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Problems With ‘The Long Term’

John Maynard Keynes once famously said “In the long run we are all dead.” But the full quote doesn’t often get hashed out. Keynes said: But this long run is a misleading guide to current affairs. In the long run we are all dead. Economists set themselves too easy, too useless a task, if in tempestuous seasons they can only tell us, that when the storm is long past, the ocean is flat again.” Keynes was pointing out that many economists use a multi-temporal approach to economics to prove very generalized points. Milton Friedman was notorious for this. He was a master of holding multiple positions at the same time thereby allowing him to change the time frame however he pleased so that his argument always worked. For instance, he might say “Oh, velocity of money isn’t having an impact in the short term, but my extensive historical data shows that in the long term, an increase in the money supply will cause velocity of money and inflation to increase” (not a real quote, just to be clear). Many people have heard some version of this in the past 5 years as we waited for the big inflation from QE to come. The use of “the long term” in economics is often another version of “My model has been wrong so far, but if we wait long enough it will be proven right!” This sort of flip flopping could allow someone to hold two opposing positions at the same time and get away with it. It’s a classic trick in economics and when someone flips between time frames it should immediately raise a red flag for you. But economics isn’t where this concept is most abused. Modern finance has been driven largely by similarly unrealistic models of the world (largely derived from the same general models that Friedman and the Chicago School of Econ devised back in the 50s, 60s and 70s). For instance, take the concept of the Efficient Frontier which generally looks something like this: Basically, stocks will outperform cash and bonds over the long term. But there’s that term again – the “long term.” As Cliff Asness recently showed the “short term” doesn’t add much clarity here because asset prices perform with a high degree of randomness over a 5 year period. But how well does the concept of the “long term” really apply to someone’s life? I want to revisit the Intertemporal Conundrum because I think it’s a crucial concept in portfolio construction that often goes overlooked when applying overly simplistic models like the ones espoused by proponents of Modern Portfolio Theory. Most people begin investing in their 20s, but don’t accumulate a significant chunk of assets until their 30s or 40s. So this gives most of us a time frame of about 25-35 years before retirement. Of course, our financial lives aren’t one clean linear ride from our 20s to retirement. There’s the wedding, the kids, the college tuitions, the new house, the cars, etc. Our financial lives don’t actually reflect a “long term” at all. They’re more like a series of short terms inside of a long term. But it gets more problematic as you apply this. Modern Portfolio Theory will tell you that as you near retirement, you should ratchet back your equity holdings to reduce the volatility in your portfolio because you no longer have a “long term” time horizon. But this is problematic because it means that the 30 year old investor with a 70/30 stock/bond allocation doesn’t really have 70% of their portfolio invested for “the long term.” When they ratchet it back to a 60/40 at age 40 10% of their 70% equity holding will have only been invested for a 10 year time horizon. And when they ratchet back to a 50/50 at age 50 almost a third of their equity portfolio will have been invested for a 20 year period. This all becomes even more problematic because our earnings tend to increase as we get older which means we are contributing more dollars per portfolio size as we age. And when you combine this with the necessary near-term spending needs that often arise as a result of life’s short-term events then our “long term” portfolios suddenly don’t mesh with the MPT story all that well. That is, our “long term” actually proves to be a series of “short terms.” Applying a “long term” to the Efficient Frontier makes it work in theory, but in reality it proves to be far less useful. I advocate treating people’s portfolios like a Savings Portfolios for a very specific reason ( see here for more detail ). I think it’s crucial to treat these portfolios as a place where we create stability and certainty in our necessarily short-term financial lives. The ideas espoused by MPT are not only unrealistic textbook models, but they often lead people to believe that they can afford to take more risk than they should simply because they believe stocks always outperform bonds in the long term. And when you combine all of this with our inherent behavioral biases, you get a situation that is ripe for mistakes. That is, our textbook models trick us into thinking we can handle a lot of risk, but when reality strikes we often realize that the textbook model led us astray. And by then it’s too late. And sadly, the vast majority of Wall Street firms rely on models that are some derivative of this sort of thinking. This doesn’t mean that the concept of the “long term” is useless and it certainly doesn’t mean that short-term market timing is necessarily good. But we have to be very careful about how we go about applying this to our actual financial lives. While the “long term” often sounds great in theory, it often turns out to be a disaster in reality.

