Smart Beta Factor Allocation Portfolios

June 8, 2016

Robert A. Gillam, CFA Chief Investment Officer McKinley Capital Management, LLC
Gregory S. Samorajski, CFA Director of Investments McKinley Capital Management, LLC

Executive Summary

McKinley Capital Management, LLC (“McKinley Capital”) has developed a proprietary smart beta factor allocation model that can provide guidance in asset allocation decisions and may generate returns above a benchmark. In this paper we describe our existing factor implementation work and the results achieved. Furthermore, we detail two model construction alternatives, their results, our preference, and how this information can inform investors.

Within the institutional investment community the concept of “smart beta” continues to gain traction. For the purpose of this paper, we assume that the systematic return component of any portfolio is primarily described by the returns of a set of factors and the portfolio’s exposure to each of these factors, referred to as “betas”. Examples of possible betas include style factors such as size, momentum, growth, value, and quality.

What We Do

McKinley Capital constructs portfolios that are systematically exposed to momentum and growth. We believe that momentum exposed securities, as traditionally defined, are associated with excess returns. Furthermore, our specific definition of momentum may lead to better returns. As for growth, portfolios of securities with traditional growth characteristics are not always expected to outperform the market. However, we believe our proprietary definition of growth is superior. Growth securities, as defined by McKinley Capital, have typically demonstrated excess returns.

Currently, we construct long-only and long-biased portfolios with systematic exposure to growth and momentum. Our first smart beta is the selection of a beta (momentum) that identifies securities expected to outperform and uses a superior/smart definition of momentum. Second, we currently construct risk neutral portfolios of securities exposed to one of our factors. For three years we have successfully managed a portfolio that is long high momentum securities and short low momentum securities, as we define it. The purpose of the portfolio is to precisely capture the expected factor return.

A third approach is to create a portfolio that is systematically exposed to multiple factors, but vary the exposure to each of the factors using a smart proprietary factor allocation model. The remainder of this paper explores this approach.

One Proposed Factor Allocation Model

Systematic returns are explained by nine fundamental style factors, as well as country, industry, currency, and overall market factors. The style factors are growth, value, volatility, medium-term momentum, short-term momentum, size, liquidity, leverage, and exchange rate sensitivity. Factor returns are measured as the returns of dollar neutral long-short portfolios with ex-ante unit exposure to the specific style factor and no ex-ante exposure to any other factor.

To analyze prospects for actively allocating among these factor portfolios, we constructed an equally weighted dollar neutral portfolio of the nine underlying style factor portfolios. For simplicity purposes and to avoid a leverage discussion, the absolute value of factor portfolio weights was constrained to sum to one. The returns of this hypothetical equally weighted factor portfolio were measured from January 2000 to March 2016.1 The simulated results are reported in Table One below. The simulated annualized return of this dollar neutral portfolio, labeled “Benchmark”, was 3.90% with a standard deviation of 1.96%.

We believe that factor performance can follow trends. To test this assumption, we generated three different factor portfolios over the same time period. The factor portfolios were ranked each month using our proprietary definition of factor trend. With these ranks, we created optimized portfolios of the underlying factor portfolios. We then estimated our own variance/covariance matrices and used our own optimization process. In each of the three test comparisons, the absolute values of the weights of the selected factor portfolio were constrained to sum to one. The difference between the three test versions was the constraint on the absolute weight of any single factor portfolio of 20%, 40%, and 60% respectively. The simulated results are reported in Table One, with the simulated comparison portfolios labeled Long-Short 20, Long-Short 40, and Long-Short 60. Depending on the specific constraint, the portfolios typically contained between three and six underlying factor portfolio positions. The weight for each underlying portfolio could be positive or negative. As indicated, each of the concentrated comparison portfolios outperformed the benchmark.

