A Bayesian Approach to Variable Selection in Discrete Regression Models with Application to Enhancing Value Investing Strategies
by
Richard Gerlach
University of Newcastle
Coauthors: Ron Bird (University of Technology)

This paper presents a fully Baysian technique for analysing both logistic (and other) regression models for discrete data. This technique uses Markov chain Monte Carlo sampling and extends the variable selection method of Smith and Kohn (1996) as well as utilising the slice sampler of Mira and Tierney (2001).

The method is advantageous as it can be applied in situations with a large number of potential variables to choose from as is illsutrated by at least one application to do with stock selection to enhance a simple value investment strategy. Value investing has been around since the 1930's and simply ranks stocks based on accounting information. We enhance this strategy by selecting a significant subset of accounting variables (from a superset of 24 potential explanators), linked to stock performance by a logistic regression model, and investing appropriately.

The results from this strategy are encouraging but not popular with classical economists.

Date received: September 14, 2001