Your Alpha Is My Beta

The term ‘alpha’ has been so abused and misused as to be almost meaningless, but when well specified, it serves an important purpose. Attribution models, which explain the sources of risk in a strategy, should not be confused with measures of ‘value added’. Alpha, as a measure of ‘value added’, is not only specific to the portfolio it might complement, but also to the investor who owns the portfolio. A couple of weeks ago, I had the pleasure of a short correspondence with Lars Kestner, a well-known quant and derivatives trader, and creator of the thoughtful K-ratio as a measure of risk-adjusted performance. We connected on the definition of alpha, and how the term has been so abused in media and marketing as to become almost meaningless. To help make his point, Lars quoted a passage from his recent whitepaper, ” My Top 8 Pet Peeves “, which I’ve taken the liberty of copying below: Incorrect casual use of the term alpha This complaint may stem from the statistician in me, but the casual use of the term alpha irritates me quite a bit. Returning to very basic regression techniques, the term alpha has a very specific meaning. rp = α + β1 r1 + β2 r2 + β3 r3 + … + ε Alpha is just one of the estimated statistics of a return attribution model. The validity of the regression outputs, whether parameter estimates such as alpha or various betas, or risk estimates such as standard errors, depend on the model used to specify the return stream. Independent variables should be chosen such that the resulting error residuals cannot be meaningfully explained further by adding independent variables to the regression. In the most prevalent return attribution model, the typical one factor CAPM model, returns are explained by one independent variable – broad market returns. Defining an appropriate return attribution model is necessary to estimate a manager’s alpha. I find it ironic that the use of the term alpha is most frequently applied to a subset of asset managers called hedge funds where defining the return attribution model is often the hardest. Long-short equity managers can display non-constant beta as their net exposures change. Fixed income arbitrage managers typically display very non-normal return distribution patterns. Managed futures traders can capture negative coskewness versus equity markets that provide additional benefits beyond their standard return and risk profile. Calculating these managers’ alpha is a difficult task if for no other reason that specifying the “correct” return attribution model is problematic. Consider the specific example of a hedge fund manager whose net exposure is not constant. In this case, a one factor market model is not necessarily optimal and other factors such as the square of market returns might need to be added to account for time varying beta. If a manager makes significant use of options, the task of specifying a proper model becomes even harder. Also, consider a manager whose product specialty is volatility arbitrage and an appropriate market benchmark may not be available. How then to estimate alpha? I prefer using the term “value-add” to be a generic catch-all for strategies that increment a portfolio’s value. Whether that incremental value is generated though true alpha, time varying beta, short beta strategies with low return drag, cheap optionality, negative coskewness to equity markets, or something else that is not able to be estimated directly from a return attribution model, it saves me from having to misuse the term alpha. Lars raises great questions about the relevance of alpha derived from a linear attribution model with Gaussian assumptions when a strategy may exhibit non-linear and/or non-Gaussian risk or payoff profiles. Unfortunately, this describes many classes of hedge funds. While this is true, his comments took me in a different direction altogether. It’s interesting to contextualize alpha not just in terms of the factors that an experienced expert might consider, but rather in terms of what a specific target investor for a product might have knowledge of, and be able to access elsewhere at less cost. In this way, a less experienced investor might perceive a product which harnesses certain non-traditional beta exposures to have delivered ‘alpha’, or more broadly ‘value added’, where an experienced institutional quant with access to inexpensive non-traditional betas would assert that the product delivers little or no alpha whatsoever. Let’s start with the simplest example: imagine a typical retail investor who invests through his bank branch. A non-specialist at the bank branch recommends a single-manager balanced domestic mutual fund, where the manager is active with the equity sleeve, exerting a value bias on the portfolio. The bond sleeve tracks the domestic bond aggregate. The fund charges a 1.5% fee. Subsequently, the