Strategy spotlight Analytic Investors October 2017 Deploying multifactor strategies in portfolios Factor-based investing has experienced a rapid increase in product innovation and development over the past several years. As traditional active management has struggled to justify its fees, investors are starting to uncover the benefits of multifactor strategies, which provide efficient and low-cost exposure to characteristics that have been proven to outperform the market over the long-term by decades of academic research. At the most basic level, multifactor portfolios aim to blend a variety of targeted-factor exposures such as Value, Size, Quality, Low Volatility, and Momentum into a single portfolio. There are a variety of ways to do this, and at Analytic Investors, we believe the best approach to harvest multiple factors is to build a portfolio from the bottom-up versus stitching together a combination of single factor portfolios from the top-down. Our approach in implementing these factors provides increased factor exposure, is cost effective, and offers low turnover and management fees. It also improves investor outcomes by delivering the equity risk premium in a more efficient way. In this paper, we conduct several case studies to examine different ways to incorporate multifactors in a portfolio and evaluate the efficacy of each approach. From our analysis, we have discovered a set of common benefits and attributes. Multifactors can help diversify a portfolio, reduce overall volatility, and enhance returns. Sharpe ratios are significantly improved. A portfolio that includes multifactors has stronger downside protection and can keep up reasonably well during market rallies. Investors do not need to use up too much of their active risk budget to incorporate multifactor strategies. In particular, our Factor Enhanced strategies have low tracking error to market-cap weighted benchmarks, which are typically used as policy benchmarks. With volatility reduced for the total portfolio, investors have more flexibility in their portfolio design to invest opportunistically in higher risk asset classes or active managers. Active manager fees are typically higher than our multifactor product suite. With higher fees, the average active manager has delivered lower excess returns over the market. portfolio: The average institutional portfolio allocation As a starting point, we put together a base-case portfolio that is meant to represent the broad asset categories that an average institutional portfolio may be invested in. As seen below, the allocations toward U.S. equities, fixed income, and alternatives are averages based on multiple consultant survey reports done over the past several years. The average institutional portfolio allocation toward public equities is half of the portfolio, with the rest spilt evenly across fixed income and alternatives. In the base case portfolio, we are assuming that the typical portfolio is using an all-index approach as the building blocks for the portfolio. Thus, we use market-cap weight indices to calculate the risk and return profile for this typical portfolio; the results are shown below. portfolio Average plan (all indices) 5-year 10-year Return 6.39 4.57 Standard Deviation 5.95 9.24 Sharpe Ratio 1.05 0.46
Case study 1: Enhancing or complementing the passive portfolio We explore the possibility that including multifactor strategies, such as the indices, can improve the risk/return profile of the base case allocation, which is composed of all passive indices. We start by allocating 50% toward within the equity portion. Within U.S. equity, we split the original 35% portfolio weight by allocating half of it to the passive Russell 3000 Index and allocate the remainder 17.5% toward the U.S. Large Cap and Small Cap indices. Since roughly 90% of the Russell 3000 is in large caps, we gave a 15.75% weight to the U.S. Large Cap Index and a 1.75% weight to the U.S. Small Cap Index. A similar approach is used to split the allocation within the international equities space by using the international and emerging market indices. We hold the fixed income and alternatives weightings the same as the base case portfolio. (all passive) Hypothetical 1 (passive + ) Russell 3000 17.5% U.S. Large Cap 15.75% U.S. Small Cap 1.75% MSCI ACWI ex-u.s. 7.5% International 6.375% Emerging Markets 1.125% 10-year risk and return 5.50 Hypothetical 1 (passive + ) Returns 5.25 5.00 4.75 4.50 4.25 Passive + Factor Enhanced 4.00 8.50 8.75 9.00 9.25 9.50 9.75 10.00 Standard deviation 50% Portfolio Sharpe Ratio 0.46 0.53 Tracking Error - 0.71 Information Ratio - 0.65 Downside Capture 100% 93.38 Up Market Capture 100% 98.17 As seen above, there are significant portfolio benefits in complementing an all-passive approach with our strategies. Overall performance is enhanced and portfolio volatility is reduced over the 5- and 10-year periods, resulting in meaningful improvements in the Sharpe Ratio compared to the base-case portfolio. Additionally, Hypothetical Portfolio 1 is also able to keep up with the original base-case portfolio during market rallies, but provides a meaningful level of downside protection during downturns. Furthermore, investors do not have to worry about using up too much of their active risk budget to enhance returns, because the tracking error (0.71) is low versus the base-case portfolio. The additional level of tracking error incurred may be worth it for some clients, given the positive Information Ratio. 2
