Who’s Being Naïve, Kay?


Satan: Dream other dreams, and better!

The Mysterious Stranger (1916)

Twain spent 11 years writing his final novel, “The Mysterious Stranger”, but never finished it. The book exists in three large fragments and is Twain’s darkest and least funny work. It’s also my personal favorite.


Stanley:   I thought you were called Lucifer.

George:   I know. “The Bringer of the Light” it used to be. Sounded a bit poofy to me. Everything I’ve ever told you has been a lie. Including that.

Stanley :   Including what?

George :   That everything I’ve ever told has been a lie. That’s not true.

Stanley :   I don’t know WHAT to believe.

George :   Not me, Stanley, believe me!

Bedazzled (1967)

A must-see movie, and I don’t mean the 2000 abomination with Brendan Fraser, but the genius 1967 version by Peter Cook and Dudley Moore. Plus Raquel Welch as Lust. Yes, please.


Henry Hill:   Ladies and gentlemen, either you are closing your eyes to a situation you do not wish to acknowledge, or you are not aware of the caliber of disaster indicated by the presence of a pool table in your community!

The Music Man (1962)

The Pied Piper legend, originally a horrific tale of murder, finds its source in the earliest written records of the German town of Hamelin (1384).

The story begins: “it is 100 years since our children left.”


As Tolstoy famously said, there are only two stories in all of literature: either a man goes on a journey, or a stranger comes to town. Of the two, we are far more familiar and comfortable with the first in the world of markets and investing, because it’s the subjectively perceived narrative of our individual lives. We learn. We experience. We overcome adversity. We get better. Or so we tell ourselves.

But when the story of our investment age is told many years from now, it won’t be remembered as a Hero’s Journey, but as a classic tale of a Mysterious Stranger. It’s a story as old as humanity itself, and it always ends with the same realization by the Stranger’s foil: what was I thinking when I signed that contract or fell for that line? Why was I so naïve?

The Mysterious Stranger today, of course, is not a single person but is the central banking Mafia apparatus in the US, Europe, Japan, and China. The leaders of these central banks may not be as charismatic as Robert Preston in The Music Man, but they hold us investors in equal rapture. The Music Man uses communication policy and forward guidance to get the good folks of River City to buy band instruments. Central bankers use communication policy and forward guidance to get investors large and small to buy financial assets. It’s a difference in degree and scale, not in kind.

epsilon-theory-whos-being-naive-kay-may-24-2016-music-man

The Mysterious Stranger is NOT a simple or single-dimensional fraud. No, the Mysterious Stranger is a liar, to be sure, but he’s a proper villain, as the Brits would say, and typically he’s quite upfront about his goals and his use of clever words to accomplish those goals. I mean, it’s not like Kay doesn’t know what she’s getting herself into when she marries into the Corleone family. Michael is crystal clear with her, right from the start. But she wants to believe so badly in what Michael is telling her when he suddenly reappears in her life, that she suspends her disbelief in his words and embraces the Narrative of legitimacy he presents. I think Michael actually believed his own words, too, that he would in fact be able to move the Family out of organized crime entirely, just as I’m sure that Yellen believed her own words of tightening and light-at-the-end-of-the-tunnel in the summer of 2014. Ah, well. Events doth make liars of us all.

epsilon-theory-whos-being-naive-kay-may-24-2016-godfather-2
epsilon-theory-whos-being-naive-kay-may-24-2016-godfather-3

Draw your own comparisons to this story arc of The Godfather, with investors playing the role of Kay and the Fed playing the role of Michael Corleone. I think it’s a pretty neat fit. It ends poorly for Kay, of course (and not so great for Michael). Let’s see if we can avoid her fate.

But like Kay, for now we are married to the Mob … err, I mean, the Fed and competitive monetary policies, as reflected in the relative value of the dollar and other currencies. The cold hard fact is that since the summer of 2014 there has been a powerful negative correlation between the trade-weighted dollar and oil, between the trade-weighted dollar and emerging markets, and between the trade-weighted dollar and industrial, manufacturing, and energy stocks. Here’s an example near and dear to the hearts of any energy investor, the trade-weighted dollar shown in green versus the inverted Alerian MLP index (ticker AMZ), a set of 43 midstream energy companies, principally pipelines and infrastructure, shown in blue.

epsilon-theory-whos-being-naive-kay-may-24-2016-bloomberg-1

This is a -94% correlation, remarkably strong for any two securities, much less two – pipelines and the dollar – that are not obviously connected in any fundamental or real economy sort of way. But this is always what happens when the Mysterious Stranger comes to town: our traditional behavioral rules (i.e., correlations) go out the window and are replaced with new behavioral rules and correlations as we give ourselves over to his smooth words and promises. Because that’s what a Mysterious Stranger DOES – tell compelling stories, stories that stick fast to whatever it is in our collective brains that craves Narrative and Belief.

