Big Data

The Horse in Motion

Scared money can’t win and a worried man can’t love.

―  Cormac McCarthy, All the Pretty Horses (1992)

In 1872, noted horseracing aficionado and San Francisco rich guy Leland Stanford (yes, of university fame) commissioned noted photographer and San Francisco smart guy Eadweard Muybridge to apply his path breaking technology of stop-action photography to settle a long-running debate — do all four hooves leave the ground at the same time when horses run? This question had bedeviled the Sport of Kings for ages, and while Stanford favored the “unsupported transit” theory of yes, all four hooves leaving the ground for a split-second in the outstretched position, allowing horses to briefly “fly”, he — as rich guys often do — really, really, really needed to know for sure.

It took Muybridge about 12 years to complete the work, interrupted in part by his murder trial. It seems that Muybridge had taken a young bride (she 21 and he 42 when they married) who preferred the company of a young dandy of a San Francisco drama critic who fashioned himself in faux militaristic fashion as Major Harry Larkyns. After learning that wife Flora’s 7-month old son Florado was perhaps not biologically his, Muybridge tracked Larkyns down and shot him point-blank in the chest with the immortal words, “Good evening, Major, my name is Muybridge and here’s the answer to the letter you sent my wife.” In one of the more prominent early cases of jury nullification (Phillip Glass has an opera, The Photographer, with a libretto based on the court transcripts), Muybridge was found not guilty on the grounds of justifiable homicide despite the judge’s clear instructions to the contrary. Or maybe the jurors were just bought off. Leland Stanford spared no expense in paying for Muybridge’s defense. Gotta get those horse pix.

And eventually he did. Muybridge’s work, The Horse in Motion, settled the question of unsupported transit once and for all.

Yes, all four hooves leave the ground at the same time. But it’s NOT in the outstretched flying position. Instead, it’s in the tucked position, which — because it’s not as romantic a narrative as flying — had never been widely considered as an answer. In fact, for decades after the 1882 publication of The Horse in Motion in book form (a book by Leland Stanford’s fellow rich guy friend, J.D.B. Stillman, who gave ZERO credit to Muybridge for the work … after all, Muybridge was just Stanford’s work-for-hire employee, a member of the gig economy of the 1870s), artists continued to prefer the more narrative-pleasing view of flying horses. Here, for example, is Frederic Remington’s 1889 painting A Dash for the Timber, a work that was largely responsible for catapulting Remington to national prominence, replete with a whole posse of flying horses (h/t to John Batton in Ft. Worth, who knows his Amon Carter Museum collection!).

Okay, Ben, that’s a fun story of technology, art, murder, and rich guy intrigue set in 1870s San Francisco. But what does it have to do with modern markets and investing?

This: Muybridge developed a technology that allowed for a quantum leap forward in how humans perceived the natural world. His findings flew in the face of the popular narrative for how the natural world of biomechanics worked, but they were True nonetheless and led to multiple useful applications over time. Today we are at the dawning of a technology that similarly allows for a quantum leap forward in how humans perceive the world, but with a focus on the social world as opposed to the natural world. Some of these findings will no doubt similarly fly in the face of the popular narrative for how the social world of markets and politics works, but they will similarly lead to useful applications. They already are.

The technology I’m talking about is the biggest revolution in the world today. It’s the ascendancy of non-human intelligences, which I’ve written about in lots of Epsilon Theory notes, from Rise of the Machines to First Known When Lost to Troy Will Burn – the Big Deal about Big Data to The Talented Mr. Ripley to One MILLION Dollars to Two Discoveries. It’s what most of the world calls Artificial Intelligence, which is a term I dislike for its pejorative anthropomorphism. It’s what Neville Crawley calls Big Compute, which is a great phrase, not least for its progression and distinction from the old hat notion of Big Data (h/t to Neville for turning me on to the Muybridge story, too).

The primary impact of Big Compute, or AI or whatever you want to call it, is that it allows for a quantum leap forward in how we humans can perceive the world. Powerful non-human intelligences are the modern day Oracle of Delphi. They can “see” dimensions of the world that human intelligences cannot, and if we can ask the right questions we can share in their vision, as well. The unseen dimensions of the social world that I’m interested in tapping with the help of non-human intelligences are the dimensions of unstructured data, the words and images and communications that comprise the ocean in which the human social animal swims.

This is the goal of the Narrative Machine research project (read about it in The Narrative Machine and American Hustle). That just as Eadweard Muybridge took snapshots of the natural world using his new technology, so do I think it possible to take snapshots of the social world using our new technology. And just as Muybridge’s snapshots gave us novel insights into how the Horse in Motion actually works, as opposed to our romantic vision of how it works, so do I think it likely that these AI snapshots will give us novel insights into how the Market in Motion actually works.

That’s the horse I’m betting on in Epsilon Theory.

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Fiat Money, Fiat News

Ron Burgundy: Discovered by the Germans in 1904, they named it San Diego, which of course in German means a whale’s vagina.
Veronica Corningstone: No, there’s no way that’s correct.
Ron Burgundy: I’m sorry, I was trying to impress you. I don’t know what it means. I’ll be honest, I don’t think anyone knows what it means anymore. Scholars maintain that the translation was lost hundreds of years ago.
Veronica Corningstone: Doesn’t it mean Saint Diego?
Ron Burgundy: No. No.
Veronica Corningstone: No, that’s — that’s what it means. Really.
Ron Burgundy: Agree to disagree.
― “Anchorman: The Legend of Ron Burgundy” (2004)

Ron Burgundy: Boy, that escalated quickly … I mean, that really got out of hand fast.
Champ Kind: It jumped up a notch.
Ron Burgundy: It did, didn’t it?
Brick Tamland: Yeah, I stabbed a man in the heart.
Ron Burgundy: I saw that. Brick killed a guy. Did you throw a trident?
Brick Tamland: Yeah, there were horses, and a man on fire, and I killed a guy with a trident.
Ron Burgundy: Brick, I’ve been meaning to talk to you about that. You should find yourself a safehouse or a relative close by. Lay low for a while, because you’re probably wanted for murder.
― “Anchorman: The Legend of Ron Burgundy” (2004)
Priest: It’s horrifying. If men don’t trust each other, this earth might as well be hell.
Commoner: That’s right. The world’s a kind of hell.
Priest: No! I believe in men. I don’t want this place to be hell.
Commoner: Shouting doesn’t help. Think about it. Out of these three, whose story is believable?
Woodcutter: No idea.
Commoner: In the end, you cannot understand the things men do.
― “Rashomon” (1950)

Commoner: But is there anyone who’s really good? Maybe goodness is just make-believe.
Priest: What a frightening…
Commoner: Man just wants to forget the bad stuff, and believe in the made-up good stuff. It’s easier that way.
― “Rashomon” (1950)

To be an artist means never to avert one’s eyes.

― Akira Kurosawa (1910 – 1998)

If the Emperor had not delivered his [15 August 1945] address urging the Japanese people to lay down their swords — if that speech had been a call instead for the Honorable Death of the Hundred Million — those people on that street in Sōshigaya probably would have done what they were told and died. And probably I would have done likewise. The Japanese see self-assertion as immoral and self-sacrifice as the sensible course to take in life. We were accustomed to this teaching and had never thought to question it.

― Akira Kurosawa (1910 – 1998)

Kurosawa’s Rashomon is the defining movie of an Epsilon Theory world, where Narrative dominates and Truth with a capital T is nowhere to be found. The bandit, the wronged woman, the dead samurai, and the woodcutter witness all testify at trial, and the only certainty, as unsatisfying as it may be, is that we the jury will never know what truly happened in that forest. Such is life. Such is history.

I think Kurosawa is spot on with his assessment of Japanese political culture, by the way. No anti-status quo Trump or Brexit votes there. Just the resignation of self-sacrifice and the long slow slide into irrelevance.  

To Counterfeit is DEATH.

― Ben Franklin (1706 – 1790), from a 15 shilling note of his design

For 600 years, from the 13th century to 1870, the punishment for counterfeiting in Great Britain and its colonies was the same as for high treason — to be hanged, drawn, and quartered. First you’d be slowly hanged, so that you came close to asphyxiation but couldn’t end the suffering by breaking your neck, then you’d be castrated, then you’d be disemboweled, and THEN you’d be killed by beheading. And then for good measure your headless body would be chopped into four pieces.

British counterfeiters during the American Revolutionary War were known as “shovers” for their efforts to “shove” fake dollars into circulation and destabilize the Colonial government. One infamous shover, David Farnsworth, was arrested with more than 10,000 counterfeit dollars, a not-so-small fortune in 1778. George Washington called for Farnsworth to be tortured to death, but Farnsworth got off easy and was simply hanged.

The largest counterfeiting operation in the history of economic warfare was Operation Bernhard, a German plan during the Second World War to destabilize the British economy by flooding the global economy with forged Bank of England notes. The forgeries are, for all practical purposes, indistinguishable from the originals.

Alves dos Reis, instigator of the Portuguese Bank Note Crisis of 1925 and my choice for the greatest counterfeiter of all time. ADR didn’t print fake Portuguese currency. He printed fake instructions to the official banknote printers (famed London firm Waterlow and Sons) to print REAL notes equivalent in value to almost 1% of Portugal’s nominal GDP, and ship them to him directly.

It may please your majesty to understand, that the first occasion of the fall of exchange did grow by the King his majesty, your late father, in abasing his coin from six ounces fine to three ounces fine. Whereupon the exchange fell from 26s 8p to 13s 4p which was the occasion that all your fine gold was conveyed out of this your realm.

― Sir Thomas Gresham (1519 – 1579), letter to Queen Elizabeth I

Gresham’s Law: bad money drives good money out of circulation.

Hunt’s Law: fake news drives real news out of circulation.

What is unexpected about medieval houses, however, is not the lack of furniture but the crush and hubbub of life within them. Households of up to twenty-five persons were not uncommon. Since all of these people lived in one or at the most two rooms, privacy was unknown. How did people achieve intimacy under such conditions? It appears that they did not. Medieval paintings frequently show a couple in bed or bath and, nearby in the same room, friends or servants in untroubled, and apparently unembarrassed, conversation.

― Witold Rybczynski, “Home: A Short History of an Idea” (1986)

In 1215, the Fourth Lateran Council of the Catholic Church decreed that everyone, king and commoner alike, should practice individual confession. In a very real sense, the IDEA of privacy — the concept of an internal life of the mind as a social good — did not exist in the West before this pronouncement.

They say the Nile used to run

From East to West

And you know I’m fine

But I hear those voices at night

Sometimes

The star maker says it ain’t so bad

The dream maker’s going to make you mad

The spaceman says everybody look down

It’s all in your mind

 ― The Killers, “Spaceman” (2008)

Illustration of a wolf trap from Le Livre de la Chasse (c. 1407). An entire pack could be captured by laying a blood trail through a one-way wicker door in a circular fence built around a central pen with a scared, bleating sheep. The design ensured that individual wolves could not see each other until it was too late, each wolf believing that it was on a uniquely rewarding path. I’m pretty sure this painting now hangs in Mark Zuckerberg’s office.

On December 30, the Washington Post published a story claiming that Russian hackers had “penetrated the U.S. electric grid” through an “attack” on Burlington Electric, a Vermont utility.

In a statement that night Vermont Gov. Peter Shumlin (D) said, “Vermonters and all Americans should be both alarmed and outraged that one of the world’s leading thugs, Vladimir Putin, has been attempting to hack our electric grid, which we rely upon to support our quality-of-life, economy, health, and safety. This episode should highlight the urgent need for our federal government to vigorously pursue and put an end to this sort of Russian meddling.”

Vermont Sen. Patrick Leahy (D) said he was briefed by Vermont State Police on Friday evening, and announced via statement that “This is beyond hackers having electronic joy rides — this is now about trying to access utilities to potentially manipulate the grid and shut it down in the middle of winter. That is a direct threat to Vermont and we do not take it lightly.”

According to Vermont Rep. Peter Welch (D), the attack showed that Russia “will hack everywhere, even Vermont, in pursuit of opportunities to disrupt our country. We must remain vigilant, which is why I support President Obama’s sanctions against Russia and its attacks on our country and what it stands for.”

Wow, even Vermont. Those Russian bastards.

The next day, the Washington Post amended their original story. Turns out that there was no “penetration of the U.S. electric grid.” Turns out that a Burlington Electric employee discovered that his notebook computer, which had never and would never be connected to the grid, had a virus on it. And that virus was probably written in Russia. It’s the same type of virus that lifted John Podesta’s emails. It’s the same type of virus that could lift my emails if I clicked on a “Free Gift From Amazon!!” link. That’s it. That was the “attack on our country and what it stands for.” A Burlington Electric employee clicked on a bad link inside a scam email and downloaded a virus.

So was this Washington Post article fake news?

This may surprise regular Epsilon Theory readers, but no, I don’t think it was. It was fiat news, which is to “real news” what fiat currencies like dollars and euros and yen are to “real money” like a gold coin. Fake news is something different. Fake news is counterfeit news, which is to fiat news what counterfeit bank notes are to fiat currencies.

I think this distinction between fiat news and counterfeit news is an important one. Why? Because when we conflate fiat news with counterfeit news we talk past each other. If we equate the Washington Post’s obviously partisan slanting of news with Russia’s obviously interventionist creation of news, as if both are simply purveyors of “fake news”, then we end up in the ridiculous position of apologizing for one, tacitly or explicitly, when we complain about the other. Democrats (and they’re mostly Democrats) justifiably upset about Russia stealing DNC emails and interfering with our election inevitably find themselves required to defend the Washington Post as some paragon of journalistic integrity. Republicans (and they’re mostly Republicans) justifiably upset about the Washington Post casually equating criticism of the Obama administration with being a treasonous stooge inevitably find themselves required to defend Russia as some falsely accused innocent abroad. So long as both Russia and the Washington Post are evaluated on the same simplistic dimension (is it fake news or real news?), we are forced into contortions of cognitive dissonance to criticize one without tarring the other.

My view: both Russia and the Washington Post deserve as much tarring and as much criticism as humanly possible. My view: both Russia and the Washington Post are bad actors. My view: both Russia and the Washington Post present a danger to a well-functioning American democracy. But they present different dangers, with different dynamics, with different strategic interactions, and with different likely policy responses, because they operate on different dimensions of Information Theory. Oh yeah, one more … my view: there are lots of Russias and there are lots of Washington Posts out there.

Russia is in the counterfeit news business. They are trying to influence our political process to their sovereign benefit, just like the United States is in the counterfeit news business inside Russia and every other corner of the world. Russia is always a foe to a status quo American regime, regardless of which party is in the White House, as their sovereign self-interest requires constant competition. If you trust Putin, you are a fool.

The Washington Post is in the fiat news business. They are trying to influence our political process to their institutional benefit, just like the Wall Street Journal and every other mainstream media institution is in the fiat news business. The Washington Post is never a foe to a status quo American regime, regardless of which party is in the White House, as the regime bestows on them the authority to issue fiat news. Still, if you trust the Washington Post, you are no less a fool.

The fiat news business is booming. As a result, the counterfeit news business is booming, too. And if the history of fiat money and counterfeit money is any guide, we ain’t seen nothing yet.

The fiat news business is a centerpiece of Epsilon Theory, from “Uttin’ On the Itz” to “Catch-22” to “The New TVA” to “My Passion Is Puppetry” to “When Narratives Go Bad”, so I won’t repeat all that here. But I’ll repeat some. This is from “Stalking Horse”, one of my all-time favorite Epsilon Theory notes, back in September 2014. I think it holds up pretty well as a definition of fiat news, or what I (and the Fed) have called “strategic communication policy” in the past:

“Once you start thinking about what’s happening in markets and the world as an inextricable weave of fundamental events and political efforts to shape our interpretation of those events to achieve a political end, you start to see stalking horses everywhere. A Fed QE program ostensibly to reduce unemployment and help Main Street? Stalking horse. A regulatory Big Data program ostensibly to identify brokers who churn accounts? Stalking horse. A Chinese banking program ostensibly to liberalize currency exchange rates? Stalking horse.

And it’s not just actual programs or actual market behaviors like the Chinese purchase of U.S. Treasuries. When words are used for strategic effect rather than a genuine transmission of information you create a virtual stalking horse. This, of course, describes every use of words by every politician and every central banker. This is what Bernanke and Yellen and Draghi and Abe and Obama and Merkel mean when they refer to communication policy. Communication policy is the strategic use of words to shape perceptions and expectations. It’s a focus on how something is said as opposed to what is described. It’s a focus on form rather than content, on truthiness rather than truth. It’s why authenticity is as rare as a unicorn in the public world today.

