An Approachable Guide to Factor Investing and Smart Beta

Transcription

1 Capital Appreciation Risk Management Income Generation Liquidity Management Investment Focus An Approachable Guide to Factor Investing and Smart Beta Christopher Huemmer, CFA Senior Investment Strategist FlexShares Exchange Traded Funds PART I: THE BASICS OF FACTORS Factor investing and smart beta are big trends in the global asset management industry. What once was the realm of select quantitative investment managers is now being embraced by investors worldwide. As the number of smart beta products continues to expand (see Exhibit 1), so too does the level of confusion about this type of investing. What do the descriptive terms smart beta and factor investing actually mean? Are they interchangeable? How do they differ from active management? The goal of this paper is to help investors understand these topics so they can make informed decisions about potentially using factor and smart beta investment strategies in their portfolios. EXHIBIT 1: THE GROWTH IN SMART BETA STRATEGIES (A.K.A. STRATEGIC BETA) HAS BEEN PROLIFIC AUM $ in Billions Strategic Beta AUM (lhs) 12/31/ /31/ /31/2012 # of Strategic Beta Products (rhs) 12/31/ /31/ /31/ /31/ /31/ # of Products Source: Morningstar Direct as of 12/31/2017.

2 Factor investing is an investment strategy in which securities are chosen based on attributes associated with higher returns. Factor investing requires investors to take into account an increased level of granularity when choosing securities. (Investopedia) A FACTOR BY ANY OTHER NAME Investment terms are often treated as synonymous. Factor investing, smart beta, strategic beta and various trade brands are all used to describe a wide range of products with varying degrees of innovation. At a high-level this imprecision is not a problem, but investors need to be diligent. Smart beta products can vary significantly from one another in goal and sophistication and it behooves investors to look beyond the descriptive term used if only to better understand how the product is situated under the smart beta umbrella. While it may be convenient to push everything that is not market-weighted passive or stock picking active into a smart beta bucket, this stymies a productive conversation about these products. For example, some fund classifiers include currency hedged versions of market-weighted strategies or generic sector funds in the smart beta category. Similarly, broad-based commodity strategies and standard sector rotation strategies also are classified by some as smart beta products. While sloppy articulations of what qualifies as a smart beta strategy can help produce eye-popping metrics showing smart beta manager and AUM growth (Exhibit 1,) they also obscure what is really going on in the space. In regards to terminology we favor factor investing over smart beta. Why? Because we think it clearly describes what most smart beta strategies look to accomplish through a non-market weighted investing approach. Factor investing links the concepts of the strategy with strong, independent academic research. This can help investors evaluate and situate a specific strategy among other factor-based products, as well as determine if it aligns with their investing goals. Just as importantly, focusing on factors touches on something academics have pointed out for decades: All investments no matter the stated purpose or objective have exposure to factors, whether the strategy intends to or not. WE ARE ALL FACTOR INVESTORS (MOST OF JT DO NOT KNOW IT) The most important takeaway from our factor investing guide is this: it does not matter if you are investing in a factor-based strategy, a market-weighted index fund or with an active portfolio manager; each and every investment option has exposure to factors. And it would be a mistake to believe that a market weighted strategy is factor neutral as Exhibit 2 demonstrates below. All strategies whether it holds three securities or 3,000 have exposures to factors such as size, value, momentum and quality. Each factor can have a meaningful impact on how a portfolio performs over both short- and longer-term horizons. The key is to focus on those factors that historically have compensated investors for the exposure taken and to understand which factors best fit a specific investment goal, time horizon and risk/ return objective. FlexShares.com An Approachable Guide to Factor Investing and Smart Beta 2

