@article {10.3844/jmssp.2017.209.219,
article_type = {journal},
title = {Stock Trading Using PE Ratio Based on Bayesian Inference},
author = {Wang, Haizhen and Chatpatanasiri, Ratthachat and Sattayatham, Pairote},
volume = {13},
year = {2017},
month = {Sep},
pages = {209-219},
doi = {10.3844/jmssp.2017.209.219},
url = {https://thescipub.com/abstract/jmssp.2017.209.219},
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 = {Journal of Mathematics and Statistics},
publisher = {Science Publications}
}