On Monday, Verizon Wireless successfully securitized more than $1 billion in cellphone contracts and sold the notes into the Asset-Backed Securities (ABS) market. Here’s the Reuters article describing the deal. Wireless guys have been selling their contracts for years in private deals, but this is the first public securitization, and the first new “asset class” to come into the ABS market in probably a decade. Nothing like selling a cool drink of water to a market with an unquenchable thirst for yield …
Old Hickory Lake, TN – visible light image (L) and infrared light image (R)
|Morpheus:||Do you believe in fate, Neo?|
|Neo:||Because I don’t like the idea that I’m not in control of my life.|
|Morpheus:||I know *exactly* what you mean. Let me tell you why you’re here. You’re here because you know something. What you know you can’t explain, but you feel it. You’ve felt it your entire life, that there’s something wrong with the world. You don’t know what it is, but it’s there, like a splinter in your mind, driving you mad.|
– “The Matrix” (1999)
|Cypher:||I know this steak doesn’t exist. I know that when I put it in my mouth, the Matrix is telling my brain that it is juicy and delicious. After nine years, you know what I realize? [Takes a bite of steak]|
|Cypher:||Ignorance is bliss.|
– “The Matrix” (1999)
|Agent Smith:||Never send a human to do a machine’s job.|
– “The Matrix” (1999)
A right-hand glove could be put on the left hand if it could be turned round in four-dimensional space.
– Ludwig Wittgenstein, “Tractatus Logico-Philosophicus” (1921)
I remember that I’m invisible and walk softly so as not awake the sleeping ones. Sometimes it is best not to awaken them; there are few things in the world as dangerous as sleepwalkers.
– Ralph Ellison, “Invisible Man” (1952)
Tell people there’s an invisible man in the sky who created the universe, and the vast majority will believe you. Tell them the paint is wet, and they have to touch it to be sure.
– George Carlin (1937 – 2008)
Invisible threads are the strongest ties.
– Friedrich Nietzsche (1844 – 1900)
This is the concluding Epsilon Theory note of a trilogy on coping with the Golden Age of the Central Banker, where a policy-driven bull market has combined with a machine-driven market structure to play you false. The first installment – “One MILLION Dollars” – took a trader’s perspective. The second – “Rounders” – was geared for investors. Today’s note digs into the dynamics of the machine-driven market structure, which gets far less attention than Fed monetary policy but is no less important, to identify what I think is an unrecognized structural risk facing both traders and investors here in the Brave New World of modern markets.
To understand that risk, we have to wrestle with the investment strategies that few of us see but all of us feel … strategies that traffic in the invisible threads of the market, like volatility and correlation and other derivative dimensions. A few weeks ago (“Season of the Glitch”) I wrote that “If you don’t already understand what, say, a gamma hedge is, then you have ZERO chance of successfully trading your portfolio in reaction to the daily ‘news’.” Actually, the problem is worse than that. Just as dark matter (which as the name implies can’t be seen with visible light or any other electromagnetic radiation, but is perceived only through its gravitational effects) makes up some enormous portion of the universe, so do “dark strategies”, invisible to the vast majority of investors, make up some enormous portion of modern markets. Perceiving these dark strategies isn’t just a nice-to-have ability for short-term or tactical portfolio adjustments, it’s a must-have perspective for understanding the basic structure of markets today. Regardless of what the Fed does or doesn’t do, regardless of how, when, or if a “lift-off” in rates occurs, answering questions like “does active portfolio management work today?” or “is now a good time or a bad time for discretionary portfolio managers?” is impossible if you ignore derivative market dimensions and the vast sums of capital that flow along these dimensions.
How vast? No one knows for sure. Like dark matter in astrophysics, we “see” these dark strategies primarily through their gravitational pull on obviously visible securities like stocks and bonds and their more commonly visible dimensions like price and volume. But three massive structural shifts over the past decade – the concentration of investable capital within mega-allocators, the development of powerful machine intelligences, and the explosion in derivative trading activity – provide enough circumstantial evidence to convince me that well more than half of daily trading activity in global capital markets originates within derivative dimension strategies, and that a significant percentage (if you held a gun to my head I’d say 10%) of global capital allocated to public markets finds its way into these strategies.