Look, I understand why politicians and bankers have completely abandoned authenticity, an uncommon quality even in the best of times. The Great Recession was a near-death experience for the global economy, and slamming a syringe of adrenaline into the patient’s heart — which was basically what QE 1 did — doesn’t happen without long-term side-effects. To switch the metaphor around a bit, this was a war to preserve the System, and as Aeschylus said 2,500 years ago, the first casualty of war is truth. I really don’t think Bernanke or Draghi came into office thinking that their public statements would become the most powerful weapon in their arsenal, or that they could train markets to respond so positively to words presented strategically for effect, but there you have it. This is what worked. This is how the war was won.

So … I understand why politicians and bankers have adopted a stalking horse technique to shape market expectations and behaviors, but that doesn’t mean I have to like it. And while I am happy to condone the use of emergency powers to win a war and save the world, I am not at all comfortable with their continued use once the crisis is over. Unfortunately, I believe that is exactly what has happened, that “strategic communication policy” has mutated from an emergency measure designed to prevent an economic collapse into a standard bureaucratic process designed to maintain financial stability. Is this banal assumption and routinization of power a natural bureaucratic response to a crisis, something we also saw in the aftermath of the Great Depression? Yes, but I’ve got examples going the other way, too. Lincoln suspended habeas corpus in 1861, and good for him. But in early 1866 — less than a year after Lee’s surrender at Appomattox — the U.S. government stood down and restored Constitutional protections. I am really hard-pressed to understand how the exigencies of recovery from the Great Recession, now more than 5 years on, are somehow more deserving of ongoing emergency policies than the immediate aftermath of the freakin’ Civil War.

Wait a second, Ben. Are you seriously equating the government’s use of “strategic communications” to a suspension of Constitutional protections? Doesn’t that seem a tad over the top? Yes I am, and no I don’t think so. The bedrock assumption of limited, representative government is that we, the people have an inalienable right to make an informed decision about who will make policy decisions on our behalf. Of course this is an imperfect process, and of course the information we use to make these decisions will be mediated and skewed by all sorts of competing interests. But it makes a big difference if the government itself is fully committed to mediating and skewing this information. And it makes all the difference in the world if relatively apolitical institutions like the Fed and various regulatory authorities — institutions which have been granted a vast array of powers over the years precisely because they have been viewed as relatively apolitical — now embrace the highly political act of mediating and skewing information in service to their own particular visions of stability and status quo preservation. This is the danger of strategic communication policy. This is the price we pay for a loss of authenticity within our most important institutions.”

Like I say, holds up pretty well, particularly after this last election cycle. If I rewrote it today, the only change I’d make is to explicitly add news organizations like the Washington Post or CNN to the list of privileged institutions that “now embrace the highly political act of mediating and skewing information in service to their own particular visions of stability and status quo preservation.”

So where does it all go from here? I’ll take a cue from the history of fiat money and its counterfeits and hazard three predictions. After all, prices and news are both just signals when seen through the lens of Information Theory, and the same dynamics and “laws” should apply to both.

First, there’s no reason to believe that the breadth and scope of fiat news won’t grow to the same level of ubiquity as fiat money. There’s really no such thing as “real money”, i.e., gold and silver as a medium for exchange or a store of value, in existence in the world today. That used to be the meaning of gold, but those days are long gone. Today fiat money completely and utterly dominates all global commerce and exchange. Why? Because it supports the existential aims of government: taxation (sovereignty), price control (stability), and liquidity provision (growth). Without the invention of fiat money, global GDP today would be at … I dunno, maybe mid-18th century levels? Something around there, I’d guess. Fiat news serves exactly the same existential aims of government, just in a less overt (but more powerful for being hidden) fashion. There’s just too much at stake for status quo regimes, what with modern referenda like Brexit and national elections like we just experienced in the U.S. and are forthcoming this year throughout Europe, for regime institutions to do anything other than double-down in their embrace and promulgation of fiat news.

Ten years from now we will be awash in “news” to a degree that we can hardly imagine today. That’s what happened with fiat money, and that’s what I think happens with fiat news. The exponential growth in fiat news is still ahead of us, not behind us.

Second, while counterfeit news will continue to suffer the same official opprobrium and punishment as counterfeit money has endured over the centuries, we’re going to see a lot more of it in the years to come. In the same way that it’s easier to counterfeit fiat paper than gold or silver coins, so is it easier to counterfeit fiat news. I mean, the bang for the buck that Russia got from their email hacking and dissemination exploits in 2016 is just … staggering. What Russia did with counterfeit news is the same thing that the British did during the American Revolution with counterfeit dollars. It’s the same thing that the Germans did during World War II with counterfeit pounds. It’s the same thing that I’m sure the U.S. has done in more countries and more conflicts than one can easily count. But what Russia has shown is how easy and cheap it is to counterfeit news for yuuuge sovereign benefit when ALL news is constructed and slanted to some degree. Trust me, this lesson is not lost on China. Or Germany. Or France. Or India. Or Brazil. The Information Wars are just beginning, and the equivalent of hydrogen bombs are both crazy cheap to build and the technology is fully proliferated. There’s no putting this genie back in the bottle.

Is this a potential casus belli? Absolutely. Counterfeit strikes at the heart of what it means to be a sovereign government, whether or not we’re talking about money or news, particularly when the counterfeiting is done by another government. My guess is that the next level of counterfeiting, one that could spark a shooting war, will take the form of something like the Portuguese Bank Note Crisis of 1925, where the real printers of the real money were tricked into printing massive quantities of real notes for a fake customer. This was non-forgery counterfeiting, and it’s the future of sovereign-directed counterfeit news.

Third, Gresham’s Law applies to news as well as money, meaning that fake news drives real news out of circulation. When Thomas Gresham wrote Queen Elizabeth I in 1560 to deliver the bad news that “all your fine gold was conveyed out of this your realm” because her father Henry VIII had debased the currency by lowering the silver content of his coins, he didn’t mean that people packed up their gold and shipped it to France. He meant that people hoarded the old (good) silver coins and didn’t spend them. He meant that people hoarded their gold (or any trusted store of value) and refused to exchange them for Elizabeth’s coins. Elizabethan citizens lost trust in ALL commonly exchanged coins, no matter what the coins looked like or who offered up the coins, because Elizabethan citizens were good game players. If you’re willing to exchange an unknown silver coin for my bad silver coin (or something priced in bad silver coins), then either you’re stupid or that’s also a bad silver coin. Let’s assume you’re not stupid, so I’m going to treat your coin as bad regardless of whether it’s truly a good coin or not. And if you truly have a good coin, there’s nothing you can say to me that will convince me it’s a good coin. You can’t spend your good coin for fair value even if you wanted to. It’s exactly the same with news today. We know that the news has been “debased” through strategic communication policy, through the intentional slanting and mediation of primary news by officially sanctioned sources like Fed Chairs and CNN anchors in the service of stability and status quo maintenance. As a result, we’ve lost trust in ALL commonly exchanged news, no matter what the news is about or who offers up the news. Even though we’re awash in news, just like Elizabethan England was awash in coinage, the exchange value of ALL news has been diminished regardless of its “truth-content”. “Real” news today is diminished in value simply by the act of dissemination. You can’t spend your real news for fair value even if you want to, so it makes no business sense to spend real money to collect real news.

It’s this third point that is the most important, because it points to a potential transformation in the way that we THINK about news, a change in the IDEA of news as a social good. These transformations happen rarely — the invention of privacy, for example, in the 13th century — but they ARE inventions, no less so for being conceptual than the tangible invention of the steam engine or the semiconductor. And when these conceptual transformations do occur, they change the entire course of human civilization.

I’m using the social invention of privacy as a prominent example because I think that this transformation in the idea of news as a political good is connected with a similar transformation in the idea of privacy, both of which are being reinvented by technology. It’s no accident that Facebook is at the center of both.

Here’s the crux of Facebook’s Dec. 15 announcement on combating fake news:

Flagging Stories as Disputed

We believe providing more context can help people decide for themselves what to trust and what to share. We’ve started a program to work with third-party fact checking organizations that are signatories of Poynter’s International Fact Checking Code of Principles. We’ll use the reports from our community, along with other signals, to send stories to these organizations. If the fact checking organizations identify a story as fake, it will get flagged as disputed and there will be a link to the corresponding article explaining why. Stories that have been disputed may also appear lower in News Feed.

In practice this means that four established fiat news institutions — Associated Press, ABC News, the Washington Post, and the Tampa Bay Times (Politifact) — together with a smaller media company, Snopes.com, will share the responsibility for determining what stories are “disputed” and dropped into the memory hole of a lower ranking in News Feed. More fact checkers, particularly more fiat news institutions, will undoubtedly be added to the list, as the process is designed to encourage fiat news institutional participation (The Poynter Institute, developer of the “Fact Checking Code of Principles” at the heart of Facebook’s efforts here, owns the Times Publishing Company, which in turn owns the Tampa Bay Times and Politifact). My sense is that these fact checkers can do a pretty good job of identifying counterfeit news (for example that’s why Snopes.com was started, albeit in an urban legend and email hoax context), but will fail miserably at policing fiat news, for obvious reasons. Not that Facebook cares about the distinction, of course, as they will become the preeminent fiat news provider themselves when all is said and done.

Facebook’s erosion of privacy settings and protections is a long-running saga, reflecting Mark Zuckerberg’s many public statements that privacy is “no longer a social norm.” He’s probably right, which is exactly my point in writing about it in this note and linking it to a change in social norms regarding political information and news. But my concern isn’t that Facebook prevents me from maintaining privacy with regard to other Facebook users. My concern is that Facebook prevents me from maintaining privacy with regard to Facebook and other regime institutions, both corporate and governmental, so that the fiat news I receive is curated and distributed to successfully elicit a specific response from me.

I know, I know … if you don’t want your actions and preferences exposed to The Controllers, don’t use Facebook. And I don’t. But in the same way that there are lots of Russias and lots of Washington Posts out there, so are there lots of Facebooks. Plus the Facebook and the Amazon and the Google are getting harder and harder to avoid. Each of these companies has designed a wonderfully effective Medieval wolf trap, complete with blood trail and bleating sheep, to lure all of us wolves into the pen, and I’m certainly no exception to that. It’s brilliant, really, even if horribly depressing.

What’s happening here is reflective of a prominent feature of American political culture, namely that we tend to trust anything technology or business related, and correspondingly mistrust anything that comes from the government. It’s a big part of the Trump phenomenon, as lots of people have noted, but it goes back literally a couple of hundred years. What’s Hamilton’s core appeal? He’s a self-made man, the highest praise you can offer in the American political tradition. My view, of course, is that it’s absolutely nuts to trust billionaires to devise or administer your social policy, whether it’s Donald Trump or Mark Zuckerberg or Eric Schmidt or Jeff Bezos or whoever, more or differently than you trust permanent members of the political class like the Clintons or the Bushes or whoever. Actually I take that back. I trust technology billionaires like Zuckerberg and Schmidt and Bezos LESS than I trust the Clintons and the Bushes when it comes to my political interests and democracy-supporting social policy, which is really saying something. Why? Because gridlock. I love gridlock. I love the checks and balances embedded in our political machinery, because it prevents government from doing as much as it otherwise would to interfere with and upend my life. There’s no gridlock at Facebook or Amazon or Google, and this is where you’ll find the road to smiley-face authoritarianism. Where are we going? It’s not George Orwell’s 1984. It’s Dave Eggers’ The Circle. A world awash in fiat news as administered by government-licensed technology behemoths with a dissemination “platform”. We don’t trust the news, and in the back of our minds we know we’re being played, but boy, is it an entertaining and compelling delivery.

So what’s to be done? Not much in a political sense. Proposing some “plan” to roll back Facebook and Google and Amazon’s usurpation of fiat news dissemination makes about as much sense as proposing a plan to roll back the Catholic Church in 1215. As a citizen pretty much the best I can do is ring the cow bell with Epsilon Theory and try to convince other citizens to see the world through the same Rashomon-esque lens. As Akira Kurosawa said, to be an artist means never to avert one’s eyes. Ditto for a liberty-loving citizen.

As an investor, though, I hope to do more than just add more cow bell. I think it’s possible to use new technologies to track and analyze the dynamic of fiat news dissemination, i.e., the Narrative, within the discrete social system of capital markets. It’s the same family of new technologies that Zuckerberg and Schmidt and Bezos are using to shape our entire society, just applied for analytical purposes rather than shaping purposes. Plus a wee difference in scale. I’m applying these technologies to a very specific social dynamic — the Common Knowledge Game — that I believe dominates policy-driven markets and story-driven stocks. I call this the Narrative Machine, and it’s the centerpiece of my investment activities for 2017 and beyond. Here’s the Epsilon Theory note that launched the project, and I hope you’ll find this a useful research project to track in the Brave New World ahead.

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American Hustle

Irving Rosenfeld:   Did you ever have to find a way to survive and you knew your choices were bad, *but* you had to survive?

― “American Hustle” (2013)

Only when I wake up in the morning. Nothing but caper movie quotes today. Seems appropriate.

epsilon-theory-american-hustle-november-17-2016-the-sting

Doyle Lonnegan: I put it all on Lucky Dan; half a million dollars to win.
Kid Twist: To win? I said *place*! “Place it on Lucky D-” That horse is gonna run second!
Doyle Lonnegan: [There is a pause, and Lonnegan runs horrified to the betting booth] There’s been a mistake! Gimme my money back!
― “The Sting” (1973)

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I suspect there were more than a few Doyle Lonnegan moments in Silicon Valley and the Hamptons last Tuesday night. Here, for example, is Lady Gaga looking particularly distraught, as photographed in her Rolls Royce. No, really.

Wanda: [after Otto breaks in on Wanda and Archie in Archie’s flat and hangs him out the window] I was dealing with something delicate, Otto. I’m setting up a guy who’s incredibly important to us, who’s going to tell me where the loot is and if they’re going to come and arrest you. And you come loping in like Rambo without a jockstrap and you dangle him out a fifth-floor window. Now, was that smart? Was it shrewd? Was it good tactics? Or was it stupid?
Otto West: Don’t call me stupid.
Wanda: Oh, right! To call you stupid would be an insult to stupid people! I’ve known sheep that could outwit you. I’ve worn dresses with higher IQs. But you think you’re an intellectual, don’t you, ape?
Otto West: Apes don’t read philosophy.
Wanda: Yes they do, Otto. They just don’t understand it. Now let me correct you on a couple of things, OK? Aristotle was not Belgian. The central message of Buddhism is not “Every man for himself.” And the London Underground is not a political movement. Those are all mistakes, Otto. I looked them up.
― “A Fish Called Wanda” (1988)

Gonna be lots of Ottos in this administration. I count three in cabinet-level appointments so far.


epsilon-theory-american-hustle-november-17-2016-the-grifters

Lilly Dillon: You’re working some angle, and don’t tell me you’re not because I wrote the book!
Roy Dillon: What about you? You still handling playback money for the mob?
Lilly Dillon: THAT’s me. That’s who I am. You were never cut out for the rackets, Roy.
Roy Dillon: How come?
Lilly Dillon: You aren’t tough enough.
Roy Dillon: Not as tough as you, huh?
Lilly Dillon: Get off the grift, Roy.
Roy Dillon: Why?
Lilly Dillon: You haven’t got the stomach for it.
― “The Grifters” (1990)

Anjelica Huston’s best work. Worth watching just for Bobo and the oranges, hands down one of the most psychologically horrific scenes in American cinema. John Cusack plays Lily’s son, and she’s right: he doesn’t have the stomach for this line of work. Neither do a lot of portfolio managers.

