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Forecasting Returns: Simple Is Not Simplistic

“It is far better to foresee even without certainty than not to foresee at all.” -Henri Poincaré 1 Another year, another body blow delivered by the market to “cheap” investments. One popular definition of cheap (i.e., value) has now underperformed growth on a total return basis for six of the last nine years. Can we blame the investor who is considering throwing in the towel, dropping to the canvas, and taking a 10 count on value strategies? Is it now time to leave the ring, sell value, and pick up the growth gloves, or is a better option to stay in the ring and buy even cheaper cheap assets? To make this important determination, a reliable expected returns model is a good referee. The choice of model is important. After all, a model’s forecasted return for an asset class is only as good as its structure, assumptions, and inputs allow it to be. In this article, we compare three models. Each can be classified as simple in contrast to the quite complex models used by many institutional investors. One of the three is the model used by Research Affiliates, which although simple has performed well, not only in terms of making long-term asset class forecasts, but in combining undervalued asset classes to build alpha-generating portfolios. This latter consideration is a prime attribute of a successful model. The Rational Return Expectation Let’s begin our analysis with the return we should rationally expect from the investments we make. Whether an investor practices top-down asset allocation or bottom-up security selection, investing is about nothing more than securing cash flows at a reasonable price. After all, the price of an asset is simply the sum of its discounted cash flows, which can be affected by two forces: 1) changes in the cash flows and/or 2) changes in the discount rate. If the cash flows and discount rate remain constant over the holding period, the asset’s value will remain the same throughout its life as on the day it was purchased. Therefore, it is a change in the cash flows and/or the discount rate that ultimately drives an asset’s realized return over time, and the possibility of such changes that drives an asset’s expected return over time. As mentioned in the introduction, the implementer of a value strategy would have experienced a long string of annual negative returns over the past several years. Figure 1 illustrates quite vividly the disappointing returns associated with a U.S. equity value strategy compared with a U.S. equity growth strategy since 2007. Click to enlarge Although this period of underperformance may be disheartening for many value investors, the precepts of finding, and then investing in, undervalued assets will, tautologically, 2 be rewarded with outperformance in the long run. The question then becomes, does “cheap” mean undervalued? To aid in answering this question, a variety of expected return models are available in the marketplace, including the model on the Research Affiliates website. 3 From the first day we published our long-term expected returns on the site, we have received questions from clients and peers on the efficacy of our model. The question usually posed is: “What’s the R 2 of your expected return model for [insert favorite asset class here]?” 4 Granted, it seems like a pretty obvious question, but we would argue it is actually not all that relevant. A better question, and the one we address here, is how our model compares with other commonly used models. Because investors need some method or modeling system to estimate forward returns, the issue is not just a matter of how “good” a single model is, but also how it compares to available alternatives; simply improving on the alternatives can be quite beneficial. A Comparison of Expected Return Models The first model is a simple rearview mirror investment approach in which we assume returns for the next 10 years will equal the realized returns of the previous 10 years. Although this is a very simple model, it also happens to be the way that many investors behave. The second model assumes that in the long run all assets should have the same Sharpe ratio, and calculates expected returns based on the realized volatility of each asset. The third model is the Research Affiliates model, as described in the methodology documents on our website. For the comparison, we’ll use expected and realized returns for a set of 16 core asset classes, over the period 1971-2005. Asset returns are included in the analysis as they historically became available. 