The Three-Body Problem

As much as I dislike the chickens on our farm, I love my bees. Do they sting? Of course they sting. The swarm is a wild animal. But after a few painful years I’m no longer a ham-handed goofball with my hives, and a morning spent in sync with this amazing animal is never a bad morning. Not only are bees low maintenance, not only do they pay a wonderful rent, but they demonstrate a genius and an optimism — there’s just no other word for it — that makes me feel more creative and alive.

The Connecticut winters are tough, though. I do what I can to support the bees, which is mostly just building a wind break with bales of straw, making sure that the hive stays ventilated enough to prevent water vapor condensation, and preventing mice from taking up residence. That and avoiding original sins like poor hive placement or collecting too much rent. But ultimately it’s a battle between the animal and Mother Nature. It’s up to them to survive. Or not.

Honeybees don’t hibernate (bumblebees do, but hive colony bees don’t), and they can’t fly south for the winter. To survive a Northern winter, bees change the composition of the swarm by shrinking the overall population, caulking the hive, getting rid of the deadweight males (i.e., ALL of the males), and laying just enough eggs to preserve a minimal survivable population through the winter and into spring. They cluster together in the center of the hive, keeping the queen in the center, shivering their wings to create kinetic energy, occasionally sending out suicide squads to retrieve honey stores from the outer combs. They lower their metabolism by creating a cloud of carbon dioxide in the hive. Yes, a carbon dioxide cloud.

All of this preparation takes time. To survive winter, the swarm starts to change its behaviors — from brood patterns to pollen collection to comb creation — not when the weather starts getting cold, but in the middle of summer when the dog days of August are still in front of us. And not just on some random date, but on a completely predictable day.

In 2018 my bees will begin to prepare for winter on Friday, June 22nd.

Why? Because bees can measure the angle of the sun’s rays. They can remember this from one day to the next. When today’s midday sun is ever so slightly lower in the sky than yesterday’s midday sun, a bee will know it. And the entire colony will begin to change.

Bees recognize the freakin’ summer solstice with as much accuracy as any human civilization ever did.

See? Genius. But we’re just getting started.

When bees act on their awareness of the summer solstice, they are trading a derivative. And they expertly manage the basis risk of that trade.

Huh? Time out, Ben. What are you talking about?

A derivative, in the broadest sense of the word, is something that’s related to something else you care about (the “underlying”), but for whatever reason you choose to interact with the derivative-something rather than the underlying-something. For humans, you might care about the stock price of company XYZ, so that’s the underlying, but you think something momentous is going to happen to the company three months from now, so you interact with a derivative on the stock, in this case a three-month option contract. For bees, the thing they truly care about is how cold it gets, so from their perspective the temperature is the underlying and the sunlight angles are the derivative thing that they analyze and interact with. In truth, of course, it’s the tilt of the Earth’s axis and the resultant sunlight angles that cause seasonality and temperature changes, so a curmudgeonly reader might accurately say that actually, it’s the temperature that’s the derivative here, but I trust we’re all open-minded enough to take a bee’s eye view of the world for the duration of this note.

Why do bees take their behavioral cues from sunlight angles rather than temperature change directly? Because the algorithm for predicting seasonality:

IF (maximum incident angle of sunlight today is less than the maximum incident angle of sunlight yesterday)

THEN (prepare for winter)

is enormously simpler, more predictive, more timely, and less volatile than any sort of temperature time-series analysis, or at least any temperature time-series analysis available to bees and pre-weather satellite humans. The genius (and fatal flaw) of bees and humans is their ability to create complex social systems on the basis of simple algorithms like this. Modern computing systems of the Big Data sort have a very different type of genius.

Hold that thought.

But first let’s make sure we understand what basis risk means, and why it’s The Most Important Thing to understand when you’re dealing with derivatives. “Basis” is the relationship between the derivative and the underlying, and so basis risk is how bad things could get if the relationship between derivative and underlying isn’t as tight as you thought it was. For bees, basis risk takes the form of cold weather coming sooner or later than normal. Shrinking the colony like clockwork based on the summer solstice works great if the first big freeze comes in November, not so well if you get a big snow in mid-October.

The key to managing basis risk is to keep your risk antennae (literally antennae when it comes to bees) focused on how well the derivative thing is tracking with the underlying thing. You need to watch the correlation. So to manage their basis risk, bees are also sensitive to temperature (the underlying) and all of the other derivative things related to changing temperature, like flower bloom patterns or prevailing winds. Nothing will totally override the summer solstice trade (even tropical bees make some small colony adjustments based on seasonality), but bees are adaptive investors, able to accelerate their winter preparation if cold weather comes early or delay it if cold weather comes late. Efficient management of basis risk is a balancing act between sticking with the original trade and adapting your behavior to changing correlations (you don’t want to mistake an Indian Summer for spring!), but that’s the beauty of evolution — billions of bee colonies over millions of years have lived and died and reproduced to naturally select the combination of hard-wired nervous system algorithms that allows honeybee species to thrive across a wide range of ecosystems and a wide range of seasonal weather variations.

But it’s only a range. Bees can’t live in as wide a range of ecosystems and weather variations as, say, ants. I doubt there’s a bee colony on Earth that can survive six months straight of sub-50 degree weather. If you’re a bee colony and you’ve moved that far north and that’s the magnitude of your downside basis risk, it really doesn’t matter how amazing you are in your solar declination calculations … you’re not going to make it. Maybe you get lucky for a couple of years, but if it’s possible that you could have four or five months of harshly cold weather, then sooner or later that severe basis risk catches up with you. This is a basis risk that you can’t insure against, that you can’t hedge against with extra preparation or precaution. It’s an unmanageable basis risk. For most of North America, though, even pretty far up into Canada, cold weather is a manageable basis risk, particularly if you’ve got a beekeeper able to lend a helping hand. Sometimes the bees will get a bad roll of the weather dice and you’ll lose a hive to basis risk, but it doesn’t threaten the species.

Species risk comes into play when you get a major climactic event that lasts for a long time in terms of a colony’s lifespan but not long at all in terms of evolution, genetic mutation, and natural selection. Like, say, what if spring no longer followed winter? What if it snowed in August and flowers bloomed in January? What if winter disappeared for a decade? What if it lasted that long? What if your weather basis risk was unknowable, as in Game of Thrones? Even a short Westerosi winter of a couple of years would kill every bee colony on the continent, which is why I don’t think I’ve ever seen a bee hive on Game of Thrones. [Hmm … I’ve just been informed by Grand Maester Guinn that “one of the Baratheon vassal houses of the Reach is House Beesbury, with a family seat of Honeyholt and a family motto of Beware Our Sting.” Sigh. You see what I have to put up with? Okay, we’ll stipulate that Dornish latitudes are safe. But The North is no place for bees when winter comes!]