TABLE ONE: Smart Beta Dollar Neutral Strategy Simulated Returns from January 2000 to March 2016

Strategy Annualized Return (%) Standard Deviation (%) Sharpe Ratio
Long-Short 20 4.57 1.80 1.5581
Long-Short 40 6.65 2.37 2.0619
Long-Short 60 6.65 2.31 2.1139
Benchmark 3.90 1.96 1.0853
MSCI All Country World Index 3.37 17.94 0.0896

Source: Axioma and Zephyr StyleADVISOR, 6/8/2016

This simulation suggests that our proprietary factor allocation model may add value in achieving positive investment returns. As formulated, the main use of the model is to provide guidance in the asset allocation decision. Identifying which factors are likely to perform best may help inform rebalancing decisions facing sponsors and their consultants.

It may not be efficient to use the described long-short model portfolios as actual investments since transaction costs could be high. Not only is factor turnover an issue, but the names in each factor portfolio are adjusted on a monthly basis. An alternate approach might be to construct regular long only portfolios with a tilt towards “in favor” factor exposures.

Our Preferred Factor Allocation Model

To test this idea, we ranked the nine style factors on a quarterly basis from January 2000 to March 2016, using our proprietary model of factor trend. The goal of the rankings is to identify which factors are expected to associate best with positive excess returns in the next quarter. From these ranks the firm created a quarterly quantitative score for each security in its global equity universe. A security’s score is the sum of its exposure to each factor multiplied by that quarter’s factor weight in the model. The quarterly factor weight is that factor’s rank divided by 45 (the sum of the ranks). The top ranked factor has a rank of nine, the last a rank of one.

With these quantitative scores as input, we constructed simulated optimized long-only portfolios quarterly with a 25% per quarter turnover constraint, realistic transaction costs, and a constant and equal risk/return objective function trade-off. The return of each of these portfolios for the next quarter was observed and the portfolio rebalanced. While the number of securities was not constrained, typical portfolios contained eighty securities or less.

The results of the research are reported in Table Two. The long-only, smart beta tilted portfolio achieved a simulated annualized return of 10.01% compared to 3.37% for the MSCI All Country World Index (“ACWI”) benchmark. The annualized standard deviation was 16.14% compared to 17.94% for the benchmark. The Sharpe ratio was 0.5111 compared to 0.0896 for the benchmark; and, when compared to the ACWI benchmark, the tracking error was 6.30% generating an information ratio calculation of 1.05.

TABLE TWO: Long-Only Strategy Simulated Returns Using McKinley Capital Smart Beta Tilts from January 2000 to June 2015

Strategy Annualized Return (%) Standard Deviation (%) Sharpe Ratio Tracking Error (%) Information Ratio
Smart Beta Tilted Portfolio 10.01 16.14 0.5111 6.30 1.05
MSCI ACWI 3.37 17.94 0.0896

Source: Axioma and Zephyr StyleADVISOR, 6/8/2016

We believe this simulation provides evidence of the utility of our proprietary smart beta factor allocation model in a long-only implementable portfolio environment.

Charts 3a to 3i show the computed factor ranks (nine forecasted best to one forecasted least effective) over the time period of the study. Most of the factors have ranks which vary significantly over time. Two of the ranks are more stable. In this framework a higher ranking for volatility means lower volatility is expected to have a positive contribution going forward.

Negative short-term momentum usually ranks well. Size (large preferred to small) is typically the least effective (though on average, the largest market cap securities did outperform the smallest market cap securities in the global space over this time period).

Conclusion

McKinley Capital has an extensive history of successfully building portfolios with specific factor exposure that have outperformed the relevant market benchmark. We believe this paper details a process that extends this skill set into a much broader application, and provides actionable information to investors.
As always, your McKinley Capital account representative is available to discuss our research and investment process.

RESULTS OF SIMULATED PORTFOLIO FACTOR RANKS

Chart 3a: Exchange Rate Sensitivity

Chart 3b: Growth

Chart 3c: Leverage

Chart 3d: Liquidity

Chart 3e: Medium-Term Momentum

Chart 3f: Negative Short-Term Momentum

Chart 3g: Size

Chart 3h: Value

Chart 3i: Volatility
Source for all charts: Axioma, 06/06/2016

1 For the purpose of this study, we ignored transaction costs as well as the offsetting benefit of short-stock rebates.

Disclosure

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