Case study 2: Replacing underperforming or mediocre active managers On the other end of the spectrum, many clients are still implementing their asset allocation program using mostly active managers. Some of these managers may be serving their clients well by delivering a high active return (or alpha), while others may be lagging their benchmarks or peers. We examine what the asset allocation results could look like if the plan were implemented with all active managers versus a plan that was executed with only the index strategies, as seen in Hypothetical Portfolio 2. We build Hypothetical Portfolio 3 below to serve as a proxy for a portfolio that is executed with all active managers in the equity portion. For simplicity, we use the median active manager net-of-fee returns from four different evestment universes that represent the U.S. large, U.S. small, international, and emerging markets asset allocation sleeves. The weights assigned to each category are in line with the strategic asset allocation of the base-case portfolio, which is 35% U.S. equities and 15% international equities. We continue to use the Bloomberg Barclays Aggregate and Wilshire Liquid Alternative indices as proxies for our fixed income and alternative exposures. In Hypothetical Portfolio 2, we substitute the equity sleeves with all indices net of fees. Below, we observe the performance results of the active versus Factor Enhanced portfolio from a net-of-fees perspective. Hypothetical 2 () Hypothetical 3 (Active Manager) U.S. Large Cap 31.5% U.S. Large Cap Core Median 31.5% Fixed Income 25% International Equities 15% Fixed Income 25% U.S. Small Cap 3.5% International 12.75% Emerging Markets 2.25% Fixed Income 25% U.S. Small Cap Core Median 3.5% EAFE Large Cap Core Median 12.75% Emerging Markets All Cap Median 2.25% 10-year risk and return Hypothetical 2 () Hypothetical 3 (Active Manager) 5.50 5.25 Factor 5.00 Enhanced 4.75 Porfolio 4.50 Active Portfolio 4.25 4.00 8.40 8.60 8.80 9.00 9.20 9.40 9.60 9.80 10.00 Returns Standard deviation Portfolio Active Portfolio Sharpe Ratio 0.46 0.55 0.46 Tracking Error - 1.12 0.46 Information Ratio - 0.51-0.27 Downside Capture 100% 89.42 97.41 Up Market Capture 100% 95.88 97.11 To calculate the median active manager net of fees, we found the average fee for each of the four evestment universes based on a $400 million account level. We deducted 42 bps for U.S. large cap core, 72 bps for U.S. small cap core, 50 bps for EAFE large cap core, and 77 bps for Emerging Markets All Cap. We deducted 12 bps from the Wells Fargo indices. From a net-of-fees perspective, the active portfolio underperforms both the base-case portfolio as well as the portfolio. In addition, the active portfolio carries a higher level of portfolio volatility versus the portfolio, resulting in significantly lower Sharpe Ratios over the 10-year period. The portfolio also does a significantly better job at protecting on the downside than the active portfolio over the 10-year period (89% versus 97%). Furthermore, the majority of the active managers have underperformed after fees, resulting in a negative information ratio, whereas, the portfolio has provided a positive information ratio. By accounting for fees in this case study, it is clear that investors should consider whether their investment managers are charging a fee that is appropriate relative to the level of excess returns they provide. At Analytic, we think of this relationship as the FER ratio (or fees versus excess returns ratio) which investors should strive to minimize. In this case study, the strategies have a higher excess return while charging significantly lower fees than the median active managers, translating into a significantly lower FER ratio. 3
Case study 3: Getting more out of your risk budget A common theme that we see across Hypothetical Portfolios 1 and 2, which include either half or all within the equity portion, is that overall portfolio standard deviation is always lower than the base case and the active portfolio. This lower volatility attribute suggests that including can potentially accomplish one of two goals for investors either lower overall portfolio volatility while getting similar or better returns, or free up the risk budget and allocate more toward higher-risk managers or asset classes while maintaining the same level of volatility. For example, Hypothetical Portfolio 2 (all ) has a 10-year standard deviation of 8.58% versus 9.24% for the base case portfolio. If the goal is to keep volatility the same and enhance returns, we could