5 All returns are real returns. Model One . Figure 2 is created using the first model. It compares the 10-year forecast, which is based on the past, to the subsequent 10-year return. On the x axis, 10-year expected returns for each asset class are grouped into nine buckets. Each blue bar represents a 2% band of expected return in a range from −4% to 14%. The height of the blue bars represents the median subsequent 10-year annualized return for the assets in that bucket. The 10-year realized return is calculated using rolling 10-year periods, month by month, starting in 1971. The orange diamonds and gray dots represent the best and worst subsequent returns, respectively, for each bucket. Click to enlarge The first model clearly underestimates the returns of assets that have performed poorly in the past, and overestimates the returns of assets that have recently performed well. For example, the actual median return for assets with a forecasted return between −2% and 0% was an amazing 11.6% a year! This pattern of bad forecasting is consistent across the range of forecasted returns. Although common sense argues that past is not prologue, using past returns to set future return expectations is the norm for many practitioners who attempt to “fix” the problem by using a very long time span. But let’s consider the half-century stock market return at the end of 1999 that was north of 13%, or 9.2% net of inflation. Many investors did expect future returns of this magnitude to continue! But because 4.1% of that outsized return was a direct consequence of the dividend yield tumbling from 8% to 1.2%, the real return for stocks was a much more modest 5.1%. Model Two . Figure 3 shows the results of the second model, which assumes a constant Sharpe ratio for all assets. In this case, we assume a Sharpe ratio equal to 0.3. This model performs better than the historical returns model. The median realized return grows as the expected return grows, however, the long-term forecasted returns are constrained on both the upper and lower ends of the forecast range (i.e., no forecasted returns less than 0% nor greater than 12% are generated). Negative returns in this model are impossible to get without a very negative real risk-free rate, and by definition, large expected returns are not possible without very high volatility. Click to enlarge Model Three. Let us now turn to the Research Affiliates model. Figure 4 shows our 10-year forecasted returns 7 for the 16 core asset classes compared to their actual subsequent 10-year returns. The trend of rising expectations and rising subsequent returns is what we should expect from a model, although it’s not perfect. Click to enlarge As Figure 4 shows, when our return expectations have been less than 2%, realized returns have often been higher than expected. Although we were apparently overly bearish, our return forecasts were well within the bounds of best and worst realized returns. It is also worth mentioning that market valuation levels have been generally rising, and yields falling, since 1971, so it is possible that our forecasts were correct, net of the (very long) secular trend in valuation levels. For forecasted returns higher than 2%, the median return for each bucket is in line with expectations, with the gap between the minimum and maximum returns becoming smaller as the expected return gets larger. It’s important to recognize our expected returns are based on yield, a contrarian signal which echoes our investment belief that the largest and most persistent active investment opportunity is long-horizon mean reversion. Investing using a yield-based signal does not come without its challenges. One big challenge is that a yield signal is a valuation signal that does not come with a timing signal. Because the yield is signaling an asset is attractive today does not mean it will not continue to get more attractive. If the asset’s price falls further, increasing the long-term return outlook, unrealized losses in the portfolio can be uncomfortable. This discomfort is not due to dollars actually lost, but by the sickening feeling that accompanies downside volatility. As American investor and writer Howard Marks has said, “The possibility of permanent loss is the risk I worry about.” We agree. Volatility should not be confused with risk. The permanent loss of capital, 8 which happens when investors succumb to fearful thoughts and thus sell at inopportune times, is the investor’s true risk. Putting It All Together The primary purpose of an expected return model is to classify what we know about assets in an economically intuitive framework for the purpose of building portfolios . Or said a different way, a model’s value is in the collection of forecasts it encompasses – that is, the system itself – and not in the individual forecasts. Figure 5 shows the results of an equally weighted portfolio using our forecasts. In this case the median realized returns line up very well with expectations, and the dispersion is smaller than that observed in Figure 4 for the individual asset classes. Are our expectations perfect? Absolutely not! Is our methodology a crystal ball for the future? No way! Can there be a ton of variability in our forecast returns versus realized returns? Most certainly, yes! But instead of lamenting these uncertainties, we