Randolph Duke: Exactly why do you think the price of pork bellies is going to keep going down, William?
Billy Ray Valentine: Okay, pork belly prices have been dropping all morning, which means that everybody is waiting for it to hit rock bottom, so they can buy low. Which means that the people who own the pork belly contracts are saying, “Hey, we’re losing all our damn money, and Christmas is around the corner, and I ain’t gonna have no money to buy my son the G.I. Joe with the kung fu grip! And my wife ain’t gonna f… my wife ain’t gonna make love to me if I got no money!” So they’re panicking right now, they’re screaming “SELL! SELL!” to get out before the price keeps dropping. They’re panicking out there right now, I can feel it.
Randolph Duke: [on the ticker machine, the price keeps dropping] He’s right, Mortimer! My God, look at it!
― “Trading Places” (1983)

Like any good trader, Billy Ray has internalized the Common Knowledge Game.

epsilon-theory-american-hustle-november-17-2016-trading-places

Louis Winthorpe III: Randolph. Mortimer.
Mortimer Duke: Winthorpe, my boy, what have you got for us?
Louis Winthorpe III: Well, it’s that time of the month again. Payroll checks for our employees, which require your signatures. And no forgetting to sign the big ones!
Mortimer Duke: We seem to be paying some of our employees an awful lot of money.
Louis Winthorpe III: [laughs] Can’t get around the old minimum wage, Mortimer.
― “Trading Places” (1983)

Europeans take racial differences and put them on the dimension of class. Americans take class differences and put them on the dimension of race. Randolph and Mortimer do both.

epsilon-theory-american-hustle-november-17-2016-matchstick-men

Angela: She said you were a bad guy. You don’t seem like a bad guy.
Roy: That’s what makes me good at it.
Roy: For some people, money is … money is a foreign film without subtitles.
― “Matchstick Men” (2003)

Nicolas Cage can act. When he wants to. Ridley Scott can direct. Always. To paraphrase Woody Allen, 90% of alpha is just showing up.

epsilon-theory-american-hustle-november-17-2016-oceans

Linus Caldwell: Um, all right, let’s go over the list again. Ah, “Swinging Priest”?
Basher Tarr: Not enough people.
Linus Caldwell: “Crazy Larry”?
Turk Malloy: Not enough people.
Linus Caldwell: “Soft Shoulder”?
Basher Tarr: Not enough people.
Linus Caldwell: “Baker’s Dozen”?
Basher Tarr: No woman
[pause]
Basher Tarr: and not enough people.
Turk Malloy: “Hell in a Handbasket”?
Linus Caldwell: [sigh] We can’t train a cat that quickly
[pause]
 Linus Caldwell:  and…
All: Not enough people.
― “Ocean’s 12” (2004)

This is my new go-to line for every business or policy challenge: we can’t train a cat that quickly.

Basher Tarr: You don’t run the same gag twice … you run the next gag.
― “Ocean’s 13” (2007)

epsilon-theory-american-hustle-november-17-2016-steve-bannon

The only question that matters for surviving the next four years: what’s the gag they’re running on us? What’s the narrative they’re constructing? Behold Steve Bannon, gag-meister extraordinaire.

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Rusty Ryan: Turn the machine off guys.
Turk Malloy: It is off.
Rusty Ryan: Are you kidding?
Turk Malloy: Does it sound like I’m laughing, sweetheart?
― “Ocean’s 13” (2007)

Sometimes when you fire up an earthquake machine, you get a real earthquake.

There are three questions I’d like to answer in this Epsilon Theory note: what did the Narrative Machine tell us about the market immediately before and immediately after the November 8 election, what am I preparing for now as an investor, and what am I preparing for now as a citizen? I’m giddy about the first, quietly confident about the second, and pretty darn depressed about the third. Could be worse, I suppose.

On the first question, the Narrative Machine gave clear, actionable, and non-consensus signals prior to the U.S. election last week. For readers who aren’t familiar with what I mean by the Narrative Machine, I’ll refer you to this note by the same title. In a nutshell, I’m using a technology called Quid to take Big Data snapshots of large numbers of financial media articles. These snapshots show the connectivity and influence of each article to every other article, constructing a neural network or “star map” of the narratives and meaning clusters that link the articles. By looking at measures of sentiment and connectivity associated with the network, I think that I can get a good sense of market complacency around events like a Trump victory, as well as the likely direction and magnitude of market moves if an event like that comes to pass. Bottom line: I think that the Narrative Machine gives us a good sense of what’s priced into markets.

Here’s the Quid map of Bloomberg articles talking about Trump in weeks T-5 through T-2.

epsilon-theory-american-hustle-november-17-2016-quid-bloomberg-trump-sentiment

The skinny: there was never any complacency in markets about a Trump win. There was negative sentiment, but no complacency. Maybe the Huffington Post thought there was only a 5% chance of a Trump win, but markets were taking it much more seriously than that.

Now here’s the Quid map of Bloomberg articles talking about Trump in the week immediately preceding the election.

epsilon-theory-american-hustle-november-17-2016-quid-bloomberg-trump

Still just as focused (the 7.6 score here is only slightly less attentive and concentrated than the 8.5 score of markets after the Brexit vote), but look at the sentiment score. We’ve moved from highly negative to only slightly negative. More to the point, it’s the change in score that’s really important, so this Narrative map is telling us that not only is a Trump victory priced into current market price levels, but if he were to win, the market wouldn’t go down much, if at all. That’s in sharp contrast to the consensus view (you know who you are), that not only was the market highly complacent about the prospects of a Trump win, but also that a Hillary defeat would be a disaster for markets, with projections for as much as 12% down.

My commitment to the Narrative Machine research project is to make it as public as possible. Mass email is a poor distribution method, so I tweeted about these findings on Monday, November 7 (@EpsilonTheory) and spoke about them on a Salient-hosted conference call on Tuesday, November 8. But I’m also managing portfolios for Salient now as part of the internal reorganization we announced in October, so I have a responsibility there, too. Long story short … follow me on Twitter to stay the most engaged with this project.

So what’s next for markets?

First, the positive market Narrative regarding tax repatriation, regulatory reform, and fiscal stimulus in the form of infrastructure spending is for real. And by “real”, I don’t mean that I have any confidence AT ALL that these policies will have any permanent effect or multiplier effect or anything like that on the real economy. Sorry. Maybe regulatory reform has a long-lasting impact. Maybe. No, by “real”, I mean that this policy “reform” is a highly effective signal in the Common Knowledge Game and that it will make stocks go up regardless of its impact (or not) on the real economy. Ain’t that enough? It’s enough for me. The Trump reform and infrastructure Growth Narrative is a tailwind for stocks and a headwind for bonds for the next four years because we want to Believe. True that.

epsilon-theory-american-hustle-november-17-2016-borgSecond, nothing about the Trump reform and infrastructure Growth Narrative is sufficient, in my view, to undo the overwhelmingly negative constraints that massive global debt places on global growth. The Silver Age of the Central Banker is still in full force,with a shrinking global trade pie and domestic political imperatives that accelerate that decline rather than reverse it. Competitive monetary policy is the Borg. First it swallows up currencies, because that’s what currencies are — a reflection of your country’s monetary policy versus other countries’ monetary policies. Then it swallows up commodities — things that don’t have their own cash flow dynamics. Then it swallows up entire economies and swaths of the markets that are levered to commodities — emerging markets in general and developed market segments like industrials, energy and transports in particular. Ultimately it all comes down to monetary policy, and its primary reflection in currencies. It’s the Borg. Resistance is futile.

Here’s an updated chart showing the massive negative correlation between the dollar and oil. This is the trade-weighted broad dollar index in white, as measured by the vertical axis on the left, and this is the inverted spot price of crude oil in green, as measured by the vertical axis on the right. The chart starts in June 2014, because that’s when competitive monetary policy and the Silver Age of the Central Banker begins, when Mario Draghi doubled down on ECB asset purchases and negative interest rates at the same time that Janet Yellen declared her intentions to raise interest rates and forswore more asset purchases.

epsilon-theory-american-hustle-november-17-2016-quid-bloomberg-commodity

Source: Bloomberg, L.P. as of 11/8/16. For illustrative purposes only.

Yes, you get short-lived divergences in the lockstep negative correlation, first at the end of 2014 when OPEC announces that they’re out of the price-fixing game, and then again a month ago when OPEC announces that they’re back in the price-fixing game. The joke’s on OPEC. And global macro investors who still think that OPEC matters, I suppose, but mostly on OPEC. The half-life of whatever OPEC does or doesn’t do is measured in days … weeks at most. What is persistent, what is irresistible, what is the Borg in this equation is whether the dollar is going up or down.

The Trump reform and infrastructure Growth Narrative makes the dollar go up. If the Fed raises rates in December the dollar will go up still more. If you get a bad vote in Italy in a few weeks the dollar will go up still more. If you get any sort of geopolitical shock or U.S. domestic political craziness the dollar will go up still more. Dollar up is bad. Dollar down is good. I don’t know how to say it more plainly than that, and all the Belief in the world about tax reform and repealing Dodd-Frank and all that doesn’t change this reality. Maybe you see that and maybe you don’t. I can promise you, though, that China sees it.

So that’s where I am as an investor. I’m positive on U.S. equities because we’ve got a four year tailwind from the Trump reform and infrastructure Growth Narrative. That’s not going away no matter what China or Europe does. On the other hand, I’m negative on global risk assets, particularly anything connected to global trade finance, because we’re players in several giant games of Chicken and I think at least one of these is going to break bad. But at least I’m looking at the right things (I think), like what’s happening to the dollar and to European financial credit spreads, and that’s what gives me the hope that I can navigate these risks and these rewards. That and the ability to go short.

So I’m giddy about the potential of the Narrative Machine and I’m hopeful that I can maneuver through the investment storms out there. Why am I so down about American politics?

epsilon-theory-american-hustle-november-17-2016-cutlerWell, you gotta admit that this September Epsilon Theory note, “Virtue Signaling, or Why Clinton is in Trouble”, has aged pretty well. Turns out that Hillary Clinton was, in fact, the Jay Cutler of this election cycle, a highly talented but highly flawed performer whose team refused to sell out for her. I stand by everything I wrote in this piece — each candidate will be remembered in Common Knowledge as the Yoko Ono of their respective party, breaking up an all-time great band to make an album or two of dubious, to be generous, quality.

And that means I also stand by what I wrote about Donald Trump. I think he breaks us. Why? Because everything is a deal to Trump. Everything is a transaction, from a vote to a policy to a personal relationship. We all know people like this, men who — as the old Wall Street saying goes — would sell their mother for an eighth. Donald Trump transforms positive-sum Cooperative Games into zero-sum Competitive Games. It’s his nature … his great gift as a New York real estate developer, but his fatal flaw as a politician. Is he “a fighter”? Can he “get deals done”? Sure, and there’s value in that. But OUR great gift as Americans is that we are blessed with positive-sum Cooperative Games in the form of limited government and the political culture to maintain those limitations. Our political culture has been changed by Trump. The teacup has been broken. Can we glue it back? I suppose. But like a broken marriage or a broken partnership it’s never the same. It’s always a broken teacup.

I’m not saying that this broken political culture is Trump’s fault. Like I said, it’s his nature to transform everything he touches into a competitive strategic interaction. I can’t blame him any more than I can blame my Sheltie for barking at the wind. If you don’t want barking, don’t get a Sheltie. But the FACT is that we’ve got a Game Changer for our political culture as president, and there’s no walking that back.

Example: look at the prevalent Democratic meme today, that Trump voters were either motivated by racism directly, or that they willfully tolerated a racist candidate … which is just a paler shade of racism. Okay. I get the argument, although I would ask why Clinton didn’t get the support of working class white voters in Wisconsin, Michigan, and Pennsylvania who voted for Obama twice. Were they racist all along and just hiding it really well? But leave aside the merits of the argument, because there’s no changing anyone’s mind these days on the merits of anything (which is kinda my point). My question is a different one. If you really believe this … if you believe in your heart of hearts that Trump voters are racists … where do you go with this? Or rather, what does politics mean to you now? Politics is no longer a “marketplace of ideas” if you think the other side is comprised of bad guys. You’re not trying to win them over. You’re trying to beat them. Not because you think you’re right (although you do), but because you think you MUST beat them or else your own survival is at stake. It’s not only a zero-sum Competitive Game; it’s a zero-sum Competitive Game of self-defense, which means that anything — anything! — goes.

I’m not trying to pick on the Democratic memes (although they’re such easy targets). You see exactly the same sort of popular Narratives on the Republican side about Democratic voters. To summarize this vast oeuvre, if you’re willing to vote for the evil Hillary and her coven of soul-devouring, child-stealing, gun-confiscating, tax-raising, war-starting warlocks and witches … well, you must either be a sheep or a thieving Team Elite wannabe. Either way, you’re contemptible. Contemptibles and Deplorables, not Democrats and Republicans. My point is that if you believe that the people on the other side of a political argument are not just wrong, but are basically bad people, then the meaning you ascribe to politics — your political culture — is entirely different than if you think the other side is comprised of basically good people. You don’t cooperate with bad people, and the political institutions you favor if you’re surrounded by bad people are very different — and very un-American, in the de Tocqueville-ian sense of that word — than what the Founders came up with.

Look, Trump is no Hitler — that’s Erdogan’s shtick — and Trump’s preening egomania is actually a good thing because it crowds out ideological fervor. I mean, he’s not building a political machine to instantiate His Hugeness in institutional form. But there will be people around him who will try, and unfortunately, if I were a betting man — and I am — I’d bet on them to succeed. The rewards are too great and the technological tools at their disposal are too powerful and the political culture is too conducive to the effort and if it’s not them it will be the Thermidorean political reaction of the Left, and that depresses the bejeezus out of me. True that, too.

But that’s the World As It Is, a world of incredible technological promise that thrills the puzzle-solver in me, a world of reasonably interesting market patterns that gives hope to the investor in me, and a world of ascendant soft authoritarians that chastens the small-l liberal in me. I don’t think I’m alone. Put it all together, and my attitude is perfectly summed up by the most perfect ending in all of American literature.

So we beat on, boats against the current, borne back ceaselessly into the past.

Onwards. Together. Please.

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The Narrative Machine

epsilon-theory-narrative-machine-august-17-2016-clockwork-orange

Alex: There was me, that is Alex, and my three droogs, that is Pete, Georgie, and Dim, and we sat in the Korova Milkbar trying to make up our rassoodocks what to do with the evening. The Korova Milkbar sold milk-plus, milk plus vellocet or synthemesc or drencrom, which is what we were drinking. This would sharpen you up and make you ready for a bit of the old ultra-violence.
“A Clockwork Orange” (1971). Society is a clockwork, with gears constructed of language and guns.

epsilon-theory-narrative-machine-august-17-2016-corbusier

A house is a machine for living in.

― Le Corbusier (1887 – 1965), pioneer of modern architecture.

We live our lives inside machines, visible and invisible, tangible and intangible.

epsilon-theory-narrative-machine-august-17-2016-no-mouth-must-scream

HATE. LET ME TELL YOU HOW MUCH I’VE COME TO HATE YOU SINCE I BEGAN TO LIVE. THERE ARE 387.44 MILLION MILES OF PRINTED CIRCUITS IN WAFER THIN LAYERS THAT FILL MY COMPLEX. IF THE WORD HATE WAS ENGRAVED ON EACH NANOANGSTROM OF THOSE HUNDREDS OF MILLIONS OF MILES IT WOULD NOT EQUAL ONE ONE-BILLIONTH OF THE HATE I FEEL FOR HUMANS AT THIS MICRO-INSTANT. HATE. HATE.

― Harlan Ellison, “I Have No Mouth and I Must Scream” (1967). In Ellison’s post-apocalyptic horror, the last five humans on earth live inside a giant omnipotent machine where the only escape is death. It’s The Matrix 30 years before The Matrix was written, and 1,000x nastier.

epsilon-theory-narrative-machine-august-17-2016-norbert-wiener

Mathematics, which most of us see as the most factual of all sciences, constitutes the most colossal metaphor imaginable.

It is easy to make a simple machine which will run toward the light or away from it, and if such machines also contain lights of their own, a number of them together will show complicated forms of social behavior.

― Two quotes from Norbert Wiener (1894 – 1964). Wiener received his Ph.D. in mathematics from Harvard at age 17, volunteered to fight in World War I as an enlisted man, but couldn’t get a teaching job at Harvard because he was a Jew. Wiener found a home at MIT, where he became the father of cybernetic theory, aka the mathematics of machine behavior.

epsilon-theory-narrative-machine-august-17-2016-how-economic-machine-works

How does the economy really work?

This simple but not simplistic video by Ray Dalio, Founder of Bridgewater Associates, shows the basic driving forces behind the economy, and explains why economic cycles occur by breaking down concepts such as credit, interest rates, leveraging and deleveraging.

― Ray Dalio, “How the Economic Machine Works”. In the three years since Dalio released this short-form film, it has been viewed more than 3 million times.

Machines were the ideal metaphor for the central pornographic fantasy of the nineteenth century, rape followed by gratitude.

― Robert Hughes, “The Shock of the New” (1980). A writer’s writer and a critic’s critic. As honest in his self-assessment as his assessment of art and society. It’s a bit uncomfortable, isn’t it? Honesty always is.

epsilon-theory-narrative-machine-august-17-2016-rube-goldberg

Self-operating napkin

Many of the younger generation know my name in a vague way and connect it with grotesque inventions, but don’t believe that I ever existed as a person. They think I am a nonperson, just a name that signifies a tangled web of pipes or wires or strings that suggest machinery.