3 BARRA E3L Risk Model Largest 1,500 Stocks, plus smaller stocks which are added to ensure an adequate basis for estimating industry returns. Stocks with prices below five dollars are usually excluded, but S&P 500 members are always included. EXHIBIT 2: S&P 500 FACTOR EXPOSURE Barra Factor Exposure Factor 0.07 Earnings Yield Value Votality Leverage Source: Factset - Data as of 12/31/2017 from the Barra E3L risk model. Earnings Variation But why are factor exposures important? To better understand the impact of factors it is helpful to think back to your middle school math class Dividend Yields Growth 0.36 Size UNDERSTANDING FACTORS BEGINS WITH UNDERSTANDING BETA Where do these factors come from? Are they really active management re-packaged, or are they something completely new and unproven? Answers to these questions begin with the original equity factor, market beta. The term beta is widely recognized as any market-weighted strategy that covers a particular segment of the market. But what is not commonly known is beta is steeped in academic history and ultimately tied to basic concepts we all learned in algebra class, starting with the equation of a line: Where m is the slope of the line and b is where the line intersects the y-axis. Y = mx + b Academics would write this slightly different, but with the same concepts. Y = bx + a This version is where the terms beta and alpha originate. Professor William Sharpe used this linear relationship to find a variable that explained stock returns. His discovery became the basis for his capital asset pricing model (CAPM). The genius of Professor Sharpe s work was that a single quantifiable factor the strategy s price volatility relative to the market had very strong explainable power not only over a single equity security s expected returns, but to a basket of equity securities, such as a mutual fund portfolio, as well. Further research on the topic estimated that CAPM explains 70% of all mutual fund returns. It does not matter if you are comparing a broad index fund or a concentrated stock portfolio, each can be evaluated in terms of this concept. Investors may do this every day when evaluating their portfolio s Sharpe ratio. FlexShares.com An Approachable Guide to Factor Investing and Smart Beta 3

4 Sharpe ratio is a measure for calculating riskadjusted return, and this ratio has become the industry standard for such calculations. The Sharpe ratio is the average return earned by a single stock or a mutual fund portfolio of stocks in excess of the risk-free rate per unit of volatility of total risk. Generally, the greater the value of the Sharpe ratio the more attractive the risk-adjusted return. (Investopedia) CAPM was only the beginning of academia s impact on the world of factor investing. In the early 1990s Professors Eugene Fama and Kenneth French expanded on Sharpe s work. They determined that in addition to sensitivity to market volatility (beta), two other factors incrementally helped explain mutual fund returns. By examining stock and mutual fund returns going back to 1926 the two professors found that two additional factors size exposure, which they named small minus big or SMB, and value exposure, which they called high minus low or HML expanded the explainable power of CAPM. Similar to quantifying a fund s exposure to market volatility (its market beta) each fund s sensitivity to size (its SMB beta) and value (its HML beta) provides a better idea of its exposures to different factors and helps explain how returns are generated. Subsequent research shows that Fama and French s three-factor model explains 90% of equity fund returns 1. This was groundbreaking for several reasons. First, it improved on the explanatory power of the CAPM model and the Sharpe ratio. Second, it helped investors further understand the biases of the funds in which they were investing. In other words, two funds may have the same market beta, but the difference in exposure to size and value may adversely or beneficially affect returns. For example, this can be used to evaluate two value portfolios by comparing their relative exposures to the value factor or see if one is incorporating other factors such as size into its approach 2. Finally, from a conceptual level, the Fama-French three-factor model affects the way we think about manager skill. If we go back to the original line equation (Y = bx + α), there are two chief variables: beta and alpha. And while CAPM includes a risk-free rate and an error term, it is the chief variables of beta and alpha that drive the model. So under CAPM, if beta explains 70% of returns then tacitly alpha was left to explain the remaining 30%. The Fama-French three-factor model realigns those dynamics; if market, size and value explain 90% of returns then only 10% is explained by alpha. Since Fama and French published their work on the three factor model there have been many advances. While their initial work focused on U.S. equities, supplemental work by Fama and French, as well as many other academics, has replicated their results across global equity markets. Thus the conclusions are not a -only phenomenon but something that exists everywhere, with only adjustments being made to the premium associated with each of the factors. The research did not end there. In 1997 Mark Carhart expanded on Fama and French s work by including momentum as a fourth factor which further increased the explainable power of the model. Recently, Fama and French published a working paper that eschews momentum for two new factors, direct investment and profitability. Additional work has looked at expanding factor research beyond equity investing into areas such as real estate and fixed income, searching for unique factors that explain returns in these asset classes. Smart beta, then, is a much evolved transformation of Professor Sharpe s original research started way back in the 1960 s. 1 Fama, French (1993): Common risk factors in the returns on stocks and bonds. 2 Ibid. FlexShares.com An Approachable Guide to Factor Investing and Smart Beta 4