investor meets a more sophisticated Advisor and they briefly discuss his portfolio. The Advisor consults his firm’s software and determines the fund’s returns are completely explained by the bond aggregate index returns, domestic equity returns, and the Fama French (FF) value factor. In fact, after accounting for these factors, the mutual fund delivers -2% annualized alpha. The Advisor suggests that the client move his money into his care, where he will preserve his exact asset allocation vis-a-vis stocks and bonds, but invest the bond component via a broad domestic bond ETF, and use a low-cost value-biased equity ETF for the equity sleeve. The Expense Ratio (ER) of the ETF portfolio is 0.1% per year, and the Advisor proposes to charge the client 0.9% per year on top, for a total of 1% per year in expenses. The Advisor, by identifying the underlying exposures of the client’s first fund and engineering a solution to replicate those factors with lower cost, has generated 1% per year in alpha (1.5% mutual fund fee – 1% all-in Advisor fee + 0.5% by eliminating the negative mutual fund alpha). At the client’s next annual review, the Advisor recommends that the client diversify half of his equities into international stocks, at a fee of 0.14%. An unbiased estimate of non-domestic equity returns would be similar to domestic returns, minus the 0.6*0.5*(0.14-0.1) = 0.012% increase in total portfolio fees. However, currency and geographic diversification are expected to lower portfolio volatility by 0.5% per year, so the result is similar returns with lower risk = higher risk-adjusted returns = higher value added = higher alpha. After another year or so, the new Advisor discusses adding a second risk factor to the equity sleeve to complement the existing value tilt: a domestic momentum ETF with a fee of 0.15%. Based on the relatively low correlation between value and momentum tilts (keeping in mind they are all long domestic equity portfolios), the Advisor believes the new portfolio will deliver the same returns over the long run, but diversification between value and momentum tilts will slightly reduce the portfolio volatility by another 0.2%. Same returns with less risk = higher alpha. At each stage, the incremental increase in returns and reduction in portfolio ‘beta’ (vis-a-vis the original fund) results in a higher ‘alpha’ for the client. Now obviously the actions that the Advisor took are not traditional sources of alpha – that is, they are not the result of traditional active bets – but they nevertheless add meaningful value to the client. Now let’s extend the logic into a more traditional institutional discussion. The institution is generally applying attribution analysis for one or both of the following purposes. The two applications are obviously linked in process, but have substantially different objectives. To discover how well systematic risk factors explain portfolio returns over a sample period. For example, we might determine that a long-short equity manager derives some returns from idiosyncratic equity selection, some from the Fama French value factor, and some returns from time-varying beta. If we hired the manager for exposure to these factors, this would confirm our judgement. Otherwise it might prompt some questions for the manager about ‘style drift’ or some other such nonsense. To determine if a manager has delivered “value added”, or alpha. For example, perhaps the manager delivered excess returns, but we discover that the excess returns can be explained away by adding traditional Fama French equity factors to the regression. Since it is a simple and inexpensive matter to replicate these risk factor exposures through ‘passive’ allocations to these factors (using ETFs or DFA funds for example), it might be reasonable to discount this source of ‘value added’ for most investors, and trim the alpha estimate accordingly. This should be pretty straightforward so far. Using a long-short equity mandate as our sandbox, we discussed how a manager’s returns might result from exposure to the FF factors, time-varying exposure to the market, and an idiosyncratic component called alpha. But now let’s get our hands dirty with some nuance. Let’s assume the long-short manager has been laying on a derivative strategy with non-linear positive payoffs. Imagine as well that a wily quant suspects he knows the method that the manager is using, can replicate the return series from the derivative strategy, and includes this factor in his attribution model. Once this factor is added, the manager’s alpha is stripped away. While the quant may feel that there is no ‘value add’ in the derivative strategy because he can replicate it for cost, surely an average investor would have no way to gain exposure to such an exotic beta. As such, the average investor might perceive the strategy as ‘value added’, or ‘alpha’ while the quant would not. Ok, let’s back out the derivative strategy, and assume our long-short manager exhibits positive and significant alpha after standard FF regression. In other words, the manager’s excess returns are not exclusively due to systematic (positive) exposure to market beta or standard equity factors, such as value, size, or momentum. Of course, since it is a ‘long-short’ strategy, the manager can theoretically add value by varying the portfolio’s aggregate exposure to the market itself. When he is net long, the strategy should exhibit positive beta risk, and when he is net short, it should manifest negative beta risk. How might we determine if this time-varying beta risk explains portfolio returns? Engel (1989) demonstrated how regressing portfolio returns on squared CAPM returns will tease out time-varying beta effects. So let’s assume that adding a squared CAPM beta return series to the attribution model explains away a majority of this ‘alpha’ source. Therefore, including this factor in the model increases the explanatory power (R2) of the model, and reduces the alpha estimate. But is this fair or relevant in the context of ‘value added’? After all, while we can say that the manager is adding value by varying CAPM beta exposure, we have not demonstrated how an investor might generate these excess returns in practice. I have yet to see a product that delivers the squared absolute returns of CAPM beta, but please let me know if I’ve missed something. I submit that it’s useful to identify the time-varying beta decisions for attribution purposes. This source of returns may represent true “value add” or (dare I say alpha), because it cannot (presumably) be inexpensively and passively replicated by the investor. To the extent an investor is experienced enough, and/or sophisticated enough to identify factors which can inexpensively replicate the time-varying beta decisions (such as via bottom-up security selection, or top-down timing models), then, and only then, might the investor discount this source of ‘value added’. So far we’ve discussed hypothetical examples, but a recent lively debate on APViewpoint is a great real-life case study. Larry Swedroe at Buckingham has long militated against traditional active management in favour of DFA style low-cost factor investing. It took many by surprise, then, when Larry wrote a compelling argument for including a small allocation to AQR’s new risk premia fund (MUTF: QSPIX ) in traditional portfolios. After all, at first glance this fund is a major departure from Larry’s usual philosophy, with high fees, and leveraged long and short exposures to a wide variety of more exotic instruments. Thus ensued 100 short dissertations from a host of respected and thoughtful Advisors and managers on APViewpoint’s forum about why the fund’s leverage introduces LTCM style risk; why the factor premia the fund purports to harvest cannot exist in the presence of efficient markets, and; why the fund’s high fees present an insurmountable performance drag. Notwithstanding these potentially legitimate issues, I’m uniquely interested in how one might view this fund in terms of alpha and beta. The fund’s strategy involves making pure risk-neutral bets on well-documented factors, such as value, momentum, carry, and low beta, across a variety of liquid asset classes. In fact, AQR published a paper describing the strategy in great detail. Presumably even a low-level analyst with access to long-term return histories from the factors the fund has exposure to could explain away all of the fund’s returns. From this perspective then, the fund would deliver zero alpha. However, it is far easier to gather the return streams from these more ‘exotic’ factors than it is to operationalize a product to effectively harvest them. So for most investors, this product represents a strong potential source of ‘value add’. The goal of this missive was to demonstrate that, when it comes to alpha, where you stand depends profoundly on where you sit. Different investors with varying levels of knowledge, experience, access, and operational expertise will interpret different products and strategies as delivering different magnitudes of value added. At each point, an investor may be theoretically ‘better off’ from adding even simple strategies to the mix, perhaps at lower fees, and even after a guiding Advisor extracts a reasonable fee on top. More experienced investors may be able to harness a broader array of risk premia directly, and thus be willing to pay for a smaller set of more exotic risk premia. It turns out that ‘alpha’ is a remarkably personal statistic after all. Disclosure: The author has no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it. The author has no business relationship with any company whose stock is mentioned in this article.