This is basis uncertainty, where you’re not even sure that any basis exists at all, as opposed to mere basis risk. Basis uncertainty is an unknowable basis risk, which is much more damaging to species development than the occasional bout of severe basis risk.

[Long parenthetical: understanding the distinction between risk and uncertainty is crucial in every aspect of life. A risky decision is when you have a pretty good sense of the odds and the pay-offs. It lends itself to statistical analysis and econometrics, particularly if it’s a decision you will have the opportunity to make multiple times. An uncertain decision is when you don’t have a good sense of odds and pay-offs. Here, statistical analysis may very well kill you, particularly if you’re not going to get many cracks at the game, or if you don’t know how many times you’ll get to make a choice. You need game theory to make sense of decisions made under uncertainty.]

Basis uncertainty is the core problem facing every investor today.

It’s not just that we endure large basis risks here in the Hollow Market, unmanageable for many. It’s not just that all of our old signposts and moorings for navigating markets aren’t working very well. It’s not just difficult to identify predictive/derivative patterns in today’s markets. There is a non-trivial chance that structural changes in our social worlds of politics and markets have made it impossible to identify predictive/derivative patterns. THIS is basis uncertainty, and it’s as problematic for humans facing markets that don’t make sense as it is for bees facing weather patterns that don’t make sense.

Well, that’s just crazy talk, Ben. What do you mean that it might be impossible to identify predictive/derivative patterns? What do you mean that basis might not exist at all? Of course there’s a pattern to markets and everything else. Of course spring follows winter.

Nope. This is the Three-Body Problem.

Or rather, the Three-Body Problem is a famous example of a system which has no derivative pattern with any predictive power, no applicable algorithm that a human (or a bee) could discover to adapt successfully and turn basis uncertainty into basis risk. In the lingo, there is no “general closed-form solution” to the Three-Body Problem. (It’s also the title of the best science fiction book I’ve read in the past 20 years, by Cixin Liu. Truly a masterpiece. Life and perspective-changing, in fact, both in its depiction of China and its depiction of the game theory of civilization.)

What is the “problem”? Imagine three massive objects in space … stars, planets, something like that. They’re in the same system, meaning that they can’t entirely escape each other’s gravitational pull. You know the position, mass, speed, and direction of travel for each of the objects. You know how gravity works, so you know precisely how each object is acting on the other two objects. Now predict for me, using a formula, where the objects will be at some point in the future.

Answer: you can’t. In 1887, Henri Poincaré proved that the motion of the three objects, with the exception of a few special starting cases, is non-repeating. This is a chaotic system, meaning that the historical pattern of object positions has ZERO predictive power in figuring out where these objects will be in the future. There is no algorithm that a human can possibly discover to solve this problem. It does not exist.

To visualize the Three-Body Problem, here’s a simulation of the orbits of green, blue, and red objects with random starting conditions, each exerting a gravitational pull on the others. What Poincaré proved is that there is no formula where you can plug in the initial information and get the right answer for where any of the objects will be at any future point in time. No human can predict the future of this system.

But a computer can. Not by using an algorithm, which is how biological brains — human and bee alike — evolved to make sense of the world, but by brute force calculations. Remember, you know everything about these three objects … none of the physics here is a mystery. If you can do the calculation quickly enough, you can compute where all three objects will be one second from now. And one second from then. And one second from then. And so on and so on. With enough processing power (and this can require a LOT of processing power) you can calculate where the three objects will be 100 years from now, even though it is impossible to solve for this outcome.

It’s a hard concept to wrap your head around, this difference between calculating the future and predicting the future, but it will change the way you see the world. And your place in it.  

Now here’s an observation that I can’t emphasize strongly enough, although I’ll try:

THIS IS NOT HOW WE USE COMPUTERS IN OUR INVESTING STRATEGIES TODAY

The way that computers can calculate an answer to the Three-Body Problem is straightforward — they can be programmed with the physics rules for how one object influences another object, so they can simulate where each object will go next. There is ZERO examination of where the objects have been in the past. This is entirely forward looking.

The way that computers can NOT calculate an answer to the Three-Body Problem is by examining the historical data of where the objects have been. In a chaotic system, it doesn’t matter how hard or how fast or how deeply you look at the historical data. There is NO predictive pattern, NO secret algorithm hiding in the data. And yet this is exactly what we all have our computers doing … examining historical data to look for patterns that will give us the magic algorithm for predicting what’s next! The only thing that the past gives you in a chaotic system is inertia, which can look like a pattern or an algorithm for some period of time, depending on how all the objects are aligned. But it’s a mirage. It will not last. Examining the past of a chaotic system can give you lots of little answers, like sparks off a bonfire, none lasting more than a few seconds. And certainly if you’re efficient with your inertia-identifying spark-capturing effort, you can make some money using computers this way. But this examination of the past through naïve induction will never give you The Answer. Because The Answer does not exist in the past. The Answer — which is another word for algorithm, which is another word for “general closed-end solution” — doesn’t exist at all in a chaotic Three-Body System.

But we can approximate The Answer. We can calculate the future in small computational chunks even if we can’t predict the future in one big algorithmic swoop, but only if we can program the computer with the “physics” of how “gravity” works in social systems like markets. What’s our financial world equivalent of a theory of gravity? I think it’s a theory of narrative. This, to me, is a more interesting research program than identifying small inertias or capturing brief sparks. But it’s not where our computing resources are being allocated, because there’s no money in it. Yet.

Exploring a theory of narrative, what I’ve called the Narrative Machine, is basic research. Like all basic research, it’s not immediately remunerative and thus is difficult to fund. But that’s not the biggest obstacle. No, the biggest obstacle to basic research in computational finance is that humans are hard-wired to look for algorithms and have a really hard time imagining that it’s even possible to pursue a non-anthropomorphic (how about that for a $10 word) research design that doesn’t pore through historical data looking for predictive algorithms at every turn. We can’t help ourselves!

What if I told you that algorithms and derivatives are as much at the heart of how humans prepare for their financial future as they are for bees preparing for their seasonal future? What if I told you that the dominant strategies for human discretionary investing are, without exception, algorithms and derivatives? And what if I told you that these algorithms and derivatives were perhaps “evolved” under a “benign” configuration of the Three-Body Problem that not only might never repeat, but in fact is certain to never repeat because it is a chaotic system?