believe there is value in measuring them. Click to enlarge For a visual representation, Figure 6 shows our expected return for the commodities asset class along with the variability (unexpected return) around the expectation. This variability could be due to changes in the shape of future term structures that differ from the past; faster or slower reversion of spot prices to expected means; or a plethora of other unknown idiosyncratic criteria. Click to enlarge Risk & Portfolio Methodology document 10 on our website describes an approach to constructing portfolios that incorporates the variability around each return expectation. A Simple Forecasting System Can Win the Round Jason Zweig noted in his commentary to The Intelligent Investor that “as [Ben] Graham liked to say, in the short run the market is a voting machine, but in the long run it is a weighing machine.” 11 We concur. We are not interested in attempting to navigate short-term price fluctuations and the random chaos that causes them. We seek instead to discern an asset’s currently unacknowledged investment heft and the likelihood that the market will recognize this value over the subsequent decade. We are long-term investors. Asset classes with higher long-term expected returns are generally unloved and overlooked for quite some time before their fortunes reverse. Uncovering value does not require a complex model. We find that a simple, straightforward returns-modeling system for constructing multi-asset portfolios works quite well. We have chosen to stay in the ring for the long term, holding today’s undervalued and unloved asset classes, confident in the compelling opportunities signaled by the simple and straightforward metric of yield. Endnotes 1. Poincaré (1913, p. 10). 2. If it fails to eventually outperform, it’s not undervalued! 3. http://www.researchaffiliates.com/assetallocation . 4. Although measuring the R 2 of our models is possible, the result is not very useful because samples overlap over long-term horizons. Take U.S. equities for which data are readily available since the late 1800s, roughly 150 years. We analyze 10-year returns, calculated monthly. As a result, we have only 15 unique samples. Any regression using monthly data points for 10-year returns will show misrepresented R 2 values, because each data point shares 119 of its 120 months with the next data point. Going to non-overlapping returns means we don’t have enough samples for robust results. For example, imagine the same test for the Barclays U.S. Aggregate Bond Index, which started in 1976-four samples anyone? 5. Indices were added as data became available: 8/1971, Russell 2000; 12/1988, MSCI EAFE; 1/1990, Barclays Corporate High Yield; 1/1992, Barclays U.S. Treasury Long; 5/1992, Barclays U.S. Aggregate; 5/1992, JPMorgan EMBI+ (Hard Currency); 4/1994, Barclays U.S. Treasury 1-3yr; 1/1997, Bloomberg Commodity Index; 3/1997, JPMorgan ELMI+; 1/2001, Barclays U.S. Treasury TIPS; 7/2003, FTSE NAREIT. Analysis is monthly and ends in 2005, the most recent date for which 10-year subsequent returns can be calculated. 6. The range for each of the bars in the chart should be interpreted as including the lower bound but not the upper bound of the range. For example, the range −2% to 0% includes returns from, and including, −2% up to, but not including, 0%. This standard also applies to the charts in Figures 3-5. 7. These forecasted returns represent return expectations that our methodology would have delivered in past decades. The core elements of the methodology were first described by Arnott and Von Germeten (1983); thus, the methodology is not a data-mining exercise of fitting past market returns. 8. Marks (2013, p. 45). 9. The 4% to 6% bucket is an outlier here; however, this result only occurred in 13 months of the entire 34-year period. 10. http://www.researchaffiliates.com/Production%20content%20library/AA-Asset-Class-Risk.pdf?print=1 . 11. Graham (2006, p. 477). References Arnott, Robert, and James Von Germeten. 1983. ” Systematic Asset Allocation .” Financial Analysts Journal, vol. 39, no. 6 (November/December): 31-38. Graham, Benjamin. 2006 (1973). The Intelligent Investor-Fourth Revised Edition, with new commentary by Jason Zweig. New York: HarperCollins Publisher. Marks, Howard. 2013. The Most Important Thing Illuminated. New York: Columbia University Press. Poincaré, Henri. 1913. The Foundations of Science. New York City and Garrison, NY: The Science Press. This article was originally published on researchaffiliates.com by Jim Masturzo . Disclaimer: The statements, views and opinions expressed herein are those of the author and not necessarily those of Research Affiliates, LLC. Any such statements, views or opinions are subject to change without notice. Nothing contained herein is an offer or sale of securities or derivatives and is not investment advice. Any specific reference or link to securities or derivatives on this website are not those of the author.

