Predictive Modeling: An Optimized and Dynamic Solution Framework for Systematic Value Investing
Abstract
This paper defines systematic value investing as an empirical optimization problem. Predictive modeling is introduced as a systematic value investing methodology with dynamic and optimization features. A predictive modeling process is demonstrated using financial metrics from Gray & Carlisle and Buffett & Clark. A 31-year portfolio backtest (1985 - 2016) compares performance between predictive models and Gray & Carlisle's Quantitative Value strategy. A 26-year portfolio backtest (1990 - 2016) uses an expanded set of predictor variables to show financial performance improvements. This paper includes secondary novel contributions. Quantitative definitions are provided for Buffett & Clark's value investing metrics. The "Sak ratio" is proposed as an extension to the Benjamini-Hochberg procedure for the inferential identification of false positive observations.
Keywords
Cite
@article{arxiv.1709.03226,
title = {Predictive Modeling: An Optimized and Dynamic Solution Framework for Systematic Value Investing},
author = {R. J. Sak},
journal= {arXiv preprint arXiv:1709.03226},
year = {2017}
}