I’ll give you two examples of influential investment algorithms/derivatives. There are many more.

GOOD COMPANIES => GOOD STOCKS

GOOD COUNTRIES => GOOD GOVERNMENT BONDS

These are the central tenets of stock-picking and sovereign bond-picking, respectively. In both cases, goodness (like beauty) is in the eye of the beholder, so I’m not saying that there is some single standard for what makes a “good” company or what makes a “good” set of macroeconomic policies. What I’m saying is that everyone reading this note (including me!) believes that there is a direct relationship between the quality of a company or an economy (however you define quality) and the future price of whatever stocks or bonds are connected to that company or economy. What I’m saying is that everyone reading this note believes that tracking the measurable quality of a company or an economy (the derivative) according to some standardized and repeatable process (the algorithm) will, over time, have a predictive correlation with the future price of the related stock and bond securities (the underlying).

What stocks do we want to own? Why, the stocks of high quality companies, of course … companies with stellar management teams, fortress balance sheets, and wonderful products or services that everyone wants to buy. Ditto for government bonds and currencies and broad market indices and the like. Maybe it will take some time for this faith in Quality to pay off, but we all believe that it WILL pay off. It’s only natural, right? As natural as spring following winter. As natural as flowers blooming in May and snow falling in December. Maybe the flowers will bloom a few weeks late and maybe the snows will fall a few weeks early, but that’s just basis risk, and we can manage for that.

But what if spring doesn’t follow winter anymore?

Look, I’m not asking us to abandon our faith in Quality. One of the key corrolaries of the Three-Body Problem is that we don’t have to reject our belief that Objects 1 and 2 exist. We don’t have to deny our faith that the Quality-of-Companies is an actual thing and that it has a big gravitational pull on the price of stocks. We don’t have to deny our faith that the Quality-of-Governments is an actual thing and that it has a big gravitational pull on the price of government bonds.

What we have to accept is that there is an Object 3 that has moved into a position such that its gravity absolutely swamps the impact of Objects 1 and 2. This Object 3, of course, is extraordinary monetary policy, specifically the purchase of $20 TRILLION worth of financial assets by the Big 4 central banks — the Fed, the ECB, the BOJ, and the PBOC.

$20 trillion is a lot of mass. $20 trillion is a lot of gravity.

Here’s the impact of all that gravity on the Quality-of-Companies derivative investment strategy.

The green line below is the S&P 500 index. The white line below is a Quality Index sponsored by Deutsche Bank. They look at 1,000 global large cap companies and evaluate them for return on equity, return on invested capital, and accounting accruals … quantifiable proxies for the most common ways that investors think about quality. Because the goal is to isolate the Quality factor, the index is long in equal amounts the top 20% of measured  companies and short the bottom 20% (so market neutral), and has equal amounts invested long and short in the component sectors of the market (so sector neutral). The chart begins on March 9, 2009, when the Fed launched its first QE program.

Over the past eight and a half years, Quality has been absolutely useless as an investment derivative. You’ve made a grand total of not quite 3% on your investment, while the S&P 500 is up almost 300%.

This is not a typo.

Have the Quality stocks in your portfolio gone up over the past eight and a half years? Sure, but it’s not because of the Quality-ness of the companies. It’s because ALL stocks have gone up ever since Object 3, the balance sheets of central banks, started exerting its massive gravity on everything BUT Quality. That’s not an accident, by the way. Central banks don’t care about rewarding “good” companies. In fact, if they care about anything on this dimension, they care about keeping “bad” companies from going under.

This is what it looks like when spring does not follow winter.

And here’s the impact of all that gravity on the Quality-of-Countries derivative investment strategy.

The gold line below is the spread (difference) between Portugal’s 10-year bond yield and the U.S. 10-year bond yield, and the blue line is the spread between Italy’s 10-year note yield and the U.S. equivalent. In “normal” times, a country with a weaker set of macroeconomic characteristics (high levels of national debt, say, or maybe low productivity) will have to offer investors a higher rate of interest to borrow their money than a country with a stronger set of macroeconomic characteristics. So in the summer of 2012, when Portugal and Italy were both looking like deadbeat countries, they had to pay investors a much higher rate of interest than the U.S. did to attract the investment … about 9% more (this is per year, mind you) for Portugal and 4% more for Italy. Those are enormous spreads in the world of sovereign debt!

This chart begins in the summer of 2012, when the ECB announced its intentions to prop up the European sovereign debt market directly. Since that announcement — even though both Portugal and Italy have higher debt-to-GDP ratios today than in 2012 — the spread versus U.S. interest rates has done nothing but decline. Driven by the commitment of the ECB to “do whatever it takes” and to be not only a last-resort buyer but also a first-in-line buyer of Portuguese and Italian debt, it now costs LESS for these countries to borrow money for 10 years than the U.S.

This is nuts. It’s an understandable nuts when you consider that the German 10-year bond yield is currently about 30 basis points, and was actually negative (meaning that you had to pay the German government for the privilege of lending them money for the next 10 years) for about six months in 2016. Meaning that at least with Italian and Portuguese debt you’re being paid something (a little less than 2% per year). It’s an understandable nuts when you consider that the Swiss 10-year bond still sports a negative interest rate and has been negative for the past two and a half years. There’s about $10 trillion worth of negative yielding sovereign bonds out there today, something that is IMPOSSIBLE under a [good country => good bond] derivative algorithm. No country is that good! But it’s entirely possible under the immense gravitational force of massive central bank asset purchases.

Here’s the kicker. Below is the spread between Greek 10-year sovereign bonds and U.S. 10-year notes. In 2012 you were paid 24% more to lend money to Greece. Per year! Today you are paid less than 2% more to lend money to Greece rather than the United States. For ten years. To Greece.

Again, I’m not saying that the Quality derivative doesn’t exist as a real thing or that it isn’t an important factor in the history of successful stock-picking or bond-picking. What I’m saying is that the Quality derivative hasn’t mattered for eight and a half years with stocks and five years with sovereign debt. What I’m saying is that it might not matter for another eighty years. Or it might matter again in eight months. A Three-Body System is a chaotic system. As the boilerplate says, past performance is not a guarantee of future results. In fact, the only thing I can promise you is that past performance will NEVER give you a predictive algorithm for future results in a chaotic system.

This is basis uncertainty. This is the biggest concern that every investor should have, that the signals (derivatives) and processes (algorithms) that we ALL use to make sense of the investing world are no longer connected to security prices.