2016 Investment Strategy With ETFs: Part 2

As we saw in Part 1 of this series , ETFs have been very popular. Understanding the trends in this area is helpful in being able to select ETFs in the right way and for the right purposes. This second part of the series will continue the discussion to understand ETFs in greater detail so that investors can make better choices. One of the important predicted changes is that institutional investors are likely to become more diverse. This will be seen on a global scale. Active ETFs Active ETFs are one area that many industry pundits believe will be the way of the future. As outlined by PWC (2013) : “After a slow start, active ETFs are picking up steam and are likely to become major drivers of a wider range of uses and greater share of wallet across a more diverse client base.” It is believed that this will create challenges in a range of areas, such as in the need for innovative approaches to the regulations associated with portfolio transparency. This has held back active ETFs until now. Additionally, it is not believed that everyone will benefit from active ETFs, and there is unlikely to be a broad-based move away from so-called “style box investing”. ETFs’ Pros and Cons It is thought that ETFs are going to continue to experience some issues as they grow and develop. Some of the problems that have been outlined include performance tracking problems, trade settlement and liquidity. Regulatory challenges, operational risks and poor technical understanding are also likely to hold back demand in some areas. Nonetheless, overall it is anticipated that ETFs are going to have a critical role in the asset management industry in the medium term. It is considered by PWC to be unlikely that ETFs will experience a slowing up of growth or even a reduction in growth in the short to medium term, as they are still very popular. Other changes in this area are likely to include increased customisation. Looking at the changes to ETFs from a different perspective, The Wall Street Journal (2013) asked experts in this area what they think . Like PWC, the Journal documents the increased likelihood of actively managed funds. While to-date these have not done particularly well in attracting investment, it seems this is likely to change in the future. Other projections for this industry include an increase in competition in this area. Some worry that ETFs may be too popular, and that there is too much chopping them around. It is thought that as a result of this, there is potential for some consolidation in this market. There were worries among some of the experts that there could be an increased likelihood of failure of new ETFs produced. The problem is perceived to be that while some of the products have big names behind them and will be able to achieve critical mass, others definitely do not, and these may struggle to attract investment. Some believe that these funds will start looking quite a bit more like managed funds in the future. Investment Strategy It seems that ETFs are here to stay, and their popularity continues to increase. This means that an ETF strategy is a useful component in any investment approach. The strategy used needs to consider the increased number of ETFs worldwide. It is suggested that there are several approaches to make money from expertise with ETFs. One of the suggestions is creating opportunistic products that are based around marketplace events. A second is looking at them as the base for packaged solutions, for annuities or allocation funds. A third is to go down the actively managed route, taking outcome-focused strategies that use ETFs. A final option is looking back and creating products that are sold in an ETF format instead. In doing this, the asset manager needs to understand the ETF system and the opportunities faced. This means also being able to see how distribution platforms and databases can be used. It also involves looking at the ways that investors can be educated, so that people understand what ETFs are and the value that they bring to the table. Differentiating is considered to be particularly important in attracting attention.