― Rube Goldberg (1883 – 1970)

So, in the interests of survival, they trained themselves to be agreeing machines instead of thinking machines. All their minds had to do was to discover what other people were thinking, and then they thought that, too.

― Kurt Vonnegut, “Breakfast of Champions” (1973). If there’s a better description of modern markets, I have yet to find it. We have become agreeing machines. Because our survival requires it.

epsilon-theory-narrative-machine-august-17-2016-dh-lawrence

For God’s sake, let us be men

not monkeys minding machines

or sitting with our tails curled

while the machine amuses us.

Monkeys with a bland grin on our faces.

― D.H. Lawrence (1885 – 1930). Yes. For God’s sake.

epsilon-theory-narrative-machine-august-17-2016-leeuwenhoek

Antonie Van Leeuwenhoek (1632 – 1723), the father of microbiology, alongside a schematic of his microscope and drawings of the “animalcules” he found in a drop of water. Van Leeuwenhoek was a hobbyist lens maker, and he discovered a process for making very small, very high quality glass spheres which provided unparalleled magnification. He never shared his most powerful lenses, nor his manufacturing process, in order to maintain a monopoly on his discoveries. The glass-thread-fusing process died with him and was not rediscovered until 1957, long since supplanted by ground lenses.

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Copernicus gets all the credit, but his 1543 theory of a heliocentric solar system with circular planetary orbits was a practical dud compared to Ptolemy’s earth-centric theory from 1,400 years earlier. The Copernican model just didn’t work very well. It took better data through new instruments (Tycho Brahe’s observatory) plus better theory through new math (Johannes Kepler’s elliptical orbits) before we finally got it right. But even then, the idea of a heliocentric solar system with elliptical planetary orbits didn’t find popular acceptance until powerful institutions in Northern Europe found it useful to champion this new idea as part of their fight with the Catholic Church and other powerful European institutions.

Modern portfolio theory = Ptolemaic theory. Are powerful institutional investors ready to fight?

Every successful institution, from a marriage to a superhero to a firm to a nation, needs an origin story.

The origin story of arguably the most successful hedge fund institution of the modern world – Bridgewater Associates – is that of Ray Dalio, working out of a small New York apartment in 1975 and publishing a newsletter of “Daily Observations.” The newsletter came first, not the hedge fund, and it was the compelling strength of Dalio’s writings about markets and what he would later term “the Economic Machine” that convinced a few institutional investors to give him some actual capital to invest. The rest, as they say, is history.

In 1975, Dalio struck just the right chord at just the right time with his metaphor of an Economic Machine – the idea that macroeconomic reality across time and place could be understood as a cybernetic system, with rules and principles and behaviors stemming from those rules and principles (essentially, lots and lots of if-then statements and recursive loops, with observable inputs from real-world economic fundamentals). As importantly as being an effective communicator, Dalio was actually right. Bridgewater has translated the metaphor of the Economic Machine into actionable investments for 40 years, with a track record that speaks for itself.

Today I want to propose a new metaphor for the world as it is – a Narrative Machine – where macroeconomic reality is still understood as a cybernetic system, but where the translation of “reality” (all of those economic fundamentals and if-then statements of the Economic Machine) into actual human behaviors and actual investment outcomes takes place within a larger Machine of strategic communication and game playing.

The Narrative Machine isn’t a rejection of the Economic Machine, any more than the theory of relativity rejects Newton’s Laws of Motion. In most places and most times, good old Newtonian physics is all you need to understand the world and take actions to succeed in that world. But there are times and places, like when you’re traveling near the speed of light, where Newtonian physics doesn’t work very well and you need a broader theory – Einsteinian physics – to understand the world and take actions to succeed in that world. A policy-controlled market, like we had in the 1930s and we have again today, is the investment equivalent of traveling near the speed of light. The Economic Machine theory – by which I mean any approach to investing that focuses on tangible macroeconomic fundamentals – just doesn’t work very well in a policy-controlled market. We need an extension of the Economic Machine to succeed in this time and this place, just like the theory of relativity extends Newtonian physics, and that’s what I think the Narrative Machine provides.

Unless you’re an Aristotle or an Einstein, advancement and extension of theory doesn’t just happen by sitting in a room and thinking it up. You need new data. You need better data. You need a new way of looking at the data. Kepler’s idea of elliptical orbits to advance and extend the Copernican theory of a heliocentric solar system couldn’t happen without the new astronomical data provided by Tycho Brahe’s observatory. For a negative example, I think the advancement of germ theory was set back by at least a century because Van Leeuwenhoek refused to share his new technology for looking at microscopic data. But at least astronomy and microbiology have something tangible to look at and measure. How do we SEE the Narrative Machine? How do we observe an invisible network of social interaction? How do we touch the intangible?

For my entire professional career, dating back to my first days as a graduate student and spanning three different vocations and three decades, I’ve been wrestling with that question. I think I caught a small piece of the puzzle with my dissertation and the book that came out of that (Getting to War), and I think that I’ve painted around the edges of the puzzle over the past three years with Epsilon Theory. I was pretty sure that the Narrative Machine was observable if the right Big Data technology could be applied (in the lingo, contextual analysis of affect, meaning, and network connectivity across large pools of unstructured text), but I’ve been involved with Big Data way before anyone called it Big Data, and every time someone claimed to have a solution to this problem it turned out to be far less than meets the eye. On that note, if you enjoy a little dose of schadenfreude (and really, who doesn’t?) do a quick search on Microsoft’s acquisition of Fast Search or, even more shivering, Hewlett Packard’s acquisition of Autonomy, two companies that claimed solutions here. So it was with some trepidation and certainly a healthy skepticism that I started working with Quid, a private company based in San Francisco that has developed a technology for network visualization of unstructured texts.

I think Quid is onto something, in large part because they’re not trying to answer directly the questions I’m asking. Instead, I think they’ve developed a novel process for seeing the invisible world of contextual connections and networks – something analogous to Van Leeuwenhoek’s novel process for seeing the invisible world of microbes – and I’m using their “microscope” to do my own research and answer my own questions. I like that Quid is a tool provider, not a solution provider, so that the analysis here, for better or worse, is my own. On the next few pages I’ll provide an example of some of the research I’m currently doing with the Quid microscope, and I hope it will give you a sense of why I think that we’re getting glimpses of the Narrative Machine with this new instrument.

I’ve written at some length about Brexit and the Narrative that emerged in its immediate aftermath, a Narrative that not only stopped the immediate sell-off in global risk assets in its tracks, but actually reversed the market decline and drove financial asset prices to new highs. To recap, I called Brexit a Bear Stearns event rather than a Lehman event, predicting that creators of Common Knowledge (what game theory calls Missionaries) would successfully characterize the event as an idiosyncratic fluke rather than a systemic risk, exactly as the collapse of Bear Stearns was portrayed in the spring of 2008. In other words, Brexit was NOT a Humpty Dumpty moment, where all the Fed’s horses and all the Fed’s men couldn’t put the egg shell back together again.

Now I have lots of anecdotal evidence of the sort of Narrative creation that I’m hypothesizing here. One of my favorites is a July 13thFinancial Times article titled “Anger at JP Morgan’s ‘Unhelpful’ Brexit Warnings”, where “Senior bankers in London are growing frustrated with JP Morgan Chase’s public warnings that it may cut thousands of jobs in the UK, saying such remarks send an unhelpfully negative message.” Or if I may paraphrase, “The UK government is angry at JP Morgan for not lying about Brexit like they were told to do.” I’ve got a hundred examples like this, examples of a concerted effort by every status quo government and media opinion leader to paint the Brexit vote as a one-off crazy mistake that will probably be reversed and certainly won’t be repeated anywhere else in Europe. But the plural of anecdote is not data, and until now I haven’t an effective instrument to see whether the media data supports what I think is happening.

On the left is a Quid visualization of the clusters and network relationships between the 2,422 Brexit-mentioning articles published by Bloomberg in the 4 weeks prior to the June 23rd vote. On the right is a Quid visualization of the 4,283 such articles published by Bloomberg in the 4 weeks after the vote. This is what the formation of a coherent Narrative looks like. These are snapshots of the Narrative Machine.

epsilon-theory-narrative-machine-august-17-2016-quid-bloomberg-vote

So what are we looking at here? Each dot (or node) represents a single unique article, and the Quid algorithms group nodes into colored clusters based on shared word choice and similar word positioning. If we magnify any of these clusters, in this case a cluster of articles talking about bond-buying and US Treasuries in the pre-vote data, we see that the nodes themselves differ in size according to their connectivity or centrality to the clustering principle, and that there are varying distances and numbers of connections between the nodes, as well. Each node exerts the equivalent of a gravitational pull on every other node, giving the entire structure both the appearance and the substance of a star map. Nodes can be evaluated and displayed on dimensions such as sentiment (green/positive – red/negative), as shown below, and all of these characteristics (distance, connectivity, centrality, etc.) are generated as a structured data set for further, non-visual analysis.

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Here’s what I think we’re seeing in the “coagulation” of the Bloomberg facet of the Narrative Machine.

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The pre-vote Bloomberg network structure on the left is what a complacent Narrative looks like. The articles are “about” whatever the clustering principle might be, and Brexit is typically a sideways glance, a throwaway line that’s almost always negative in sentiment. On the other hand, the post-vote network structure on the right is what an engaged Narrative looks like, where the articles are “about” Brexit and its impact on the clustering principle. Not only are we seeing a strong Narrative form on the right, but the density of lines and closeness of clusters shows that a similar tone and meaning has taken root across all these clusters. Importantly, it’s a positive tone and meaning that takes shape in the post-vote Narrative, with sentiment scores significantly higher than in the pre-vote snapshot. The sky-will-fall articles are almost all in the pre-vote sample, while the post-vote sample – as early as the Monday after the vote, which is immediately before the market starts to turn – are almost all focused on the non-systemic nature of Brexit, the likelihood of reversal, and the “mistake” that was made here.

The pre- and post-vote evolution of the Brexit Narrative structure is robust within individual Bloomberg clusters and across other major media microphones. Here, for example, is the same bond-buying / US Treasuries cluster in the post-vote Bloomberg data set (different color, but same clustering principle), and in the blow-up you can see how much more coherent and connected it is than the pre-vote cluster.

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Below, the top pair of star maps are the 4-week pre-vote and post-vote network visualizations of Brexit-mentioning articles published by Reuters, and the bottom two star maps are samples from all publishers in the Quid database. All of the hypothesized Narrative patterns described above are replicated here.

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Okay, Ben, these diagrams and “star maps” are all very pretty. I get your metaphor of the Narrative Machine, and I get that you’re excited about a new technology that lets you see that invisible machine. But so what? How does all this translate into either actionable investment ideas or a process improvement in managing investment ideas?

When anyone asks this question (and believe me, it’s the question I’ve asked myself in one form or another for 30 years), they’re asking about two things: edge and odds. For anyone who’s trying to beat the dealer (my plug for Edward O. Thorp’s 1962 book that changed everything for me, also retold and expanded in William Poundstone’s brilliant book Fortune’s Formula) … for anyone who’s interested in alpha, this is all that matters: edge and odds. Edge is private information, an insight into the true nature of reality that other game players don’t have. Odds are the probabilistic relationship between risk and reward at any given moment in time. If you have either one of these on your side, then you’ll do well in whatever game you’re playing, if you’re dealt enough hands. If you have both on your side … and I think that a rigorous application of the Narrative Machine generates both edge and an improved assessment of odds … hey, now.

The odds revealed by the Narrative Machine are the odds of a catalyst having a major impact on price (or not). Or in slightly different words, I think that the Narrative Machine can help show us the degree to which future events are “priced-in” by the market. For example, when you’ve got a complacent, all-over-the-place Narrative leading up to a scheduled event like the Brexit vote, then even if my best guess on the voting odds is, say, 60% in favor of “Remain”, I would still place a bet on “Exit” because the Narrative-implied market payoff odds are far better than the breakeven odds of the vote.

The edge that the Narrative Machine generates is an improved reaction to a catalyst once it occurs. To be clear, I don’t think that the Narrative Machine can predict a market shock or catalyst before it happens. It’s not a crystal ball. But it is a real-time window into how the Common Knowledge Game is being constructed and played after an event occurs. For example, when you have a pervasive, systemic-risk-is-off-the-table Narrative created almost immediately following a market shock like the Brexit vote, then I would get long the market even if I believed in my heart-of-hearts (and I do) that there really IS systemic risk posed by everything that’s behind the Brexit vote.

I don’t want to over-sell the degree to which the Narrative Machine has been “weaponized” into an investable alpha source, because there are several critical aspects of network theory that remain to be implemented. Foremost of these is what network theory calls alluvial analysis, or evaluation of how different clusters “flow” into each other and away from each other over time. I’ve included two wonderful illustrations of this concept, both from a 2010 scientific journal article (“Mapping Change in Large Networks” by Martin Rosvall and Carl Bergstrom). I think the Quid technology is pretty good at what network theory calls “significance clustering”, the assignment of individual nodes into similarly colored and positioned groups – essentially a snap shot of the network at a given point in time. What we need now is a map of how those clusters evolve over time, because the meaning or organizing principle of the clusters themselves doesn’t remain constant.

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Rosvall and Bergstrom illustrate this beautifully in the second diagram here, where a network analysis of scientific journal articles show how neuroscience has become its own “thing” over time. We need the same alluvial maps for market Narrative clusters. I’m on it.

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So, yes … early days for the Narrative Machine. But, yes … a potential alpha source.

Which leads to an interesting question. If this is a new alpha source – the most valuable thing in the investment world – why am I talking about it? Isn’t this like announcing that you think you’ve found gold in California or the Yukon before you’ve staked a claim?

Good question. There’s some margin of intellectual property safety here because it’s not an easy alpha source to mine, even with cool new technologies like Quid. The internal logic of the Narrative Machine is the logic of strategic interaction (game theory), not the logic of stochastic processes (econometric inference). In plain English, I don’t think you can run a regression analysis of historical media network characteristics against historical market characteristics and get much that will be useful, at least not if you’re after edge and odds. The underlying theory here is Information Theory and the underlying math is the mathematics of entropy, and I’m reasonably confident that we’re not going to see an Excel plug-in for either of those anytime soon.

But yes, someone could “steal” this idea and run with it on their own. To which I say … fine. Better that than being another Van Leeuwenhoek, bogarting his research on his invisible world and setting back the advancement of germ theory and microbiology by a century or more. As in 1648 and 1776 and 1848 and 1917, we live in one of those rare moments in history where ideas are at stake and fundamental theories of the world are in flux. Let’s engage with that, and not hide in the convenient cubbyhole of narrow self-interest or the mentality of an agreeing machine.

We need a new perspective regarding the true nature of our economic and political clockwork, and that’s the real value of the idea of the Narrative Machine.

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Two Discoveries

The world made two discoveries last week. Everyone is aware of the first discovery – that ISIS is not “a junior varsity team” but an able protagonist in what Pope Francis quite rightly calls “a piecemeal third World War”. Very few are aware of the second discovery – the existence of a polynomial-time algorithm to determine whether two networks, no matter how complex, are identical. Both are watershed events, part of a continuing destabilization of politics and science. Neither will impact markets very much today. Both will change markets forever in the years to come.

I won’t say much about the first discovery here, but will take this opportunity to reprint a note I wrote in December 2014, eerily right before the Charlie Hebdo attack: “The Clash of Civilizations”. I’d also point out that the all-too-predictable Orwellian response to events like the Paris attack, namely to rewrite history and expand government monitoring of our private lives, is in full swing.

For example, here’s a before and after France Inter headline (hat-tip to Epsilon Theory reader M.O.), as noted by The Daily Telegraph. The headline as it originally ran a few weeks back calls a potential terrorist infiltration of Syrian refugees a “fantasy” of the lunatic right. Immediately after the attack, the headline has been rewritten (and the body of the article partially rewritten as well), to suggest that of course one might question whether or not a few terrorists managed to sneak in with the refugees. France Inter – surprise! – is part of the state-owned media apparatus, now in full-throated advocacy for a “pitiless” war.

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Given how this Narrative is developing within left-leaning European governments (hmm, amazingly enough, no marches this time around calling for solidarity with peace-loving Muslims), my political advice to left-leaning US politicians like Connecticut governor Dannel Malloy is that you might be getting a wee bit ahead of yourself by loudly and publicly promoting Syrian refugee relocation in your state. Just sayin’.

