What are Smart-Beta Strategies?

Transcription

1 Active Return What are Smart-Beta Strategies? And how can we improve them? Charlie Bassignani President, CIO Standard & Poor s Investment Advisory Services (SPIAS) 55 Water Street New York, NY charlie.bassignani@mhfi.com March 24, 2015 Smart-Beta is an increasingly popular term in the asset management industry. It is based on the definitions that alpha is the skill in selecting assets to outperform the market and that beta is the return achieved from exposure to the overall market. For example, the returns an investor would achieve by buying the SPDR S&P 500 ETF Trust would be effectively all beta and no alpha to the large cap US equity market. The theory behind smart-beta approaches is based on terminology first introduced by the pivotal Fama-French capital asset pricing model (CAPM) which earned them a Nobel Prize in economics. Grad students still study the research though the CAPM factors have expanded from one factor in the 1960 s to five factors in The five latest factors are market, size, value, profitability and investment. ABOUT SMART-BETA STRATEGIES Smart-beta strategies seek to outperform the broader market or asset class by deviating from traditional capitalization weighted index tracking approaches. There seems to be an endless variety of smart-beta approaches as there are a surprisingly large number of factors that seek to profit from market anomalies. One of the simplest smart-beta approaches is to give each market constituent equal weight. For example, take all 500 stocks in the S&P 500 index and assign each stock a weight of.20% (1/500), rather than the index weight which is based on market capitalization. This achieves exposure to smaller capitalization stocks. Smaller capitalization stocks have historically outperformed larger cap stocks over the past 25 years as shown in Figure 1. Figure 1. Erin Gibbs Equity Chief Investment Officer Standard & Poor s Investment Advisory Services (SPIAS) 55 Water Street New York, NY egibbs@spcapitaliq.com 6% 5% 4% 3% 2% 1% 0% -1% Annualized Average Active Return Per Market Cap Decile Active = Stock Return Difference from S&P 500 Total Return Index Source: Largest Smallest The chart represents the average annual returns of hypothetical baskets of stocks representing the smallest decile (tenths) of S&P 500 companies based upon year end market capitalization. Returns are calculated using annual equal weighted baskets from December 31, 1989 to December 31, Return data are gross of fees and expenses and do not include dividends. Inclusion of fees and expenses would reduce returns. The average returns of the baskets do not represent the performance of any SPIAS strategy, nor is it meant to forecast, imply or guarantee the future performance of any investment strategy. Indices are unmanaged, statistical composites. Index

2 Active Return returns do not include payment of any sales charges or fees an investor would pay to purchase the securities represented in the index. It is not possible to invest directly in an index. Inclusion of fees and expenses in the index would lower performance. Past performance is not indicative of future results. Another basic approach known as fundamental indexing is to weight each stock by a financial characteristic, such as its dividend yield or price to book value. These achieve exposure to a specific characteristic, frequently either value or income. The volatility approach, which is frequently mentioned, weights stocks according to volatility, favoring low volatility. The theory is to gain exposure to low volatility stocks which have historically achieved higher risk adjusted returns in the US. We caution readers that smart-beta strategies are far from a holy grail in the asset management world. There are downsides to adding beta exposure to any characteristic. The equal weighting approach puts a larger emphasis on small cap companies, but these companies are often more volatile, illiquid and expensive for a large manager to trade. Using the market capitalization decile example above, investing in the smallest 10% of companies would have resulted in underperformance of the S&P 500 Index in 11 of the 25 years tested. The bottom 10% by market cap also produced significantly higher average volatility. So though the smallest caps outperform on average over a long period of time, it is not a strategy for those seeking to higher risk adjusted returns, weak stomachs or an investment horizon of under 6 years. Figure 2. Active Return Smallest 10% by Market Cap for the S&P 500 Index 80% 60% Smart-Beta Strategies have underperformed for extended periods of time. 40% 20% 0% -20% -40% Source: The chart represents the average annual returns of hypothetical baskets of stocks representing the smallest decile (tenths) of S&P 500 companies based upon year end market capitalization. Returns are calculated using annual equal weighted baskets from December 31, 1989 to December 31, Return data are gross of fees and expenses and do not include dividends. Inclusion of fees and expenses would reduce returns. The average returns of the baskets do not represent the performance of any SPIAS strategy, nor is it meant to forecast, imply or guarantee the future performance of any investment strategy. Indices are unmanaged, statistical composites. Index returns do not include payment of any sales charges or fees an investor would pay to purchase the securities 2