What ‘Smart Beta’ Means To Us

Summary The absence of a generally accepted definition of “smart beta” has given people license to describe a wide range of products as smart beta strategies. In equity investing, we use smart beta to refer to valuation-indifferent strategies that break the link between the price of an asset and its weight in the portfolio while retaining. By sharing our thoughts about the term, we hope to guide the discussion towards the real issue: how best to manage investor assets. As with most new expressions, “smart beta” is in the process of seeking an established meaning. It is fast becoming one of the most overused, ill-defined, and controversial terms in the modern financial lexicon. Unfortunately, the success of so-called smart beta products has attracted a host of new entrants purporting to be smart beta products when, frankly, they aren’t! They stretch the definition of smart beta to encompass their products, a natural business strategy. Without a simple, generally accepted meaning, the term “smart beta” risks becoming meaningless. Is that a bad thing? Probably not to the critics of the term smart beta. These are mainly the definitional purists. Bill Sharpe, who coined and defined “alpha” and “beta” in his seminal work (1964), famously remarked that the term makes him “definitionally sick.” His objection is completely legitimate: Bill defined beta as merely a measure of the non-diversifiable risk of a portfolio, measured against the capitalization-weighted market, and defined alpha as the residual return that’s not attributable to the beta. Some providers of traditional cap-weighted indices similarly object, either because they believed that there is only one “true” beta or because they infer from the smart beta label that its advocates believe that cap weighting is “stupid beta.” C’mon folks, is the beta relative to the S&P 500 Index-an actively selected broad-market core portfolio- really the one true beta?! Also, the practitioner community has increasingly embraced the notion of seeking beta (which has already morphed in meaning to refer to exposure to chosen markets, not the total market portfolio of investable assets, as CAPM originally defined it) for free, and paying for alpha. Viewed in this context, smart beta actually can mean something useful: a smarter way for investors to buy beta with alpha . After all, if one can find a more reliable alpha, and pay less for it, that would be pretty smart. The early critics of our Fundamental Index™ work were quick to point out that it was just a backtest and was merely clever repackaging of value investing. Well, it was a backtest, and it has a value tilt against the cap-weighted market. (Or, just to be provocative, does the cap-weighted market have a growth tilt against the broad macroeconomy, providing investors with outsized exposure to companies that are expected to grow handily, and skinny exposure to troubled companies?) It’s not a backtest anymore, as we approach our 10th anniversary of live results; and it has outperformed the cap-weighted market in most of the world, during a time when value generally underperformed growth . Critics have become more muted, as the efficacy of the Fundamental Index method (and other so-called smart beta strategies) is better understood. Defining Smart Beta for Equity The term smart beta grew out of attempts by people in the industry to explain the Fundamental Index approach vis-à-vis existing passive and active management strategies. When Towers Watson, a leading global investment consulting firm, coined the expression smart beta, it was not their intent to label cap-weight as “dumb beta.” Indeed, they referred to it as “bulk beta,” because it could be purchased for next-to-nothing. There is nothing “dumb” about cap-weighted indexing. If an investor wants to own the broad market, wants to pay next to nothing for market exposure, and doesn’t want to play in the performance-seeking game, cap-weighted indexing is the smartest choice, by far. People are beginning to understand that the dumb beta is the fad-chasing investor who buys whatever is newly beloved and sells whatever is newly loathed, trading like a banshee. Fortunately or unfortunately, these folks are legion, as is well documented in Russ Kinnel’s important “Mind the Gap” white papers (2005, 2014). As the debate over the smart beta label grew, Towers Watson (2013) sought to clarify the meaning of their expression with the following definition: Smart beta is simply about trying to identify good investment ideas that can be structured better… smart beta strategies should be simple, low cost, transparent and systematic. This straightforward definition indicates what investors ought to expect of a smart beta product. Our research suggests, however, that many alternative beta strategies fall short of this definition. Some are overly complex or opaque in the source of value added. Others will incur unnecessary implementation costs. Many so-called alternative beta strategies don’t seem so smart, by Towers Watson’s definition. The problem may be that even this definition is not clear enough. The absence of a rigorous, generally accepted definition gives me-too firms enough latitude to stamp smart beta on anything that’s not cap-weighted indexing. The way the term is bandied about, without much regard for meaning, is a disservice to investors. We don’t presume to define smart beta for the industry, but