Improving Smart Beta Attribution Analysis II
Differentiating between factor exposures versus factor returns
December 2024. Reading Time: 10 Minutes. Author: Nicolas Rabener.
SUMMARY
- Attribution analysis is important for understanding the risk and return drivers of a fund`s performance
- However, this can be attributed to factor performance and factor exposures
- Which needs to be further differentiated as exposures can be positive or negative
INTRODUCTION
In our last research article (read Improving Smart Beta Attribution Analysis), we highlighted the complexity of attribution analysis, which incorporates asset class and factor performance and a fund`s exposure to these. A value-focused smart beta ETF can severely underperform the general stock market, but this is mostly a consequence of the poor performance of the value factor rather than the stock selection process.
Factors are as cyclical as stock markets, which makes single-factor funds risky investments. Multi-factor funds try to mitigate this risk by allocating to multiple factors, but this has not worked well in the U.S. over the last decade (read Market-Neutral versus Smart Beta Factor Investing).
However, how do you run an attribution analysis when the exposure to the factor is time-varying?
In this research article, we will explore an attribution analysis framework for dynamic multi-factor funds.
ATTRIBUTION ANALYSIS OF STATIC SMART BETA ETFS
First, we run an attribution analysis for a smart beta ETF, Alpha Architect`s U.S. Quantitative Value ETF (QVAL), that provides static factor exposures. The ETF was launched in 2014, is actively managed, and holds a concentrated portfolio of 50 equal-weighted U.S. stocks sorted on quality and valuation metrics.
The analysis highlights that almost all returns can be attributed to the U.S. stock market (S&P 500), which is expected for a long-only fund. The value factor provided the largest return contribution, followed by low volatility and size.
We observe that the negative contribution from the valu
