Using PCA and Factor Regressions to Assess Smart Beta Diversification

Summary

This research summary examines four MSCI smart beta equity indexes—value weighted, equal weighted, momentum, and minimum volatility—against a capitalization-weighted benchmark. Using monthly observations from 1999 through 2014, it applies principal component analysis (PCA) to separate common and strategy-specific return components, then regresses those components on Fama–French factors and the Carhart momentum factor. The dominant component reflects the broad market, while momentum forms the largest distinct strategy component. Factor exposures overlap, making it difficult to isolate pure strategies.

The reported results suggest that equal weighting and value weighting add relatively little distinct portfolio behavior, while fundamental value weighting provides exposure to the value factor. The study also finds that smart beta components are broadly similar across global, EAFE, and emerging markets, but their explanatory role is smaller in emerging markets. These findings are tied to the indexes, sample, and models studied; the summary provides no evidence that the exposures persist beyond the sample or translate into superior net returns. It also notes differences in index risk, including higher volatility for equal weighting than minimum volatility.

Key ideas

• PCA separates shared market returns from distinct components across smart beta indexes.\nThe market component dominates, while momentum is the largest strategy-specific component in the U.S. sample.\nOverlapping factor exposures limit diversification among smart beta approaches.\nValue-weighted indexes show value exposure, while equal weighting contributes relatively little distinct portfolio behavior.\nThe findings are sample- and market-dependent, with weaker smart beta influence reported for emerging markets.

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