There’s nothing particularly new about this phenomenon in markets, as there have always been “story stocks”, especially in the technology, media, and telecom (TMT) sector where you have more than your fair share of charismatic management storytellers and valuation multiples that depend on their efforts.

My favorite example of a “story stock” is Salesforce.com (ticker CRM), a $55 billion market cap technology company with 19,000 employees and about $6.5 billion in revenues. I’m pretty sure that Salesforce.com has never had a single penny of GAAP earnings in its existence (in FY 2016 the company lost $0.07 per share on a GAAP basis). Instead, the company is valued on the basis of non-GAAP earnings, but even there it trades at about an 80x multiple (!) of FY 2017 company guidance of $1.00 per share. Salesforce.com is blessed with a master story-teller in its CEO, Marc Benioff, who – if you’ve ever heard him speak – puts forth a pretty compelling case for why his company should be valued on the basis of bookings growth and other such metrics. Of course, the skeptic in me might note that it is perhaps no great feat to sell more and more of a software service at a loss, particularly when your salespeople are compensated on bookings growth, and the cynic in me might also note that for the past 10+ years Benioff has sold between 12,500 and 20,000 shares of CRM stock every day through a series of 10b5-1 programs. But hey, that’s why he’s the multi-billionaire (and a liquid multi-billionaire, to boot) and I’m not. Here’s the 5-year chart for CRM:

epsilon-theory-whos-being-naive-kay-may-24-2016-bloomberg-2

Not bad. Up 138% over the past five years. A few ups and downs, particularly here at the start of 2016, although the stock has certainly come roaring back. But when you dig a little deeper …

There are 1,272 trading days that comprise this 5-year chart. 21 of those trading days, less than 2% of the total, represent the Thursday after Salesforce.com reports quarterly earnings (always on a Wednesday after the market close). If you take out those 21 trading days, Salesforce.com stock is up only 35% over the past five years. How does this work? What’s the causal process? Every Wednesday night after the earnings release, for the past umpteen years, Benioff appears on Mad Money, where Cramer’s verdict is always an enthusiastic “Buy, buy, buy!” Every Thursday morning after the earnings release, the two or three sell-side analyst “axes” on the stock publish their glowing assessment of the quarterly results before trading begins. It’s not that every investor on Thursday believes what Cramer or the sell-side analysts are saying, particularly anyone who’s short the stock (CRM always has a high short interest). But in a perfect example of the Common Knowledge Game, if you ARE short the stock, you know that everyone else has heard what Cramer and the sell-side analysts (the Missionaries, in game theory lingo) have said, and you have to assume that everyone else will act on this Common Knowledge (what everyone knows that everyone knows). The only logical thing for you to do is cover your short before everyone else covers their short, resulting in a classic short squeeze and a big up day. Now to be sure, this isn’t the story of every earnings announcement … sometimes even Marc Benioff and his lackeys can’t turn a pig’s ear of a quarter into a silk purse … but it’s an incredibly consistent behavioral result over time and one of the best examples I know of the Common Knowledge Game in action.

But wait, there’s more. Now let’s add the Fed’s storytelling and its Common Knowledge Game to Benioff’s storytelling and his Common Knowledge Game. Over the past five years there have been 43 days where the FOMC made a formal statement. If you owned Salesforce.com stock for only the 43 FOMC announcement days and the 21 earnings announcement days over the past five years, you would be UP 167%. If you owned Salesforce.com stock for the other 1,208 trading days, you would be DOWN 8%.

