Tag Archives: modern

Revisiting A Paradigm Shift: Allocation Decisions In The Absence Of Theory

Problems with CAPM and EMH suggest that Modern Portfolio Theory is not useful for individual investors. As a result, modern finance is in the midst of a paradigm shift similar to those discussed by Kuhn (1962). On an interim basis, using James Montier’s trinity of risk plus behavioral finance as an overlay may work. James Montier, the famous value investor now at GMO Asset Management, has written extensively about the huge contradictions between academic theory and real world observations when it comes to the dynamic between risk and reward in the markets. This is a topic I have been interested in for a long time, because the disjunction between theory and practice should (but usually doesn’t) strongly affect how investment managers view risk and construct client portfolios. Montier began his argument with a review of the evolution of market theory, and especially that part of theory called the Capital Asset Pricing Model, or CAPM. In the 1950’s future Nobel Laureate Harry Markowitz wrote his Ph.D. thesis on a mathematical model for asset allocation called portfolio optimization. His model could theoretically be used to construct portfolios that combined maximum gain with minimum risk for any investor whose assets were diversified. Eventually this approach led, in conjunction with the CAPM, to what is now called Modern Portfolio Theory (MPT). Many institutions today use a modified version of MPT to develop their recommended asset allocations. The CAPM part of modern theory was introduced by Nobel Laureate William Sharpe and his colleagues in the 1960’s. In brief, the CAPM assumed that all investors would use Markowitz’s optimization method, so that a single mathematical factor could be isolated that would distinguish between stocks of differing risk levels, and that factor is called beta (i.e., beta is that part of a stock’s risk that can be attributed to market fluctuations that are systematic and undiversifiable, and this in turn depends in part on a stock’s correlation to the market, as represented by the S & P 500). The final component of MPT was the development of a concept called the Efficient Market Hypothesis by Nobel Laureate Eugene Fama. As part of his work Fama attempted to prove that information is equally available to all players in the markets, so therefore the markets are efficient and all stocks are correctly priced. Over time this idea led directly to the notion that the best investment approach is to use passive indexes to fill out a portfolio allocation, since no one should expect to beat efficient markets for any substantial period of time. It is important to note that this idea of efficient markets is really just an assumption used to make mathematical treatment possible. There is abundant evidence that the assumption of market efficiency is false, as has been discussed by Warren Buffett, John Mauldin and many others. One only needs to think back to the NASDAQ bubble in the late 1990’s and its subsequent collapse, or the carnage of the Great Financial Crisis in 2008, to find glaring examples of inefficient markets. A basic tenet of CAPM is that risk and reward are directly proportional. This means that as risk increases, so does reward. However, a study in the late 2000s by JPMorgan has shown just the opposite trend for real world data. In other words, when 20 years of actual market (the Russell indexes) data through 2008 were plotted, they indicated a strong linear relationship between risk and reward all right, but it was reciprocal. Thus, if risk increases in the real markets, reward can actually decrease. Indeed, Fama and his long-time colleague French published a paper in 2004 showing that for the period from 1923 to 2003, using all stocks on the NYSE, AMEX and NASDAQ, the highest risk (highest beta) stocks considerably underperformed relative to the predictions of the CAPM. The reverse was also true, in that the lowest risk (lowest beta) stocks considerably outperformed relative to the predictions of the CAPM. Over the long run, there was essentially no relationship between beta and stock returns. Yet another study was conducted a few years ago by Jeremy Grantham of GMO Asset Management, who found that for the 600 largest U.S. stocks (for the time period from 1963 to 2006), those with the lowest beta have had the highest returns. Montier himself has studied the risk-return relationship for European stocks for the period from 1986 to 2006 as well, with essentially the same result. Montier’s explanation for the failure of the CAPM over shorter time frames is based on the many questionable assumptions that have to be made for the model to “work” mathematically. Amongst the more questionable assumptions are: 1) no taxes are paid, so investors are indifferent between dividends and capital gains; 2) all investors use Markowitz portfolio