… Okay, Ben, you’ve exhausted me. It’s a weird and strange way of looking at the world, but let’s go with it for a minute. What’s the pay-off here? What do we DO in a chaotic system? What does that even mean, to say that we are investors in a chaotic system?

Four suggestions.

First, I think we should adopt a philosophy of what I’ve called profound agnosticism when it comes to investing, where we don’t just embrace the notion that no one has a crystal ball in this system, but we actually get kinda annoyed with those who insist they do. I think that risk balancing strategies make a ton of sense in a chaotic system, so that we think first about budgeting our risk agnostically across geographies and asset classes and sectors, and secondarily think about budgeting our dollars.

Second, and relatedly, I think we should adopt a classic game theory strategy for dealing with uncertain systems — minimax regret. The idea is simple, but the implications profound: instead of seeking to maximize returns, we seek to minimize our maximum regret. Keep in mind that our maximum regret may not be ruinous loss! I know plenty of people whose maximum regret is not keeping up with the Joneses. In fact, from a business model perspective, that’s more common than not. Or if you’ve bought into Bitcoin north of $15,000 per coin, I think you know what I’m talking about, too. The point being that we need to be painfully honest with ourselves about our sources of regret and target our investments accordingly. If we can be this honest with ourselves, it’s a VERY powerful strategy.

Third, I think we should reconsider our approach to computer-directed investment strategies. Using computers in an anthropomorphic way, where we treat them like a smarter, faster human, set loose in a vast field of historical data to search for patterns and algorithms … it’s a snipe hunt. Or at least I think we’ve squeezed just about all the juice out of this inductive orange that we’re likely to get. With the massive processing power at our fingertips today, not to mention the orders-of-magnitude-greater processing power that quantum computing will bring to bear in the future, there’s much bigger game afoot with computational approaches that take a more deductive, forward-looking strategy.

Fourth, and perhaps most importantly, I think we need to accept that we’re never going to fully understand the reality of a chaotic system, but that it’s never been more important to try. The brains of both bees and humans are hard-wired for algorithms. Both species see patterns even when patterns don’t exist, and both species tend to do poorly in environments where derivative signals are plagued by basis uncertainty rather than mere basis risk. Every bee in the world will follow its hard-wired algorithms even unto death. And most humans will, too. But humans have the capacity to think beyond their biological and cultural programming … if they work at it.

Where do we lose good people? When they convince themselves that they’ve found The Answer — either in the form of a charismatic person or, more dangerously still, a charismatic idea — in a chaotic system where no Answer exists. A chaotic system like markets, yes, but also a chaotic system like politics.

The Answer is, by nature, totalitarian. Why? Because it’s a general closed-form solution. That’s the technical definition of The Answer, and that’s the practical definition of totalitarian thought. We’re hard-wired to want the all-encompassing algorithm, which is why it’s so difficult to resist. But if we care about liberty. If we care about justice. If we care about liberty and justice for all … we have to resist The Answer.

Because we’ve lost enough good people.

As wise as serpents, as harmless as doves …

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It’s Still Not About the Nail

Reader reaction to the March 31 Epsilon Theory note, “It’s Not About the Nail”, was probably the strongest and most positive for any note to date. The message in a nutshell: financial advisors of all stripes and sizes would be well-served to do more than serve up old-school diversification platitudes in this Brave New World of a bull market that everyone hates, and the behavioral insights of regret minimization are an effective framework for making that adaptation.

This is a message that bears repeating, and thanks to Institutional Investor that’s what’s happening. A condensed version of “It’s Not About the Nail” can be found on the Institutional Investor website here, that piece will appear in the print magazine later this month in their “Unconventional Wisdom” column, and I’ve appended it below.

I think the reason this message strikes a chord is that it not only puts into words what a lot of people are feeling in an inchoate fashion, but also suggests a toolkit for improving the strained dialog between advisors and investors. It’s possible to take our tried and tested (but tired) notions of portfolio construction and energize them with the tools of game theory and behavioral economics, so that we get to the meaning of words like “diversification” and “de-risking”.

In the note I presented one way of thinking about all this in simple graphical terms, by taking the historical risk and reward of a portfolio or a subset of a portfolio and just seeing what the impact of a diversifying strategy would actually have been as seen in risk/reward space.

epsilon-theory-its-still-not-about-the-nail-april-14-2015-historical-risk-reward

The goal here is to move the original portfolio (the gold ball) up and to the left into the green triangle that marries both the traditional meaning of diversification (maximization of reward per unit of risk) and the behavioral meaning of de-risking in a bull market (minimization of the risk of underperformance). There ARE strategies that accomplish this goal, but the trick is finding the strategies that do this for the actual portfolio you have today, not some hypothetical portfolio or index.

We’ve built a set of tools at Salient within our systematic strategies group to analyze the historical impact of a wide range of diversifying strategies from a wide range of asset managers on actual portfolios, and then to map the impact of various diversifying strategies in risk/reward space. It’s not rocket science, and I’m sure any number of Epsilon Theory readers could develop a similar toolkit, but we’ve found it to be a very useful process for not only evaluating, but also communicating how diversifying strategies can make an existing portfolio better for an investor’s needs. Sometimes Salient strategies show up well in this analysis; sometimes they don’t. If you’re familiar with the Progressive Car Insurance commercials with Flo, you get the idea.

If you’re an investment professional and/or financial advisor with a portfolio you’d like to have analyzed in this manner, reply to this email or drop me a note at bhunt@salientpartners.com , and I’d be delighted to set it up for you.

As with all things Epsilon Theory-related, there’s no fee or obligation associated with this analysis. Thanks again to my partners and colleagues here at Salient for their commitment to releasing useful intellectual property into the wild. I think it’s a smart, non-myopic view of what it means to be an asset manager in the modern age, but a rare bird nonetheless.

All the best,
Ben


There’s a massive disconnect between advisors and investors today, and it’s reflected in both declining investment activity as well as a general fatigue with the consultant-client conversation. Consultants continue to preach the faith of diversification, and their clients continue to genuflect in its general direction. But diversification as it’s currently preached is perhaps the most oversold concept in financial advisor-dom, and the sermon isn’t connecting. Fortunately, behavioral economics offers a fresh perspective on portfolio construction, one that lends itself to what we call Adaptive Investing.