epsilon-theory-whos-being-naive-kay-may-24-2016-salesforc

Okay, Ben, how about other stocks? How about entire indices? Well, let’s look again at that Alerian MLP index. Over the past five years, if you had owned the AMZ for only the 43 FOMC announcement days over that span, you would be UP 28%. If you owned it for the other 1,229 trading days you would be DOWN 39%. Over the past two years, if you had owned the AMZ for only the 16 FOMC announcement days over that span, you would be UP 18%. If you owned it for the other 487 trading days you would be DOWN 48%. Addition by subtraction to a degree that would make Lao Tzu proud.

epsilon-theory-whos-being-naive-kay-may-24-2016-alerian

I’ll repeat what I wrote in Optical Illusion / Optical Reality … it’s hard to believe that MLP investors should be paying a lot more attention to G-7 meetings and reading the Fed governor tea leaves than to gas field depletion schedules and rig counts, but I gotta call ‘em like I see ‘em. In fact, if there’s a core sub-text to Epsilon Theory it’s this: call things by their proper names. That’s a profoundly subversive act. Maybe the only subversive act that really changes things. So here goes. Today there are vast swaths of the market, like emerging markets and commodity markets and industrial/energy stocks, that we should call by their proper name: a derivative expression of FOMC policy. Used to be that only tech stocks were “story stocks”. Today, almost all stocks are “story stocks”, and the Common Knowledge Game is more applicable to helping us understand market behaviors and price action than ever before.

You see this phenomenon clearly in the entire S&P 500, as well, although not as starkly with a complete plus/minus reversal in performance between FOMC announcement days and all other days. Over the past five years, if you had owned the SPX for only the 43 FOMC announcement days over that span, you would be UP 17%. If you owned it for the other 1,229 trading days you would be UP 28%. Over the past two years, if you had owned the SPX for only the 16 FOMC announcement days over that span, you would be UP 5%. If you owned it for the other 487 trading days you would be UP 2%.

epsilon-theory-whos-being-naive-kay-may-24-2016-s-p-500

What do I take from eyeballing these charts? The Narrative effect and the impact of the Common Knowledge Game have accelerated over the past two years (ever since Draghi and Yellen launched the Great Monetary Policy Schism of June 2014); they’re particularly impactful during periods when stock prices are otherwise declining, and they’re spreading to broader equity indices. That’s what it looks like to me, at least.

So what does an investor do with these observations? Two things, I think, one a practical course of action and one a shift in perspective. The former being more fun but the latter more important.

First, there really is a viable research program here, and what I’ve tried to show in this brief note is that there really are practical implementations of the Common Knowledge Game that can support investment strategies dealing with story stocks. I want to encourage anyone who’s intrigued by this research program to take the data baton and try this on your favorite stock or mutual fund or index. You can get the FOMC announcement dates straight from the Federal Reserve website. This doesn’t require an advanced degree in econometrics to explore.

I don’t know where this research program ends up, but it’s my commitment to do this in plain sight through Epsilon Theory. Think of it as the equivalent of open source software development, just in the investment world. I suspect it’s hard to turn the Common Knowledge Game into a standalone investment strategy because you’re promising that you’ll do absolutely nothing for 98 out of 100 trading days. Good luck raising money on that. But it’s a great perspective to add to our current standalone strategies, especially actively managed funds. Stock-pickers today are being dealt one dull, low-conviction hand after another here in the Grand Central Bank Casino, and the hardest thing in the world for any smart investor, regardless of strategy, is to sit on his hands and do nothing, even though that’s almost always the right thing to do. Incorporating an awareness of the Common Knowledge Game and its highly punctuated impact makes it easier to do the right thing – usually nothing – in our current investment strategies.

And that gets us to the second take-away from this note. The most important thing to know about any Mysterious Stranger story is that the Stranger is the protagonist. There is no Hero! When you meet a Mysterious Stranger, your goal should be simple: survive the encounter.

This is an insanely difficult perspective to adopt, that we (either individually or collectively) are not the protagonist of the investing age in which we live. It’s difficult because we are creatures of ego. We all star in our own personal movie and we all hear the anthems of our own personal soundtrack. But the Mysterious Stranger is not an obstacle to be heroically overcome, as if we were Liam Neeson setting off (again! and again!) to rescue a kidnapped daughter in yet another “Taken” sequel. At some point this sort of heroism is just a reflection of bad parenting in the case of Liam Neeson, and a reflection of bad investing in the case of stock pickers and other clingers to the correlations and investment meanings of yesterday.

