Smart Beta ETF Construction: High versus Low Factor Exposures

Portfolio construction matters

September 2024. Reading Time: 10 Minutes. Author: Nicolas Rabener.

SUMMARY

  • Factor portfolios can be structured to offer high or low factor exposures
  • Portfolio concentration and equal weighting increase factor exposures
  • However, higher factor exposures do not necessarily lead to better returns

INTRODUCTION

Investors want outperformance, but they dislike large tracking errors to benchmarks. Naturally, the former is difficult without the latter, which is one of the reasons why most mutual funds underperform. Fund managers understand that they need to be different to generate alpha, but their investors will redeem their capital if they underperform significantly. Given this career risk, many fund managers minimize the tracking error and therefore their ability to outperform after fees.

Unfortunately, this challenging problem is not unique to active fund managers but extends to systematic products. There are plenty of smart beta products that offer low factor exposures, which essentially makes them expensive index trackers.

In this research article, we will evaluate the main drivers of factor exposures when constructing smart beta portfolios.

PORTFOLIO CONCENTRATION

Creating a factor portfolio requires many assumptions and we will evaluate portfolio concentration, weighting schemes, rebalancing frequencies, and market capitalization thresholds. Our investible universe includes all stocks trading in the U.S. stock market. We use five factors, namely momentum, quality, low volatility, size, and value, with factor definitions in line with industry standards.

The standard assumptions for the long-only factor portfolios in this analysis are using the top 30% of the stocks ranked by factors, equal-weighting, rebalancing monthly, and only considering stocks with a minimum market capitalization of $1 billion.

First, we evaluate portfolio concentration by selecting the top 10%, 20%, and 30% of stocks ranked by the factors, e.g. in the case of the value factor, stocks that trade low on price-to-earnings and price-to-book multiples. We then measure the betas of these portfolios to the factors, highlighting that more concentrated portfolios feature higher betas for all factors, which is intuitive (try Finominal’s Alpha Analyzer).

Factor Betas of Long-Only Factor Portfolios Portfolio Concentration

Source: Finominal

WEIGHTING SCHEMES

Next, we compare market capitalization versus equal-weighting, which shows that equal-weighting stocks in factor portfolios leads to higher betas for all factors, except for value. We can explain this by market cap-weighting have more exposure to financials, which rank high on value metrics.

Factor Betas of Long-Only Factor Portfolios Weighting Schemes

Source: Finominal

REBALANCING FREQUENCIES

Then we evaluate whether rebalancing more or less frequently has an impact, but the betas remain relatively constant for all five factors. Given that more frequent rebalancing leads to higher transaction costs, this robustness can be viewed as a positive feature of factor investing.

Factor Betas of Long-Only Factor Portfolios Rebalancing Frequencies

Source: Finominal

MARKET CAPITALIZATION THRESHOLDS

Varying the market capitalization thresholds does not have a significant impact on the factor betas, except for the size factor. Many investors believe that factor investing works better in small caps, but this analysis highlights that investors can ignore small and micro-caps, which again reduces transaction and market impact costs when trading these stocks (read Factor Returns: Small vs Large Caps and Factor Investing in Micro & Small Caps).

Factor Betas of Long-Only Factor Portfolios Minimum Market Cap Thresholds ($ billions)

Source: Finominal

HIGH VERSUS LOW FACTOR EXPOSURE PORTFOLIOS

So far we have learned that higher portfolio concentration and equal-weighting leads to higher factor betas, so we create a multi-factor portfolio using all five factors and the intersectional model. We observe that the multi-factor portfolio using the top 10% of the stocks and equal-weighting exhibited significantly higher betas to the quality, low volatility, size, and value factor than the portfolio selecting the top 30% and market capitalization-weighting. Oddly, there was no difference in the beta to the momentum factor.

Multifactor Model Top 30% Market Cap-Weighted versus Top 10% Equal-Weighted

Source: Finominal

PERFORMANCE

Finally, we compare the performance of the two multi-factor portfolios, which show abysmal returns. Each of the five factors had some difficult periods within the last two decades and despite theoretical diversification benefits, combining all five would not have created an attractive portfolio.

When we compare the two portfolios we see that the one with higher factor exposures, i.e. the more concentrated and equal-weighted portfolio, was more volatile, which is expected. If factor performance would have been better, then this portfolio would have generated higher excess returns.

Excess Returns of Multifactor Models Top 30% Market Cap versus Top 10% Equal-Weighted

Source: Finominal

FURTHER THOUGHTS

Fund managers used to be active stock pickers with concentrated portfolios, but as the investment industry grew, the focus shifted from generating alpha for clients to management fees for asset management firms. Fortunately, this trend is reversing as ETFs have made index huggers unacceptable and technology has made them visible.

However, it takes two to tango, and investors need to accept that active funds should have a higher tracking error, so they have a chance to create alpha.

RELATED RESEARCH

Factor Investing Is Dead, Long Live Factor Investing!
How Painful Can Factor Investing Get?
Combining Smart Beta Funds May Not Be Smart
Factor Construction: Portfolio Rebalancing
Factor Construction: Portfolio Scenarios
Smart Beta vs Factors in Portfolio Construction
Factor Returns: Small vs Large Caps
Factor Investing in Micro & Small Caps

 

ABOUT THE AUTHOR

Nicolas Rabener is the CEO & Founder of Finominal, which empowers professional investors with data, technology, and research insights to improve their investment outcomes. Previously he created Jackdaw Capital, an award-winning quantitative hedge fund. Before that Nicolas worked at GIC and Citigroup in London and New York. Nicolas holds a Master of Finance from HHL Leipzig Graduate School of Management, is a CAIA charter holder, and enjoys endurance sports (Ironman & 100km Ultramarathon).

Connect with me on LinkedIn or Twitter.