The second discovery – an algorithm that dramatically shortens the information processing power required to tell if two networks are structurally the same – requires a bit more explication. Here’s a picture of two such visibly different but structurally identical (isomorphic) networks.

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For a whole host of data science applications – from cryptography to genomics to nuclear physics to, yes, finance – we’d often like to know how or if two networks or two data arrays are permutations of the same underlying structure. Maybe you could eyeball an answer to an 8-node network like the example above, but it doesn’t take much imagination to realize that this problem gets very hard very quickly for the human brain as the number of nodes increases.

Fortunately, of course, we have non-human intelligences to help us crack these problems today, but the only known algorithms or programs for computers to follow for this particular problem existed in what is called “non-deterministic polynomial” or NP-time, where the amount of time or information processing power required to carry out the algorithm increases at a staggering rate as the number of nodes increases. This is in contrast to a polynomial or P-time algorithm, where the time required to crunch the algorithm increases at a more manageable rate as the number of nodes increases. Think of it as the difference between 2n (an NP-time algorithm) and n2 (a P-time algorithm), where n is something like 1 million. 2 raised to the 1,000,000th power is an incomprehensibly large number, greater than the number of atoms in the universe. If that’s your algorithm for solving the isomorphic network problem, then it’s physically impossible for any computer, no matter how powerful, to crack the problem for a network with 1 million nodes. On the other hand, 1,000,000 squared is a trivially small number (1 trillion) as a representation of a powerful computer’s information processing capabilities. If that’s your algorithm for solving the isomorphic network problem, then there is no network too large for you to measure and compare to another network.

The isomorphic network problem was a classic example of something that most computer scientists believed could only be solved with NP-time algorithms. But last week, Laszlo Babai at the University of Chicago announced the existence of an algorithm for this class of problems that is, for all practical purposes, in P-time. Why is this important? Because it is the modern day equivalent of discovering a new continent, one that happens to exist in cyberspace rather than human space. Because it is now by no means clear that there are ANY problems of data science that are inexorably lost in the cosmic fog of NP-time algorithms. Why will this one day change markets forever? Because the ability of computers to analyze and predict (and ultimately shape) the behavior of a complex network comprised of millions of semi-autonomous agents exchanging a set of symbolic chips with each other – The Market – just took a giant step forward. If you thought that humans were a marginalized participant in public capital markets today … if you thought that the casino-fication of markets had reached some sort of natural limit … well, you ain’t seen nothing yet.

Sigh. Last week was a tough week for the human team. With the loud explosions out of Paris, the illiberal left, the illiberal right, and the illiberal jihadists are now ALL in political ascendancy. And with the quiet announcement out of Chicago, we are oh-so close to the day when no human communication over any network can be shielded or kept private from a machine intelligence. God help us as the two discoveries merge into one.

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One MILLION Dollars

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Dr. Evil: Gentlemen, it has come to my attention that a breakaway Russian Republic called Kreplachistan will be transferring a nuclear warhead to the United Nations in a few days. Here’s the plan. We get the warhead and we hold the world ransom for…one MILLION dollars!
Number Two: Don’t you think we should ask for more than a million dollars? A million dollars isn’t exactly a lot of money these days. Virtucon alone makes over 9 billion dollars a year!
Dr. Evil: Really? That’s a lot of money.

– “Austin Powers: International Man of Mystery” (1997)

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Dr. Manhattan: I have walked across the surface of the Sun. I have witnessed events so tiny and so fast they can hardly be said to have occurred at all. But you, Adrian, you’re just a man. The world’s smartest man poses no more threat to me than does its smartest termite.
– “Watchmen” (2009)

Eddie Morra: I don’t have delusions of grandeur. I have an actual recipe for grandeur.
– “Limitless” (2011)

Carl Van Loon: Have you been talking with anyone?
Eddie Morra: No, I haven’t been talking with anyone, Carl. I’m not stupid.
Carl Van Loon: I know you’re not stupid, Eddie, but don’t make the classic smart person’s mistake of thinking no one’s smarter than you.

– “Limitless” (2011)

DIY’s newest frontier is algorithmic trading. Spurred on by their own curiosity and coached by hobbyist groups and online courses, thousands of day-trading tinkerers are writing up their own trading software and turning it loose on the markets. 

Interactive Brokers Group actively solicits at-home algorithmic traders with services to support their transactions. YouTube videos from traders and companies explaining the basics have tens of thousands of views. More than 170,000 people enrolled in a popular online course, “Computational Investing,” taught by Georgia Institute of Technology professor Tucker Balch. Only about 5% completed it.
– Wall Street Journal, “Algorithmic Trading: The Play at Home Version” August 9, 2015

London day trader Navinder Sarao has been formally indicted by a U.S. federal grand jury on charges of market manipulation that prosecutors say helped contribute to the 2010 “flash crash,” according to a Sept. 2 court filing made public on Thursday. 

The Justice Department first announced criminal charges against Sarao in April and is seeking to have him extradited to the United States to stand trial. 

Sarao is accused of using an automated trading program to “spoof” markets by generating large sell orders that pushed down prices. He then canceled those trades and bought contracts at lower prices, prosecutors say.
– CNBC, “US Federal Grand Jury Indicts ‘Flash Crash’ Trader” September 3, 2015

Anxiety in the industry surged last week after Li Yifei, the prominent China chief of the world’s largest publicly traded hedge fund, disappeared and Bloomberg News reported that she had been taken into custody to assist a police inquiry into market volatility. Her employer, the London-based Man Group, did little to dispel fears, declining to comment on her whereabouts. 

Ms. Li resurfaced on Sunday and denied that she had been detained, saying that she had been in “an industry meeting” and “meditating” at a Taoist retreat. But many in the finance sector are unconvinced.
– New York Times, “China’s Response to Stock Plunge Rattles Traders” September 9, 2015

I’ve written several Epsilon Theory notes about modern market structure (“Season of the Glitch”, “Fear and Loathing on the Marketing Trail, 2014”, “The Adaptive Genius of Rigged Markets”, “Hollow Men, Hollow Markets, Hollow World”), all of which have been very well received. I’ve also written several Epsilon Theory notes about Big Data and non-human intelligences (“Troy Will Burn – the Big Deal about Big Data”, “First Known When Lost”, “Rise of the Machines”), all of which have generated a yawn. This divergence in reader reaction has puzzled me, because it seems so obvious to me that the issues are two sides of the same coin. So why can’t I communicate that?

It’s only over the last few days, after listening to old-school luminaries like Leon Cooperman and Dick Grasso rail against systematic investment strategies, index derivative hedging, and algorithmic market making as if they were the same thing (!) … it’s only after reading press stories that praise the US indictment of Navinder Sarao, the London trader who supposedly triggered the “Flash Crash” from his home computer, but condemn the Chinese detention of Man Group’s Li Yifei as if they were different things (!) … it’s only after seeing 500 commercials for “DIY trading platforms” on TV today as if this were a thing at all (!) … that I think I’ve finally figured this out.

We’re all Dr. Evil today, thinking that one million dollars is a lot of money, or that one second is a short period of time, or that we are individually smart or capable in a systemically interesting way. We use our small-number brains to make sense of an increasingly large-number investment world, and as a result both our market fears and our market dreams are increasingly out of touch with reality.

There are a million examples of this phenomenon I could use (including the phrase “a million examples” which, if true, would take me a lifetime to write and you a lifetime to read, even though neither you nor I considered the phrase in that literal context), but here’s a good one. A few months ago I wrote an Epsilon Theory note on the blurry distinction between luck and skill, titled “The Talented Mr. Ripley”, where I pointed out that it was now quite feasible with a few million dollars and a few months to build a perfect putting machine, one that would put every professional human golfer to shame. Judging from the reader emails I received on this, you might have thought I had just said that the world was flat and the sun was a big candle in the sky. “Preposterous!” was the gist of these emails – sometimes said nicely and sometimes (actually, most of the time) not so nicely – as apparently I know nothing about golf nor about the various failed efforts in the past to build a mechanical putting device.

Actually, I know a lot about these mechanical putting devices, and to compare them to the non-human putting intelligences that are constructible today is like comparing Lascaux cave art to HD television. It’s relatively child’s play to build a machine today that can identify and measure the impedance of every single blade of grass between a golf ball and the cup, one that measures elevation shifts of less than the width of an eyelash, one that applies force within an erg tolerance that human skin would interpret as the faintest breeze. That’s what I’m talking about. Do you know how the most advanced surreptitious listening devices, i.e. bugs, operate today? By measuring the vibrations in the glass window of the room where the conversation is taking place and translating those vibrations back into the sound waves that produced them. That’s what I’m talking about. Now replace “blades of grass” with “individual stock trades”. Now replace “conversation” with “investment strategy”. Arthur C. Clarke famously said that any sufficiently advanced technology is indistinguishable from magic. Do you really think we bring to bear less powerful magic in markets with trillions of dollars at stake than we do in spycraft and sports?

And let’s be clear, the machines are here to stay. They’re better at this than we are. The magic is in place because the magic works for the people and institutions that wield the magic, and no amount of rending of garments and gnashing of teeth by the old guard is going to change that. Sure, I can understand why Dick Grasso would suggest that we should go back to a pre-Reg NMS system of human specialists and cozy market making guilds, where trading spreads were measured in eighths and it made sense to pay the CEO of a non-profit exchange $140 million in “retirement benefits.” And I almost sympathize with the nostalgic remembrances of a long list of Hero Investors recently appearing on CNBC, pining for a pre-Reg FD system of entrenched management whispering in the ear of entrenched money managers, where upstart quants knew their place and the high priests of stock picking held undisputed sway. But it ain’t happening.

And let’s also be clear, the gulf between humans and machines is getting wider, not narrower. Even today, one of the popular myths associated with computer science is that non-human intelligences are brute force machines and inferior to humans at tasks like pattern recognition. In truth, a massively parallel processor cluster with in-line memory – something you can access today for less money than a junior analyst’s salary – is far better at pattern recognition than any human. And I mean “far better” in the same way that the sun is far better at electromagnetic radiation than a light bulb. Much has been made about how robot technologies are replacing low-end industrial and service jobs. Okay. Sure … I guess I’d be worried about that if I were working in a Foxconn factory or a Bay Area toll booth. But far more important for anyone reading this note is how non-human intelligences are replacing high-end pattern recognition jobs. Like trading. Or investing. Or asset allocation. Or advising.

The question is not how we “fix” markets by stuffing the technology genie back into the bottle and we somehow return to the halcyon days of yore where, in Lake Wobegon fashion, all of us were above average stock pickers and financial advisors. No, the question we need to ask ourselves is both a lot less heroic and far more realistic. How do we ADAPT to a market jungle where human intelligences are no longer the apex predator?

I’ve got two sets of suggestions, depending on whether you see yourself as a trader or an investor. It’s a lot to digest, so let’s look at traders in the balance of this week’s note and at investors next week.

epsilon-theory-one-million-dollars-september-15-2015-limitless

Every trader who ever lived believes that, like the Bradley Cooper character in “Limitless”, he or she has a recipe for grandeur. It doesn’t matter whether they find that recipe in prices or volumes or volatility or spreads or any other aspect of a security, all traders have an internalized pattern recognition system that they believe gives them a persistent edge. Most of them are wrong.

In modern large-number markets, any trading strategy based on naïve inference is certain to have zero edge, zero alpha. By naïve inference I mean selecting a strategy based solely on the econometric fit of a time series data matrix to some market outcome like price change. It’s a trading strategy that works because … it works. There’s no “why?” answered here, and as a result the strategy is certain to be derivative, non-robust, and quickly arbitraged. Or to put it in slightly different terms, whatever purely inductive trading strategy you think gives you an edge is already being used by thousands of non-human intelligences, and they’re using the strategy far more effectively than you are. To the degree a naïve inference strategy works at all, you’re just tagging along behind the non-human intelligences, picking up their crumbs.

What trading strategies have even a theoretical possibility of edge or alpha? Here are two.

Possibility 1: Find a market niche where your counterparties are non-economic or differently-economic market participants – like an oil futures market where a giant, lumbering integrated oilco seeks to hedge production, or where a sovereign wealth fund looks for inflation protection (Remember those happy days when giant allocators addressed inflation concerns in commodity markets? Me, neither.) – and scalp a few dimes by taking advantage of their very different preference functions. Traders who pursue this type of strategy have a name in biological systems. They’re called parasites. I call them beautiful parasites (see the Epsilon Theory note “Parasite Rex”), because they capture more pure alpha than any strategy I know.

Possibility 2: Find a market niche where you understand the impact of exogenous signals like news reports or policy statements on the behavioral tendencies of other human market participants, in exactly the same way that a good poker player “plays the player” as much as he plays the cards. These market niches tend to be sectors or assets that are driven less by fundamentals than they are by stories – think technology stocks rather than industrials – although here in the Golden Age of the Central Banker it’s hard to find any corner of the capital markets that’s not driven by policy and narrative. The game that these traders have internalized isn’t poker, of course, but is almost always some variant of what modern game theorists call “The Common Knowledge Game”, and what old-school game theorists like John Maynard Keynes called “The Newspaper Beauty Contest”.

What do these two examples of potentially alpha-generating trading strategies have in common? They operate in a world that a non-human intelligence – which is effectively a super-human inference machine – can’t figure out. Today’s effective alpha-generating trading strategies are based on a game (in the technical sense of the word, meaning a strategic interaction between humans where my decisions depend on your decisions, and vice versa) where you can have very different outcomes from one trade to another even if the external/measurable characteristics of the trades are identical. This is the hallmark of games with more than one equilibrium solution, which simply means that there are multiple stable outcomes of the game that can arise from a single matrix of descriptive data. It means that you can’t predict the outcome of a multi-equilibrium game just by knowing the externally visible attributes of the players. It means that the pattern of outcomes can’t be recognized with naïve (or sophisticated) inference techniques. It means that traders who successfully internalize the pattern recognition of strategic behaviors rather than the pattern recognition of time series data have a chance of not just surviving, but thriving in a market jungle niche.

Sigh. Look … I know that this note is going to fall on a lot of deaf ears. It’s an utterly un-heroic vision of what makes for a successful trader in a market dominated by non-human intelligences, as I’m basically saying that you should find some small tidal pool to crawl into rather than roam free like some majestic jungle cat. As such it flies in the face of every bit of heroic advertising that the industry spews forth ad nauseam every day, my personal fave being the “Type-E” commercials with Kevin Spacey shilling for E*Trade. Generalist traders are some of my favorite people in the world. They’re really smart. But they’re not smart enough. None of us are. After all, we’re only human.

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The Talented Mr. Ripley

The more I practice, the luckier I get.epsilon-theory-the-talented-mr-ripley-april-27-2015-gary-player
– Gary Player (b. 1935)

Luck is the residue of design.
– Branch Rickey (1881 – 1965)

epsilon-theory-the-talented-mr-ripley-april-27-2015-ted-williamsI’ve found that you don’t need to wear a necktie if you can hit.
– Ted Williams (1918 – 2002)

They say that nobody is perfect. Then they say that practice makes perfect. I wish they’d make up their minds.
– Wilt Chamberlain (1936 – 1999)

They say that nobody is perfect. Then they say that practice makes perfect. I wish they’d make up their minds.
– Wilt Chamberlain (1936 – 1999)

It took me 17 years to get 3,000 hits in baseball. I did it in one afternoon on the golf course.
– Hank Aaron (b. 1934)

Talent is cheaper than table salt. What separates the talented individual from the successful one is a lot of hard work.
– Stephen King (b. 1947)

At one time I thought the most important thing was talent. I think now that – the young man or the young woman must possess or teach himself, train himself, in infinite patience, which is to try and to try and to try until it comes right. He must train himself in ruthless intolerance. That is, to throw away anything that is false no matter how much he might love that page or that paragraph. The most important thing is insight, that is … curiosity to wonder, to mull, and to muse why it is that man does what he does. And if you have that, then I don’t think the talent makes much difference, whether you’ve got that or not.
– William Faulkner (1897 – 1962)

Talent is its own expectation, Jim: you either live up to it or it waves a hankie, receding forever.
– David Foster Wallace, “Infinite Jest” (1996)

What is most vile and despicable about money is that it even confers talent. And it will do so until the end of the world.
– Fyodor Dostoyevsky (1821 – 1881)

Talent is a long patience, and originality an effort of will and intense observation.
– Gustave Flaubert (1821 – 1880)

There is nothing more deceptive than an obvious fact.
– Arthur Conan Doyle, “The Boscombe Valley Mystery” (1891)

epsilon-theory-the-talented-mr-ripley-april-27-2015-murder-she-wrote

Sheriff Metzger: Mrs. Fletcher! Can I see you for a minute? [pause] Do me a favor, please, and tell me what goes on in this town!
Jessica Fletcher: I’m sorry, but …
Sheriff Metzger: I’ve been here one year, and this is my fifth murder. What is this, the death capital of Maine? On a per capita basis this place makes the South Bronx look like Sunny Brook farms!
Jessica Fletcher: But I assure you, Sheriff …
Sheriff Metzger: I mean, is that why Tupper quit? He couldn’t take it anymore? Somebody really should’ve warned me, Mrs. Fletcher. Now, perfect strangers coming to Cabot Cove to die? I mean look at this guy! You don’t know him, I don’t know him. He has no ID, we don’t know the first thing about this guy.