optimization at all times; and 3) investors can take any position (long or short) without affecting the market price. These assumptions are implicitly accepted by all who use MPT and CAPM to manage portfolios, such as many institutional asset managers. These may indeed be valid over very long time frames, but then they may not be appropriate for mere mortals to use with their personal investments. This assumed validity reaches its ultimate level of absurdity in the obsession many financial institutions have for so-called short term “tracking error”. Tracking error measures the variability in the difference between a fund manager’s portfolio returns and the returns of the appropriate stock index. Many institutional managers have been compensated on the basis of tracking error. Thus, the variance in investor portfolio returns has not always been considered; rather, a manager’s relative performance against an index is the criterion by which they are commonly judged. This means that if the market loses 20% in a given year and the manager only loses 18%, that manager may very well bonus for outperformance on a tracking error basis, even though their clients lost significant money. Modern hedge funds are in part the profession’s response to client angst over this state of affairs. Many hedge funds attempt to provide steady absolute returns, and that is why they have become so popular amongst high net worth clients. Unfortunately, retail clients until recently had no sophisticated risk-control strategies available to them, but that is changing. If you accept for the purposes of argument that both CAPM and the Efficient Markets Hypothesis are invalid or at least suspect, then you are presented with a dilemma. MPT doesn’t really work except during 50 year periods and longer, which is way too long for use with retail clients, but it is the only theory with any mathematical rigor that is widely accepted. This situation is reminiscent of the problems faced in the physical sciences when an old foundational theory or paradigm has been tossed out, but a new one has not yet appeared. The classic examples are the paradigm shift that occurred when Newton proposed his gravitational theory, and again when Einstein proposed his theories about relativity. This problem was written about brilliantly by Thomas S. Kuhn in his book on “The Structure of Scientific Revolutions,” published in 1962. What generally happens is that the old guard defends the old paradigm even while it is being destroyed as an explanatory tool by new data, so that only younger scientists like Newton and Einstein can break through to new paradigms, and then only when the old guard stops fighting. A famous quote on the matter is attributed to the physicist Max Planck in the early 1900s: “Science progresses one funeral at a time.” I believe, as do others, that this is where we now find ourselves with respect to MPT. Grad schools still teach it, but applying it during the sequential bubbles of the last 20 years has yielded awful results on a risk-adjusted basis. Over an even longer period, since 1982, 30-year zero coupon bonds have beaten the S & P 500 by an absolutely huge margin, as Gary Shilling has been pointing out for many years. The careful practitioner then has a dilemma, assuming that he or she now rejects the old MPT paradigm: there is no mathematically rigorous new paradigm to replace it with. How do we go about asset allocation then? Many others have explored this question in recent years, but with a possible new bear market ahead of us sometime in 2016 or 2017, there is renewed urgency to the quest for answers. Behavioral finance has provided a rich and powerful explanation for what happens in the real world of the markets. It should be a major part of the equation, and goes a long way towards explaining the problems with MPT, but it is not inherently mathematical itself. I am reconciled to thinking that since human beings are involved in economics and markets, there will be no mathematical solution. I personally have been using Montier’s “trinity of risk” concept as a template for making allocation decisions. His trinity consists of valuation risk, business/earnings risk, and balance sheet/financial risk. These can be applied in some way to most asset classes. But a behavioral finance overlay can be useful as well. It is on this basis that I have written elsewhere that I strongly favor certain bonds over stocks, and possibly even over cash, in 2016. The most important conclusion for investors to draw from this discussion is that the assumptions that underlie an asset manager’s approach should be examined carefully and judged for their conformity with that investor’s investment goals. Most will reject the notion that periodic 50% losses are acceptable, so a more risk-aware approach is needed. I realize that I have not really answered all of the questions I have posed; clearly this is a work in progress.