3 represented in the index. It is not possible to invest directly in an index. Inclusion of fees and expenses in the index would lower performance. Past performance is not indicative of future results.. What was once a largely niche quant terminology has possibly become one of the new buzz words among advisors. Smart-beta strategies have been gaining market share and public awareness. Morningstar has smart-beta strategies at $529B in assets under management globally, a fivefold increase since One estimate shows the strategies reaching $6 trillion in assets in the next five years 2. We theorize that the rapid pace of smart-beta growth is due to investors disillusionment with active management and the high fees associated with it. At the same time, investors might have a better understanding that passive index investing, which is typically market capitalization weighted, might not offer the best returns. Since many investors seek outperformance to the broad market, and perhaps are weary of unfulfilled promises of alpha, it comes as no surprise to us that smart-beta strategies have become more popular. ADDRESSING SMART-BETA CRITIQUES VOLATLITY As we saw in our Smallest Cap hypothetical portfolio in Figure 2, smart-beta strategies can have significantly elevated volatility compared to the market returns. This volatility can produce extended periods of underperformance before a single period of large outperformance, resulting in the long term average of outperformance. If an investor chooses a smart-beta fund based on recent outperformance, chasing returns, they may have a significant wait before the fund returns to that pattern of performance. There is evidence that most single factor smart-beta strategies produce only slightly improved risk adjusted returns 3. At SPIAS, we focus our research on producing attractive risk-adjusted returns as well capital appreciation. Our research attempts to avoid large swings in relative market performance and deliver a smoother return stream than single factor smart-beta strategies. CONCENTRATED RISK Related to higher volatility, one of the bigger smart-beta critiques is they are not diversified across risk exposures. This does not mean they select too few stocks, but rather that the selection factors cause the smart-beta portfolios to have a concentrated risk to those factors. This concentration may lead to inconsistent performance when a factor is out of favor, as shown by our earlier Small Cap portfolio example. As quant researchers with extensive experience, we understand factor-specific underperformance from both a theoretical and practical perspective. SPIAS attempts to achieve true alpha, not just beta, in its strategic alpha equity strategies using unique factor combinations and factor diversification. SPIAS researches for optimal combinations of factors that complement each other to produce risk adjusted returns in a variety of market conditions. TURNOVER Smart-beta strategies generally rebalance at regular intervals and are frequently referenced as lower turnover styles. We have concerns that some rebalance schedules are chosen arbitrarily or follow a norm rather than make an active decision based on empirical research. The SPIAS strategic alpha equity strategies typically follow regular intervals as well, however SPIAS includes the tradeoff between higher turnover and potential alpha versus higher transaction costs in its construction research. Optimal rebalance intervals and calendar periods are sought and actively incorporated into the portfolio construction process for our clients. LIQUIDITY Smart-beta strategies generally have increased exposure to smaller cap stocks compared to index weighted benchmarks and this may introduce liquidity concerns. SPIAS strategies tend have a smaller average market capitalization than their respective benchmarks but to address 3