Investors aren’t asking for diversification, which isn’t that surprising after six years of a bull market. Investors only ask for diversification after the fire, as a door-closing exercise when the horse has already left the burning barn. What’s surprising is that investors are asking for de-risking, similar in some respects to diversification but different in crucial ways. What’s also surprising is that investors are asking for de-risking rather than re-risking, which is what you’d typically expect at this stage of such a powerful bull market.

Why is this the most mistrusted bull market in recorded history? Because no one thinks it’s real. Everyone believes that it’s a by-product of outrageously extraordinary monetary policy actions rather than the by-product of fundamental economic growth and productivity — and what the Fed giveth, the Fed can taketh away.

This is a big problem for the Federal Reserve, as its efforts to force greater risk-taking in markets through large-scale asset purchases and quantitative easing have failed to take hold in investor hearts and minds. Yes, we’re fully invested, but just because we have to be. To paraphrase the old saying about beauty, risk-taking is only skin deep for today’s investor, but risk-aversion goes clear to the bone.

It’s also the root of our current adviser-investor malaise. How so? Because de-risking a bull market is a very different animal than de-risking a bear market. As seen through the lens of behavioral economics, de-risking is based on regret minimization (not risk–reward maximization like diversification), and the simple fact is that regret minimization is driven by peer comparisons in a bull market. In a bear market your primary regret — the thing you must avoid at all costs — is ruin, and that provokes a very direct physical reaction. You can’t sleep. And that’s why de-risking Rule No. 1 in a bear market is so simple: Sell until you can sleep at night. Go to cash.

In a bull market, your primary regret is looking or feeling stupid, and that provokes a very conflicted, very psychological reaction. You want to de-risk because you don’t understand this market, and you’re scared of what will happen when the policy ground shifts. But you’re equally scared of being tagged “a panicker” and missing “the greatest bull market of this or any other generation.” And so you do nothing. You avoid making a decision, which means you also avoid the consultant-client conversation. Ultimately everyone — advisor and investor alike — looks to blame someone else for their own feelings of unease. No one’s happy, even as the good times roll.

So what’s to be done? Is it possible to both de-risk a portfolio and satisfy the regret minimization calculus of a bull market?

In fact, our old friend diversification is the answer, but not in its traditional presentation as a cure-all bromide. Diversification can certainly de-risk a portfolio by turning down the volatility, and it’s well suited for a bull market because it can reduce volatility without reducing market exposure. The problem is that diversification can take a long time to prove itself, and that’s rarely acceptable to investors who are seeking the immediate portfolio impact of de-risking, whether it’s the bear market or bull market variety.

What we need are diversification strategies that can react quickly. That brings me back to adaptive investing, which has two relevant points for de-risking in a bull market.

First, your portfolio should include allocations to strategies that can go short. If you’re de-risking a bull market, you need to make money when you’re right, not just lose less money. Losing less money pays off over the long haul, but the path can be bumpy.

Second, your portfolio should include allocations to trend-following strategies, which keep you in assets that are working and get you out of those that aren’t. The market is always right, and that’s never been more true — or more difficult to remember — than now in the Golden Age of the Central Banker.

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It’s Not About the Nail

epsilon-theory-its-not-about-the-nail-march-31-2015-yoda

Do, or do not. There is no try.”

– Yoda, “Star Wars: Episode V – The Empire Strikes Back” (1980)

I see it all perfectly; there are two possible situations – one can either do this or that. My honest opinion and my friendly advice is this: do it or do not do it – you will regret both.
Soren Kierkegaard, “Either/Or: A Fragment of Life” (1843)

The only victories which leave no regret are those which are gained over ignorance.
Napoleon Bonaparte (1769 – 1821)

Maybe all one can do is hope to end up with the right regrets.
Arthur Miller, “The Ride Down Mt. Morgan” (1991)

Of all the words of mice and men, the saddest are, “It might have been.”
Kurt Vonnegut, “Cat’s Cradle” (1963)

One can’t reason away regret – it’s a bit like falling in love, fall into regret.
Graham Greene, “The Human Factor” (1978)

epsilon-theory-its-not-about-the-nail-march-31-2015-cash.jpg

I bet there’s rich folks eatin’

In a fancy dining car.

They’re probably drinkin’ coffee

And smokin’ big cigars.

Well I know I had it comin’.

I know I can’t be free.

But those people keep-a-movin’

And that’s what tortures me.

– Johnny Cash, “Folsom Prison Blues” (1955)

epsilon-theory-its-not-about-the-nail-march-31-2015-paul-anka

Regrets…I’ve had a few.

But then again, too few to mention.

– Paul Anka, Frank Sinatra “My Way” (1969)

The Moving Finger writes; and, having writ,
Moves on: nor all thy Piety nor Wit
Shall lure it back to cancel half a Line,
Nor all thy Tears wash out a Word of it.
Omar Khayyam, “Rubaiyat” (1048 – 1141)

You can tell it any way you want but that’s the way it is. I should of done it and I didn’t. And some part of me has never quit wishin’ I could go back. And I can’t. I didn’t know you could steal your own life. And I didn’t know that it would bring you no more benefit than about anything else you might steal. I think I done the best with it I knew how but it still wasn’t mine. It never has been.”
Cormac McCarthy, “No Country for Old Men” (2005)

Jesse: Yeah, right, well, great. So listen, so here’s the deal. This is what we should do. You should get off the train with me here in Vienna, and come check out the capital.
Celine: What?
Jesse: Come on. It’ll be fun. Come on.
Celine: What would we do?
Jesse: Umm, I don’t know. All I know is I have to catch an Austrian Airlines flight tomorrow morning at 9:30 and I don’t really have enough money for a hotel, so I was just going to walk around, and it would be a lot more fun if you came with me. And if I turn out to be some kind of psycho, you know, you just get on the next train.

Alright, alright. Think of it like this: jump ahead, ten, twenty years, okay, and you’re married. Only your marriage doesn’t have that same energy that it used to have, y’know. You start to blame your husband. You start to think about all those guys you’ve met in your life and what might have happened if you’d picked up with one of them, right? Well, I’m one of those guys. That’s me, y’know, so think of this as time travel, from then, to now, to find out what you’re missing out on. See, what this really could be is a gigantic favor to both you and your future husband to find out that you’re not missing out on anything. I’m just as big a loser as he is, totally unmotivated, totally boring, and, uh, you made the right choice, and you’re really happy.

Celine: Let me get my bag.

