machine learning in finance: Major applications, issues, metrics, and future trends," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-32, September.
    17. Alina Barbulescu & Cristian Stefan Dumitriu, 2021. "Markov Switching Model for Financial Time Series," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 193-198, August.
    18. Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.
    19. Eva DEZSI & Ioan Alin NISTOR, 2016. "Can Deep Machine Learning Outsmart The Market? A Comparison Between Econometric Modelling And Long- Short Term Memory," Romanian Economic Business Review, Romanian-American University, vol. 11(4.1), pages 54-73, december.
    20. Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
    21. Saerom Park & Jaewook Lee & Youngdoo Son, 2016. "Predicting Market Impact Costs Using Nonparametric Machine Learning Models," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    22. Mishra, Sasmita & Padhy, Sudarsan & Mishra, Satya Narayan & Misra, Satya Narayan, 2021. "A novel LASSO – TLBO – SVR hybrid model for an efficient portfolio construction," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).

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