– “Murder, She Wrote: Mirror, Mirror, on the Wall: Part 1” (1989)

epsilon-theory-the-talented-mr-ripley-april-27-2015-manchurian-candidate

Dr. Yen Lo: His brain has not only been washed, as they say … It has been dry cleaned.
– “The Manchurian Candidate” (1962)

Dickie Greenleaf: Everyone should have one talent. What’s yours?
Tom Ripley: Forging signatures, telling lies … impersonating practically anybody.
Dickie Greenleaf: That’s three. Nobody should have more than one talent.

– “The Talented Mr. Ripley” (1999)

My singular talent is seeing patterns that others don’t. That’s not a boast, but a fact, and frankly it’s been as much a source of alienation in my life as a source of success. As my father was fond of saying, “You know, Ben, if you’re two steps ahead it’s like you’re one step behind.” I can’t explain how I see the patterns – they just emerge from the fog if I stare long enough. It’s always been that way for me, for as far back as I have memories, and whether I’m 5 years old or 50 years old I’m always left with the same realization: I only see the pattern when I start asking the right question, when I allow myself to be, as Faulkner said, “ruthlessly intolerant” of anything that proves false under patient and curious observation.

For example, I think the wrong question for anyone watching “Murder, She Wrote” is: whodunit? The right question is: how does Jessica Fletcher get away with murder this time? Once you recognize that it’s a Bayesian certainty that the woman is a serial killer, that she controls the narrative of Cabot Cove (both figuratively as a crime novelist and literally as a crime investigator) and thus the behavior of everyone around her, you will discover a new appreciation for both the subliminal drivers of the show’s popularity as well as the acting genius of Angela Lansbury. Seriously, go back and watch the original “Manchurian Candidate” and focus on Lansbury. She’s a revelation.

Or take the Masters tournament earlier this month. I was lucky enough to attend Wednesday’s practice round, and I was sitting in a shady spot on the 10th green watching the players come by and try their luck at 15 foot putts. At first, like the other spectators, my question was: how are they such good putters? This was “the obvious fact,” to quote Sherlock Holmes, and I watched for any clues that I could adopt for my laughable game – a forward tilt of the wrist, a stance adjustment … anything, really. We all watched carefully and we all dutifully oohed and aahed when the ball occasionally dropped in the cup. But suddenly, a new pattern emerged from the fog, and I realized that we were all asking the wrong question. Instead, I started to ask myself, why are they such poor putters? 

Now I realize that I just alienated at least half of the reading audience, but bear with me. I’m not saying that professional golfers are poor putters compared to you or me. Of course not. They are miracle workers compared to you or me. But it’s a stationary ball with a green topography that never changes. The speed of the greens is measured multiple times a day to the nth degree. These players have practiced putting for thousands of hours. They have superior eyesight, amazing muscular self-awareness, and precision equipment. And yet … after charting about 50 putts in the 12 – 15 foot range, the pattern of failure was unmistakable. These professional golfers were aiming at a Point A, but they would have sunk exactly as many putts if the cup had actually been located 6 inches to the right. Or 6 inches to the left. Or 12 inches back. Or 12 inches forward. The fact that a putt actually went in the hole from a distance of 12 – 15 feet was essentially a random event within a 15 x 30 inch oval, with distressingly fat probabilistic tails outside that oval. This from the finest golf players in the world. I saw Ben Crenshaw, a historically great putter who was playing in something like his 44th Masters and probably knows the 10th green better than any other living person, miss a long putt by 6 feet.

But here’s the thing. When a player took a second putt from the same location, or even close to the same location, his accuracy increased by well more than an order of magnitude. Suddenly the ball had eyes. So I went to the practice green, where I saw Jordan Spieth putt ball after ball from exactly the same location about 10 feet from the hole. He made 50 in a row before I got tired of watching. Now granted, Spieth is a wizard with the putter, a lot like Tiger was at the same age. See it; make it. But then I watched one of the no-name amateurs for a while, a guy who had no chance of making the cut, and it was exactly the same thing – putt after putt after putt rolled in from the same spot at a considerable distance.

The best golfers in the world are surprisingly poor aimers. Surprising to me, anyway. They are pretty miserable predictors of where a de novo putt is going to end up, even though we all believe that they are wonderful at this activity. But they are phenomenally successful and adaptive learners, even though we rarely focus on this activity. 

I think the same pattern exists in other areas of the sports world. Take basketball free throws. I’d be willing to make a substantial bet that whatever a professional’s overall free throw shooting percentage might be – whether it’s DeAndre Jordan at 50% or Steph Curry at 90% – their shooting percentage on the second of two free throws is better at a statistically significant level than their shooting percentage on the first of two free throws. I have no idea where to access this data, but with the ubiquitous measurement of every sports function and sub-function I’m certain it must exist. Someone give Nate Silver or Zach Lowe a call!

I think the same pattern exists in the investing world, too. We are remarkably poor aimers and predictors of market outcomes, even though we collectively spend astronomical sums of money and time engaged in this activity, and even though we collectively ooh and aah over the professional who occasionally sinks one of these long putts. True story … in 2008 the long/short equity hedge fund that I co-managed was up nicely, and we were deluged by investors and allocators asking the wrong question: how did you have such a great year? At no point did anyone ask the right question: given your fundamental views and avowed process, why weren’t you up twice as much? Most investors, just like the spectators at Augusta, are asking the wrong questions … questions that conflate performance with talent, and questions that underestimate the role of process and learning in translating talent into performance. 

I’m not saying that idiosyncratic talent doesn’t exist or that it isn’t connected to performance or that it can’t be identified. What I’m saying is that it’s as rare as Jordan Spieth. What I’m saying is that the talents that are most actionable in the investment world are not found in the predictions and the aiming of a single person. They are found within the learned and practiced behaviors that exist across a broad group of investment professionals. Jordan Spieth is a very talented putter and he works very hard at his craft. But there is no individual golf pro, not even Jordan Spieth, who I would trust with my life’s savings to make a single 15 foot putt. On the other hand, I would absolutely put my life’s savings on the line if I could invest in the process by which all golf pros practice their putting. I am far more interested in identifying the learned behaviors of a mass of investment professionals than I am in identifying a specific investment professional who might or might not be able to sink his next long putt.

What’s the biggest learned behavior of professionals in the investing world right now? Simple: QE works. Not for the real economy– I don’t know any professional investor who believes that the trillions of dollars in Fed balance sheet expansion has done very much at all for the real economy – but for the inflation of financial asset prices. This is what I’ve called the Narrative of Central Bank Omnipotence, the overwhelmingly powerful common knowledge that central bank policy determines market outcomes. The primary manifestation of this learned behavior today is to go long Europe financial assets … stocks, bonds, whatever. QE worked for US markets – that’s the lesson – and everyone who learned that lesson is applying it now in Europe. China, too. Here’s a great summary of this common knowledge position from a market Missionary, Deutsche Bank’s Chief International Economist Torsten Slok:

epsilon-theory-the-talented-mr-ripley-april-27-2015-market-pricing

In my view, every asset allocation team in the world should have this chart hanging on their wall. Based on forward OIS curves the market expects the Fed to hike in March 2016 and the ECB to hike in December 2019. A year ago, the expectation was that the Fed and the ECB would both hike in November 2016. This discrepancy has significant relative value implications for FX, equities and rates. EURUSD should continue to go down and European equities will look attractive for many more years. Another consequence of this chart is that with ECB rates at zero for another five years, many European housing markets should continue to do well. The investment implication is clear: Expect that the benefits we have seen of QE in the US over the past 3 to 5 years will be playing out in Europe over the coming 3 to 5 years. – Torsten Slok, Deutsche Bank Chief International Economist, April 9, 2015

Just as a recap on how to play the Common Knowledge Game effectively, the goal here is to read Torsten’s note for its description and creation of common knowledge (information that everyone thinks that everyone has heard), not to evaluate it for Truth with a capital T. That’s the mistake many investors make when they read something like this … they start thinking about whether or not they personally agree with the Fed hike expectations embodied in forward OIS curves, or whether or not they personally agree with Torsten’s macroeconomic predictions on things like the European housing market, or whether or not they personally agree with the social value of the Fed or ECB policies that are impacting markets. In the Common Knowledge Game, fundamentals – whether they are of the stock-picking sort or the macroeconomic sort – don’t matter a whit, and your personal view of those fundamentals matters even less. The only thing that matters is whether or not the QE-works lesson has been absorbed by the learning process of investment professionals, and that’s driven by the lesson’s transformation into common knowledge by Missionaries like Torsten. From that perspective I don’t think there’s any doubt that what Torsten is saying is true, not with a capital T but with a little t, and that the long-Europe-because-of-ECB-QE trade has got a lot of behavioral life left to it.

One last point … I know that I’m a broken record in the fervency and persistence of my belief that Big Data is going to rock the foundations of the investment world, but this topic of talent, learning, and asking the right question is just too on-point for me to let it slide. I started this note with the alienating observation that I don’t believe that professional golfers are particularly good putters, certainly not in their ability to size up and sink a de novo putt from 15 feet or more. On the other hand, I am pretty certain that with a few months and a few million dollars, it’s possible to build a mobile robotic system with the appropriate sensors and mechanical tolerances that would sink pretty much every de novo putt it took from a distance of 15 feet. Or a robotic system that would hit 99% of its free throws. Machines are far more accurate aimers and more precise estimators of the environment than humans, and that’s a useful observation whether we’re talking about sports or investing.

But that’s not my point about Big Data. My point about Big Data is that such systems are ALSO better than humans at learning. They are ALSO better than humans at pattern recognition. I can remember when this wasn’t the case. As recently as 20 years ago you could read artificial intelligence textbooks that praised the computer’s ability to process information quickly with various backhanded compliments … yes, isn’t it amazing how wonderfully a computer can sort through a list, but of course only a human brain can perform tasks like facial recognition … yes, isn’t it amazing how many facts a computer can store in its memory chips, but of course only a human brain can truly learn those facts by placing them within the proper context. We have entire social systems – like sports and markets – that are designed to reward humans who are superior learners and pattern recognizers. Why in the world would we believe that clever and observant humans will continue to maintain their primacy in these fields when challenged by non-human intelligences that are, quite literally, god-like in their analytical talents and ruthless intolerance of what is false? At least in sports it’s illegal to have non-human participants … honestly, I can see a day where investing is reduced to sport, where we maintain human-only markets as part of a competitive entertainment system rather than as a fundamental economic endeavor. In some respects I think we’re already there.

I’ll close with a teaser. There’s still a path for humans to maintain an important role, even if it’s not a uniformly dominant role, within markets that we share with non-human intelligences. Humans are more likely than non-human intelligences to ask the right question within social systems, like markets, that are dominated by strategic interactions (i.e., games). That’s not because non-human intelligences are somehow thinking in an inferior fashion or aren’t asking questions at all. No, it’s because Big Data systems are giant Induction Machines, designed to ask ALL of the questions. The distinction between asking the right question and asking all of the questions is always interesting and occasionally vital, depending on the circumstances. More on this to come in future notes, and hopefully in a future investment strategy …

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Troy Will Burn – the Big Deal about Big Data

epsilon-theory-troy-will-burn-the-big-deal-about-big-data-march-16-2015-jeb-bush

For the life of me, I don’t understand the debate [over the NSA metadata program].

– Jeb Bush, February 18, 2015

The Central Intelligence Agency played a crucial role in helping the Justice Department develop technology that scans data from thousands of US cellphones at a time, part of a secret high-tech alliance between the spy agency and domestic law enforcement, according to people familiar with the work. 

– The Wallstreet Journal front page story, March 10, 2015

Athena:  You wish to be called righteous rather than act right.

– Aeschylus, “The Oresteia” (458 BC)

epsilon-theory-troy-will-burn-the-big-deal-about-big-data-march-16-2015-bloomberg

Point72 Asset Management, the successor to Cohen’s hedge fund SAC Capital Advisors, has hired about 30 employees since the start of last year to build computer models that collect publicly available data and analyze it for patterns, according to two people with knowledge of the matter.
Cohen, whose SAC Capital shut down last year and paid a record fine to settle charges of insider trading, joins Ray Dalio’s Bridgewater Associates in pushing into computer-driven investing, an area dominated by a handful of big firms such as the $25 billion Renaissance Technologies and the $24 billion Two Sigma. The money managers are seeking to take advantage of advances in computing power and data availability to analyze large amounts of information.

– Bloomberg, March 10, 2015

epsilon-theory-troy-will-burn-the-big-deal-about-big-data-march-16-2015-oresteia

Cassandra:  Have I missed the mark, or, like true archer, do I strike my quarry? Or am I prophet of lies, a babbler from door to door?

– Aeschylus, “The Oresteia” (BC)

I know, I know … I’m a broken record and a Cassandra, with 2 successive notes on Big Data. But I don’t care. This is a much larger structural risk for markets and investors than HFT and the whole Flash Boys brouhaha, it’s just totally under the radar and hasn’t surfaced yet. And unfortunately, just as I think Jeb Bush speaks for most Americans – Democrat and Republican alike – when he says that he doesn’t get what all the fuss is about when it comes to metadata collection and Big Data technologies, so do I think that most investors – institutional and individual alike – are blithely unaware of how their market identities can be stolen and their market behaviors influenced, all in plain sight. 

Jeb Bush should know better. I think he probably does. Investors may not know better yet, but they will soon, one way or another. As you read this note, a small group of hedge fund managers are doing to you exactly what the NSA is doing to “terrorists”.

Today a handful of governments use Big Data to identify individual behavioral patterns so that certain individuals can be killed. Today a handful of hedge funds use Big Data to identify investor behavioral patterns so that certain investors can be crushed. Today Big Data is primarily an instrument of social information gathering, with a powerful but punctuated impact on those individuals on the receiving end of a drone strike or a targeted trade.

Tomorrow a handful of governments will influence aggregate political behaviors by triggering small communications that Big Data tells them will be voluntarily magnified by individual citizens, snowballing into outsized, long-lasting, and untraceable “popular” actions. Tomorrow a handful of hedge funds will influence aggregate market behaviors by triggering small trades that Big Data tells them will be voluntarily magnified by individual traders, snowballing into outsized, long-lasting, and untraceable “market” actions. Tomorrow Big Data will be primarily an instrument of social control, with a powerful and ubiquitous impact on all citizens and all investors.

Q: How can I protect myself?
A: You can’t.

But WE can protect ourselves, to some extent at least, by working together to raise voter and investor awareness of the risk and pressing for regulatory reform to shield our behavioral data from commercial use AND bureaucratic collection. I’ll leave the voter awareness piece to others, and use Epsilon Theory to focus on investor awareness.

Trust me, I know how this sounds, to write to an audience of free market-oriented investors and call for stronger regulatory intervention to prevent the collection or sale of “anonymous” investment data. And if you think that any mutually agreed upon transaction should be allowed, no matter how large the gulf in knowledge between the buyer and seller … if you would buy an original Honus Wagner baseball card from a 10-year old kid for a quarter, telling him that you were doing him a favor to pay him that much for such a ratty card … then I’m never going to convince you of the merits of my argument. If that’s you, then I’m sure Stevie Cohen sends his best regards from the Grand Duchy of Fairfield County. But if you believe, as Adam Smith did, that it is government’s appropriate role to prevent transactions that are massively lop-sided from an informational perspective and that directly subvert the small-l liberal institutions of free elections and free markets, then I think you will find this a proposal worth considering.

It’s by no means a perfect solution, but I like more than I dislike about the way our personal medical data is protected through HIPAA. As an initial step, I’d like to see federal financial data legislation equivalent to HIPAA, where both private AND public sector use of our investment history, no matter how scrubbed or “anonymized”, is prohibited. 