Richard Linklater, “Before Sunrise” (1995)

For it falls out
That what we have we prize not to the worth
Whiles we enjoy it, but being lacked and lost,
Why, then we rack the value, then we find
The virtue that possession would not show us
While it was ours.
William Shakespeare, “Much Ado About Nothing” (1612)

When to the sessions of sweet silent thought
I summon up remembrance of things past,
I sigh the lack of many a thing I sought,
And with old woes new wail my dear time’s waste:
William Shakespeare, “Sonnet 30” (1609)

epsilon-theory-its-not-about-the-nail-march-31-2015-nirvana

No, I don’t have a gun.

– Nirvana, “Come As You Are” (1992)

I spend a lot of my time speaking with investors and financial advisors of all stripes and sizes, and here’s what I’m hearing, loud and clear. There’s a massive disconnect between advisors and investors today, and it’s reflected in both declining investment activity as well as a general fatigue with the advisor-investor conversation. I mean “advisor-investor conversation” in the broadest possible context, a context that should be recognizable to everyone reading this note. It’s the conversation of a financial advisor with an individual investor client. It’s the conversation of a consultant with an institutional investor client. It’s the conversation of a CIO with a Board of Directors. It’s the conversation of many of us with ourselves. The wariness and weariness associated with this conversation runs in both directions, by the way.

Advisors continue to preach the faith of diversification, and investors continue to genuflect in its general direction. But the sermon isn’t connecting. Investors continue to express their nervousness with the market and dissatisfaction with their portfolio performance, and advisors continue to nod their heads and say they understand. It reminds me of Jason Headley’s brilliant short film, “It’s Not About the Nail”, with the advisor reprising Headley’s role. Yes, the advisor is listening. But most find it impossible to get past what they believe is the obvious answer to the obvious problem. Got a headache? Take the nail out of your head. Nervous about the market? Diversify your portfolio. But there are headaches and then there are headaches. There is nervousness and then there is nervousness. It’s not about the nail, and the sooner advisors realize this, the sooner they will find a way to reconnect with their clients. Even if it’s just a conversation with yourself.

epsilon-theory-its-not-about-the-nail-march-31-2015-nail

Investors aren’t asking for diversification, which isn’t that surprising after 6 years of a bull market. Investors never ask for diversification after 6 years of a bull market. They only ask for it after the Fall, as a door-closing exercise when the horse has already left the burning barn. What’s surprising is that investors are asking for de-risking, similar in some respects to diversification but different in crucial ways. What’s surprising is that investors are asking for de-risking rather than re-risking, which is what you’d typically expect at this stage of such a powerful bull market.

Investors are asking for de-risking because this is the most mistrusted bull market in recorded history, a market that seemingly everyone wants to fade rather than press. Why? Because no one thinks this market is real. Everyone believes that it’s a by-product of outrageously extraordinary monetary policy actions rather than the by-product of fundamental economic growth and productivity, and what the Fed giveth … the Fed can taketh away.

This is a big problem for the Fed, as their efforts to force greater risk-taking in markets through LSAP and QE (and thus more productive risk-taking, or at least inflation, in the real economy) have failed to take hold in investor hearts and minds. Yes, we’re fully invested, but only because we have to be. To paraphrase the old saying about beauty, risk-taking is only skin deep for today’s investor, but risk-aversion goes clear to the bone.

It’s also the root of our current advisor-investor malaise. De-risking a bull market is a very different animal than de-risking a bear market. And neither is the same as diversification.

Let’s take that second point first.

Here’s a simple representation of what diversification looks like, from a risk/reward perspective.

epsilon-theory-its-not-about-the-nail-march-31-2015-historical-risk-rewardFor illustrative purposes only.

The gold ball is whatever your portfolio looks like today from a historical risk/reward perspective, and the goal of diversification is to move your portfolio up and to the left of the risk/reward trade-off line that runs diagonally through the current portfolio position. Diversification is all about increasing the risk/reward balance, about getting more reward per unit of risk in your portfolio, and the goodness or poorness of your diversification effort is defined by how far you move your portfolio away from that diagonal line. In fact, as the graph below shows, each of the Good Diversification outcomes are equally good from a risk/reward balance perspective because they are equally distant from the original risk/reward balance line, and vice versa for the Poor Diversification outcomes.

epsilon-theory-its-not-about-the-nail-march-31-2015-historical-risk-reward-2

For illustrative purposes only.

Diversification does NOT mean getting more reward out of your portfolio per se, which means that some Poor Diversification changes to your portfolio will outperform some Good Diversification changes to your portfolio over time (albeit with a much bumpier ride).

epsilon-theory-its-not-about-the-nail-march-31-2015-historical-risk-reward-3

For illustrative purposes only.

It’s an absolute myth to say that any well-diversified portfolio will outperform all poorly diversified portfolios over time. But it’s an absolute truth to say that any well-diversified portfolio will outperform all poorly diversified portfolios over time on a risk-adjusted basis. If an investor is thinking predominantly in terms of risk and reward, then greater diversification is the slam-dunk portfolio recommendation. This is the central insight of Harry Markowitz and his modern portfolio theory contemporaries, and I’m sure I don’t need to belabor that for anyone reading this note.

The problem is that investors are not only risk/reward maximizers, they are also regret minimizers (see Epsilon Theory notes “Why Take a Chance” and “The Koan of Donald Rumsfeld” for more, or read anything by Daniel Kahneman). The meaning of “risk” must be understood as not only as the other side of the reward coin, but also as the co-pilot of behavioral regret. That’s a mixed metaphor, and it’s intentional. The human animal holds two very different meanings for risk in its brain simultaneously. One notion of risk, as part and parcel of expected investment returns and the path those returns are likely to take, is captured well by the concept of volatility and the toolkit of modern economic theory. The other, as part and parcel of the psychological utility associated with both realized and foregone investment returns, is captured well by the concepts of evolutionary biology and the toolkit of modern game theory.

The problem is that diversification can only be understood as an exercise in risk/reward maximization, has next to nothing to say about regret minimization, and thus fails to connect with investors who are consumed by concerns of regret minimization. This fundamental miscommunication is almost always present in any advisor-investor conversation, but it is particularly pernicious during periods of global debt deleveraging as we saw in the 1870’s, the 1930’s, and today. Why? Because the political consequences of that deleveraging create investment uncertainty in the technical, game theoretic sense, an uncertainty which is reflected in reduced investor confidence in the efficacy of fundamental market and macroeconomic factors to drive market outcomes. In other words, the rules of the investment game change when politicians attempt to maintain the status quo – i.e., their power – when caught in the hurricane of a global debt crisis. That’s what happened in the 1870’s. That’s what happened in the 1930’s. And it’s darn sure happening today. We all feel it. We all feel like we’ve entered some Brave New World where the old market moorings make little sense, and that’s what’s driving the acute anxiety expressed today by investors both large and small. Recommending old-school diversification techniques as a cure-all for this psychological pain isn’t necessarily wrong. It probably won’t do any harm. But it’s not doing anyone much good, either. It’s not about the nail.

