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An efficient portfolio construction model using stock price predicted by support vector regression

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  • Mishra, Sasmita
  • Padhy, Sudarsan

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  • 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).
    • Handle: RePEc:eee:ecofin:v:50:y:2019:i:c:s1062940818302481
    DOI: 10.1016/j.najef.2019.101027
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    References listed on IDEAS

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    1. Panda, Chakradhara & Narasimhan, V., 2007. "Forecasting exchange rate better with artificial neural network," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 227-236.
    2. Hwarng, H. Brian, 2001. "Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures," Omega, Elsevier, vol. 29(3), pages 273-289, June.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    5. Li Wang & Ji Zhu, 2010. "Financial market forecasting using a two-step kernel learning method for the support vector regression," Annals of Operations Research, Springer, vol. 174(1), pages 103-120, February.
    6. 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.
    7. Altaf Hossain & Mohammed Nasser, 2011. "Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(3), pages 533-551, November.
    8. Chiang, W. -C. & Urban, T. L. & Baldridge, G. W., 1996. "A neural network approach to mutual fund net asset value forecasting," Omega, Elsevier, vol. 24(2), pages 205-215, April.
    9. Jensen, Michael C., 1978. "Some anomalous evidence regarding market efficiency," Journal of Financial Economics, Elsevier, vol. 6(2-3), pages 95-101.
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    Cited by:

    1. Xu, Qifa & Li, Mengting & Jiang, Cuixia, 2021. "Network-augmented time-varying parametric portfolio selection: Evidence from the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. Chen, Wei & Zhang, Haoyu & Jia, Lifen, 2022. "A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    3. Vera Ivanyuk, 2021. "Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation," Economies, MDPI, vol. 9(3), pages 1-19, June.
    4. 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).
    5. Xuelin Xie & Jingfang Shen, 2021. "Waterlogging Resistance Evaluation Index and Photosynthesis Characteristics Selection: Using Machine Learning Methods to Judge Poplar’s Waterlogging Resistance," Mathematics, MDPI, vol. 9(13), pages 1-19, July.
    6. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

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