Research Article Open Access

Stock Trading Using PE Ratio Based on Bayesian Inference

Haizhen Wang1, Ratthachat Chatpatanasiri2 and Pairote Sattayatham2
  • 1 Guizhou University of Finance and Economics, China
  • 2 Suranaree University of Technology, Thailand

Abstract

The Price Earnings (PE) ratio is one of the most widely applied tool for the firm valuation in a security market. Unfortunately, recent academic developments in financial econometrics and machine learning have rarely looked at this tool. In the paper, we propose to formalize a process of fundamental PE ratio estimation by employing Dynamic Bayesian Network (DBN) methodology. Forward-backward inference and Expectation Maximization (EM) parameter estimation algorithms are derived with respect to our proposed DBN structure. A simple but practical trading strategy is invented based on the result of Bayesian inference. We make stock trading experiments using Thai stocks and American stocks, respectively. Extensive experiments show that our trading strategy statistically outperforms the buy-and-hold strategy.

Journal of Mathematics and Statistics
Volume 13 No. 3, 2017, 209-219

DOI: https://doi.org/10.3844/jmssp.2017.209.219

Submitted On: 31 May 2017 Published On: 29 September 2017

How to Cite: Wang, H., Chatpatanasiri, R. & Sattayatham, P. (2017). Stock Trading Using PE Ratio Based on Bayesian Inference. Journal of Mathematics and Statistics, 13(3), 209-219. https://doi.org/10.3844/jmssp.2017.209.219

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Keywords

  • Dynamic Bayesian Network
  • Fundamental Investment
  • PE Ratio
  • Statistical Significance
  • Expectation Maximization