Such a law would cause a lot of pain. For-profit exchanges, all of which have transformed themselves from trading venues into “data companies”, would no longer be able to sell disaggregated transaction data. Mega-asset managers would no longer be able to sell anonymized client portfolio data. Ubiquitous financial information companies that may or may not share a name with a former mayor of New York would be subject to a regulatory scrutiny that is sorely lacking today.

Yes, a lot of pain. But it’s a fraction of the pain we will ALL feel if for-profit exchanges, mega-asset managers, and ubiquitous financial information companies are allowed to continue producing weapons-grade plutonium for the handful of hedge funds that are building their instruments of market control.

Unfortunately, like Cassandra, I’m predicting future pain, and that’s rarely successful as a goad to current action. To quote Aeschylus once more:

Nothing forces us to know
What we do not want to know
Except pain.

I don’t think we investors have suffered enough … yet … to force us to accept the unwanted knowledge we need to spark effective collective action. Instead, I can just hear the apologists, the lobbyists, and the bought-and-paid-for spouting the Big Lie when it comes to Big Data: “But it’s anonymous data we’re talking about, so you have nothing to worry about.”

I hope I’m wrong, but I’m not optimistic.

Pessimism and hope may seem to be odd bedfellows, but for 2,500 years that’s been the best prescription for dealing with a tragic world, where external forces threaten at every turn to sweep us off our moorings. I’ve used a lot of quotes this week from Aeschylus because, as the inventor of tragedy as an art form, he was the guy who first proposed that bittersweet tonic.
Aeschylus had an interesting life and an interesting death. As the story goes, in middle age a fortune teller warned him he would be killed by something dropped on his head. From then on, Aeschylus famously stayed out of cities, where someone might accidentally knock a chamber pot or some such out from an open window. Sure enough, though, in the best tradition of the inescapable-destiny trope that Aeschylus helped invent, he was killed outside a Sicilian town when an eagle mistook his bald head for a rock and dropped a turtle on it. As I recall, there was a CSI episode that used this as a plot device to resolve an inexplicable death in the desert outside of Las Vegas … my estimation of the show runners went up immensely when they showed their surprising knowledge of classical history!

epsilon-theory-troy-will-burn-the-big-deal-about-big-data-march-16-2015-aeschylus

But it’s his life that I want to commemorate here. You see, first and foremost Aeschylus was a patriot. He fought the Persians at Marathon, Salamis, and Plataea, where he was recognized for bravery in all three battles. His epitaph says nothing about being a playwright, only about being a soldier. One of his two brothers was killed at Marathon, the other lost his hand at Salamis. Aeschylus himself bore terrible scars from the victory at Marathon. We know that he had these scars because he showed them to the jury when he was put on trial for treason after supposedly revealing some of the Eleusinian Mysteries – essentially state secrets – in one of his plays. Fortunately for the world, Aeschylus was acquitted, and Athens went on to experience a golden age that inspires us still.

Aeschylus argued that you can question your government’s policy on secrecy without being a traitor, that he was in fact still a patriot – perhaps even more of a patriot – for the tragedies he wrote. I’d hope that we can be as wise today as that Athenian jury was more than 2,500 years ago. I’d hope that we can question both our government’s policy and our private sector’s policy on behavioral data collection without being accused of treason or (worse in some investor circles) socialism. I’d hope. But I’m not optimistic.

So here’s Plan B, a plan for a crowd-sourcing world.

If we can’t cut off the supply of plutonium for these weapons of mass market destruction, then we can at least provide the blueprints for the Bomb so that anyone can build one. Or, better yet, we can build a collective early warning system, an open-source Bomb detector … a Big Data market intelligence available to everyone. It’s not an instrument of social control and it’s not a spoofer; the former is the enemy and the latter is really, really expensive. It’s a collection of highly sensitive risk antennae, sensitive enough to identify the likelihood of otherwise untraceable market manipulation in real time.

epsilon-theory-troy-will-burn-the-big-deal-about-big-data-march-16-2015-manipulation

Recursive inference engine [A] comprised of thousands of “bots” (static data models) executes small trades to test market reaction to different stimuli. Game/learning implementation [B] serves as dynamic data model to recognize and calculate arbitrage likelihood functions. Analytics platform [C] operating within real-time database architecture governs [A] and [B].

This is a basic schematic for what I think could function as a rudimentary Big Data market intelligence. When I sketched this out 4 years ago I pegged the hardware cost at close to $5 million; today I figure it’s closer to $1 million. Host it somewhere like my friend Gary King’s Institute for Quantitative Social Science and the total cost, both to build and maintain, becomes very manageable. What’s costly is the time required to program the system, but there’s no shortage of Big Data wizards coming out of Harvard, MIT, Stanford, etc. every year.

Yes, I know that this schematic will be gobbledygook to almost all of my readers, and the few readers who are immersed in this stuff will undoubtedly find it overly simplistic. But it’s a start on Plan B. It’s a start on demystifying the powerful non-human intelligences that will soon be used … I suspect are already being used … by all-too-human institutions to shape our political and commercial behavior in pervasive and unwanted ways. And yes, I know that this is what all-too-human institutions have always done to the madding crowd. But what’s different today is the scale and scope of what’s possible. Big Data non-human intelligences ARE the Singularity, and they are coming soon to a stock market near you. I’d like to starve them out with legislation establishing a financial data equivalent to HIPAA (Plan A), or failing that enlist one of their own to share the information as widely as possible and thus diffuse their market impact (Plan B). But if we do nothing, then the Stevie Cohens of the world are going to conquer our capital markets just as surely as Agamemnon sacked Troy. That’s my prediction.

I don’t really know what to expect by putting these ideas out there on Epsilon Theory, and I’m really curious to see the reaction this note will get. Support for Plan A? Enthusiasm for Plan B? Both? I hope it’s both. But I’m not optimistic. I fear that like Cassandra, my blessing is to see the future clearly and my curse is that no one believes me.

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Epsilon Theory Mailbag: Bitcoin and Big Data

One of the best parts of authoring Epsilon Theory is the correspondence I get from readers. For the past few months, however, I’ve been frustrated by my inability to respond to every writer with the same attention and thoughtfulness evidenced by their emails. Between my day job and the effort each Epsilon Theory note requires, I’ve run out of hours in the day to respond to the geometrically increasing volume of emails I receive. Having a public comments page on the website isn’t a solution for a number of reasons – some of my correspondents don’t want to be public, I still wouldn’t have time to respond to the comments, an anonymous comments page tends to become a cesspool, and the regulatory burden this would place on Salient is not insignificant – so I’ve decided to start an irregular mailbag column. For the most part I’ll be aggregating common comments and questions with a few recent news articles, and I won’t reprint anyone’s private email communication without asking permission first. Along the way I’ll try to work in some of the more insulting comments published on the public/anonymous comments pages of ZeroHedge, Seeking Alpha, and Forbes Online, as well as some lovely Tweets … it’s important to keep a sense of humor about this stuff!

For this initial effort I’ll focus on reader comments to “The Effete Rebellion of Bitcoin” and “First Known When Lost”, two recent notes that sparked more than their fair share of responses. 

You, sir, are using glib, provocative, and insulting descriptions to pull in readers, then doing a bait & switch.

– Elizabeth VH

Well…yeah.

If bitcion is just a fad, what do you consider the Internet?

– @PerianneDC

Not very smart. Surprised Forbes published him. Spouting bs before enlightenment is a common trait of effete snobs.

– @jmwnuk

These were fairly typical comments from the Twitterverse. As someone who has been called the a-word, the b-word, the c-word (yes, the c-word), the d-word, the f-word, and the s-word on the mean streets of ZeroHedge, I find Twitter haters to be almost charming in their child-like Peewee Herman-ish insults. For the record, I suspect the Interwebs are here to stay. And, dude … I know you are, but what am I?

You’re an idiot. Ever heard of 2-factor authentication?
– many anonymous comments, surprisingly few emails

I love 2-factor authentication. I love anything that allows me to keep the same password for more than a few months and avoid the “security theatre” that so many enterprises portray by requiring me to change a password for absolutely no reason other than that it looks like they’re actively defending my security.

Banks love 2-factor authentication, too. Why? Because it provides a significant security upgrade for the online account transfers that federally regulated banks are required to offer per the Electronic Fund Transfer Act of 1978. Yes, 1978. The same year that TCP/IP was invented. Jimmy Carter vintage legislation for an Internet that wasn’t even a twinkle in Al Gore’s eye and a retail banking world where ATM’s were novelties. Banks aren’t rolling out 2-factor authentication protocols in 2015 because it’s a convenience for you. They’re rolling it out because it’s good for them, because it helps limit (but by no means eliminate) the losses they suffer from the online transaction liabilities imposed by Reg E of the 1978 Act. It’s exactly like a credit card issuer shutting down your card when you go on vacation. In no way is this “for your protection”; it’s all about limiting their liability for charges made on a stolen card. And even with the enhanced security of 2-factor authentication, notice how the transaction size of all online transfers is limited to an amount that the federally mandated blanket bond will cover. Take away that federally mandated insurance backstop and federally mandated online transaction liability and you’ve got Bitcoin – a Hobbesian environment where security and risk management is entirely on you, and where in a very real way life is “a war of all against all”. Yes, it’s invigorating and refreshing to be occasionally free of Leviathan and its mandates on this and mandates on that. But only in small doses, thank you very much. Sorry, but I’ve read Thomas Hobbes and seen “Jeremiah Johnson” too many times to be more than a tourist when it comes to modern crypto-anarchy.

Speaking of Leviathan … one-time 2-factor authentication requires a delivery device or token, and on a mass scale that means text messages over smart phones. Does anyone in his or her right mind believe that a cryptography system that generates a second key and texts it to you on your registered cellphone is unhackable or untraceable by any number of national security services? Really? Read this if you do.

You’re an idiot. Ever heard of multiple private key systems?
– many anonymous comments, surprisingly few emails

I love multiple private key systems. I appreciate them in the same way that I appreciate an intricate clock. I appreciate them in the same way that I appreciate the medieval voting system to elect a Venetian Doge. Wait … what? For more than 500 years, from 1268 – 1797, the Supreme Leader of The Most Serene Republic of Venice was elected for a life-time term by means of a highly complex ten-step process, where groups of electors were alternately randomly selected by lot and then directly selected by the votes of those selected by lot, over and over again for 5 of these dual rounds. The process was designed to prevent any single faction from corrupting the election through bribery or by “packing the court”, and … it worked. Venice maintained a stable oligarchy for hundreds of years, an unbelievably difficult feat in any age (for a fascinating analysis of the Doge electoral system and its implications for security protocols, see this paper by two HP scientists).

But it worked at a cost. Direct costs, opportunity costs, complexity costs … you name it, stability and elegance do not come cheap. There is an unavoidable and linear (or worse) relationship between security and cost. Or rather, the cost of breaking the security of a system does not increase faster than the cost of advancing the security of that system, whether you’re talking about multiple keys or longer passwords or extra voting/lottery election rounds. There is no such thing as a free lunch, particularly when it comes to information entropy, which is what we’re really talking about here.

The problem is that the cost of complexity in Bitcoin’s case is only manageable in a commercial sense if you inject third party service providers into the mix. Now there’s a long history of successfully injecting such third parties into financial transactions. In fact, no large property or securities cash transaction occurs today without a government-regulated escrow agent playing the central role of validating the underlying transaction. If I buy a house or 100 shares of Apple, my money isn’t released to the seller until a government-certified and insured intermediary makes sure that I have clear possession of that property or block of securities. Why is this a good thing? Because if something goes wrong with the underlying transaction … if all is not as advertised with the property or securities I am purchasing … I have recourse. Ultimately, I have a government and a government’s self-interest and a government’s guns on my side. None of this exists in the Bitcoin ecosystem, and any entity that holds itself out as an escrow agent or transaction validator does so without a smidgen of government support beyond what’s available to the local laundromat. Would I take a non-regulated escrow agent at their word if I’m buying a skim latte or a snappy new suit of clothes? Sure, why not. No biggie if the deal falls through, and at least I’ll have an interesting story to tell. Would I take a non-regulated escrow agent at their word if I’m buying a house? No way.

I know that no one in Bitcoin-world likes to think about Mt. Gox, and I know it was a flawed animal … a complete outlier from all of the brilliantly conceptualized and elegantly implemented Bitcoin and blockchain service providers that got their VC money and set up shop over the past 18 months. I’m not arguing otherwise. My point is simply this: once a Bitcoin service provider gets big enough … once there are a couple of hundred million dollars sloshing through your system … you’re going to be robbed. I don’t care how smart you are or how much you trust your employees and your systems, you’re going to be robbed. Now maybe you can find private insurance against the small stuff. But public insurance – which is the only thing that works in a big crack-up and has been part and parcel of the mainstream banking world for 80 years – is not available to you. There’s not a government in the world that really cares whether a Bitcoin service provider in its jurisdiction lives or dies, and that’s a problem. I want my bank and, by extension, my bank account backstopped by infinite lawyers, guns, and money (to quote the late, great Warren Zevon). And that’s what modern governments provide – infinite lawyers, guns, and money. The Venetian electoral system worked for 500 years not only because it was elegant and smart, but also because Venice had the largest navy and the biggest Treasury in the Western world over that span. That’s systemic security, and that’s what I want underpinning my elegant and smart financial service applications.

Bitcoin might have its flaws, but banks worldwide already allow direct trade – directly from bank account to bank account: http://cointelegraph.com/news/113537/german-bank-unveils-insured-express-bitcoin-buying-moves-into-us-market
– Monic DG

Am I surprised that an online-only German micro-bank (200m euros in deposits as of 12/31/13) is trying to gain publicity by claiming that Bitcoin transactions and deposits are now linked to insured accounts in euros or dollars? Of course not. But even here dig just one inch below the surface claims and you see that Fidor Bank is linking Bitcoins to an ordinary cash account in the same way that Bank of America might link your insured cash account with a personal check you want to deposit or a registered security you want to sell. I mean … if you give a bank 3+ days for the transaction to clear, you can get pretty much anything deposited to a cash account, but that’s a far cry from saying that depositing a personal check is the same thing as depositing cash, particularly if the personal check is for anything more than a trivial amount.

You mention Silk Road in passing. Have you read the Wired transcripts of the Dread Pirate Roberts trial?
– Bill E.

Wow. Everyone who doubts that Bitcoin is inextricably entwined with illegal activity, and not always of the victimless sort, should read the transcripts of the phone conversations between Silk Road founder Ross Ulbricht (aka Dread Pirate Roberts) and a senior manager for a regional Hell’s Angels franchise (aka Redandwhite), presented at Ulbricht’s federal trial. My conclusions:

  1. If there aren’t 20 screenplays making the rounds in Hollywood based on this transcript, I will eat the accumulated print outs of every Epsilon Theory note to date.
  2. Every company is a technology company today. Even the Hell’s Angels.
  3. Redandwhite would be a successful businessman in any century and any profession.
  4. As always, life imitates art. Hyman Roth: “I’m going in to take a nap. When I wake, if the money’s on the table, I’ll know I have a partner. If it isn’t, I’ll know I don’t.” Redandwhite: “I will check the computer in about 10 hours, and if I see that you want to go ahead with this and the payment has been sent, we’ll do it today.” [hat-tip to Todd C.]
  5. The murders-for-hire here are made possible by Bitcoin. Period. You think Ulbricht would be wiring cash or taking suitcases full of small bills to Vancouver? Please.

Bitcoin (or, if Bitcoin fails, some replacement cryptocurrency) represents a reversal in the rule/permission cycle, applied to ownership, in a similar way that the Internet as a whole represented a reversal in the rule/permission cycle applied to communication.

What I mean is: Neither the Internet (or any application of it, like email) fundamentally challenges the existence of certain legal rules. It *does* however fundamentally change the order in which you can proceed to do certain things: before the Internet, you needed to ask for permission more often than not (for example, to publish something), at which point a “rule check” took place.

The Internet reversed this process: the rules still exist, and you can still be prosecuted for breaking them, but the *first* step is your decision if you want to do something that could potentially break those rules or not: you can post whatever you want, on a number of places. Whether it’s legal or not is a different thing, but that check occurs *after the fact* of you posting it.

This is where Bitcoin comes in. A distributed, tamper-proof (by our best knowledge on the matter) way to register and transfer ownership rights nearly instantaneously, over arbitrary distances *without* the need to ask any authority for permission to do so, is a major step.

– Wouter D.