On the other hand, the concept of de-risking has a lot of meaning within the context of regret minimization, which makes it a good framework for exploring a more psychologically satisfactory set of portfolio allocation recommendations. But to develop that framework, we need to ask what drives investment regret. And just as we talk about different notions of volatility-based portfolio constructions under different market regimes, so do we need to talk about different notions of regret-based portfolio constructions under different market regimes.

Okay, that last paragraph was a bit of a mouthful. Let me skip the academic-ese and get straight to the point. In a bear market, regret minimization is driven by existential concerns. In a bull market, regret minimization is driven by peer comparisons.

In a bear market your primary regret – the thing you must avoid at all costs – is ruin, and that provokes a very direct, very physical reaction. You can’t sleep. And that’s why Rule #1 of de-risking in a bear market is so simple: sell until you can sleep at night. Go to cash. Here’s what de-risking in a bear market looks like, as drawn in risk/reward space.

epsilon-theory-its-not-about-the-nail-march-31-2015-historical-risk-reward-4

For illustrative purposes only.

Again, the gold ball is whatever your portfolio looks like today from a historical risk/reward perspective. De-risking means moving your portfolio to the left, i.e. a lower degree of risk. The question is how much reward you are forced to sacrifice for that move to the left. Perfect De-Risking sacrifices zero performance. Good luck with that if you are reducing your gross exposure. Average De-Risking is typically accomplished by selling down your portfolio in a pro rata fashion across all of your holdings, and that’s a simple, effective strategy. Good De-Risking and Poor De-Risking are the result of active choices in selling down some portion of your portfolio more than another portion of your portfolio, or – if you don’t want to go to cash – replacing something in your portfolio that’s relatively volatile with something that’s relatively less volatile.

In a bull market, on the other hand, your primary regret is looking or feeling stupid, and that provokes a very conflicted, very psychological reaction. You want to de-risk because you don’t understand this market, and you’re scared of what will happen when the policy ground shifts. But you’re equally scared of being tagged with the worst possible insults you can suffer in our business: “you’re a panicker” … “you missed the greatest bull market of this or any other generation”. Again, maybe this is a conversation you’re having with yourself (frankly, that’s the most difficult and conflicted conversation most of us will ever have). And so you do nothing. You avoid making a decision, which means you also avoid the advisor-investor conversation. Ultimately everyone, advisor and investor alike, looks to blame someone else for their own feelings of unease. No one’s happy, even as the good times roll.

So what’s to be done? Is it possible to both de-risk a portfolio and satisfy the regret minimization calculus of a bull market?

Through the lens of regret minimization, here’s what de-risking in a bull market looks like, again as depicted in risk/reward space:

epsilon-theory-its-not-about-the-nail-march-31-2015-historical-risk-reward-5

For illustrative purposes only.

Essentially you’ve taken all of the bear market de-risking arrows and moved them 45 degrees clockwise. What would be Perfect De-Risking in a bear market is only perceived as average in a bull market, and many outcomes that would be considered Good Diversification in pure risk/reward terms are seen as Poor De-Risking. I submit that this latter condition, what I’ve marked with an asterisk in the graph above, is exactly what poisons so many advisor-investor conversations today. It’s a portfolio adjustment that’s up and to the left from the diagonal risk/reward balance line, so you’re getting better risk-adjusted returns and Good Diversification – but it’s utterly disappointing in a bull market as peer comparison regret minimization takes hold. It doesn’t even serve as a Good De-Risking outcome as it would in a bear market.

Now here’s the good news. There are diversification outcomes that overlap with the bull market Good De-Risking outcomes, as shown in the graph below. In fact, it’s ONLY diversification strategies that can get you into the bull market Good De-Risking area. That is, typical de-risking strategies look to cut exposure, not replace it with equivalent but uncorrelated exposure as diversification strategies do, and you’re highly unlikely to improve the reward profile of your portfolio (moving up vertically from the horizontal line going through the gold ball) by reducing gross exposure. The trick to satisfying investors in a bull market is to increase reward AND reduce volatility. I never said this was easy.

epsilon-theory-its-not-about-the-nail-march-31-2015-historical-risk-reward-6

For illustrative purposes only.

The question is … what diversification strategies can move your portfolio into this promised land? Also (as if this weren’t a challenging enough task already), what diversification strategies can work quickly enough to satisfy a de-risking calculus? Diversification can take a long time to prove itself, and that’s rarely acceptable to investors who are seeking the immediate portfolio impact of de-risking, whether it’s the bear market or bull market variety.

What we need are diversification strategies that can act quickly. More to the point, we need strategies that can react quickly, all while maintaining a full head of steam with their gross exposure to non-correlated or negatively-correlated return streams. This is at the heart of what I’ve been calling Adaptive Investing.

Epsilon Theory isn’t the right venue to make specific investment recommendations. But I’ll make three general points.

First, I’d suggest looking at strategies that can go short. If you’re de-risking a bull market, you need to make money when you’re right, not just lose less money. Losing less money pays off over the long haul, but the long haul is problematic from a regret-based perspective, which tends to be quite path-sensitive. Short positions are, by definition, negatively correlated to the thing that they’re short. They have a lot more oomph than the non-correlated or weakly-correlated exposures that are at the heart of most old-school diversification strategies, and that’s really powerful in this framework. Of course, you’ve got to be right about your shorts for this to work, which is why I’m suggesting a look at strategies that CAN go short as an adaptation to changing circumstances, not necessarily strategies that ARE short as a matter of habit or requirement.

Second, and relatedly, I’d suggest looking at trend-following strategies, which keep you in assets that are working and get you out of assets that aren’t (or better yet, allow you to go short the assets that aren’t working). Trend-following strategies are inherently behaviorally-based, which is near and dear to the Epsilon Theory heart, and more importantly they embody the profound agnosticism that I think is absolutely critical to maintain when uncertainty rules the day and fundamental “rules” change on political whim. Trend-following strategies are driven by the maxim that the market is always right, and that’s never been more true – or more difficult to remember – than here in the Golden Age of the Central Banker.

