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Financial technical indicator based on chaotic bagging predictors for adaptive stock selection in Japanese and American markets

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  • Suzuki, Tomoya
  • Ohkura, Yuushi

Abstract

In order to examine the predictability and profitability of financial markets, we introduce three ideas to improve the traditional technical analysis to detect investment timings more quickly. Firstly, a nonlinear prediction model is considered as an effective way to enhance this detection power by learning complex behavioral patterns hidden in financial markets. Secondly, the bagging algorithm can be applied to quantify the confidence in predictions and compose new technical indicators. Thirdly, we also introduce how to select more profitable stocks to improve investment performance by the two-step selection: the first step selects more predictable stocks during the learning period, and then the second step adaptively and dynamically selects the most confident stock showing the most significant technical signal in each investment. Finally, some investment simulations based on real financial data show that these ideas are successful in overcoming complex financial markets.

Suggested Citation

  • Suzuki, Tomoya & Ohkura, Yuushi, 2016. "Financial technical indicator based on chaotic bagging predictors for adaptive stock selection in Japanese and American markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 50-66.
    • Handle: RePEc:eee:phsmap:v:442:y:2016:i:c:p:50-66
    DOI: 10.1016/j.physa.2015.08.042
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    References listed on IDEAS

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    Cited by:

    1. Xueling Wu & Junyang Wang, 2023. "Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China," IJERPH, MDPI, vol. 20(6), pages 1-18, March.
    2. Thiago Christiano Silva & Benjamin Miranda Tabak & Idamar Magalhães Ferreira, 2019. "Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies," Complexity, Hindawi, vol. 2019, pages 1-14, December.

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