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.

I Know It Was You, Fredo

If you want to read more about the Epsilon Theory perspective on polarized politics and the use of game theory to understand this dynamic, read “ Inherent Vice ”, “ 1914 Is the New Black ”, and “ The New TVA ”. Hollow Markets Whatever shocks emanate from polarized politics, their market impact today is significantly greater than even 10 years ago. That’s because we have evolved a profoundly non-robust liquidity provision system, where trading volumes look fine on the surface and appear to function perfectly well in ordinary times, but collapse utterly under duress. Even in the ordinary times, healthy trading volumes are more appearance than reality, as once you strip out all of the faux trades (HFT machines trading with other HFT machines for rebates, ETF arbitrage, etc.) and positioning trades (algo-driven rebalancing of systematic strategies and portfolio overlays), there’s precious little investment happening today. Here’s how I think we got into this difficult state of affairs. First, Dodd-Frank regulation makes it prohibitively expensive for bulge bracket bank trading desks to maintain a trading “inventory” of stocks and bonds and directional exposures of any sort for any length of time. Just as Amazon measures itself on the basis of how little inventory it has to maintain for how little a span of time, so do modern trading desks. There is soooo little risk-taking or prop desk trading at the big banks these days, which of course was an explicit goal of Dodd-Frank, but the unintended consequence is that a major trading counterparty and liquidity provider when markets get squirrelly has been taken out into the street and shot. Second, the deregulation and privatization of market exchanges, combined with modern networking technologies, has created an opportunity for technology companies to provide trading liquidity on a purely voluntary basis. To be clear, I’m not suggesting that liquidity was provided on an involuntary basis in the past or that the old-fashioned humans manning the old-fashioned order book at the old-fashioned exchanges were motivated by anything other than greed. As Don Barzini would say, “after all, we are not Communists”. But there is a massive and systemically vital difference between the business model and liquidity provision regime (to use a good political science word) of humans operating within a narrowly defined, publicly repeatable game with forced participation and of machines operating within a broadly defined, privately unrepeatable game with unforced participation. Whatever the root causes, modern market liquidity (like beauty) is only skin deep. And because liquidity is only skin deep, whenever a policy shock hits (say, the Swiss National Bank unpegs the Swiss franc from the euro) or whenever there’s a technology “glitch” (say, when a new Sungard program misfires and the VIX can’t be priced for 10 minutes) everything falls apart, particularly the models that we commonly use to calculate portfolio risk. For example, here’s a compilation of recent impossible market events across different asset classes and geographies (hat tip to the Barclays derivatives team) … impossible in the sense that, per the Central Tendency on which standard deviation risk modeling is based, these events shouldn’t occur together over a million years of market activity, much less the past 4 years. Source: Barclays, November 2015. So just to recap … these market dislocations DID occur, and yet we continue to use the risk models that say these dislocations cannot possibly occur. Huh? And before you say, “well, I’m a long term investor, not a trader, so these temporary market liquidity failures don’t really affect me”, ask yourself this: do you use a trader’s tools, like stop-loss orders? do you use a trader’s securities, like ETFs? If you answered yes to either question, then you can call yourself a long term investor all you like, but you’ve got more than a little trader in you. And a trader who doesn’t pay attention to the modern realities of market structure and liquidity provision is not long for this world. If you want to read more about the Epsilon Theory perspective on hollow markets and the use of game theory to understand this dynamic, read “ Season of the Glitch ”, “ Ghost in the Machine ”, and “ Hollow Men, Hollow Markets, Hollow World ”. Adaptive Investing and Aware Investing Okay, now for the big finish. What does one DO about this? How does one invest in a world of bimodal uncertainty and a market of skin-deep liquidity? Both of these investment goblins – Political Polarization and the Hollow Market – are so thoroughly problematic because our perceptions of both long-term investment outcomes and short-term trading outcomes are so thoroughly infected by The Central Tendency and a quasi-religious faith in econometric modeling. But while