The Warren Buffett Way: High Quality Stocks In Emerging Markets

By Baijnath Ramraika, CFA This article originally appeared on advisor perspectives . “Shares are not mere pieces of paper. They represent part ownership of a business. So, when contemplating an investment, think like a prospective owner.” – Warren E. Buffett ” It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price.” – Warren E. Buffett “The risk of paying too high a price for good quality stocks – while a real one – is not the chief hazard confronting the average buyer of securities. Observation over many years has taught us that the chief losses to investors come from the purchase of low quality securities at times of favorable business conditions.” – Benjamin Graham In the movie The Silence of the Lambs , Dr. Hannibal Lecter helps Agent Clarice find a window to the mind of the killer. He says: First principles, Clarice. Simplicity. Read Marcus Aurelius . Of each particular thing, ask, what is it in itself? What is its nature? When applied to investing in stocks, the pertinent question becomes what is the nature of equity shares. Are they mere entries in one’s investment account or is there more to them? Warren Buffett answered these questions for us when he said that shares represent part ownership of a business . Consequently, he suggested that when investing in equity shares, one should think like a prospective owner. This advice gives rise to further questions. What does it mean to be a part owner of a business ? How does it differ from owning pieces of paper with the intention of flipping them to someone else at a higher price? In the discussion that follows, we will offer our view on those questions and show how investing in equities provides an opportunity to generate superior returns as a part owner of a business. It’s all about the mindset: Part owner or speculator There are key differences between the mindset of a part owner and that of a speculator. While a part owner is chiefly concerned with the future cash-flow generation ability of his business, a speculator is primarily concerned with his ability to sell his holdings to someone else at a higher price, i.e., his ability to find a greater fool. The table below summarizes some of the primary concerns of both kinds of market participants. Exhibit 1: Part-owner vs. Speculator (click to enlarge) Being a part-owner of a business doesn’t mean that you will never sell. However, the decision rules of a part owner are (as they should be) miles apart from those of a speculator. A part owner is much less concerned with day-to-day changes in share prices and much more interested in changes in underlying business value over the long-term. Warren Buffett put it best when he wrote : Buy into a company because you want to own it, not because you want the stock to go up. … People have been successful investors because they’ve stuck with successful companies. Sooner or later the market mirrors the business. In our research paper Long-Term Sources of Investment Returns and a Simple Way to Enhance Equity Returns , we contended that over the long term, investment returns from equities are earned primarily as a result of growth in the underlying business value. This is so because sooner or later, the market mirrors the business. As evidence, we showed that the long-term returns of equity markets have approximated the growth in book value of all businesses. This in turn leads to our assertion that a simple way to generate superior investment return is to invest in a portfolio of high-quality businesses. What is a high-quality (HQ) business? The true nature of an HQ business is rather simple – it has sustainable competitive advantages. Warren Buffett wrote that: The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage. The products or services that have wide, sustainable moats around them are the ones that deliver rewards to investors. A business that enjoys sustainable competitive advantages is able to keep competition at bay. As a result, over any extended time period encompassing a full business cycle, such businesses are able to grow their economic earnings. Mirroring this, the business value grows as well. The ability to keep competition at bay manifests itself in measures of economic earnings, specifically in higher returns on capital and superior cash generation especially when adjusted for business-value growth. Superior economic profitability of an HQ business is a result of the existence of competitive advantage and not the other way around. While all businesses with sustainable competitive advantages have the capability to generate superior economic returns on capital, not all businesses that generate superior returns of capital possess sustainable competitive advantages. Quality as an investment factor Quality-driven investing – i.e., investing in shares of HQ companies as an investment strategy – has been around for quite some time. However, it has gained renewed momentum over the past decade driven by spectacular crashes experienced subsequent to the technology boom and during the global financial crisis. Academic research has also revealed the benefits of quality-driven investing. Over the last few years, several academics as well as practitioners have weighed in on the quality factor and many papers have been written to identify object criteria of companies that have it. Further, quality is now being designated by many researchers as a fifth factor explaining investment returns along with beta, size, momentum and value. This development is in sync with our long-held belief that quality is a distinct investment style. This article builds on a number of studies that have explored returns to factors such as profitability, the relationship between accounting, and economic profits and leverage. Chan, et al. (2006) showed that the difference between accounting earnings and cash flows are negatively associated with future