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

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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.

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Authors and Affiliations

  1. College of Natural Science, Can Tho University, Can Tho City, Vietnam

    Tai Vo-Van

  2. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ha Che-Ngoc & Thao Nguyen-Trang

  3. Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ha Che-Ngoc & Thao Nguyen-Trang

  4. College of Basis Science, Nam Can Tho University, Can Tho, Vietnam

    Nghiep Le-Dai

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  1. Tai Vo-Van
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  2. Ha Che-Ngoc
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  3. Nghiep Le-Dai
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  4. Thao Nguyen-Trang
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Correspondence to Thao Nguyen-Trang.

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Vo-Van, T., Che-Ngoc, H., Le-Dai, N. et al. A New Strategy for Short-Term Stock Investment Using Bayesian Approach. Comput Econ 59, 887–911 (2022). https://doi.org/10.1007/s10614-021-10115-8

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