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Bigger Is Better? For Investment Managers, Maybe Not

It’s no secret that America has long operated under an obsession with size. Over the last couple of decades, the average house size has continually increased (even as lots are shrinking ), cars have become supersized , food portions have grown, retail stores continue to sprawl, and even our waistlines have gradually expanded. Everything, it seems, is increasing in mass or breadth , as our national focus on size-above-all becomes ever more pathological with each passing year. This “bigger is better” mentality bleeds over into our financial lives, as well. Even as we rail against the “Too Big To Fail” banks for destabilizing our economy and extracting rents from working-class Americans, we continue to bank with them en masse, lured by the convenience and security of working with a known brand name. As a result, the big banks continue to get larger still, to the point that they’re now bigger than ever (and, coincidentally or not, failing some government-led stress tests). Click to enlarge Unsurprisingly, this same mentality holds true when it comes to our investments, and the advisors we choose to work with. Most individuals simply default to working with the big wirehouse brokerages (Morgan Stanley, Merrill Lynch, Wells Fargo, etc.), even when they could be obtaining better service (and true fiduciary advice ) by working with a smaller, independent Registered Investment Adviser firm. And yet, there’s a growing body of evidence that smaller (and not bigger) might actually be better for many things, including our investment returns. In early 2013, a study released by Beachhead Capital found that among approximately 3,000 long/short hedge funds, the funds managed by firms with total assets under management (AUM) between $50 million and $500 million outperformed those run by larger firms over essentially every time period studied. And the amount of outperformance was significant — 2.54% per year over five years, and 2.20% per year over ten years, with the outperformance concentrated in the years immediately preceding and following the financial crisis of 2009. Risk measures were roughly the same for the different types of firms, so the outperformance can’t be explained by greater risk-taking. And while “dispersion” measures were greater among the smaller advisors — meaning that returns varied more for smaller firms than for larger ones — the overall difference in performance is too large to be ignored. These findings run counter to what much industry research might predict. Whether at hedge funds or at investment advisory firms, scale is generally expected to improve purchasing power, and to allow for access to a broader range of investment vehicles (like certain swaps and derivatives or other over-the-counter products that smaller firms simply can’t access via their existing custodial channels). If nothing else, size is supposed to improve the terms that managers are able to negotiate, whether via lower commissions or fees or via improved investor protections in potential bankruptcies or other corporate restructurings. And yet, intuition aside, these benefits of scale simply don’t seem to be flowing through to the bottom line, for the firms or their investors. Beachhead presented a number of potential explanations for the disparity, a few of which I’ll paraphrase here. Some investments don’t benefit from scale Contrary to conventional wisdom, bigger isn’t always better in the markets; sometimes, size can be a limiting factor, constricting the types of investments that a firm can realistically add to its portfolio. Take the case of the Harvard Management Company, the group tasked with managing Harvard University’s sizeable endowment . For years, HMC’s investment performance was top-notch, consistently beating its peers as its talented managers consistently generated high double-digit annual returns. But as HMC’s portfolio continued to grow, it found itself running out of viable places to put all of its money. In many markets, they had already become the single largest owner of available shares, and to increase the size of their stakes in those investments would impede their ability to exit (or trim) those positions in the future. In some markets, HMC had effectively become the market, simply by virtue of its size. Funds (or managers) in that position are left with two basic options: either begin to branch out into ever more esoteric investments and asset classes, or else pile into the so-called “hedge fund hotels” , those few investment vehicles that have the opportunity for outsized gains, but are also large and liquid enough to accept massive inflows of capital without enduring wild market-moving price shifts. Neither option is particularly attractive, from the investment manager’s point of view. Choosing the “esoteric investments” route often means accepting significantly less liquidity (and an attendant increase in volatility), which tends to limit flexibility while also exacerbating the impact of downturns on fund returns. Indeed, this is exactly what happened to HMC during the 2009 financial crisis, a dynamic that led to a reconsideration of overall investment strategy. But the “hedge fund hotel” route is similarly problematic: for one, how can a fund distinguish itself from its peers when all funds own the same investments? Wouldn’t larger firms then, by definition, simply trend toward standard “average” market performance over time? And, perhaps more concerningly, what happens when a majority of the large funds all run for the “hotel” exits at the same time? At best, the fund is, again, forced to endure greater portfolio volatility, and at worst, the managers are trampled like so many young men in Pamplona . The fact is some investment opportunities are small enough that only a small advisor can really avail itself of the benefits — the market for the investment could be so limited that the large manager’s entrance would