returns. George and Hwang (2010) showed that stocks with low leverage have high alpha. Robert Novy-Marx (2013) showed that stocks with high gross profitability as measured by gross profits to assets outperform. We, Baijnath and Prashant (2014), showed that stocks with high returns on equity, superior free-cash-flow and low leverage tend to outperform. In the next section, we will discuss the investment returns from a simple quantitative process of selecting high-quality businesses. In valuing such businesses, market participants systematically underestimate the duration of competitive advantage. Because of this, the valuation premium assigned does not sufficiently account for the difference between the business-value-creation potential of an HQ business as compared to the average business. Indeed, a basket of HQ stocks generates significantly superior investment returns compared to publicly traded benchmarks, and it does so with significantly lower risk. Investment return to high quality stocks in emerging markets This article discusses the application of quality-driven investing in emerging markets. In the discussion that follows, we lay out the framework for selecting HQ businesses and the risk-return of a basket of HQ stocks in emerging markets. 1. Qualitative Factors At Multi-Act, we have spent more than 15 years developing and refining our process for identifying HQ businesses. Our internal research process assigns every company we follow a quality rating, referred to as its “grade.” There are several components of our process, some of which lend themselves to quantitative modeling while others don’t. As discussed above, the defining trait of HQ business is the existence of “sustainable” competitive advantage, a component that does not lend itself to quantitative modeling. The existence and sustainability of competitive advantage are the most important criteria in our classification of a business as HQ. This is driven by our assertion that much of the investment returns that accrue to investors from the quality factor depend on the ability of the business to persist with supernormal returns on capital. This in turn depends on its ability to keep competition at bay. Given our inability to model this component, we believe that our manually selected list of quality businesses will likely generate superior risk-adjusted performance as compared to the quantitatively selected basket that is discussed here. 2. Quantitative factors There are some key characteristics of an HQ business. An HQ business generates superior returns on capital – the stronger the competitive advantage, the lesser the impact of competition and the higher the returns on capital. Returns on capital of such businesses tend to be persistently high. Further, such businesses have a very healthy relationship between their accounting profits and their economic profits. Our HQ businesses possess good balance sheets, so financial risk isn’t a significant factor driving our investment returns or desire to mitigate risk. Exhibit 2: Determination of Quality Companies (click to enlarge) As shown in Exhibit 2 , our process includes three characteristics that lend them to quantitative analysis. Our research process utilizes many measures within each characteristic. For the sake of simplicity, we have chosen one measure to represent each quality characteristic. For the purposes of this article, we measured returns on capital by return on equity (RoE). A high RoE indicates the existence of competitive advantage and the persistence of this variable suggests its sustainability. It is possible for the management of a company to manage its RoE. To the extent that earnings are manipulated, they will impact RoE. Further, RoE is affected by corporate transactions including buybacks, acquisitions and restructurings. To ensure that the earnings component of the RoE is not a result of financial creativity, we use free cash flow over earnings (FCF/EPS) to calibrate the quality of earnings. We have found that this measure filters out companies with suspect accounting numbers. Finally, we measure financial safety by net debt over free cash flow (ND/FCF). This measure indicates the number of years of free cash flow needed to repay the debt. Table 1 provides summary statistics on each of the quality factors by country. Table 1. Comparison of Quality Measures This table shows average quality measures by country for the investment universe as well as for the quality basket. This table reports the average of each country’s equal-weighted average of quality measures between 2005 and 2014. We also report the difference between quality basket and all companies in the selected universe, by country. (click to enlarge) Data and quality factors Our data sample consists of 1,175 companies covering 44 [1] countries between 2005 and 2014. The 44 markets correspond to countries that are not a part of the MSCI World Developed Index as of December 31, 2014. One of the factors affecting the countries included in the study was the market capitalization of the largest company of a country. Those countries which, in any of the last 10 years, did not have a company large enough to be included in the largest 500 companies in the emerging markets space based on market capitalization, are not represented in this study. All data including fundamentals and prices are from Factset Global with returns calculated in USD with currency risk hedged away. We utilized fundamental data reported anywhere in calendar year t-1 in April of calendar year t, such that there is a minimum three-month lag from the end of the fiscal year of the company. Table 2 provides summary statistics on number of companies and market capitalization by country. Table 2. Number of Companies and Market Capitalization This table shows yearly average of number of companies and yearly average of average market capitalization for the investment universe as well as for the quality basket by country. We also report the difference between average market capitalizations of the quality basket and all companies in the universe by each country. (click to enlarge) Given the issues with data availability as well quality of historical data for much of the emerging space, our sample could start in 2005 at the earliest. 