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A New Strategy for Short-Term Stock Investment Using Bayesian Approach

Author

Listed:
  • Tai Vo-Van

    (Can Tho University)

  • Ha Che-Ngoc

    (Ton Duc Thang University
    Ton Duc Thang University)

  • Nghiep Le-Dai

    (Nam Can Tho University)

  • Thao Nguyen-Trang

    (Ton Duc Thang University
    Ton Duc Thang University)

Abstract

In this paper, an application of the Bayesian classifier for short-term stock trend prediction is presented. In order to use Bayesian classifier effectively, we transform the daily stock price time series object into a data frame format where the dependent variable is the stock trend label and the independent variables are the stock variations of the last few days. Based on the posterior probability density function, we propose a new method for stock selection and then propose a new stock trading strategy. The numerical examples demonstrate the potential of the proposed strategy for application to short-term stock trading.

Suggested Citation

  • Tai Vo-Van & Ha Che-Ngoc & Nghiep Le-Dai & Thao Nguyen-Trang, 2022. "A New Strategy for Short-Term Stock Investment Using Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 887-911, February.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:2:d:10.1007_s10614-021-10115-8
    DOI: 10.1007/s10614-021-10115-8
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    References listed on IDEAS

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    1. Rohnn Sanderson & Nancy L. Lumpkin-Sowers, 2018. "Buy and Hold in the New Age of Stock Market Volatility: A Story about ETFs," IJFS, MDPI, vol. 6(3), pages 1-14, September.
    2. J. Wiesinger & D. Sornette & J. Satinover, 2013. "Reverse Engineering Financial Markets with Majority and Minority Games Using Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 475-492, April.
    3. Roscoe, Philip & Howorth, Carole, 2009. "Identification through technical analysis: A study of charting and UK non-professional investors," Accounting, Organizations and Society, Elsevier, vol. 34(2), pages 206-221, February.
    4. George S. Atsalakis & Eftychios E. Protopapadakis & Kimon P. Valavanis, 2016. "Stock trend forecasting in turbulent market periods using neuro-fuzzy systems," Operational Research, Springer, vol. 16(2), pages 245-269, July.
    5. Xinyi Li & Yinchuan Li & Xiao-Yang Liu & Christina Dan Wang, 2019. "Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction," Papers 1908.01112, arXiv.org.
    6. Huarng, Kunhuang & Yu, Hui-Kuang, 2005. "A Type 2 fuzzy time series model for stock index forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 445-462.
    7. Robert Sollis & Paul Newbold & Stephen Leybourne, 2000. "Stochastic unit roots modelling of stock price indices," Applied Financial Economics, Taylor & Francis Journals, vol. 10(3), pages 311-315.
    8. Thao Nguyen-Trang & Tai Vo-Van, 2017. "A new approach for determining the prior probabilities in the classification problem by Bayesian method," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 629-643, September.
    9. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
    10. Zhou, Zhongbao & Jin, Qianying & Xiao, Helu & Wu, Qian & Liu, Wenbin, 2018. "Estimation of cardinality constrained portfolio efficiency via segmented DEA," Omega, Elsevier, vol. 76(C), pages 28-37.
    11. Shangkun Deng & Kazuki Yoshiyama & Takashi Mitsubuchi & Akito Sakurai, 2015. "Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 49-89, January.
    12. Pätäri, Eero & Karell, Ville & Luukka, Pasi & Yeomans, Julian S, 2018. "Comparison of the multicriteria decision-making methods for equity portfolio selection: The U.S. evidence," European Journal of Operational Research, Elsevier, vol. 265(2), pages 655-672.
    13. Jimmy E. Hilliard & Jitka Hilliard, 2018. "Rebalancing versus buy and hold: theory, simulation and empirical analysis," Review of Quantitative Finance and Accounting, Springer, vol. 50(1), pages 1-32, January.
    14. Zhi Liu & Tie Zhang, 2019. "A second-order fuzzy time series model for stock price analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(14), pages 2514-2526, October.
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