simply overwhelm the market and thus eliminate any mispricing opportunity. Even if the large fund were successful in its trade, the gross size of the gain might be so small as to barely impact total fund returns. Think of the old parable of Bill Gates stooping down to pick up a 100 dollar bill (or a mythical 45,000 dollar bill ) — reaching down to pick up that $100 would have little to no impact on his net worth, and it might even be a complete waste of his time to bother with picking it up. For a panhandler, though (or a poor college student, or me or you), that $100 would make a meaningful impact on our bottom line. The same holds true in the markets: sometimes, the available opportunity in a specific investment is limited to a set dollar amount, an amount that will certainly help improve small manager returns, but that would have little to no measurable impact on the returns of the larger fund. Beachhead refers to this dynamic as the “broader opportunity set” dynamic, and it is very real. If it weren’t, then “hedge fund hotels” would never have existed in the first place. As it stands, the larger you get, the fewer markets (or opportunities) you can find that are large and liquid enough to accommodate your increased size. Hence, in some markets, smaller advisors are at an inherent advantage in terms of percentage performance. The “talent” gap It’s generally assumed that the most talented managers will be enticed to work at the largest firms, since those firms have the greatest resources and opportunities, enabling young and talented advisors to thrive and become rich. However, there’s a counter-narrative in play that makes at least as much sense. If you’re truly talented, and capable of generating outsized returns, why would you want to sell that skill off, enabling a large corporation to profit from your work? Wouldn’t you be better off launching your own firm, so as to profit off of your own work, rather than counting on your boss (or a board of directors) to determine your ultimate compensation? Indeed, there’s an argument to be made that smaller advisors represent a specific type of self-selection: only those advisors who are very confident in their ability to survive on their own will even bother to break away and start their own operation. Yes, they’ll be smaller by definition, but their talent and ability to generate returns for investors will be unaffected by a switch in the logo on their business card. As demonstrated above, the advisors might even be able to open up their investment opportunity set by doing so. Arguably, those who choose to work at the largest firms (and stay there for the long run) are simply those who crave the stability and comfort that those firms provide or promise. Particularly for the millennial generation, there seems to be a trend toward entrepreneurship and betting on oneself , and that trend impacts the investment advisory industry as well. If you’re an investor, do you want to hire the manager who needs (or who thinks he needs) a big brand name in order to succeed, or one who trusts in his ability to swim on his own, even without the resources and advantages that the larger firm provides? That remains an open question, but the evidence is beginning to mount in favor of the smaller firm. The importance of each individual client One dynamic that Beachhead does not mention, but that may be particularly important for those looking to choose an investment manager, is what I will call the “burning platform” issue. At a large firm, complacency can often be a very powerful force. For the big wirehouse brokerages, a sudden loss of 1 or 2 or clients (or even, say, 5-10% of clients) may not be meaningful enough to really impact the bottom line over the long run. Sure, a few layoffs and restructurings might result, but the viability of the business is rarely threatened. At smaller firms, though, the experience and importance of each individual client is amplified. A period of sustained underperformance that leads to client attrition could , in fact, threaten the long-term viability of the firm, as well as the paychecks of the managers in question. The closer a manager is to the end user — and the greater the importance of each individual client — the less room for complacency and apathy there will be. At smaller firms, there’s simply less room for ignorance of client needs — you either perform or you’re history, generally speaking. At the end of the day, while we all might derive some comfort from size, research shows that betting on smaller managers can often be a savvy move. Ultimately, brand names are little more than a signalling mechanism — “we’re safe, we’ve been vetted,” say the big brands. You can trust them, they’d argue, because their size indicates that many others have (presumably) done their research and chosen to work with them already. 50 million Elvis fans can’t be wrong , right? Thus, when we blindly choose to work with the big brand name, what we’re effectively doing is outsourcing our due diligence to others. Instead of choosing to learn about the firms or managers in question, we simply rely on the brand name to protect us, because it’s the seemingly “safe” play. Increasingly, that approach doesn’t hold water. As an investor, take it upon yourself to learn more about the actual services that are offered, the actual philosophies that guide different offerings, and really get to know the diversity of service offerings. All of the various industry players have different strengths and weaknesses, the relative merits of which may or may not be important to you; don’t assume that the big guy has exactly what you want and need just because they’re big. In reality, it’s rarely the case. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. Business relationship disclosure: The author is a contract employee and partial owner of myFinancialAnswers.com, and he is compensated to provide industry commentary for the site. The opinions provided here may also be published at myFinancialAnswers.com.

Quarterly Update: Portfolio Rebalancing – A Potentially Golden Opportunity

For a variety of reasons, gold is a widely held asset class within investment portfolios. Many investors include gold in their asset allocation mix for its perceived ability to act as both a diversifier and as a potential store of value in times of uncertainty; these perceptions contribute to the concept of gold as a “core holding” in many diversified portfolios. Indeed, with the notable exception of Warren Buffett, 1 some of the investment community’s most distinguished names currently maintain investments in gold 2 . Like any investment, gold is subject to rebalancing or reallocation when its value relative to other portfolio components shifts significantly. Examining quarterly data from the beginning of 1976 (the year that gold started trading freely in the United States) through the quarter ended March 31, 2016, suggests that gold is overvalued relative to historical price relationships with the major agricultural crops of corn, wheat, soybeans and sugar. 3 In fact, the gold/soybean ratio is nearly at its all-time high. At quarter end March 31, 2016, the gold/corn ratio, defined herein as the number of bushels of corn an investor could buy with the proceeds from selling one troy ounce of gold, was 351 bushels, versus a 39-year average value of 170 bushels. Gold investors attempting to maximize portfolio performance through disciplined quarterly or annual rebalancing, may want to consider adjusting their gold holdings in tandem with their existing or anticipated agricultural sector portfolio investment mix. For example, the historical data for the gold/corn ratio suggests that a mean reversion 4 from March 31, 2016 levels of 351 bushels to the 39-year mean value of approximately 170 bushels of corn for each ounce of gold (bu/oz), could benefit an investor rebalancing gold for corn within their portfolio. Click to enlarge As illustrated in the chart on page 1, at 351 bu/oz the gold/corn ratio is approximately 107% above its nearly four-decade average of 170 bu/oz. Hypothetically, if an investor sold gold and purchased corn at the current 351 bu/oz level, and the ratio subsequently retraced to its historical mean value of approximately 170 bu/oz, the investor would then be able to sell the corn and buy back 107% more gold than was originally sold, to make the temporary reallocation from gold into corn. The gold/corn ratio may have been within 6% of its all-time high at the end of Q1 2016, but both the gold/wheat and gold/soybean related ratios were also very near historic highs over the same time period. The gold/wheat ratio was within 3% of its all-time highest value, and the gold/soybean ratio was within 1%, or virtually at, its all-time high value. The gold/sugar ratio is 41% below its all-time high. Charts for the gold/wheat, gold/soybean, and gold/sugar ratios are shown below. Click to enlarge Click to enlarge The current availability of both futures contracts and futures-based exchange traded products for gold, corn, wheat, soybeans, and sugar make rebalancing the gold and agricultural components within a portfolio easier than ever before. Investors and advisors need to make an assessment of the relative value of gold versus their other portfolio constituents, including agriculture, and appropriately adjust their allocations to suit their individual investment needs and objectives. 1 “Why Warren Buffet t Hates Gold.” NASDAQ 15 Aug. 2013: Web. October 9th, 2014. 2 Based on the 13-F filings for holders of GLD, the SPDR Gold Trust, as of 3/31/16 and found using Bloomberg Professional, April 12th, 2016. 3 Analysis & corresponding charts were prepared by Teucrium Trading, LLC, using Bloomberg Professional, April 12th, 2016. All supporting detail available upon request. 4 Mean Reversion: A theory suggesting that prices and returns eventually move back towards the mean or average. This mean or average can be the historical average of the price or return or another relevant average such as the growth in the economy or the average return of an industry. Disclosure: I am/we are long I AM/WE ARE LONG CORN, WEAT, SOYB, CANE, TAGS. Business relationship disclosure: Sal Gilbertie is the Founder, President, and CIO of Teucrium Trading, LLC, the Sponsor of the Teucrium CORN Fund ETP (NYSE Ticker “CORN”) and other agricultural ETPs listed on the NYSE under the ticker symbols “WEAT” “SOYB” “CANE” and “TAGS.” Additional disclosure: I have held in the near past, and may purchase in the near future, shares of DGZ as a proxy for short gold against my long agricultural holdings of corn, wheat, soybeans and sugar.

