How To Build An Actually Useful Factor Model

Introduction

You’ll often see new papers describing “so and so” as a new factor being discovered, and they will often cite some Sharpe/t-stat as the primary reason why this should be considered as a “factor” that explains the cross-section of stock returns.

Unfortunately, the truth is that most of them are not novel factors and can hardly be considered useful, because using t-stat as a measure of importance/success is using the wrong metric / asking the wrong questions when building a factor model.

We’ve already espoused the goodness of factor models again and again - they are a denoising technique, they allow you to focus on something actually competitive / useful / is real alpha; but we haven’t really talked about how to build a comprehensive factor model that spans many factors.

This article changes that.