Efficient “Smart Beta” Factor Investing in Global Equities

May 16, 2017

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

Smart Beta Investing

Smart beta is an investment tactic that relies on the observation that stocks can be grouped by fundamental factor exposures in a way that creates high return correlations between stocks within each factor group, but low correlations between stocks in different factor groups. Total portfolio returns can then be decomposed into a small number of factor returns and portfolio exposures to each specific factor. Some of the important fundamental factors have names like momentum, growth, value, small-size, and low volatility/low beta.

There are different notions about what it means to employ smart beta within an investment strategy. Some associate certain factor exposures with alpha generation and overweight stocks with exposure to those factors. Others use factor exposure tilts in overall portfolio construction strategies to better manage risk. Some combine both. Issues all analysts consider include: selecting the factor exposures, factor definitions, and combing factor exposures in a comprehensive portfolio strategy.

In a previous paper on smart beta investing, McKinley Capital Management, LLC (“McKinley Capital”) introduced some of the common notions — especially as applied to the search for excess returns.1 Even with a fundamental component, quantitatively based long-only investing, tilted towards certain factors, is “smart-beta” when the tilts are towards factors associated with risk mitigation, or excess returns. In some cases, superior definitions can be employed to transform risk type smart betas into those associated with excess returns. In the firm’s case, its regular portfolios tilt towards the smart betas of momentum and growth. While some do not consider generic growth as a return factor, the firm’s definition of growth as “earnings acceleration” transforms a common risk factor into an excess return factor. Of course, experienced firms like McKinley Capital use both smart definitions and one or more smart factors in combination to produce a type of smart beta portfolio. In a similar way, long/short portfolios provide even greater opportunities to exploit these variables.

Some smart beta strategies — even long only — downplay stock selection and limit exposures to smart beta tilts. One simple smart beta strategy of this sort, employs stock sub-portfolios largely exposed to each of the desired factors.2 The overall portfolio is a combination of the underlying factor portfolios; either equal-weighted or selected according to an allocation model. This strategy is analyzed and referred to in this paper as the “bolt-together” strategy. An alternate smart beta strategy employs mathematical optimization to create a single portfolio exposed to multiple factors. The factor weights in the objective function are either equal or selected according to an allocation model. This strategy is also analyzed and referred to in this paper as the “multi-factor strategy”. It is the McKinley Capital preferred approach.

An advantage cited for either approach, is the elimination of the effort required to analyze and select specific stocks. Excess returns are attributed to well-known factor effects. A related advantage cited for the bolt-together strategy is that factor portfolios can be created and packaged cheaply, and run by specialized external managers. The plan sponsor, consultant, or allocation advisor is free to concentrate on allocation among factor portfolios. However, there are some serious disadvantages with the bolt-together approach. Many plans are restricted to long-only positions. Mathematically, it is not entirely feasible to create long-only portfolios that are exposed to one factor and nothing else. As we will demonstrate, long-only “single factor exposed” portfolios will have inadvertent and often significant exposures to other factors — even if not desired. A second disadvantage is the lack of ability to form portfolios optimally exposed to the desired factors. Bolt-together smart beta strategies ignore cross-correlations between stocks in the various single factor portfolios. Ignoring these important correlations, when combining portfolios with single factor exposures, can result in very sub-optimal risk-return outcomes. The multi-factor approach employed by McKinley Capital and some other managers addresses this issue.

Roger Clarke, Harindra de Silva, and Steven Thorley (“CDT”) recently published a paper on efficient factor investing.3 The authors selected four factor exposures to study: momentum, low-beta, small-size, and value. The study focused on the U.S. equity markets from 1968 through 2015. The data was analyzed in different ways, but their conclusion was always the same. Smart beta investments which bolt together single factor portfolios are very far from efficient. It is better to create a single portfolio using an optimization routine that incorporates the desired factors in a single objective function; the method employed by McKinley Capital.