The correlations and investment meanings of today are inextricably entwined with central bankers and their storytelling. To be investment survivors in the low-return and policy-controlled world of the Silver Age of the Central Banker, we need to recognize the impact of their words and incorporate that into our existing investment strategies, while never accepting those words naïvely in our hearts.


PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/16271/

Optical Illusion / Optical Truth

epsilon-theory-optical-illusion-optical-truth-may-4-2016-tufte

epsilon-theory-optical-illusion-optical-truth-may-4-2016-cholera-2Portion of original dot map by Dr. John Snow, the founding father of epidemiology, showing the clusters of cholera cases in the London epidemic of 1854. The visual representation of Snow’s data analysis convinced local authorities to shut down the contaminated public well at ground zero of the cholera outbreak, although it would be another 20 years before Snow’s arguments in favor of germ theory and a direct connection between cholera and fecal contamination of water supply would be widely accepted.

John Snow, “On the Mode of Communication of Cholera” (1855)

Anscombe’s Quartet: four datasets that appear identical using summary statistical methods (mean, variance, correlation, linear regression), but are completely different in meaning and composition – a difference that is clearly revealed through visual inspection.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-quartet

Frank Anscombe, “Graphs in Statistical Analysis” American Statistician v.27 no.1 (1973), drawing by Schutz 

epsilon-theory-optical-illusion-optical-truth-may-4-2016-minard-map

Charles Joseph Minard, “Carte Figurative” of Napoleon’s 1812 Russian Campaign (1869)

The Minard Map: a map of Napoleon’s disastrous invasion of Russia in 1812, showing six distinct data dimensions (troop strength, temperature, distance marched, geographic latitude and longitude, direction of travel, location at event dates) in 2-dimensional form.

Mephistopheles:Here too it’s masquerade, I find:
As everywhere, the dance of mind.
I grasped a lovely masked procession,
And caught things from a horror show…
I’d gladly settle for a false impression,
If it would last a little longer, though.
epsilon-theory-optical-illusion-optical-truth-may-4-2016-mephistopheles

Edouard de Reszke as Mephistopheles
in Gounod’s opera “Faust” (c. 1880)

So, so you think you can tell
Heaven from Hell,
Blue skies from pain.
Can you tell a green field
From a cold steel rail?
A smile from a veil?
Do you think you can tell?

– Roger Waters, “Wish You Were Here” (1975)

A great deal of intelligence can be invested in ignorance when the need for illusion is deep.

– Saul Bellow, “To Jerusalem and Back” (1976)

It is difficult to get a man to understand something, when his salary depends on his not understanding it.

– Upton Sinclair, “I, Candidate for Governor: And How I Got Licked” (1935)

Knowledge kills action; action requires the veils of illusion.

– Friedrich Nietzsche, “The Birth of Tragedy” (1872)

To find out if she really loved me, I hooked her up to a lie detector. And just as I suspected, my machine was broken.

– Jarod Kintz, “Love Quotes for the Ages. Specifically Ages 19-91” (2013)

Edward Tufte is a personal and professional hero of mine. Professionally, he’s best known for his magisterial work in data visualization and data communication through such classics as The Visual Display of Quantitative Information (1983) and its follow-on volumes, but less well-known is his outstanding academic work in econometrics and statistical analysis. His 1974 book Data Analysis for Politics and Policy remains the single best book I’ve ever read in terms of teaching the power and pitfalls of statistical analysis. If you’re fluent in the language of econometrics (this is not a book for the uninitiated) and now you want to say something meaningful and true using that language, you should read this book (available for $2 in Kindle form on Tufte’s website). Personally, Tufte is a hero to me for escaping the ivory tower, pioneering what we know today as self-publishing, making a lot of money in the process, and becoming an interesting sculptor and artist. That’s my dream. That one day when the Great Central Bank Wars of the 21st century are over, I will be allowed to return, Cincinnatus-like, to my Connecticut farm where I will write short stories and weld monumental sculptures in peace. That and beekeeping.

