Estimating Fundamental PE Ratios with Dynamic Bayesian Networks for Trading

Summary

This research uses a dynamic Bayesian network to formalize estimates of fundamental price-to-earnings ratios, which are often based on expert judgment. The inferred PE ratio is intended to support human investment decisions or serve as an input to an automated trading system. The authors derive forward-backward inference and expectation-maximization procedures for their proposed network, and connect its structure to behavioral finance interpretations of volatility.

A trading strategy is built from the Bayesian inference results, and experiments compare it with standard investment benchmarks. The authors report consistent outperformance in those experiments. The document does not specify the assets, benchmark definitions, test periods, or transaction-cost assumptions in its summary, so the reported results cannot establish general performance or live-trading suitability. The method is presented as both a way to estimate valuation inputs and a basis for systematic decisions.

Key ideas

• A dynamic Bayesian network is used to estimate fundamental PE ratios.
• Forward-backward inference and expectation-maximization are derived for the proposed model.
• Inferred PE ratios can inform expert decisions or feed an automated trading system.
• The model's interpretation is linked to behavioral finance evidence about volatility.
• Experiments report that a strategy using inferred PE ratios outperforms standard benchmarks.

Tags

# Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment


# Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment









On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundamental PE ratios are often estimated only by subjective expert opinions. The purpose of this research is to formalize a process of fundamental PE estimation by employing advanced dynamic Bayesian network (DBN) methodology. The estimated PE ratio from our model can be used either as a information support for an expert to make investment decisions, or as an automatic trading system illustrated in experiments. Forward-backward inference and EM parameter estimation algorithms are derived with respect to the proposed DBN structure. Unlike existing works in literatures, the economic interpretation of our DBN model is well-justified by behavioral finance evidences of volatility. A simple but practical trading strategy is invented based on the result of Bayesian inference. Extensive experiments show that our trading strategy equipped with the inferenced PE ratios consistently outperforms standard investment benchmarks.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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