McKinley Capital is a global growth equity specialist. The firm offers long-only and long-short portfolios primarily exposed to a select group of factors,4 as well as more traditionally managed portfolios incorporating quantitative modeling in addition to a qualitative review. The firm can customize single factor or multi-factor exposed portfolios. With regard to smart beta types of investments, the firm seeks to add value through proprietary definitions of growth and momentum as well as through superior asset allocation models. The firm’s experience in global investing gives it the ability to offer some unique insights into global smart beta style equity investing.

Efficient Factor Investing Applied to Global Equities

McKinley Capital extended the work of CDT into the global space, using data from January 2003 through December 2016. During this time span, the MSCI All-Country World Index (“MSCI ACWI”) measured an annualized return of 8.56%, with an annualized standard deviation of 15.31%; resulting in a Sharpe ratio of 0.48. To begin its analysis, the firm simulated five long-only optimized portfolios with tilts towards one each of five factor exposures: value, growth, low volatility, small-size, and momentum. A score, scaled from 1 to 100, was calculated based on the generic Axioma computed factor exposures of each stock in the MSCI ACWI. The optimization was set to maximize the expected return vs. tracking error differential.5 The results of the simulation are shown in Table One and Figure One. The factor-tilted portfolios’ simulated annualized returns varied from 7.17% (growth) to 11.32% (value). Only the simulated growth-tilted portfolio underperformed the MSCI ACWI. Simulated Sharpe ratios varied from 0.29 (growth) to 0.73 (low volatility). This global data extends the CDT U.S. finding that certain smart beta, single factor portfolios have historically outperformed on a simulated basis. The relevant outperforming simulated factors portfolios, when considered on a stand-alone basis, include: value, low-volatility, small-size, and momentum.

Table One: Long-Only Optimized Single Factor Portfolio Simulations
January 2003 – December 2016

Portfolio Ann. Return Standard Deviation Tracking Error Sharpe Ratio Info. Ratio
Value 11.32% 21.07% 9.00% 0.48 0.31
Growth 7.17% 20.43% 7.93% 0.29 -0.18
Low Volatility 9.15% 10.87% 7.06% 0.73 0.08
Small Size 8.90% 21.90% 10.94% 0.35 0.03
Momentum 10.05% 19.32% 8.50% 0.46 0.18
MSCI ACWI 8.56% 15.31% 0.48

Figure One: Long-Only Optimized Single Factor Portfolio Simulations
January 2003 – December 2016

Figure One: Long-Only Optimized Single Factor Portfolio Simulations

Source: Axioma 04/13/2017. Analysis by McKinley Capital Management, LLC, 04/13/2017.

Long-only portfolios, even when tilted to a single exposure, often have inadvertent exposure to other factors. These inadvertent exposures can be the most important ex-post return drivers; occasionally in surprising ways. Table Two shows the inadvertent Axioma factor loadings embedded in each of the simulated single-factor tilted portfolios. 

Table Two: Long-Only Optimized Single Factor Portfolio Simulations
January 2003 – December 2016
(Exposure – Annualized Contribution to Return)

Portfolio Value Growth Volatility Size MTM* Liquidity Leverage
Value 2.50
(5.45%)
-0.03
(-0.04)
0.41
(-2.89%)
-0.54
(0.60%)
-0.21
(0.52%)
0.31
(0.34%)
0.11
(-0.04%)
Growth 0.07
(0.25%)
1.98
(2.05%)
0.50
(-4.15%)
-0.42
(0.41%)
0.11
(1.16%)
0.36
(0.30%)
0.06
(-0.19%)
Low Volatility 0.01
(0.16%)
-0.15
(-0.10%)
-0.83
(5.68%)
-0.15
(0.41%)
-0.02
(-1.19%)
-0.34
(-0.43%)
0.20
(-0.29%)
Small Size 0.31
(1.05%)
-0.20
(-0.25%)
0.61
(-4.75%)
-0.77
(0.83%)
-0.04
(0.17%)
0.49
(0.59%)
0.20
(-0.25%)
Momentum -0.35
(-0.49%)
0.29
(0.20%)
0.60
(-5.84%)
-0.37
(0.66%)
1.22
(6.28%)
0.47
(0.58%)
0.01
(0.15%)

*MTM – Medium-Term Momentum
Source: Axioma 04/13/2017. Analysis by McKinley Capital Management, LLC, 04/13/2017.