But until that happy day, I am inspired in my war-fighting efforts by Tufte’s skepticism and truth-seeking. The former is summed up well in an anecdote Tufte found in a medical journal and cites in Data Analysis:

One day when I was a junior medical student, a very important Boston surgeon visited the school and delivered a great treatise on a large number of patients who had undergone successful operations for vascular reconstruction. At the end of the lecture, a young student at the back of the room timidly asked, “Do you have any controls?” Well, the great surgeon drew himself up to his full height, hit the desk, and said, “Do you mean did I not operate on half of the patients?” The hall grew very quiet then. The voice at the back of the room very hesitantly replied, “Yes, that’s what I had in mind.” Then the visitor’s fist really came down as he thundered, “Of course not. That would have doomed half of them to their death.” God, it was quiet then, and one could scarcely hear the small voice ask, “Which half?”

‘Nuff said.

The latter quality — truth-seeking — takes on many forms in Tufte’s work, but most noticeably in his constant admonitions to LOOK at the data for hints and clues on asking the right questions of the data. This is the flip-side of the coin for which Tufte is best known, that good/bad visual representations of data communicate useful/useless answers to questions that we have about the world. Or to put it another way, an information-rich data visualization is not only the most powerful way to communicate our answers as to how the world really works, but it is also the most powerful way to design our questions as to how the world really works. Here’s a quick example of what I mean, using a famous data set known as “Anscombe’s Quartet”.

Anscombe’s Quartet
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

In this original example (developed by hand by Frank Anscombe in 1973; today there’s an app for generating all the Anscombe sets you could want) Roman numerals I – IV refer to four data sets of 11 (x,y) coordinates, in other words 11 points on a simple 2-dimensional area. If you were comparing these four sets of numbers using traditional statistical methods, you might well think that they were four separate data measurements of exactly the same phenomenon. After all, the mean of x is exactly the same in each set of measurements (9), the mean of y is the same in each set of measurements to two decimal places (7.50), the variance of x is exactly the same in each set (11), the variance of y is the same in each set to two decimal places (4.12), the correlation between x and y is the same in each set to three decimal places (0.816), and if you run a linear regression on each data set you get the same line plotted through the observations (y = 3.00 + 0.500x).

But when you LOOK at these four data sets, they are totally alien to each other, with essentially no similarity in meaning or probable causal mechanism. Of the four, linear regression and our typical summary statistical efforts make sense for only the upper left data set. For the other three, applying our standard toolkit makes absolutely no sense. But we’d never know that — we’d never know how to ask the right questions about our data — if we didn’t eyeball it first.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-anscombes-quartet

Okay, you might say, duly noted. From now on we will certainly look at a visual plot of our data before doing things like forcing a line through it and reporting summary statistics like r-squared and standard deviation as if they were trumpets of angels from on high. But how do you “see” multi-variate datasets? It’s one thing to imagine a line through a set of points on a plane, quite another to visualize a plane through a set of points in space, and impossible to imagine a cubic solid through a set of points in hyperspace. And how do you “see” embedded or invisible data dimensions, whether it’s an invisible market dimension like volatility or an invisible measurement dimension like time aggregation or an invisible statistical dimension like the underlying distribution of errors?

The fact is that looking at data is an art, not a science. There’s no single process, no single toolkit for success. It requires years of practice on top of an innate artist’s eye before you have a chance of being good at this, and it’s something that I’ve never seen a non-human intelligence accomplish successfully (I can’t tell you how happy I am to write that sentence). But just because it’s hard, just because it doesn’t come easily or naturally to people and machines alike … well, that doesn’t mean it’s not the most important thing in data-based truth-seeking.

Why is it so important to SEE data relationships? Because we’re human beings. Because we are biologically evolved and culturally trained to process information in this manner. Because — and this is the Tufte-inspired market axiom that I can’t emphasize strongly enough — the only investable ideas are visible ideas. If you can’t physically see it in the data, then it will never move you strongly enough to overcome the pleasant fictions that dominate our workaday lives, what Faust’s Tempter, the demon Mephistopheles, calls the “masquerade” and “the dance of mind.” Our similarity to Faust (who was a really smart guy, a man of Science with a capital S) is not that the Devil may soon pay us a visit and tempt us with all manner of magical wonders, but that we have already succumbed to the blandishments of easy answers and magical thinking. I mean, don’t get me started on Part Two, Act 1 of Goethe’s magnum opus, where the Devil introduces massive quantities of paper money to encourage inflationary pressures under a false promise of recovery in the real economy. No, I’m not making this up. That is the actual, non-allegorical plot of one of the best, smartest books in human history, now almost 200 years old.