their problematic root cause may be the same, their Epsilon Theory solutions are different. I call the former Adaptive Investing, and I call the latter Aware Investing. Adaptive Investing focuses on portfolio construction and the failure of The Central Tendency to predict long(ish)-term investment returns. Aware Investing focuses on portfolio trading and the failure of The Central Tendency to predict short(ish)-term investment returns. Each is a crucial concept. Each deserves its own book, much less its own Epsilon Theory note. But this note is going to focus on Adaptive Investing. Adaptive Investing tries to construct a portfolio that does as well when The Central Tendency fails as when it succeeds. Adaptive Investing expects historical correlations to shift dramatically as a matter of course, usually in a market-jarring way. But this is NOT a tail-risk portfolio or a sky-is-falling perspective. I really, really, really don’t believe in either. What it IS – and the stronger your internal Fredo the harder this concept will be to wrap your head around – is a profoundly agnostic investing approach that treats probabilities and models and predictions as secondary considerations. I’ll use two words to describe the Adaptive Investing perspective, one that’s a technical term and one that’s an analogy. The technical term is “convexity”. The analogy is “barbell”. In truth, both are metaphors. Both are Narratives. As such, they are applicable across almost every dimension of investing or portfolio allocation, and at almost every scale. Everyone knows what a barbell is. Convexity, on the other hand, is a daunting term. Let’s un-daunt it. The basic idea of convexity is that rather than have Portfolio A, where your returns go up and down with a market or a benchmark’s returns in a linear manner, you’d rather have Portfolio B, where there’s a pleasant upward curve to your returns if the market or benchmark does really well or really poorly. The convex Portfolio B performs pretty much the same as the linear Portfolio A during “meh” markets (maybe a tiny bit worse depending on how you’re funding the convexity benefits), but outperforms when markets are surprisingly good or surprisingly bad. A convex portfolio is essentially long some sort of optionality, such that a market surprising event pays off unusually well, which is why convexity is typically injected into a portfolio through the use of out-of-the-money options and other derivative securities. Another way of saying that you’re long optionality is to say that you’re long gamma. If that term is unfamiliar, check out the Epsilon Theory note “ Invisible Threads ”. All other things being equal, few people wouldn’t prefer Portfolio B to Portfolio A, particularly if you thought that markets are likely to be surprisingly good or surprisingly bad in the near future. But of course, all other things are never equal, and there are (at least) three big caveats you need to be aware of before you belly up to the portfolio management bar and order a big cool glass of convexity. Caveat 1: A convex portfolio based on optionality must be an actively managed portfolio, not a buy-and-hold portfolio. There’s no such thing as a permanent option … they all have a time limit, and the longer the time limit the more expensive the option. The clock works in your favor with a buy-and-hold portfolio (or it should), but the clock always works against you with a convex portfolio constructed by purchasing options. That means it needs to be actively traded, both in rolling forward the option if you get the timing wrong, as well as in exercising the option if you get the timing right. Doing this effectively over a long period of time is exactly as impossible difficult and expensive as it sounds. Caveat 2: A convex portfolio fights the Fed, at least on the left-hand part of the curve where you’re making money (or losing less money) as the market gets scorched. Yes, there are going to be more and more political shocks hitting markets over the next few years, and yes, those shocks are going to be exacerbated by the hollow market and its structurally non-robust liquidity provision. But in reaction to each of these market-wrenching policy and liquidity shocks, you can bet your bottom dollar that every central bank in the world will stop at nothing to support asset price levels and reduce market volatility. Make no mistake – if you’re long down-side protection optionality in your portfolio, you’re also long volatility. That puts you on the other side of the trade from the Fed and the ECB and the PBOC and every other central bank, and that’s not a particularly comfortable place to be. Certainly it’s not a comfortable (or profitable) place to be without a keen sense of timing, which is why, again, a convex portfolio expressed through options and derivatives needs to be actively managed and can’t be a passive buy-and-hold strategy. Caveat 