Here are a few of the interesting observations. With the exception of the simulated low-volatility portfolio, each of the simulated factor portfolios had significant exposure to high volatility stocks, and these exposures were always a major drag on simulated performance. For example, growth as a factor generated positive simulated performance attribution. Yet, the growth-tilted simulated portfolio underperformed due to its unintended exposure to volatility. Growth and value are often thought of as opposites. Yet, this is not the message in the data. The growth tilted simulated portfolio actually had a small exposure to value, while the value tilted simulated portfolio had negative, but insignificant exposure to growth. Apparently, growth and value are more independent than opposites. Stocks can be growth, value, both growth and value, or neither growth nor value. All single factor-tilted simulated portfolios had small-size exposure – with a positive attribution effect in this time frame. Apparently, stocks with almost any important factor exposure are also more likely to be smaller and more volatile. Finally, momentum, as a factor, seemed to generate the best simulated returns. However, these simulated returns were offset to some degree by inadvertent exposure to high volatility and negative value. Of the two, volatility was the more serious. Some managers combine momentum and value in their strategies. The analysis lends support to McKinley Capital’s choice to use momentum enhanced with volatility control.

Given the interaction between factors in long-only tilted portfolios, the importance of multi-factor smart beta portfolio construction becomes apparent. That was the message of the CDT U.S. focused paper, and is also the message for global equities. To illustrate, McKinley Capital tested three multi-factor, smart beta portfolio construction methodologies. The first, a bolt-together strategy, was simply an equal-weighted average of the five simulated single factor tilted portfolios (SUM in Table Three). The second method is the selection of a single simulated portfolio using optimization. The alpha component of the single objective function is specified as an equal-weighted tilt to the five separate factors (COMP in Table Three). This method makes it possible for the portfolio construction process to incorporate the interactions between factors as represented by the Axioma assigned factor exposures for each stock in the universe. The third method is similar to the second, except that objective function weights are dynamically set using the McKinley Capital proprietary factor momentum asset allocation model(ALPHA in Table Three). The simulated results are presented in Table Three and Figure Two.

Table Three: Long-Only Optimized Multi-Factor Portfolio Simulations
January 2003 – December 2016

Portfolio Ann. Return Standard Deviation Tracking Error Sharpe Ratio Info. Ratio
SUM 8.61% 19.40% 6.16% 0.38 0.01
COMP 12.66% 17.18% 5.85% 0.67 0.70
ALPHA 14.44% 18.35% 7.17% 0.72 0.82
MSCI ACWI 8.56% 15.31% 0.48

Figure Two: Long-Only Optimized Single Factor Portfolio Simulations
January 2003 – December 2016

Figure Two: Long-Only Optimized Single Factor Portfolio Simulations

Source: Axioma 04/13/2017. Analysis by McKinley Capital Management, LLC, 04/13/2017.

