Using Momentum, Size, and Residual Volatility to Improve Portfolio Utility

Summary

The document evaluates how asset characteristics jointly predict returns and how they can be used to build portfolios for investors with different risk preferences. It frames performance in terms of out-of-sample power utility and certainty equivalents, then addresses estimation error that arises when portfolio weights are linked to characteristics. To reduce overfitting, it proposes optimizing an in-sample objective that penalizes risky weights more strongly than the utility function itself.

No single characteristic improves utility for every investor. Combining momentum, firm size, and residual volatility, however, yields portfolios with higher certainty equivalents than the stated benchmarks across the investors considered. The authors attribute this result to complementarities among characteristics: momentum, in particular, helps limit overfitting from other inputs. The resulting portfolio returns also appear largely distinct from traditional factor returns. The brief account provides no sample details or numerical effect sizes, so the strength and generality of these findings cannot be assessed from the text alone.

Key ideas

• Portfolio weights estimated from asset characteristics can suffer from estimation error.
• A more concave in-sample objective is used to reduce overfitting when selecting weights.
• No individual characteristic improves utility for every investor in the analysis.
• Combining momentum, size, and residual volatility produces higher certainty equivalents than the benchmarks across investors.
• Momentum can complement other characteristics by mitigating their tendency to overfit.
• The resulting portfolio returns are largely outside the span of traditional factors.

Tags

# An Empirical Assessment of Characteristics and Optimal Portfolios


# An Empirical Assessment of Characteristics and Optimal Portfolios









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.