3: Top-down portfolio risk adjustments like convexity injection through index options or risk premia derivatives are *always* going to disappoint bottom-up stock-picking investors. I’ve written a lot about this phenomenon, from one of the first Epsilon Theory notes, “ The Tao of Portfolio Management ”, to the more recent “ Season of the Glitch ”, so I won’t repeat all that here. The basic idea is that it’s a classic logical fallacy to infer characteristics of the whole (in this case the portfolio) from characteristics of the component pieces (in this case the individual securities selected via a bottom-up process), and vice versa. What that means in more or less plain English is that risk-managing individual positions in an effort to achieve a risk-managed overall portfolio is inherently an exercise in frustration and almost always ends in unanticipated underperformance for stock pickers. Okay, Ben, those are three big problems with implementing convexity in a portfolio. I thought you said this was a good thing. You’ll notice that each of these three caveats pertain most directly to the largest population of investors in the world – non-institutional investors who create an equity-heavy buy-and-hold portfolio by applying a bottom-up, fundamental, stock-picking perspective. The caveats don’t apply nearly so much to institutional allocators who apply a systematic, top-down perspective to a portfolio that’s typically too large to engage in anything so time-consuming as direct stock-picking. They have no problem employing a staff to manage these portfolio overlays (or hiring external managers who do), and they’re not terrified by the mere notion of negative carry, derivatives, and leverage. These institutional allocators may not be large in numbers, but they are enormous in terms of AUM. I spend a lot of time meeting with these allocators, and I can tell you this – implementing convexity into a portfolio in one way or another is the single most common topic of conversation I’ve had over the past year. Every single one of these allocators is thinking in terms of portfolio convexity, even if most are still in the exploration phase, and you’re going to be hearing more and more about this concept in the coming months. So that’s all well and good for the CIO of a forward thinking multi-billion dollar pension fund, but what if it’s a non-starter to have a conversation about the pros and cons of a long gamma portfolio overlay with your client or your investment committee? What if you’re a stock picker at heart and you’d have to change your investment stripes (something no one should ever do!) and reconceive your entire portfolio to adopt a top-down convexity approach using derivatives and risk premia and the like? This is where the barbell comes in. The basic concepts of Adaptive Investing can be described as placing modest portfolio “weights” or exposures on either side of an investment dimension. This is in sharp contrast to what Johnny Ola has convinced most of us to do, which is to place lots and lots of portfolio weight right in the middle of the bar, with normally distributed tails on either end of the massive weight in the center (i.e., a whopping 5% allocation to “alternatives”). What are these investment dimensions? They are the Big Questions of investing in a world of massive debt maintenace (and are actually very similar to the Big Questions of the 1930s), questions like … will central banks succeed in preventing a global deflationary equilibrium? … is there still a viable growth story in China and in Emerging Markets more broadly, or was it all just a mirage built on post-war US monetary policy? … is there a self-sustaining economic recovery in the US? Here’s an example of what I’m talking about, a barbell portfolio around the Biggest of the Big Questions in the Golden Age of the Central Banker: will extraordinarily accommodative monetary policy everywhere in the world spur inflationary expectations and growth-supporting economic behaviors? Like all barbell dimensions, there’s really no middle ground on this. In 2016, either the market will be surprised by resurgent global growth / inflation, or the market will be surprised by anemic growth / deflation despite extraordinary monetary policy accommodation. I want to “be there” in my portfolio with modest exposures positioned to succeed in each potential outcome, as opposed to having a big exposure somewhere in the middle that I have to drag in one direction or another when I end up being “surprised” just like the rest of the market. Specifically, what might those positions look like? Everyone will have a different answer, but here’s mine: • If deflation and low global growth carry the day, then I want to be in yield-oriented securities where the cash flows are tied to real economic activity in geographies with real growth prospects, and where company management is really