4 liquidity risks SPIAS has researched and developed liquidity weighting algorithms for several of its clients equity strategies. OVERCROWDING Finally, there is an argument that as smart-beta strategies gain in popularity, the market inefficiencies they exploit will be eliminated along with the outperformance of their equity benchmarks. SPIAS incorporates proprietary factors and weighting schemes to lessen overcrowding in several strategies. Though this reduces transparency, it minimizes the potential for the strategies to be replicated. STRATEGIC ALPHA: ENHANCING SMART-BETA THEORIES At SPIAS, we seek to address the pitfalls mentioned and several others in our active equity strategies. We do this while seeking higher total returns and risk adjusted returns. We strive for our strategies to achieve pure alpha, defined as the residual outperformance unexplained by any systematic factor exposure. Using this definition, SPIAS took the daily returns of our large cap equity models and calculated the five factor exposures in the latest published Fama-French five factor model (FF5) using the S&P 500 Index constituents as our universe. The factors are: Market (CAPM Beta), Size (Market Capitalization), Value (Book to Price), Profitability (Operating Profit) and Investment (Asset Growth or Decline) 4. We also calculated each model portfolio s CAPM alpha and the Fama- French five factor alpha, the portion of the performance not explained by either CAPM beta or the FF5 model. Table 1. The Equal Weighted strategy loads heavily on the size factor, while S&P equity strategies load on a variety of factors and are more diversified. Data from Dec 4, 2007 to February 28, 2015 using the S&P 500 Index constituents Loadings on Fama-French Five Factors Strategy Market Beta Size Value Profitability Investment S&P 500 Index S&P 500 Equal Weight Index S&P S&P Competitive Advantage S&P Intrinsic Value S&P Dividend Income & Growth Source: Loadings are calculated using daily Returns from Dec 4, 2007 to February 28, All return data are gross of fees and expenses and include dividends. Inclusion of fees and expenses would reduce returns. The inception date for all S&P model portfolios is Dec 3, Past performance is not indicative of future results. Table 1 shows the loadings, or exposures to the FF5 factors in the S&P 500 Index. Our sample smart-beta strategy, Equal Weighted, has a heavy exposure to the size factor and very little 4

5 exposure to any of the other factors. So we would expect this strategy to do well when small caps do well but not necessarily when value, profitability or asset investments are driving the market s performance. The S&P large cap strategic alpha strategies have significant loadings to several of the FF5 risk factors and even have CAPM betas below 1. They all have significant exposure to the size factor, as we would expect given the weighted average market cap of the model portfolios is typically smaller than the index. But they all have balancing exposures to other factors, allowing the strategies to perform based on combinations of the value, profitability and investment drivers of risk and return. We also included a simple equal weighted backtested portfolio as an example of one of the most popular smart-beta strategies. As expected the equal weighted backtested portfolio produced some CAPM alpha but minimal FF5 alpha of 1.0%. One could argue the alpha is insignificant after estimating trading and management fees costs from the returns. Our findings on the equal weighted strategy using the S&P 500 are similar to research done for longer periods of time and larger universes of stocks. Arnott and Research Associates produced extensive calculations for The Journal of Portfolio Management using a large selection on smartbeta style strategies or as they called them inverse strategies. None of their US equity strategies produced over 1.5% in alpha using the previous Fama-French four factor model 5. At the time of their research the five factor model had yet to become that standard. The SPIAS strategic equity strategies all produced significant CAPM alphas and FF5 alphas. Since the strategies which combine and allocate across a variety risk factors and smart-beta style we conclude that thoughtful researched selection and careful combinations of these market factors can indeed lead to pure alpha. We have long heard the argument that only fundamental managers could possibly produce alpha, despite the vast majority of them underperforming the market. We feel the SPIAS results call that assumption into question. Table 2. SPIAS Large Cap Equity Strategies produce significant alpha over the Dec 4, 2007 to February 28, 2015 period. Strategy CAPM Alpha Annualized (%) Fama-French 5 Factor Alpha Annualized (%) S&P 500 Index 0.0% 0.0% S&P 500 Equal Weight Index 2.2% 1.0% S&P 4 5.7% 2.2% S&P Competitive Advantage 6.6% 2.5% S&P Intrinsic Value 7.4% 2.8% S&P Dividend Income & Growth 4.1% 2.0% Source: Alphas are calculated using daily Returns from Dec 4, 2007 to February 28, All return data are gross of fees and expenses and include dividends. Inclusion of fees and expenses would reduce returns. The inception date for all S&P model portfolios is Dec 3, Past performance is not indicative of future results. It is possible that the FF5 model does not capture all drivers of market returns. In fact there is a new six factor Fama-French model under peer review. SPIAS decided to not use that model in our research until it is the official Fama-French model. For now we feel it is possible to produce 5

