Predictive Modeling: An Optimized and Dynamic Solution Framework for Systematic Value Investing

TL;DR

This paper presents a systematic, predictive modeling framework for value investing that incorporates dynamic optimization, validated through extensive backtesting and novel statistical methods for identifying false positives.

Contribution

It introduces a new predictive modeling approach for value investing with dynamic and optimization features, including novel quantitative definitions and an extension to false discovery rate procedures.

Findings

Predictive models outperform traditional strategies in backtests.

Expanded predictor variables improve financial performance.

Novel statistical methods enhance false positive detection.

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.