distributing those cash flows to shareholders directly. • If inflation and resurgent growth carry the day, then I want to be in growth-oriented securities linked to commodities. • And yes, there are companies that can thrive in both environments. Now of course you’ll get push-back to the notion of a barbell portfolio from your client or investment committee (maybe the investment committee inside your own head), most likely in the form of some variation on these three natural questions: Q: Wouldn’t you be be better off predicting the winning side of any of these Big Questions and putting all your weight there? A: Yes, if I had a valid econometric model that could predict whether central banks will fail or succeed at spurring inflationary expectations in the hearts and minds of global investors, then I would definitely put all my portfolio weight on that answer. But I don’t have that model, and neither do you, and neither does the Fed or anyone else. So let’s not pretend that we do. Q: But if one side of your portfolio barbell ends up being right, that must mean that the other side is wrong. Wouldn’t we be just as well off putting all the weight somewhere in the middle like we usually do? A: No, that’s not how these politically-polarized investment dimensions play out, with one side clearly winning and one side clearly losing. The underlying dynamics of the Big Questions in investing today are governed by the multi-year spiraling back-and-forth of multiple equilibria games like Chicken, not The Central Tendency (read “ Inherent Vice ” for some examples). Not only is it far more capital efficient to use a barbell approach, but both sides will do relatively better than the middle. That is, in fact, the entire point of using an allocation approach that creates optionality and effective convexity in a portfolio without forcing the top-down imposition of option and derivative overlays. Q: But how do we know that you’ve identified the right positions to take on either side of these Big Questions? A: Well, that’s what you hire me for: to identify the right investments to execute our portfolio strategy effectively. But if we’re not comfortable with selecting specific assets and companies, then we might consider a trend-following strategy. Trend-following is profoundly agnostic. Unlike almost any other strategy you can imagine, trend-following doesn’t embody an opinion on whether something is cheap or expensive, overlooked or underappreciated, poised to grow or doomed to failure. All it knows is whether something is working or not, and it is as happy to be short something as it is to be long something, maybe that same thing under different circumstances. As such, a pure trend-following strategy will automatically move on its own accord from weighting one end of a barbell to the other, spending as little time as possible in the middle, depending on which side is working better. That is an incredibly powerful tool for this investment perspective. A barbell portfolio captures the essence or underlying meaning of portfolio convexity without requiring top-down portfolio overlays that are either impractical or impossible for many investors. The investments described here have a positive carry, meaning that the clock works in your favor, meaning that – unlike convex strategies that are actively trading options and volatility – these strategies fit well in a buy-and-hold, non-Fed fighting, stock-picking portfolio. I think it’s a novel way of rethinking the powerful notions of convexity and uncertainty so that they fit the real world of most investors, and whether these ideas are implemented or not I’m certain that it’s a healthy exercise for all of us to question the conceptual dominance of The Central Tendency. You know, Michael Corleone has a great line after he wised up to Fredo’s betrayal and the true designs of Johnny Ola and Hyman Roth: “I don’t feel I have to wipe everybody out … just my enemies.” It’s the same with our portfolios. We don’t have to completely reinvent our investment process to incorporate the valuable notion of convexity into our portfolios. We don’t have to sell out of everything and start fresh in order to adopt an Adaptive Investing perspective. Our investment enemies live inside our own heads. They are the ideas and concepts that we have allowed to hold too great a sway over our internal Fredo, and they can be put in their proper place with a fresh perspective and a questioning mind. Econometric modeling and The Central Tendency don’t need to be eliminated; they need to be demoted from a position of unwarranted trust to a position of respectful but arms-length business relationship. After all, let’s remember the secret of Hyman Roth’s success: he always made money for his partners. I’m happy to be partners with modeling because I think it’s a concept that can make me a lot of money. But I’m never going to trust my portfolio to it.