5 Today, we use these three- and four-factor models to help understand the performance of rules-based investing methodologies produced by index providers. Investment strategies that track rules-based indices may still generate alpha, but only after their exposure to the various other factors has been accounted for. Strong performance results attributed to superior manager skill (per attractive Sharpe or information ratios) often look more pedestrian when subject to a factor-based returns analysis. Evaluation of existing strategies was just the first part of the usefulness of factors. What came next was when quantitative investors started to explicitly target factor exposures. HARNESSING FACTORS AS AN INVESTMENT STRATEGY If factors can help explain incremental investment returns, wouldn t an effective investment strategy be to target the factors that could be expected to provide compensation to investors? For decades quantitative portfolio managers have asked and attempted to answer this question. If the idea behind passive index funds was that owning the index could give investors inexpensive beta market exposure as defined by CAPM, then shouldn t incorporating factors such as size, value and momentum be the logical next evolution? This is a good point to ask Is this really a new concept? People have talked about value investing since Benjamin Graham. In fact, the goal of most active portfolio managers is to find undervalued, quality companies. That sounds just like investing in the quality and value factors! It is true a lot of these factors can be traced to earlier times Graham s value investing in the 1930s and 1940s; Banz exhorting small-cap firms in his 1981 paper; even low volatility has been researched since the 1960s. Those early works have served as a backbone for further investigation from other contemporary academics and practitioners, but traditional active management is dramatically different than factor investing. Even if an active manager is pursuing cheap, high-quality companies most of the time they are doing so in the framework of selecting stocks from a universe. The end result is often a concentrated portfolio focused on the best ideas of a particular investment manager or team. This type of strategy looks less toward factor exposures per se and instead attempts to potentially add performance over an index by courting idiosyncratic risk from a handful of securities. Idiosyncratic risk can be stock specific, or it can be broader, with biases toward particular sectors or industries, or countries and geographic regions. By timing these individual buy and sell decisions, the manager looks to beat the market, and since these buy and sell decisions are potentially so impactful, most portfolio managers prefer not to share their current holdings with anyone. FlexShares.com An Approachable Guide to Factor Investing and Smart Beta 5

6 Factor investing takes a totally different approach. Most academic research and empirical evidence suggests that investors are not compensated for taking idiosyncratic risk. Factor investing looks to diversify away from idiosyncratic risk by having less concentration to any single security, sector or industry. Instead, these strategies focus on the compensated factors like size, value, momentum, quality or low volatility in the context of a well-diversified portfolio. Some factor strategies even look to own the whole universe of a particular index (or the majority of it) and get their factor exposure from adjusting constituent weights. This is a major part of why factor investing pairs so effectively with the exchange traded fund structure: a diversified, rules-based investment process is well suited to providing the road map of an index methodology. The more thoughtful an index methodology is designed to capture a factor, the more effectively a strategy can pursue and may benefit from its exposures. Since the factor strategy is diversified over a large number of names, disclosing daily holdings is not problematic as it is with active stock picking approaches. Is it possible to adhere to a factor approach via an active methodology instead of deploying a rules-based index approach? Sure. Just as there are closet indexers that are essentially replicating market-weighted strategies in an active process, there are active mutual funds that do an excellent job of following a factor-based mandate. These active factor investors highlight their ability to trade, to select exit and entry points, as well as use professional judgment to adjust the factor exposures, believing these actions have the potential to add value over a rules-based process. Whatever the merits of active factor investments, we believe a rules-based approach will typically provide less style drift and better adherence to factor capture. IS IT POSSIBLE TO TIME FACTORS? This question usually stems from the realization that factor investing is not a magic wand with an approach that will always produce outperformance regardless of market or economic cycles. Similar to equity assets and their sub-categories, each factor goes through cycles of outperformance and underperformance. Since we understand that factor behaviors are cyclical, it is tempting to believe that an investor can enter each factor as it is poised to outperform and likewise reverse out just before they start to underperform. In reality it is extremely difficult to time factors and the evidence is scarce that rotating between factors leads to sustained outperformance. Here is an example: the most popular factor timing strategies are either momentum-based (ride the factors with good recent performance while avoiding those that have done poorly,) or value-based (target factors that are relatively inexpensive compared to other factors, probably because they have underperformed recently.) Note the inconsistency? The two most popular strategies for timing factors are diametrically opposed to one another. One strategy targets factors that have performed well, while the other targets those that have performed poorly 3. 3 Likewise there are many rules-based factor strategies that either: poorly and inefficiently capture the intended factor exposure, or are really stock selectors in the guise of targeting factors. In particular, this latter group is one reason we tend to disassociate factor investing with the smart beta label. FlexShares.com An Approachable Guide to Factor Investing and Smart Beta 6