6 true alpha using thoughtful researched selections and allocations based on smart-beta theories while applying practical considerations to portfolio implementation. on the entire SPIAS equity strategies mentioned above, please contact Endnotes 1 Morningstar, as of December 31, 2014 Includes all exchange traded products within the Strategic Beta catagory 2 Madison Marriage, Smart Beta Bandwagon Triggers Alarms, Financial Times, September 6, Robert Arnott, Jason Hsu, Vitali Kalesnik and Phil Tindall, The Surprising Alpha From Malkiel s Monkey and Upside-Down Strategies, The Journal of Portfolio Management, Vol 39, No. 4, Summer Fama, Eugene F. and French, Kenneth R., A Five-Factor Asset Pricing Model (September 2014). Fama-Miller Working Paper. Available at SSRN: or 5 Robert Arnott, Jason Hsu, Vitali Kalesnik and Phil Tindall, The Surprising Alpha From Malkiel s Monkey and Upside-Down Strategies, The Journal of Portfolio Management, Vol 39, No. 4, Summer 2013 References David Oakley, Smart Beta gaining ground in battle for investment supremacy, Financial Times, January 29, 2015 Walter H. Prahl, Phd, What s So Smart About Beta Lord Abbett Equity Perspectives, April 14, 2014 Bruce L Jacobs and Kenneth N. Levy, Smart Beta Versus Smart Alpha, The Journal of Portfolio Management, Invited Editorial Comment, Summer Bruce L Jacobs and Kenneth N. Levy, Disentagling Equity Return Regularities: New Insights and Investment Opportunities, Financial Analyst Journal, Vol 44, No. 3 (1988), pp Fama, Eugene F. and French, Kenneth R., A Five-Factor Asset Pricing Model (September 2014). Fama-Miller Working Paper. Available at SSRN: or Robert Arnott, Jason Hsu, Vitali Kalesnik and Phil Tindall, The Surprising Alpha From Malkiel s Monkey and Upside-Down Strategies, The Journal of Portfolio Management, Vol 39, No. 4, Summer

7 Model Portfolios Disclosure Competitive Advantage, Dividend Income & Growth, Intrinsic Value, and S&P 4 Model Portfolios Each of the model portfolios shown on the preceding pages for the Competitive Advantage, Dividend Income & Growth, Intrinsic Value, and the S&P 4 (individually the Model and collectively the Models ) only represents the results of the GMI IAS model portfolios. Each Model performance has inherent limitations. The returns shown are Model results only and do not represent the results of actual trading of investor s assets. GMI IAS maintains each Model and calculates the respective Model returns shown or discussed, but does not manage actual assets. Thus, the returns shown or discussed in these materials do not reflect the impact that material economic and market factors had or might have had on decision making if actual investor s money had been managed. While the returns of a Model may have shown better results than the returns of its respective benchmark for some or all of the periods shown for that Model, the performance during any other period may not have, and there is no assurance that a Model will outperform its benchmark in the future. Model performance is calculated using a time-weighted rate of return using daily valuations and takes into account the reinvestment of dividends. Dividends are assumed to be paid at the ex-dividend date. Stocks are presumed added to, or deleted from, the Model portfolio at the close of market on the models rebalance dates. A Model performance does not consider taxes and brokerage commissions, nor does it reflect the deduction of any advisory or other fees that investors will incur when their accounts are managed in accordance with the Model, including but not limited to fees charged by GMI IAS, advisors, or other parties. The imposition of these fees and charges would cause actual performance to be lower than the performances shown. For example, if a Model returned 10 percent on a $100,000 investment for a 12-month period (or $10,000) and an annual assetbased fee of 1.5 percent were imposed at the end of the period (or $1,650), the net return would be 8.35 percent (or $8,350) for the year. Over 3 years, an annual 1.5 percent fee taken at year end with an assumed 10 percent return per year would result in a cumulative gross return of 33.1 percent, a total fee of $5,375 and a cumulative net return of 27.2 percent (or $27,200). Fees deducted on a frequency other than annual would result in a different cumulative net return in the preceding example. From inception to 9/30/2011, the Models were the responsibility of a former portfolio manager. Another portfolio manager assumed responsibility after 9/30/2011. There is no assurance that the current portfolio manager would have achieved the same performance as the previous portfolio manager. The benchmark for the Models is the S&P 500 Total Return index. An index is an unmanaged, statistical composite and its return does not reflect payment of any brokerage commissions or fees an investor would pay to purchase the securities it represents. Such costs would lower performance. It is not possible to invest directly in an index. The S&P 500 Total Return, Index includes a different number of securities and has different risk characteristics than the Model portfolios. Past performance of an index is not an indication of future returns. 7

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