To summarize, based on our analysis, we find the best performance in the presence of implementation costs to be around the 25% concentration level. If we only focus on empirical evidence, the optimal cutoff level may differ across individual factor portfolios. The goal of our analysis, however, is to most effectively capture the factor premium at a reasonable cost through a simple, transparent, and rules-based approach. Lacking a strong reason for the individual factor portfolios to be constructed using different concentration levels, we suggest investors avoid overfitting risk and use consistent coverage across all factors included in the multi-factor portfolio.
Striking the Right Balance
As investors’ interest in multi-factor smart beta investing grows, understanding how to optimally combine factors in the presence of real-world implementation costs is critical for desirable investment outcomes. Our analysis examines how best to gain the excess returns associated with factors balanced against the impact of implementation cost, considering portfolio concentration, turnover, trading cost, and capacity as crucial elements in multi-factor smart beta product design.
The momentum and size factors, perhaps surprisingly, are helpful components in a multi-factor smart beta strategy. A stand-alone momentum strategy’s high turnover generates intimidatingly high trading costs, under reasonably large AUM assumptions, and can wipe out the factor’s typically high expected value-add on paper. A size strategy is normally believed to have high implementation costs because of small-cap stocks’ lower liquidity and higher market impact in trading. When added to a multi-factor strategy, however, these detracting qualities become benefits if we strike the right balance in portfolio construction.
The addition of momentum helps lower tracking error and improves the IR because of negative or low positive correlations with other factors. These benefits are achieved without a large increase in implementation cost because offsetting trades across the factor strategies cancel each other out and because of the improved liquidity of the stocks held in a momentum strategy.
The size factor is actually rather inexpensive to trade because of its relatively broad coverage and low turnover. Thus, adding the size factor to the combination of other factors can improve the performance, and lower the tracking error, of the multi-factor strategy given the low correlation of size with the other factors, resulting in a higher IR together with a reduction in trading cost.
We find that using more-concentrated underlying factor portfolios in constructing a multi-factor strategy can improve the strategy’s performance. These benefits come, however, at the expense of higher volatility, tracking error, turnover, and trading cost. Our analysis suggests that a 25% concentration level for each factor typically produces the best risk-adjusted performance in the presence of implementation costs.
Neither extreme of maximizing paper portfolio performance, while ignoring the trading costs that reduce performance in practice, nor of focusing on low-cost implementation, while missing opportunities for better performance, will produce an optimal result for multi-factor smart beta investors. We strongly advocate the thoughtful design of a multi-factor strategy, which requires a conscious and deliberate decision to find the most advantageous balance between effectively harvesting the factor premium and implementation cost.
This article is based on “Trade-Off in Multifactor Smart Beta Investing: Factor Premium and Implementation Cost,” Journal of Portfolio Management, Quantitative Special Issue, vol. 4, no. 3 (2019):115–124.
Appendix: Factor-Based Smart Beta Construction Methodology
To construct our portfolios in the United States, we use the universe of US stocks from the CRSP/Compustat database. We define the US large-cap equity universe as stocks whose market capitalizations are greater than the median market capitalization on the NYSE, and the small-cap universe as stocks whose market capitalizations are smaller than the NYSE median. The US data extend from July 1973 through December 2018.
The factor-based smart beta portfolios, except the small size strategy, are constructed from the large-cap universe. For each of the factor characteristics in these portfolios, we use the top 30% of the NYSE as a breakpoint. For example, we construct the value portfolio from stocks above the 70th percentile on the NYSE by book-to-market ratio. Our small-size strategy consists of all the available stocks in the small-cap universe. We then capitalization weight the selected stocks except for low beta, which we weight by beta ranking. The portfolios are rebalanced annually each July, except for momentum and low beta, which are rebalanced quarterly.
The signals used to sort the various factor-based smart beta portfolios are: