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There’s no larger problem in profile danger management than the precise recognition of diversifying direct exposures, no more vital subject for an Epsilon Concept viewpoint.

Here’s my point: we position waaaay too much focus on a security’s external look – its asset class or sector – in making our profile choices. We put waaaay too much emphasis on a manager’s external look – his style box – in making our portfolio decisions. Do we need this type of streamlining classification or modeling as part of our investment evaluation procedure? Sure. However to define the diversification qualities of an investment in regards to its phenotype as opposed to its genotype … well, that’s a mistake.

I think that there’s massive room for improvement in constructing clever profiles if we can stop staring at surface appearances and begin focusing on the financial investment DNA of securities and methods.

Of course, there’s no such thing as a hereditary sequencing assay for an investment or a strategy, so exactly what does this mean in practice, that we should focus on the financial investment DNA of a security or method? If we are not going to measure the diversification of a portfolio by externally noticeable qualities such as asset class or design box, then exactly what’re we expected to do?

I think the answer is to look at the externally visible attribute that’s most carefully linked to the diversity of the human haplogroup: language. I’ve actually discussed this at length, so will not duplicate all that right here. The keynote, though, is that just as linguistic evolution maps practically perfectly to human adaptive radiation and the method our species spread out into brand-new environments out of Southern Africa, so, too, exist investment languages and grammars that map to the underlying ‘DNA’ of a security or method. The old financial investment languages are Value (together with its grammar, Reversion to the Mean) and Growth (together with its grammar, Extrapolation), and the relative mix of these languages in the description and practice of securities and techniques discloses a huge quantity about their concealed ‘genotype’.

From this Epsilon Concept viewpoint, a portfolio included numerous large-cap United States industrial and banking stocks (practically all of which talk a strong Value dialect) would get much less diversification benefit than a standard perspective would suggest from an allotment to a macro hedge fund that utilized numerous reversion-to-the-mean strategies for currency trades. Alternatively, I presume that a portfolio holding Microsoft (Value-speaking) might receive a substantial diversification gain from adding (Growth-speaking), although they’re both large-cap tech stocks. I think that there are dozens of methods to put this concentrate on investment language, financial investment grammar, and by extension – financial investment genotype – into useful use for the construction of better-diversified profiles, and I’ll be spending a lot of time in the coming months checking these applications.

To be sure, this is not really the first time in the history of the world that someone has recommended checking out surface features such as possession course to find better measurements of profile diversification.

For years, Ray Dalio and Bridgewater have been advocating something extremely just like this concept with their argument concerning the weakness of possession course relationships in identifying optimal profile appropriations. Dalio’s point – which is the theoretical foundation of Bridgewater’s All-Weather danger parity strategy – is that the relationship of returns between possession courses like stocks and bonds is neither constant nor random. The connection waxes and subsides in time, with long periods of adverse relationship and long periods of favorable connection that must reflect some underlying force.

Dalio calls this underlying force the macroeconomic ‘device’, which at any offered time reflects exactly what other people call a ‘routine’… some mix of inflation and growth qualities within a context of financial obligation cyclicality to which stocks and bonds react in predictable methods. Depending upon the existing regimen (which tends to alter slowly), stocks and bonds will certainly have either a strong or weak, positive or unfavorable relationship to each other, but there’s nothing meaningful about that relationship.

What’s significant is the relationship or connection between stocks and bonds to the macro routine. If you can measure the inflation/growth regimen precisely and you know the efficiency relationship of possession courses to this underlying force, then voilà … you can build a profile of stocks and bonds (and other properties, like commodities) that must perform as well as it’s possible to carry out within the given program, where good performance is specified as the most award for the least volatility. Or so the argument goes.

I think it’s a great argument. Dalio’s concept of why a risk-balanced portfolio works isn’t the skin-deep viewpoint embedded in a lot of portfolio construction efforts. Dalio is stating that there’s absolutely nothing special about this possession course or that asset class in figuring out a risk-balanced portfolio, no magical ratio, 60/40 or otherwise, of stocks to bonds. The Bridgewater technique is not really concentrated on ‘balancing’ asset courses at all, due to the fact that there’s truly absolutely nothing of importance to balance right here, no meaning in asset classes per se.

Securities are merely instruments that show a hidden economic routine with their performance characteristics, and a portfolio needs to be constructed on the basis of integrating these securities in the very best possible risk/reward setup provided the underlying program, duration. Occasionally this will mean a lot of stocks and a couple of bonds, even more normally this will certainly mean a great deal of bonds and a couple of stocks. In either case, the Bridgewater technique looks underneath the possession class skin of a security, and that’s a great beginning.

But it’s just a start. I wish to suggest an alternative theoretical basis for risk-balanced portfolio construction, one that doesn’t count on a deterministic design of the economy.

turtle elephantMoving from a property course conception of relationship and risk to an inflation/growth regimen conception of relationship and threat isn’t actually a basic modification in viewpoint. We are still discussing external qualities, just now we are talking about the economy as a whole rather than possession courses or specific securities. It’s like a Hindu mystic stating that it’s wrong to develop of the world being supported by four elephants, however that exactly what you truly need to look for is the turtle that supports the elephants.

The problem, naturally, is that once you accept this concept, you need to ask what the turtle is basing on. The Bridgewater answer is that the macroeconomic turtle-machine is the very first mover, the Aristotelian primum mobile, the bedrock on which everything else rests. The only appropriate complement to the beta profile in Bridgewater’s turtle-machine structure needs to be confined to the realm of ‘alpha’ or skill-based returns that can not be designed as a systematic or recognizable phenomenon.

The relationships in between possessions and the macroeconomic machine are ‘ageless and universal’ to quote Bridgewater co-CIO Bob Prince, which means that it’s challenging for their design to account for a regimen of programs, a long and unpredictable game by which political and social forces shape and transform the investment meaning and return relationship of a security to the macroeconomic qualities of inflation and growth. We believe that these political and social forces are both detectable and actionable and would be more properly determined as parts of epsilon instead of alpha.

Why’s this an issue? Because as the tale goes, it’s not nothing beneath that first turtle, but rather more and more turtles … all the means down in a limitless area of turtle-dom. In this Epsilon Theory scenario, below the financial turtle-machine is a political turtle-machine, and below that’s a social turtle-machine, and below that’s a human animal turtle-machine, etc. and so on. The lower the turtle, the more slow-moving it is, and the most likely you can ignore its existence for the sake of expedient design forecast at any given point in time.

But if you’re unfortunate sufficient to be investing on the basis of your economic turtle-machine when one of the lower turtles stumbles forward … you are in for a nasty surprise. What might this resemble? Consider that for most of the previous 2,000 years it’s been illegal to accept interest payments for a loan to a business, much less to securitize that sort of loan into a bond. Check out The Merchant Of Venice once more if you need a refresher course in the scope and power of usury laws.

Or for a more current example, think about that private domestic mortgage-backed securities barely existed prior to 2001, were a $4 trillion asset course by the end of 2007, and are now entirely moribund, just running into oblivion. I just don’t believe it’s insane to imagine large and unpredictable shifts in the economic device substantiated of political and social modification. In truth, I think it’s crazy not to expect these shifts, even if the timing and focus of the stumble is impossible to forecast.

There are 2 escapes of the infinite turtles issue. The first, which is what I envision the Bridgewater Elect are doing, is to broaden the macroeconomic machine to consist of political and social sub-machines. If you have ever checked out Isaac Asimov’s Foundation Trilogy, you can easily envision Ray Dalio as Hari Seldon, with a legion of psychohistorians figuring out increasingly more equations to integrate into a large econometric model of human society and mass habits.

The second escape (which I favor for specifically the reasons that Seldon’s design failed) is to reject the concept of ANY mechanistic model of how the world works in favor of a profound agnosticism about exactly what the future holds. The just constants I am willing to accept, especially in a duration of global deleveraging and ferocious political fragmentation within and in between nations, are the constants of human nature. My forecasts for the markets in 2014 are that worry and greed will certainly still reign supreme, that investors will certainly still talk ancient languages of Value and Growth, and that emergent habits like the Common Expertise Game will certainly drive brief to medium-term price levels in lots of securities.

I think that a risk-balanced profile – if it clearly includes both the grammar of Reversion-to-the-Mean and the grammar of Extrapolation – can be as responsive and adaptive to changing patterns and market-moving forces as you desire it to be, whether or not you’ve the best design to discuss why those patterns are moving. As just recently as 10 years ago a simplifying macroeconomic design was an outright need for making sense of all the signals that the world tosses at us minute after minute. A model, by definition, will certainly overlook certain signals. It’s exactly what designs DO. They simplify the world and sometimes miss out on important signals so that we aren’t drowned by the sheer flood of lesser signals. It’s a compromise that used to be needed … but it’s not anymore.

We’re in the middle of a details processing revolution – a quantum leap forward in inductive reasoning and inference informally called Big Data – that’s every bit as vital for profile management as the economic concept developed by Markowitz et al in the 1950’s.

Today we can determine the marketplace world – all of it – and infer the likelihood function of any given pattern or result. Our company know what the previous patterns have actually been and we’ve the devices to sound an alarm system if those patterns start to alter, for whatever reason. We no more need to model the financial world and deliberately cut ourselves off from potentially useful signals since they do not fit our preconceptions. We no more need to be the women and gentlemen that Steinbeck explained, unable to comprehend Lee if he spoke anything aside from pidgin English, since otherwise he’d not fit their model of who Lee was. We can be like Samuel, among the uncommon individuals able to separate our observations from our prejudgments. You can refrain that if you approach the world constricted by a model. Sorry, however you cannot.

The tyranny of models is rampant in nearly every aspect of our investment lives, from every main bank worldwide to every gigantic property manager on the planet to the biggest hedge funds in the world. There are very good reasons why we reside in a model-driven world, and there are great reasons model-driven institutions have the tendency to dominate their non-modeling rivals.

The use of models is wonderfully reassuring to the human animal due to the fact that it’s exactly what we perform in our own minds and our own groups and people all the time. We can’t help ourselves from applying streamlining designs in our lives due to the fact that we’re evolved and trained to do just that. But models are most beneficial in normal times, where the fundamental educational trade-off in between modeling power and modeling comprehensiveness is not a huge concern and where historical patterns don’t break. Sadly we’re residing in distinctly unusual times, a time where simplifications can blind us to structural change and where models create a threat that can not be fixed by even more or much better modeling!

It’s not a matter of using a different design or enhancing the design that we have. It’s the threat that ALL financial models posture when a bedrock presumption about politics or society shifts. If you are not prepared to look past your model … if you are unprepared, as Steinbeck composed, to separate your observations from your prejudgments … then you’ve a huge invisible risk in your profile.

I know it’s difficult to embrace exactly what I am calling an extensive agnosticism about the mechanics of how the world works. I understand it goes against our biological grain to decline the convenience and succor of a deterministic design and an Answer. In lots of respects, deep agnosticism is the utmost Other. It’s a non-human point of view on how to consider the world – a Rakshasa – and I am not expecting it to receive a warm or trusting welcome, particularly when it’s the skin of some familiar investment item.

But I believe it’s properly to look at a world wracked by political fragmentation, encumbered huge financial obligations, and took part in the best monetary policy experiments ever devised by guy. I think it’s properly to look at a world of huge uncertainty, as opposed to a world of merely significant threat, and it’s the point of view I’ll continue to take with Epsilon Theory.

[Editor’s Note: This short article is excerpted from Ben Hunt’s financial investment note on diversification.]

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