we would like to see more consistency in how the label is applied. Our definition builds on the Towers Watson definition, adding more specificity as it relates to equity strategies, where the smart beta revolution began almost a decade ago: A category of valuation-indifferent strategies that consciously and deliberately break the link between the price of an asset and its weight in the portfolio, seeking to earn excess returns over the cap-weighted benchmark by no longer weighting assets proportional to their popularity, while retaining most of the positive attributes of passive indexing. Earning Excess Returns The shortcomings of cap-weighted indices are by now well understood and widely acknowledged. Cap-weighted indices are “the market,” and they afford investors the market return. That’s indisputable. Nonetheless, because constituent weights are linked to price, they automatically increase the allocation to companies whose stock prices have risen, and reduce the weight for companies whose stock prices have fallen. If the market is not efficient, and prices some companies too high and some too low, then cap-weighted indices naturally have disproportionately large concentrations in companies that are likely to be overvalued and light allocations in companies that are disproportionately undervalued. This structure creates a return drag that is overcome by breaking the link between price and weight in a portfolio. 1 In fact, our research indicates that any structure that breaks the link between price and weight outperforms cap weighting in the long run. 2 In this sense, our work on the Fundamental Index concept is not special! 3 Equal weight, minimum variance, Shiller’s new CAPE index, and many others, all sever this link, and empirically add roughly the same alpha. This can be done simply, inexpensively, and mechanistically; these ideas show good historical efficacy all over the world; and some have live experience that roughly matches the backtests. Accordingly, this way to pursue a particular beta might rightly be considered “smart.” In periodically rebalancing to target weights that are unrelated to price, smart beta strategies engage in value investing: They buy low and sell high (we have demonstrated this result elsewhere 4 and will return to it in a moment). It will surprise many readers to learn that the value tilt is empirically a far smaller source of return than is the rebalancing process itself. 5 After all, what could be more uncomfortable than systematically trimming our holdings in the most extravagantly newly beloved companies, while topping up our holdings in the most newly feared and loathed companies? These portfolios look perfectly reasonable; their trading does not. That’s where the alpha is sourced: contratrading against the legions of investors who chase fads and shun recent disappointments . Accordingly, breaking the link with price is, in our view, the most important component to any useful definition of smart beta. Strategies that use market capitalization in selecting or weighting securities, such as cap-weighted value indices, are not smart beta using our definition: they leave money on the table due to the same return drag that afflicts any cap-weighted strategy. 6 Best Attributes of Passive Investing Compelling as it might be to define smart beta simply as those equity strategies that break the link with price, 7 we believe that tapping a reliable source of excess return is not sufficient to merit the label smart beta. As our general definition for equity market smart beta indicates, we also think smart beta solutions should retain some of the key benefits of passive investing, including: Smart beta strategies are transparent. The principles of portfolio construction and the intended sources of excess return are clearly stated and easy to understand. Investors know what they are getting. Smart beta strategies are rules-based. Their methodology is systematic and mechanically executed. Investors know that the process is disciplined. These strategies can be independently tested, including in out-of-sample tests covering new time spans or new markets. Smart beta strategies are low cost relative to active management . 8 In addition to lower fees, they have lower due diligence and monitoring costs. As a result, they offer investors affordable access to potential excess returns. Smart beta strategies have large capacity and the liquidity to accommodate easy entrance and exit. Smart beta strategies are well-diversified and/or span the macro economy. Because stock weights are uncoupled from prices, smart beta strategies do not expose investors to sector and industry concentrations arising from misvaluations. We think of these traits as family traits. Few will have every one of these traits; we’d be inclined to apply the smart beta label to a strategy that displays most or all of them. To us, the trait in our primary definition is sacrosanct: Any strategy that is not valuation-indifferent, that does not break the link between the weight in the portfolio and price (or market cap), is not smart beta. Performance Record We’ve described what smart beta means to us, and, in the process, indicated what we think investors should expect of products that are marketed as smart beta strategies. Is it also reasonable to expect long-term outperformance relative to cap-weighted indices? We cannot know the future. Perhaps, in the years ahead, investors