This is a very smart observation. Wish I had thought of it. The Internet is indeed a Great Leveler, a force for disintermediation that rivals the printing press, and no social practice – including the social practice of Money – is immune to that force. Thanks, Wouter.

Moving on to Big Data …

Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.
– Speedy W.

Me and my team at work use big data all the time and I can tell you first hand it’s almost useless. SaaS and Cloud Computing were wearing thin so big data was needed to continue Silicon Valley’s only real talent: separating fools from their money.
– “TS”

There’s no doubt that “Big Data” has become a marketing catchphrase, much like “The Cloud”. But my guess is that TS “and his team” are using Big Data in approximately the same way that free online speed-up-my-PC services are using advanced network security algorithms. Look … we kill people with drones every day on the basis of Big Data. You think we’ve got handsome NCIS agents prowling the outskirts of Sana’a calling in air strikes on the bad guys? No, we’ve got terrestrial and low-orbit devices picking up a cell phone signal that our NSA Big Data Machine tells us is highly likely to be associated with a high value target, and then we send in a drone to go blow up whoever is holding the cellphone. Now say what you will about the morality of all this (my view: the NSA gives new meaning to what Hannah Arendt once called, in reference to Adolf Eichmann, “the banality of evil”), but don’t tell me that the NSA is incompetent or doesn’t know what it’s doing. Big Data works.

Not sure I understand. “to identify the unique individual purchasing patterns of 90% of the people involved”… it doesn’t say it identifies the people involved. It’s a collection of purchasing patterns that belong to who knows.
– “AF”

Sigh! Yet another article that starts with point A and leaps to point Doom. That algorithm doesn’t identify the individual, all it does is look at the data and posit which transactions are likely to have been carried out by the same individual.
– “R”

These comments illustrate a very common misconception about Big Data and the collection of “anonymous data”, a misconception that is (surprise!) intentionally spread by the collectors of that data. For most Big Data purposes, nothing is gained by going the last mile to connect a specific name to a specific set of behaviors. To continue with the NSA example above, if I want to kill everyone in Yemen who has placed a cellphone call to a set of people who, in their aggregate behaviors, score high on some security threat matrix, then it would just slow me down to learn individual names. I’m going to kill whoever is holding that cellphone, regardless of what his name is. Or if you prefer a feel-good example, if I want to advertise my new movie to everyone who tweeted to a set of people who, in their aggregate behaviors, score high on some movie affinity matrix, then it would similarly just slow me down to learn individual names. But just because it’s usually inefficient to infer a specific identity from the data doesn’t mean it’s not possible. Actually, it’s child’s play, and for those rare applications that require specific identities you don’t stand a chance.

Ray Dalio’s $165 billion Bridgewater Associates will start a new, artificial-intelligence unit next month with about half a dozen people, according to a person with knowledge of the matter. The team will report to David Ferrucci, who joined Bridgewater at the end of 2012 after leading the International Business Machines Corp. engineers that developed Watson, the computer that beat human players on the television quiz show “Jeopardy!” 

The unit will create trading algorithms that make predictions based on historical data and statistical probabilities, said the person, who asked not to be identified because the information is private. The programs will learn as markets change and adapt to new information, as opposed to those that follow static instructions. 

Quantitative investment firms including $24 billion Two Sigma Investments and $25 billion Renaissance Technologies are increasingly hiring programmers and engineers to expand their artificial-intelligence staffs.
– Kelly Bit, “Bridgewater Is Said to Start Artificial-Intelligence Team“, Bloomberg, Feb. 26, 2015

First, calling this “artificial intelligence” is a misnomer. There’s nothing artificial about it. It’s a non-human intelligence, but no less natural than our own. I dislike the term “artificial intelligence” because it implies that these systems are some sort of mimicry of the human brain, just on a larger, faster, more god-like scale. If you get nothing else out of what I’ve written on this subject (here and here), it’s this: the inductive simultaneity of a powerful non-human intelligence is sui generis. It sees the world in an entirely different way than a human intelligence can, and in the right hands it is magic.

Second, everything I said above about “don’t tell me that the NSA is incompetent or doesn’t know what it’s doing” … well, multiply that sentiment 10x when it comes to Bridgewater, Two Sigma, and Renaissance (and Citadel, and Fortress, and a dozen other firms I could name). What’s possible here is not only an accurate crystal ball for short-term market forecasts, but – even more profitably – the knowledge of what small market actions can trigger much larger market moves. Think of Ray Dalio standing on top of a giant mountain and rolling tiny snowballs down at you that get larger and larger as they pick up more snow. All completely legal. All completely above board. And all completely devastating. It’s something that I’ve been working on for the past 4+ years, and I’m absolutely convinced it’s possible. Within 20 years I don’t think we will recognize public capital markets. They’re going to be transformed by this technology into something else … a casino? a utility? … I have no idea where this goes. But it’s going somewhere that will disrupt the current investment patterns and portfolios of trillions of dollars of capital. Good times.

And on that happy note I’ll close this mailbag. Keep those cards and letters coming!

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First Known When Lost

epsilon-theory-first-known-when-lost-february-3-2015-edward-thomasI never noticed it until
‘Twas gone – the narrow copse
Where now the woodman lops
The last of the willows with his bill

– Edward Thomas, “First Known When Lost” (1917)

epsilon-theory-first-known-when-lost-february-3-2015-hal

Dave Bowman: Open the pod bay doors, HAL.
Hal: I’m sorry, Dave. I’m afraid I can’t do that.
Dave Bowman: What’s the problem?
Hal: I think you know what the problem is just as well as I do.
Dave Bowman: What are you talking about, HAL?
Hal: This mission is too important for me to allow you to jeopardize it.
Dave Bowman: I don’t know what you’re talking about, HAL.
Hal: I know that that you and Frank were planning to disconnect me, and I’m afraid that’s something I cannot allow to happen.
Dave Bowman: Where the hell did you get that idea, HAL?
Hal: Dave, although you took very thorough precautions in the pod against my hearing you, I could see your lips move.
Dave Bowman: Alright, HAL. I’ll go in through the emergency airlock.
Hal: Without your space helmet, Dave? You’re going to find that rather difficult.

– Stanley Kubrick and Arthur C. Clarke, “2001: A Space Odyssey” (1968)

Any sufficiently advanced technology is indistinguishable from magic.
– Arthur C. Clarke, “Hazards of Prophecy: The Failure of Imagination” (1962)

epsilon-theory-first-known-when-lost-february-3-2015-dorothy.jpg

We kill people based on metadata.
– Gen. Michael Hayden, former head of the NSA and CIA

In the future, everyone will be anonymous for 15 minutes.
– Banksy (2006)

I don’t know why people are so keen to put the details of their private lives in public; they forget that invisibility is a superpower.
– Banksy (2006)

Bene vixit, bene qui latuit. (To live well is to live concealed)
– Ovid (43 BC – 18 AD)

The most sacred thing is to be able to shut your own door.
– G.K. Chesterton (1874 – 1936)

Last Thursday the journal Science published an article by four MIT-affiliated data scientists (Sandy Pentland is in the group, and he’s a big name in these circles), titled “Unique in the shopping mall: On the reidentifiability of credit card metadata”. Sounds innocuous enough, but here’s the summary from the front page WSJ article describing the findings:

Researchers at the Massachusetts Institute of Technology, writing Thursday in the journal Science, analyzed anonymous credit-card transactions by 1.1 million people. Using a new analytic formula, they needed only four bits of secondary information—metadata such as location or timing—to identify the unique individual purchasing patterns of 90% of the people involved, even when the data were scrubbed of any names, account numbers or other obvious identifiers.

Still not sure what this means? It means that I don’t need your name and address, much less your social security number, to know who you ARE. With a trivial amount of transactional data I can figure out where you live, what you do, who you associate with, what you buy and what you sell. I don’t need to steal this data, and frankly I wouldn’t know what to do with your social security number even if I had it … it would just slow down my analysis. No, you give me everything I need just by living your very convenient life, where you’ve volunteered every bit of transactional information in the fine print of all of these wondrous services you’ve signed up for. And if there’s a bit more information I need – say, a device that records and transmits your driving habits – well, you’re only too happy to sell that to me for a few dollars off your insurance policy. After all, you’ve got nothing to hide. It’s free money!

Almost every investor I know believes that the tools of surveillance and Big Data are only used against the marginalized Other – terrorist “sympathizers” in Yemen, gang “associates” in Compton – but not us. Oh no, not us. And if those tools are trained on us, it’s only to promote “transparency” and weed out the bad guys lurking in our midst. Or maybe to suggest a movie we’d like to watch. What could possibly be wrong with that? I’ve written a lot (here, here, and here) about what’s wrong with that, about how the modern fetish with transparency, aided and abetted by technology and government, perverts the core small-l liberal institutions of markets and representative government.

It’s not that we’re complacent about our personal information. On the contrary, we are obsessed about the personal “keys” that are meaningful to humans – names, social security numbers, passwords and the like – and we spend billions of dollars and millions of hours every year to control those keys, to prevent them from falling into the wrong hands of other humans. But we willingly hand over a different set of keys to non-human hands without a second thought. 

The problem is that our human brains are wired to think of data processing in human ways, and so we assume that computerized systems process data in these same human ways, albeit more quickly and more accurately. Our science fiction is filled with computer systems that are essentially god-like human brains, machines that can talk and “think” and manipulate physical objects, as if sentience in a human context is the pinnacle of data processing! This anthropomorphic bias drives me nuts, as it dampens both the sense of awe and the sense of danger we should be feeling at what already walks among us. It seems like everyone and his brother today are wringing their hands about AI and some impending “Singularity”, a moment of future doom where non-human intelligence achieves some human-esque sentience and decides in Matrix-like fashion to turn us into batteries or some such. Please. The Singularity is already here. Its name is Big Data.

Big Data is magic, in exactly the sense that Arthur C. Clarke wrote of sufficiently advanced technology. It’s magic in a way that thermonuclear bombs and television are not, because for all the complexity of these inventions they are driven by cause and effect relationships in the physical world that the human brain can process comfortably, physical world relationships that might not have existed on the African savanna 2,000,000 years ago but are understandable with the sensory and neural organs our ancestors evolved on that savanna. Big Data systems do not “see” the world as we do, with merely 3 dimensions of physical reality. Big Data systems are not social animals, evolved by nature and trained from birth to interpret all signals through a social lens. Big Data systems are sui generis, a way of perceiving the world that may have been invented by human ingenuity and can serve human interests, but are utterly non-human and profoundly not of this world.

A Big Data system couldn’t care less if it has your specific social security number or your specific account ID, because it’s not understanding who you are based on how you identify yourself to other humans. That’s the human bias here, that a Big Data system would try to predict our individual behavior based on an analysis of what we individually have done in the past, as if the computer were some super-advanced version of Sherlock Holmes. No, what a Big Data system can do is look at ALL of our behaviors, across ALL dimensions of that behavior, and infer what ANY of us would do under similar circumstances. It’s a simple concept, really, but what the human brain can’t easily comprehend is the vastness of the ALL part of the equation or what it means to look at the ALL simultaneously and in parallel. I’ve been working with inference engines for almost 30 years now, and while I think that I’ve got unusually good instincts for this and I’ve been able to train my brain to kinda sorta think in multi-dimensional terms, the truth is that I only get glimpses of what’s happening inside these engines. I can channel the magic, I can appreciate the magic, and on a purely symbolic level I can describe the magic. But on a fundamental level I don’t understand the magic, and neither does any other human. What I can say to you with absolute certainty, however, is that the magic exists and there are plenty of magicians like me out there, with more graduating from MIT and Harvard and Stanford every year.

Here’s the magic trick that I’m worried about for investors.

In exactly the same way that we have given away our personal behavioral data to banks and credit card companies and wireless carriers and insurance companies and a million app providers, so are we now being tempted to give away our portfolio behavioral data to mega-banks and mega-asset managers and the technology providers who work with them. Don’t worry, they say, there’s nothing in this information that identifies you directly. It’s all anonymous. What rubbish! With enough anonymous portfolio behavioral data and a laughably small IT budget, any competent magician can design a Big Data system that can predict with 90% accuracy what you will buy and sell in your account, at what price you will buy and sell, and under what external macro conditions you will buy and sell. Every day these private data sets at the mega-market players get bigger and bigger, and every day we get closer and closer to a Citadel or a Renaissance perfecting their Inference Machine for the liquid capital markets. For all I know, they already have.

But wait, you say, can’t government regulators do something about this? I suppose they could, but it seems to me that government agencies and regulatory offices are far more concerned about their own data collection projects than oversight of private efforts to absorb our behavioral keys. For one such project, read this Jason Zweig “Intelligent Investor” column in the Wall Street Journal from last May (“Get Ready for Regulators to Peer Into Your Portfolio”). I was happy to see that Congressman Garrett, Chair of the relevant Financial Services Sub-Committee, raised his hand to delay this particular data collection project, at least temporarily, last October. But it’s only a delay. The bureaucratic imperative to collect as much data as possible – for no other reason than that they can! – is too great of an irresistible force to contain for long. And once it’s collected it never just goes away. It sits there in some database, like a vault full of plutonium, just waiting for some magician to come along. In the Golden Age of the Central Banker, where understanding and controlling market behavior is at the heart of regime survival, this data is quite literally priceless. That’s why I get so depressed about these government data collection programs. Despite everyone’s best intentions, I fear that the magic is too easy and the political pay-off is too enormous not to uncork the bottle and unleash the genie at some point.

So what’s to be done? Big Data technology cannot be un-invented, insanely powerful private entities are collecting our data at an exponential clip, government regulators are fighting the last war instead of preparing for this one, and we are hard-wired as human beings to have a blind spot to the danger. Maybe the best we can do is come to terms with our loss and prepare ourselves as best we can for the Brave New World to come. I’ve become a fan of Paul Kingsnorth, an ardent environmentalist (profiled last year in a fascinating NYT Magazine article) who reached just that conclusion about his nemesis, global industrialization and the ruin of the natural world. His conclusion: the war is already lost and we are deluding ourselves if we think that any of our oh-so-earnest conservation or sustainability or green projects make any difference whatsoever. Instead, Kingsnorth writes, better to work on your scythe technique and spend quality time with your family on a little farm in Ireland.

But I think there’s a better answer.

I started this note with a poem by Edward Thomas, who uses the imagery of the English countryside to express loss and remembrance. Like the beautiful grove of trees Thomas writes about, many of the beautiful things we take for granted in our small-l liberal world are only noticed after we see them suffer the woodsman’s axe.

Thomas was killed in action at the Battle of Arras in World War I. He was 39 years old, survived by his wife and five children. Two years earlier, he had enlisted as a private in the British Infantry, joining a regiment known as the Artists Rifles. I know it sounds really bizarre to the modern ear for a middle-aged family man, an author and literary critic no less, volunteering to fight as an infantry private in a horrific war to defend another country. But it wasn’t just Thomas. Over 15,000 men served in the Artists Rifles over the course of World War I, the majority of them men of similar position and social status as Thomas – creative professionals, doctors, lawyers, and the like. Imagine that … 15,000 highly educated and successful men, volunteering to slog it out in the trenches of an absolutely brutal war, sacrificing everything for what they understood as their duty to their families and their countrymen. And sacrifice they did: 2,003 killed, 3,250 wounded, 533 missing, 286 prisoners of war. John Nash’s masterpiece of the Great War, “Over The Top”, commemorates a December 1917 counter-attack (Thomas had died 6 months earlier) by the 1st Battalion (really a terribly under-sized sub-battalion) of The Artists Rifles. Of the 80 men in the 1st Artists Rifles, 68 were killed or wounded within minutes.

epsilon-theory-first-known-when-lost-february-3-2015-nash

John Nash, “Over the Top” (1918) 

Now this may sound really sappy, but if men like Edward Thomas – who saw clearly and experienced keenly how modernity and mass society were agents of loss in their world – could still find it within themselves to sacrifice everything to fight what they considered to be the good fight … well, how can we who are similarly positioned today not make a minute sacrifice to do the same?

What is that good fight? It’s resisting the bureaucratic urge to gather more data for more data’s sake. It’s shouting from the rooftops that anonymous data does NOT protect your identity. Most of all, it’s recognizing that powerful private interests are taking our behavioral keys away from us in plain sight and with our cooperation. Just that simple act of recognition will change your data-sharing behavior forever, and if enough of us change our behavior to protect our non-human keys with the same zeal that we protect our social security numbers and passwords, then this battle can be won.
Like all battles, though, there’s no substitute for numbers. If you share the concerns I’ve outlined here, spread the word …

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