Stock Trading Using PE Ratio Based on Bayesian Inference
- 1 Guizhou University of Finance and Economics, China
- 2 Suranaree University of Technology, Thailand
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.
Copyright: © 2017 Haizhen Wang, Ratthachat Chatpatanasiri and Pairote Sattayatham. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Dynamic Bayesian Network
- Fundamental Investment
- PE Ratio
- Statistical Significance
- Expectation Maximization