A Better Way To Run Bootstrap Return Tests: Block Resampling

Developing confidence about a portfolio strategy’s track record (or throwing it onto the garbage heap), whether it’s your own design or a third party’s model, is a tricky but essential chore. There’s no single solution, but a critical piece of the analysis for estimating return and risk, including the potential for drawdowns and fat tails , is generating synthetic performance histories with a process called bootstrapping. The idea is based on simulating returns by drawing on actual results to see thousands of alternative histories to consider how the future may unfold. The dirty little secret in this corner of Monte Carlo analysis is that there’s more than one way to execute bootstrapping tests. To cut to the chase, block bootstrapping is a superior methodology for asset pricing because it factors in the reality that market returns exhibit autocorrelation. The bias for momentum – positive and negative – in the short run, in other words, can’t be ignored, as it is in standard bootstrapping. There’s a tendency for gains and losses to persist – bear and bull markets are the obvious examples, although shorter, less extreme runs of persistence also mark the historical record as well. Conventional bootstrapping ignores this fact by effectively assuming that returns are independently distributed. They’re not, which is old news. The empirical literature demonstrates rather convincingly a strong bias for autocorrelation in asset returns. Designing a robust bootstrapping test on historical performance demands that we integrate autocorrelation into the number crunching to minimize the potential for generating misleading results. The key point is recognizing that sampling historical returns for analysis should focus on multiple periods. Let’s assume that we’re looking at monthly performance data. A standard bootstrap would reshuffle the sequence of actual results and generate alternative return histories – randomly, based on monthly returns in isolation from one another. That would be fine if asset returns weren’t highly correlated in the short run. But as we know, positive and negative returns tend to persist for a stretch, sometimes in the extreme. The solution is sampling actual histories in blocks of time (in this case several months) to preserve the autocorrelation bias. The question is how to choose the length for the blocks, along with some other parameters. Much depends on the historical record, the frequency of the data, and the mandate for the analysis. There’s a fair amount of nuance here. Fortunately, R offers several practical solutions, including the meboot package (“Maximum Entropy Bootstrap for Time Series”). As an illustration, let’s use a couple of graphics to compare a standard bootstrap to a block bootstrap, based on monthly returns for the US stock market (S&P 500). To make this illustration clear in the charts, we’ll ignore the basic rules of bootstrapping and focus on a ridiculously short period: the 12 months through March 2016. If this was an actual test, I’d crunch the numbers as far back as history allows, which runs across decades. I’m also generating only ten synthetic return histories; in practice, it’s prudent to create thousands of data sets. But let’s dispense with common sense in exchange for an illustrative example. The first graph below reflects a standard bootstrap – resampling the historical record with replacement. The actual monthly returns for the S&P (red line) are shown in context with the resampled returns (light blue lines). As you can see, the resampled performances represent a random mix of results via reshuffling the sequence of actual monthly returns. The problem is that the tendency for autocorrelation is severed in this methodology. In other words, the bootstrap sample is too random – the returns are independent from one another. In reality, that’s not an accurate description of market behavior. The bottom line: modeling history through this lens could, and probably will, lead us astray as to what could happen in the future. Let’s now turn to block bootstrapping for a more realistic profile of market history. Note that the meboot package does most of the hard work here in choosing the length of the blocks. The details on the algorithm are outlined in the vignette. For now, let’s just look at the results. As you can see in the second chart below, the resampled returns resemble the actual performance history. It’s obvious that the synthetic performances aren’t perfectly random. Depending on the market under scrutiny and the goal of the analytics, we can adjust the degree of randomness. The key point is that we have a set of synthetic returns that are similar to, but don’t quite match, the actual data set. Note that no amount of financial engineering can completely wipe away uncertainty. The future can and probably will deliver surprises, for good and ill, no matter how clever our analytics. Nonetheless, bootstrapping historical data (or in-sample returns via backtests) can help separate the wheat from the chaff when looking into the rearview mirror as a preview of what lies ahead. But the details on how you run a bootstrap test are critical for developing comparatively high-confidence test results. In short, we can’t ignore a simple fact: market returns have an annoying habit of exhibiting non-random behavior.