3. Methodology Before proceeding with our calculations, we performed exclusions for size and for suitability and data applicability. Every year, we start with the largest 500 companies from all emerging markets after excluding some industries as discussed below. Limiting our universe of companies to 500 companies minimizes size factor’s contribution to our investment returns and ensures that the stocks that qualified through our process were, in fact, tradable. Without this filter, there would indeed be a much larger number of companies over which we would apply the quality factor. Consequently, it is possible that there are differences in the list of HQ businesses between the limited universe of 500 companies and a wider universe. Further, we excluded some industries that do not lend themselves to existence of sustainable competitive advantages [2] in our assessment. This is not to say that there cannot be a business with sustainable competitive advantage in these industries. However, the probability of finding a business with sustainable competitive advantage in these industries is significantly lower. Further, calculating cash-flow data presents a practical problem with some of these industries, especially in the case of banking and insurance where cash flow is affected by changes to loans, investments, and deposits and thus loses its sanctity. Accordingly, we have excluded these industries from our samples. We calculated the quality factors discussed earlier for all the remaining stocks in our data sample. We then applied absolute cutoffs that a business must meet in order to qualify as HQ business. Businesses that met these cutoffs were sorted by their market capitalization in a descending order. Finally, we selected 50 of the largest businesses from all qualifying businesses as our quality basket. This process resulted in a basket of 50 HQ stocks every year. We tested the performance of the baskets over a period of 10 years, from 2005 to 2014. 4. R isk and returns of the quality basket We now turn to risk and returns of quality businesses. Figure 1 shows the performance of the quality basket on a gross basis [3] as compared to that of MSCI EFM (emerging + frontier markets) Standard index. Because we selected our basket from a universe of all countries excluding developed markets, we consider this index to be the appropriate benchmark. The quality basket generated a compounded annual return of 15.0% as compared to 8.4% for MSCI EFM Standard index. What is more, annualized standard deviations of monthly returns were lower for the quality basket at 14.2% as compared to 23.4% for the benchmark index. Figure 1 (click to enlarge) Figure 2 shows drawdown [4] profiles of the quality basket and of the benchmark index. Clearly, the quality basket is significantly less risky when compared to MSCI EFM Standard index, as drawdowns aren’t only shallower; recovery to peak is quicker as well. In fact, the MSCI EFM Standard index has been underwater since 2007. We estimate the quality basket’s relative risk to be 50% [5] of that of the benchmark index. (click to enlarge) Summary The defining trait of HQ business is the existence of sustainable competitive advantage. Multi-Act’s definition of quality includes quantitative as well as qualitative variables and sustainability of competitive advantage is a key factor. A simple three-factor quantitative process for selecting emerging-market HQ stocks outperforms the publicly traded benchmarks and does so with lower risk. [1] The following industries were excluded for the purposes of this paper: Aluminum, Steel, Pharmaceuticals: Generic, Pharmaceuticals: Major, Pharmaceuticals: Other, Financial Conglomerates, Investment Banks/Brokers, Life/Health Insurance, Major Banks, Multi-Line Insurance, Property/Casualty Insurance, Real Estate Investment Trusts, Regional Banks, Specialty Insurance, Biotechnology, Apparel/Footwear Retail, Major Telecommunications, Electronics/Appliance Stores, and Specialty Stores. [2] Excluding dividends. [3] We include all countries that are not a part of the MSCI World Developed Index. Countries included in MSCI World Developed Index are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Hong Kong, Ireland, Israel, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, United Kingdom, and United States. [4] The peak-to-trough decline during a specific record period of an investment, fund or commodity. A drawdown is usually quoted as the percentage between the peak and the trough. (Source: Investopedia) [5] The worst drawdown of the quality basket is 38% while that of the benchmark index is 61%. The relative risk is estimated as log(1-38%)/log(1-61%) = 50%. At 50% of the benchmark’s risk, relative risk of the quality basket is half that of the benchmark index. This means that it takes about two back-to-back losses of 38% to produce one 61% loss. For more on this, refer to here . References Ramraika, Baijnath, Long-Term Sources of Investment Returns and a Simple Way to Enhance Equity Returns (October 29, 2014). Available at SSRN . Ramraika, Baijnath and Trivedi, Prashant, Investment Returns to Quality in Developed Markets (October 30, 2014). Available at SSRN . Chan, K., Chan, L.K.C., Jegadeesh, N., Lakonishok, J., (2006), “Earnings quality and stock returns,” Journal of Business George, Thomas J., and C.Y. Hwang (2010), “A Resolution of the Distress Risk and Leverage Puzzles in the Cross Section of Stock Returns,” Journal of Financial Economics Novy-Marx, Robert (2013), “The Other Side of Value: The Gross Profitability Premium,” Journal of Financial Economics