use the remaining risk budget of 66 basis points (bps) to invest in higher-risk small caps. We can do this by strategically decreasing our fixed income allocation given the low yield environment, and allocate it toward more equities, while maintaining a similar level of risk as the base case portfolio. For example, in Hypothetical Portfolio 4, we decreased our fixed income allocation by 3.75% and allocated it toward small cap, represented by the Russell 2000 Index. By doing so, we increased our overall equity allocation from the original 50% to 53.75% and decreased our fixed income to 21.25% from the original 25%. As you can see from the risk/return chart, we were able to significantly enhance returns while keeping overall portfolio risk the same. The ability to reduce portfolio risk means investors can get more out of their risk budget and have more investment flexibility to accomplish a variety of investment goals. Hypothetical 4 ( Portfolio with small caps) U.S. Large Cap 31.5% 10-year risk and return 8.00 Fixed Income 21.25% Hypothetical 4 ( Portfolio with small caps) U.S. Small Cap 3.5% Russell 2000 Index 3.75% International 12.75% Emerging Markets 2.25% Returns 7.00 6.00 5.00 Portfolio with small caps 4.00 8.00 8.20 8.40 8.60 8.80 9.00 9.20 9.40 9.60 9.80 10.00 Standard deviation Portfolio with small caps Sharpe Ratio 0.46 0.54 Tracking Error - 0.90 Information Ratio - 0.84 Downside Capture 100% 97.55 Up Market Capture 100% 103.46 4
Case study 4: Core-Satellite approach The core-satellite approach to portfolio construction is an approach where investors combine actively managed funds with index tracking products in a single portfolio. The conventional view of constructing a core-satellite portfolio is to use market-cap weighted index funds to fill in the core because they are safer and a lower cost, and use high active share managers for the satellite portion of the portfolio. The appeal of this approach is that it establishes a risk-controlled portfolio while also securing some prospects of outperformance. However, we encourage investors to look outside of market-cap weighted indices and consider other types of strategies that can control risk, such as the multifactor strategies. In this case study, we put together Hypothetical Portfolio 5 that uses a core-satellite approach (80/20 split) within the equity portion of the base case portfolio. The resulting allocation within the equity sleeve is a 40% weight to the same four indices and the remaining 10% with a high active share WellsCap manager. As seen in the illustration below, the Factor Enhanced core-satellite portfolio is able to enhance returns and reduce overall risk, while maintaining a minimal level of tracking error. It is worthwhile to note that the volatility reduction is strong, especially during market downturns as seen with the 90.95% downside capture ratio over the 5-year period. During market rallies, the core-satellite portfolio is mostly able to keep up as well. Hypothetical 5 (Core Satellite) U.S. Large Cap 25% U.S. Small Cap 3% EverKey Focused Global Equity 10% 5-year risk and return International 10.5% Emerging Markets 1.5% 7.00 Hypothetical 5 (Core Satellite) Returns (%) 6.50 Core Satellite Portfolio 6.00 5.50 5.75 6.00 Standard deviation (%) Core Satellite Portfolio Sharpe Ratio 1.05 1.14 Tracking Error - 0.89 Information Ratio - 0.3 Downside Capture 100% 90.95 Up Market Capture 100% 97.68 5
Closing thoughts The case studies above show that there are a variety of ways to use multifactor strategies in an investor s portfolio, which will largely depend on the individual needs and investment goals of the client. Multifactor strategies can help investors diversify their portfolio, reduce portfolio volatility, and increase returns (both absolute and risk-adjusted). We believe our transparent, rules-based approach to accessing multiple factor exposures through our index suite is a cost effective way to do so, as seen in our low FER (fee to excess return ratio). Lastly, the ability to provide strong volatility reduction and downside protection allows clients the flexibility to be more creative in their portfolio construction and to invest more opportunistically while staying within their risk budgets. Simulated performance results have many inherent limitations, some of which are described below. No representation is being made that any account will or is likely to achieve results similar to those shown. In fact, there are frequently sharp differences between simulated performance results and the actual performance results subsequently achieved by any particular trading program. One of the limitations of simulated performance results is that they are generally prepared with the benefit of hindsight. In addition, simulated trading does not involve financial risk, and no simulated trading record can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses are material points which can also adversely affect actual trading results. 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