So what I’m going to ask of you, dear reader, is to look at some pictures of market data, with the hope that seeing will indeed spark believing. Not as a temptation, but as a talisman against the same. Because when I tell you that the statistical correlation between the US dollar and the price of oil since Janet Yellen and Mario Draghi launched competitive monetary policies in mid-June of 2014 is -0.96 I can hear the yawns. I can also hear my own brain start to pose negative questions, because I’ve experienced way too many instances of statistical “evidence” that, like the Anscombe data sets, proved to be misleading at best. But when I show you what that correlation looks like …

epsilon-theory-optical-illusion-optical-truth-may-4-2016-bloomberg-1

© Bloomberg Finance L.P., for illustrative purposes only

I can hear you lean forward in your seat. I can hear my own brain start to whir with positive questions and ideas about how to explore this data further. This is what a -96% correlation looks like.

What you’re looking at in the green line is the Fed’s favored measure of what the US dollar buys around the world. It’s an index where the components are the exchange rates of all the US trading partners (hence a “broad dollar” index) and where the individual components are proportionally magnified/minimized by the size of that trading relationship (hence a “trade-weighted” index). That index is measured by the left hand vertical axis, starting with a value of about 102 on June 18, 2014 when Janet Yellen announced a tightening bias for US monetary policy and a renewed focus on the full employment half of the Fed’s dual mandate, peaking in late January and declining to a current value of about 119 as first Japan and Europe called off the negative rate dogs (making their currencies go up against the dollar) and then Yellen completely back-tracked on raising rates this year (making the dollar go down against all currencies). Monetary policy divergence with a hawkish Fed and a dovish rest-of-world makes the dollar go up. Monetary policy convergence with everyone a dove makes the dollar go down.

What you’re looking at in the magenta line is the upside-down price of West Texas Intermediate crude oil over the same time span, as measured by the right hand vertical axis. So on June 18, 2014 the spot price of WTI crude oil was over $100/barrel. That bottomed in the high $20s just as the trade-weighted broad dollar index peaked this year, and it’s been roaring back higher (lower in the inverse depiction) ever since. Now correlation may not imply causation, but as Ed Tufte is fond of saying, it’s a mighty big hint. I can SEE the consistent relationship between change in the dollar and change in oil prices, and that makes for a coherent, believable story about a causal relationship between monetary policy and oil prices.

What is that causal narrative? It’s not just the mechanistic aspects of pricing, such that the inherent exchange value of things priced in dollars — whether it’s a barrel of oil or a Caterpillar earthmover — must by definition go down as the exchange value of the dollar itself goes up. More impactful, I think, is that for the past seven years investors have been well and truly trained to see every market outcome as the result of central bank policy, a training program administered by central bankers who now routinely and intentionally use forward guidance and placebo words to act on “the dance of mind” in classic Mephistophelean fashion. In effect, the causal relationship between monetary policy and oil prices is a self-fulfilling prophecy (or in the jargon du jour, a self-reinforcing behavioral equilibrium), a meta-example of what George Soros calls reflexivity and what a game theorist calls the Common Knowledge Game.

The causal relationship of the dollar, i.e. monetary policy, to the price of oil is a reflection of the Narrative of Central Bank Omnipotence, nothing more and nothing less. And today that narrative is everything.

Here’s something smart that I read about this relationship between oil prices and monetary policy back in November 2014 when oil was north of $70/barrel:

I think that this monetary policy divergence is a very significant risk to markets, as there’s no direct martingale on how far monetary policy can diverge and how strong the dollar can get. As a result I think there’s a non-trivial chance that the price of oil could have a $30 or $40 handle at some point over the next 6 months, even though the global growth and supply/demand models would say that’s impossible. But I also think the likely duration of that heavily depressed price is pretty short. Why? Because the Fed and China will not take this lying down. They will respond to the stronger dollar and stronger yuan (China’s currency is effectively tied to the dollar) and they will prevail, which will push oil prices back close to what global growth says the price should be. The danger, of course, is that if they wait too long to respond (and they usually do), then the response will itself be highly damaging to global growth and market confidence and we’ll bounce back, but only after a near-recession in the US or a near-hard landing in China.