Consistent with the CDT findings, both simulated multi-factor single portfolio models (COMP and ALPHA) significantly outperformed both the market index, and the bolt-together portfolio formed as the average of the five simulated single factor portfolios (SUM). The respective simulated annualized returns were 8.61% for SUM, 12.66% for COMP, and 14.44% for ALPHA. Also, as indicated in the table, the volatility was lower than the simulated single portfolio approaches. The resulting simulated Sharpe ratios were 0.38 for SUM, 0.67 for COMP, and 0.72 for ALPHA. Table Four — the Axioma portfolio factor loadings — shed light on the reasons for improvement. Relative to SUM, the two simulated multi factor single portfolio options, COMP and ALPHA, showed significantly reduced exposure to high volatility stocks, but significantly increased exposure to stocks with momentum, value, and growth. Whether in the U.S. or global spaces, it is probable that constructing long-only smart beta portfolios as a combination of single smart beta portfolios (bolt-together) is a suboptimal approach. Instead, a better method might be to construct a single portfolio using a multi-factor objective function. Keep in mind that, while important, these results are simulated and illustrative only. They cannot be directly compared to actual portfolio returns that are constrained by considerations such as turnover and transaction costs.

Table Four: Long-Only Optimized Multi-Factor Portfolio Simulations
January 2003 – December 2016
Exposure (Annualized Contribution to Return)

Portfolio Value Growth Volatility Size MTM* Liquidity Leverage
SUM 0.60
(1.27%)
0.34
(0.41%)
0.31
(-3.01%)
-0.47
(0.57%)
0.23
(1.42%)
0.30
(0.33%)
0.11
(-0.12%)
COMP 1.10
(2.36%)
0.68
(0.68%)
-0.01
(-0.42%)
-0.63
(0.68%)
0.25
(1.67%)
-0.01
(0.04%)
0.05
(-0.04%)
ALPHA 1.02
(2.48%)
0.70
(0.65%)
0.10
(-1.64%)
-0.66
(0.85%)
0.42
(2.37%)
-0.02
(0.07%)
0.02
(0.05%)

*MTM – Medium-Term Momentum
Source: Axioma 04/13/2017. Analysis by McKinley Capital Management, LLC, 04/13/2017.

Summary

Smart beta investing is a current trend in institutional investment management. Often, a plan sponsor hires one or multiple managers to construct single factor smart beta portfolios. The plan then bolts together the single factor portfolios into an overall portfolio using either fixed weights or dynamic weights and a factor allocation model. CDT studied the efficiency of this approach in U.S. stocks, and found it wanting. Combining pre-formed, single factor portfolios does not provide the benefit of considering correlations across the single factor portfolio components. The construction of a single portfolio using a multi-factor objective function has the potential to both increase expected returns and reduce risk. McKinley Capital, a global growth equity manager, extended the simulated results into the global universe. The firm concluded that single factor, long-only, portfolios have significant, and detrimental unintended exposure — in particular to high volatility. McKinley Capital concurs with CDT. Constructing a single portfolio, with a multi-factor objective function is likely to be the preferred method. Your McKinley Capital account executive would be pleased to discuss this study and the firm’s customization capabilities with you.

 

 

1 Robert A. Gillam, CFA, Gregory S. Samorajski, CFA, Smart Beta Factor Allocation Portfolios, McKinley Capital Management, LLC, June 8, 2016.

2 Factor calculations are often generic – as provided by attribution analysis firms like Axioma. Some firms, like McKinley Capital, develop proprietary factor definitions in an attempt to add value compared to generic definition based strategies.

3 Roger Clark, Harindra de Silva, CFA, and Steven Thorley, CFA, “Fundamentals of Efficient Factor Investing,” Financial Analysts Journal Vol. 72, No. 6, (November/December 2016), 9-26.

4 Typical McKinley Capital managed portfolios are exposed to risk-adjusted relative return — the firm’s proprietary definition of momentum, and earnings acceleration — the firm’s proprietary definition of growth.

5 Each simulation portfolio in the study was unconstrained with the exception of liquidity. Positions were limited to the value of one day’s average daily trading volume. The assumed AUM was $100 million. Trading costs were based on ITG cost curves, when available, or 1.5% for each buy and sell transaction.

6 The Axioma exchange rate sensitivity and short-term momentum factor numbers were excluded. For each tested simulated factor portfolio, the exposure to these two factors was insignificant, without important attribution.

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