7 Instead, we believe it is recommended that investors understand when each factor has tended to historically outperform and to underperform. Likewise, it typically pays to stay invested in a factor through the periods of underperformance so as to profit during the periods of outperformance. However, just as it is important to consider a client s equity risk tolerance when constructing a portfolio, it is also important to evaluate a client s risk appetite for different factors and to choose those factors that are best suited to a client s specific goals, investment objectives, risk tolerances and preferences. Another way to help mitigate the factor cycle is by using a multi-factor approach. By combining factors that are low or negatively correlated to one another, an investor can potentially smooth out the various factor cycles and potentially experience less overall volatility. We think the key consideration is how best to combine factors, which factors to choose and how to implement the multi-factor approach. FIND OUT MORE The FlexShares approach to index-based investing is, first and foremost, investor-centric and goal oriented. We pride ourselves on our commitment to developing products that are designed to meet real-world objectives for both institutional and individual investors. If you would like to discuss the attributes of any of the ETFs discussed in this report in greater depth or find out more about the index methodology behind them please don t hesitate to call us at FlexETF ( ) or visit www. FlexShares.com. IMPORTANT INFORMATION Before investing, carefully consider the FlexShares investment objectives, risks, charges and expenses. This and other information is in the prospectus and a summary prospectus, copies of which may be obtained by visiting Read the prospectus carefully before you invest. Foreside Fund Services, LLC, distributor. An investment in FlexShares is subject to numerous risks, including possible loss of principal. Fund returns may not match the return of the respective indexes. The Funds are subject to the following principal risks: asset class; authorized participant, calculation methodology; commodity; concentration; counterparty; currency; derivatives; dividend; emerging markets; equity securities; financial sector, fluctuation of yield; foreign securities; geographic; high portfolio turnover; income; industry concentration; inflation; infrastructurerelated companies; interest rate; issuer; liquidity; large cap; management; market; market trading; mid cap stock; MLP; momentum; natural resources; new funds; non-diversification; passive investment; privatization; securities lending; small cap stock; tracking error; value investing; and volatility risk. A full description of risks is in the prospectus. MANAGED BY NORTHERN TRT FlexShares.com An Approachable Guide to Factor Investing and Smart Beta 7

8 DEFINITIONS Momentum Captures sustained relative performance and its effect on risk. Volatility Captures relative volatility using measures of both long-term historical volatility (such as historical residual standard deviation) and near-term historical volatility (such as high-low price ratio, daily standard deviation, cumulative range over the last 12 months). Other proxies for volatility (volume beta) are also included in index. Size Captures systematic return and risk differences between large-cap and small-cap stocks. Value Distinguishes between value stocks and growth stocks using the ratio of book-value of equity to market capitalization. Earnings Yield Combines current and historical earnings per share (E/P) ratios with a measure of analyst-predicted E/P ratios. Dividend Yield Computes a measure of predicted dividend yield using the past history of dividends and the market price behavior of the stock. Growth Characterizes a firm s growth in a number of aspects, particularly earnings. Leverage Measures the firm s financial leverage that is the degree to which a company uses fixed-income securities such as debt and preferred equity. The more debt financing a company uses, the higher its financial leverage. Earnings Variation Measures a company s historical earnings variability and cash flow fluctuations. Book Value The net asset value of a company, calculated as total assets minus intangible assets (patents, goodwill) and liabilities. CONTACT FINANCIAL PROFESSIONALS By Phone Consult Consultants are available Monday Friday: 9AM to 5PM ET FlexETF ( ) INDIVIDUAL INVESTORS Your Financial Professional By Mail/Overnight Delivery FlexShares ETFs c/o Foreside Fund Services, LLC 3 Canal Plaza, Suite 100 Portland, ME Follow Us on LinkedIn FlexShares FlexShares.com An Approachable Guide to Factor Investing and Smart Beta FS00095 (4/18)