Third, these graphs of portfolio adjustments in risk/reward space are not hypothetical exercises. Take the historical risk/reward of your current portfolio, or some portion of that portfolio such as the real assets allocation, and just see what the impact of including one or more liquid alternative strategies would be over the past few years. Check out what the impact on your portfolio would be since the Fed and the ECB embarked on divergent monetary policy courses late last summer, creating an entirely different macroeconomic regimeSeriously, it’s not a difficult exercise, and I think you’ll be surprised at what, for example, a relatively small trend-following allocation can do to de-risk a portfolio while still preserving the regret-based logic of managing a portfolio in a bull market. For both advisors and investors, this is the time to engage in a conversation about de-risking and diversification, properly understood as creatures of regret minimization as well as risk/reward maximization, rather than to avoid the conversation. As the old saying goes, risk happens fast. Well … so does regret. 

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Why Take a Chance?

epsilon-theory-why-take-a-chance-february-24-2015-casino

Vinny Forlano: He won’t talk. Stone is a good kid. Stand-up guy, just like his old man. That’s the way I see it.
Vincent Borelli: I agree. He’s solid. An effin’ Marine.
Americo Capelli: He’s okay. He always was. Remo, what do you think?
Remo Gaggi: Look… why take a chance? At least, that’s the way I feel about it.

— “Casino” (1995)

epsilon-theory-why-take-a-chance-february-24-2015-casino-ace

Ace Rothstein: Four reels, sevens across on three $15,000 jackpots. Do you have any idea what the odds are?
Don Ward: Shoot, it’s gotta be in the millions, maybe more.
Ace Rothstein: Three effin’ jackpots in 20 minutes? Why didn’t you pull the machines? Why didn’t you call me?
Don Ward: Well, it happened so quick, 3 guys won; I didn’t have a chance …
Ace Rothstein: [interrupts] You didn’t see the scam? You didn’t see what was going on?
Don Ward: Well, there’s no way to determine that …
Ace Rothstein: Yes there is! An infallible way, they won!

— “Casino” (1995)

There’s only one question that matters in the Golden Age of the Central Banker: why isn’t QE working? Why hasn’t the largest monetary stimulus in the history of man – trillions of dollars of liquidity with trillions more euros and yen to come – sparked a self-sustaining recovery in the global economy?

If you’re a true-believer in modern economic orthodoxy or a central bank apparatchik the answer is simple: something must be getting in the way of our elegant theories of Zero Interest Rate Policy (ZIRP) and Large Scale Asset Purchases (LSAP), so if $4 trillion isn’t enough to break through to the Promised Land we better do $4 trillion more.

If you see the world through the lens of behavioral economics, however, you come to a very different conclusion. Something IS blocking the effectiveness of QE, but that something is human nature. Behavioral economics suggests that a little QE can change human behavior at the margins, but no amount of QE is enough to change human nature at its core.

The High Priests of the IMF, the Fed, and the ECB are blind to this because all of modern economic theory – ALL of it – is based on a single bedrock assumption: humans are economic maximizers. If something is good, then more is better and “MOAR!” is best. And if that assumption holds true, then QE works. You will indeed force productive risk-taking in the real world economy (more loans to small businesses, more growth-oriented investments in people and equipment, etc.) by making it increasingly difficult for investors to play it safe in capital markets (negative 10-year Swiss bonds, anyone?). But if that assumption is flawed, then you get exactly what we’re seeing: pervasive non-productive risk-taking in the real world economy (stock buy-backs, for example) and massive wealth transfers from savers to speculators in the capital markets.

Yes, we are maximizers of reward. But we are also minimizers of regret. That’s not because we are irrational or stupid, but because most of us draw on our portfolios for real world needs. Our investment portfolios are a means to an end, not an end in themselves. We understand that a) periodic losses are inevitable in a risky investment portfolio, no matter how well it maximizes long-term gains, and b) if we’re unlucky and suffer losses such that our portfolios decline below a certain level, then we are faced with real world risks and tough real world decisions that overshadow whatever investment logic the Fed would prefer us to have.

Regret minimization is not just for financial investors. It holds true for investors of all sorts, from a CEO deciding how to allocate cash flows to a general deciding how to allocate troops to a farmer deciding how to allocate land. For all of these decision makers, it doesn’t matter how meager the reward of playing it safe might be if an unlucky roll of the investing dice would create existential risk. In the immortal words of “Casino” mob boss Remo Gaggi as he tacitly ordered a hit on a trusted lieutenant, “Look … why take a chance?”

To be sure, some investors are paralyzed by the unreasonable fear of rolling snake-eyes 500 times in a row. Still others, as we saw with the Swiss National Bank debacle, have no idea of the risks they’re taking when they intend to play it safe. Human behavior may be governed by concerns of risk and regret, but neither concept comes easily to us. All of us, no matter how comfortable we might be swimming in the ocean of randomness that surrounds us, occasionally channel our inner Don Ward, the hapless casino employee who thinks that it’s possible that three separate slot machine jackpots could trigger within minutes of each other simply by chance.

Fortunately, a branch of game theory called “Minimax Regret” can help apply analytical rigor to both our human nature and our human failings. As the name implies, the goal of Minimax Regret is to minimize the maximum regret you might experience from a decision choice. Developed in 1951 by Leonard “Jimmie” Savage – a colleague of John von Neumann and Milton Friedman, and in general one of the most brilliant American mathematicians of the 20th century – the Minimax Regret criterion is widely used in fields as diverse as military strategy and climate science … any situation requiring a choice between extremely costly options and where the results of your decision will not become apparent for years. Are you listening, Mr. Draghi?

Unfortunately, I’m certain that neither Mr. Draghi nor the other High Priests of monetary policy are listening at all. We seem destined to learn the hard way … once again … that you can’t change human nature by government fiat. But individual investors and allocators can listen and learn from these old good ideas, and that’s how you survive the Golden Age of the Central Banker.

I wrote an introductory note about Minimax Regret strategies in October 2013 (“The Koan of Donald Rumsfeld”), and – seeing as how Central Bankers outside the US are doubling down on the QE bet – it’s time for me to dust off this line of analysis. I think that Minimax Regret is the right micro toolbox to go along with the macro toolbox of political analysis (see “Finest Worksong” and “Now There’s Something You Don’t See Every Day, Chauncey” for recent notes on this thread), and together they create the Adaptive Investing framework that’s at the heart of a practical Epsilon Theory perspective. I’ll be putting some Minimax Regret resources on the website over the next few weeks, along with some brief email and Twitter distributions to guide the effort. If you’re not already an email subscriber or Twitter follower, now would be a good time to sign up.

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