Oh wait, I wrote that. Good stuff.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-economist

But that was a voice in the wilderness in 2014, as the dominant narrative for the causal factors driving oil pricing was all OPEC all the time. So what about that, Ben? What about the steel cage death match within OPEC between Saudi Arabia and Iran and outside of OPEC between Saudi Arabia and US frackers? What about supply and demand? Where is that in your price chart of oil? Sorry, but I don’t see it in the data. Doesn’t mean it’s not really there. Doesn’t mean it’s not a statistically significant data relationship. What it means is that the relationship between oil supply and oil prices in a policy-controlled market is not an investable relationship. I’m sure it used to be, which is why so many people believe that it’s so important to follow and fret over. But today it’s an essentially useless exercise in data analytics. Not wrong, but useless … there’s a difference!

Of course, crude oil isn’t the only place where fundamental supply and demand factors are invisible in the data and hence essentially useless as an investable attribute. Here’s the dollar and something near and dear to the hearts of anyone in Houston, the Alerian MLP index, with an astounding -94% correlation:

epsilon-theory-optical-illusion-optical-truth-may-4-2016-bloomberg-2

© Bloomberg Finance L.P., for illustrative purposes only

Interestingly, the correlation between the Alerian MLP index and oil is noticeably less at -88%. Hard to believe that MLP investors should be paying more attention to Bank of Japan press conferences than to gas field depletion schedules, but I gotta call ‘em like I see ‘em.

And here’s the dollar and EEM, the dominant emerging market ETF, with a -89% correlation:

epsilon-theory-optical-illusion-optical-truth-may-4-2016-bloomberg-3

© Bloomberg Finance L.P., for illustrative purposes only

There’s only one question that matters about Emerging Markets as an asset class, and it’s the subject of one of my first (and most popular) Epsilon Theory notes, “It Was Barzini All Along”: are Emerging Market growth rates a function of something (anything!) particular to Emerging Markets, or are they simply a derivative function of Developed Market central bank liquidity measures and monetary policy? Certainly this chart suggests a rather definitive answer to that question!

And finally, here’s the dollar and the US Manufacturing PMI survey of real-world corporate purchasing managers, probably the most respected measure of US manufacturing sector health. This data relationship clocks in at a -92% correlation. I mean … this is nuts.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-bloomberg-4

© Bloomberg Finance L.P., for illustrative purposes only

Here’s what I wrote last summer about the inexorable spread of monetary policy contagion.

Monetary policy divergence manifests itself first in currencies, because currencies aren’t an asset class at all, but a political construction that represents and symbolizes monetary policy. Then the divergence manifests itself in those asset classes, like commodities, that have no internal dynamics or cash flows and are thus only slightly removed in their construction and meaning from however they’re priced in this currency or that. From there the divergence spreads like a cancer (or like a cure for cancer, depending on your perspective) into commodity-sensitive real-world companies and national economies. Eventually – and this is the Big Point – the divergence spreads into everything, everywhere.

I think this is still the only story that matters for markets.

The good Lord giveth and the good Lord taketh away. Right now the good Lord’s name is Janet Yellen, and she’s in a giving mood. It won’t last. It never does. But it does give us time to prepare our portfolios for a return to competitive monetary policy actions, and it gives us insight into what to look for as catalysts for that taketh away part of the equation.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-cholera

Most importantly, though, I hope that this exercise in truth-seeking inoculates you from the Big Narrative Lie coming soon to a status quo media megaphone near you, that this resurgence in risk assets is caused by a resurgence in fundamental real-world economic factors. I know you want to believe this is true. I do, too! It’s unpleasant personally and bad for business in 2016 to accept the reality that we are mired in a policy-controlled market, just as it was unpleasant personally and bad for business in 1854 to accept the reality that cholera is transmitted through fecal contamination of drinking water. But when you SEE John Snow’s dot map of death you can’t ignore the Broad Street water pump smack-dab in the middle of disease outcomes. When you SEE a Bloomberg correlation map of prices you can’t ignore the trade-weighted broad dollar index smack-dab in the middle of market outcomes. Or at least you can’t ignore it completely. It took another 20 years and a lot more cholera deaths before Snow’s ideas were widely accepted. It took the development of a new intellectual foundation: germ theory. I figure it will take another 20 years and the further development of game theory before we get widespread acceptance of the ideas I’m talking about in Epsilon Theory. That’s okay. The bees can wait.

PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/16276/