will be rewarded by owning more of whatever is most expensive and less of whatever is least expensive. Personally, I doubt it. We can know the past. So-called smart beta strategies have produced value-added returns in long-term historical testing, all over the world, and on many 9 live-asset portfolios. And this outperformance has been driven, in large part, by the inherently value-based trading that takes place when smart beta portfolios are rebalanced to non-price-related weights. In long-term simulations, smart beta strategies have generated excess returns relative to cap-weighted indices. For instance, Figure 1 traces the hypothetical cumulative returns of a fundamentally weighted U.S. index and the comparable returns of two cap-weighted indices-a broad market index and a traditional value style index-over the 35-year period from 1979 through 2013. The fundamentally weighted index outperformed both of the indices whose weighting methods incorporate market prices. 10 A cautionary note is in order. As with any strategies, smart beta investing is a long-term strategy. Only a charlatan would encourage customers to expect 100% probability of future outperformance. There have been prolonged periods of underperformance, especially in secular bull markets. Smart beta strategies are contrarian, and they make sense only for investors with long-term planning horizons and a willingness to tolerate uncomfortable (even profoundly uncomfortable) portfolio rebalancing trades. In Closing Smart beta has been roundly dismissed as a marketing buzzword, rather than a significant development in finance theory and investment practice. We like the name, partly because it is jarring and controversial, but we don’t for a moment deny that it has been misused to flog me-too products. We hope that, by sharing our thoughts about the nomenclature, we can nudge the discussion in the direction of the real issue: how to best manage investor assets. Endnotes 1 To be sure, the cap-weighted index of the market cannot have a performance drag relative to itself. Here, we refer to a performance drag relative to the opportunity set. 2 Brightman (2013); Arnott, Hsu, Kalesnik, and Tindall (2013). 3 How many investment managers will say this about their own best products?! 4 Arnott, Hsu, Kalesnik, and Tindall (2013). 5 Chaves and Arnott (2012). 6 Hsu (2014). Note also that cap-weighted value strategies have a powerful, statistically significant negative Fama-French alpha. They derive value-added from their value tilt and then lose much of it due to cap weighting. 7 For bonds and other asset classes, our core definition can still apply. But, it’s a bit more nuanced. Do we want to weight a bond portfolio by the debt appetite of a borrower, and then be forced to buy more of the issuer’s debt as they seek to borrow more? That’s what cap weighting will do in bonds. Alternatively, do we want to weight a bond portfolio by the debt service capacity of the borrower, which is loosely related to the aggregate economic scale of the borrower? That’s one of many ways to construct a smart beta strategy in bonds. Historically, it works. 8 It should go without saying, but these strategies cannot price-compete with conventional cap weighting, nor should they. Did Vanguard charge 7 bps for their first S&P 500 fund? No, they did not. Should product innovation be rewarded? Of course. Reciprocally, these strategies must charge much less than the active strategies that purport to offer similar incremental returns, in order to justify their relevance. 9 We can’t say “most” because we don’t have access to the track record of all practitioners in this space. But, I personally am confident that the word “most” would be accurate… even though value has underperformed growth in most of the past decade! 10 Kalesnik (2014). References Arnott, Robert D., Jason Hsu, Vitali Kalesnik, and Phil Tindall. 2013. ” The Surprising Alpha from Malkiel’s Monkey and Upside Down Strategies .” Journal of Portfolio Management , vol. 39, no. 4 (Summer):91-105. Brightman, Chris. 2013. ” What Makes Alternative Beta Smart? ” Research Affiliates (September). Chaves, Denis B., and Robert D. Arnott. 2012. ” Rebalancing and the Value Effect. ” Journal of Portfolio Management , vol. 38, no. 4 (Summer):59-74. Hsu, Jason. 2014. ” Value Investing: Smart Beta vs. Style Indexes. ” Journal of Index Investing , vol. 5, no. 1 (Summer):121-126. Kalesnik, Vitali. 2014. “Smart Beta: The Second Generation of Index Investing.” IMCA Investments & Wealth Monitor (July/August): 25-29, 47. Kinnel, Russ. 2005. “Mind the Gap: How Good Funds Can Yield Bad Results.” Morningstar FundInvestor (July). —. 2014. “Mind the Gap 2014.” Morningstar Fund Spy (February 27). Sharpe, William F. 1964. “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk.” Journal of Finance , vol. 19, no. 3 (September):425-442. Towers Watson. 2013. “Understanding Smart Beta.” Insights (July 23). This article was originally published on researchaffiliates.com by Rob Arnott and Engin Kose . Disclaimer: The statements, views and opinions expressed herein are those of the author and not necessarily those of Research Affiliates, LLC. Any such statements, views or opinions are subject to change without notice. Nothing contained herein is an offer or sale of securities or derivatives and is not investment advice. Any specific reference or link to securities or derivatives on this website are not those of the author.