An Empirical Assessment of Characteristics and Optimal Portfolios

TL;DR

This paper empirically evaluates how different characteristics influence portfolio utility, demonstrating that combining momentum, size, and residual volatility improves out-of-sample performance and certainty equivalents beyond traditional factors.

Contribution

It introduces a method to mitigate estimation error by maximizing an in-sample loss function and highlights the benefits of characteristic complementarities in portfolio optimization.

Findings

Conditioning on momentum, size, and residual volatility improves utility.

Characteristic complementarities reduce overfitting and enhance portfolio performance.

Optimal portfolios often lie outside traditional factor spans.

Abstract

We analyze characteristics' joint predictive information through the lens of out-of-sample power utility functions. Linking weights to characteristics to form optimal portfolios suffers from estimation error which we mitigate by maximizing an in-sample loss function that is more concave than the utility function. While no single characteristic can be used to enhance utility by all investors, conditioning on momentum, size, and residual volatility produces portfolios with significantly higher certainty equivalents than benchmarks for all investors. Characteristic complementarities produce the benefits, for example momentum mitigates overfitting inherent in other characteristics. Optimal portfolios' returns lie largely outside the span of traditional factors.