Let me stick with that last structural change – the explosive growth in derivative trading activity – as it provides the best connection to a specific dark strategy that we can use as a “teachable moment” in how these invisible market dimensions exert such a powerful force over every portfolio, like it or not. The chart on the right, courtesy of Nanex’s Eric Scott Hunsader, shows the daily volume of US equity and index option quotes (not trades, but quotes) since mid-2003. The red dots are daily observations and the blue line is a moving average. In 2004 we would consistently see 100,000 options quotes posted on US exchanges on any given day. In 2015 we can see as many as 18 billion quotes in a single day. Now obviously this options activity isn’t being generated by humans. There aren’t millions of fundamental analysts saying, “Gee, I think there’s an interesting catalyst for company XYZ that might happen in the next 30 days. Think I’ll buy myself a Dec. call option and see what happens.” These are machine-generated quotes from machine-driven strategies, almost all of which see the world on the human-invisible wavelength of volatility rather than the human-visible wavelength of price.
There’s one and only one reason why machine-driven options strategies have exploded in popularity over the past decade: they work. They satisfy the portfolio preference functions of mega-allocators with trillions of dollars in capital, and those allocators in turn pay lots of money to the quant managers and market makers who deliver the goods. But volatility, like love if you believe The Four Aces, is a many splendored thing. That is, there’s no single meaning that humans ascribe to the concept of volatility, so not only is the direct relationship between volatility and price variable, but so is the function that describes that relationship. The definition of gamma hasn’t changed, but its meaning has. And that’s a threat, both to guys who have been trading options for 20 years and to guys who wouldn’t know a straddle from a hole in the head.
Okay, Ben, you lost me. English, please?
The basic price relationship between a stock and its option is called delta. If the stock moves up in price by $2.00 and the option moves up in price by $1.00, then we say that the option has a delta of 0.5. All else being equal, the more in-the-money the option’s strike price, the higher the delta, and vice versa for out-of-the-money options. But that delta measurement only exists for a single point in time. As soon as the underlying stock price change is translated into an option price change via delta, a new delta needs to be calculated for any subsequent underlying stock price change. That change in delta – the delta of delta, if you will – is defined as gamma.
One basic options trading strategy is to be long gamma in order to delta hedge a market neutral portfolio. Let’s say you own 100 shares of the S&P 500 ETF, and let’s assume that an at-the-money put has a delta of 0.5 (pretty common for at-the-money options). So you could buy two at-the-money put contracts (each contract controlling 100 shares) to balance out your 100 share long position. At this point you are neutral on your overall market price exposure; so if the S&P 500 goes up by $1 your ETF is +$100 in value, but your puts are -$100, resulting in no profit and no loss. But the delta of your puts declined as your S&P ETF went up in price (the options are now slightly out-of-the-money), which means that you are no longer market neutral in your portfolio but are slightly long. To bring the portfolio back into a market neutral position you need to sell some of your ETF. Now let’s say that the S&P goes down by $2. You’ve rebalanced the portfolio to be market neutral, so you don’t lose any money on this market decline, but now the delta of your puts has gone up, so you need to buy some S&P ETF to bring it back into market neutral condition. Here’s the point: as the market goes back and forth, oscillating around that starting point, you are constantly buying the ETF low and selling it high without taking on market risk, pocketing cash all the way along.
There are a thousand variations on this basic delta hedging strategy, but what most of them have in common is that they eliminate the market risk that most of us live with on a daily basis in favor of isolating an invisible thread like gamma. It feels like free money while it works, which attracts a lot of smart guys (and even smarter machines) into the fray. And it can work for a long time, particularly so long as the majority of market participants and their capital are looking at the big hazy market rather than a thread that only you and your fellow cognoscenti can “see”.
But what we’re experiencing in these dark strategies today is the same structural evolution we saw in commodity market trading 20 or 30 years ago. In the beginning you have traders working their little delta hedging strategies and skinning dimes day after day. It’s a good life for the traders plucking their invisible thread, it’s their sole focus, and the peak rate of return from the strategy comes in this period. As more and larger participants get involved – first little hedge funds, then big multistrat hedge funds, then allocators directly – the preference function shifts from maximizing the rate of return in this solo pursuit and playing the Kelly criterion edge/odds game (read “Fortune’s Formula” by William Poundstone if you don’t know what this means) in favor of incorporating derivative dimension strategies as non-correlated return streams to achieve an overall portfolio target rate of return while hewing to a targeted volatility path. This is a VERY different animal than return growth rate maximization. To make matters even muddier, the natural masters of this turf – the bank prop desks – have been regulated out of existence.
It’s like poker in Las Vegas today versus poker in Las Vegas 20 years ago. The rules and the cards and the in-game behaviors haven’t changed a bit, but the players and the institutions are totally different, both in quantity and (more importantly) what they’re trying to get out of the game. Everyone involved in Las Vegas poker today – from the casinos to the pros to the whales to the dentist in town for a weekend convention – is playing a larger game. The casino is trying to maximize the overall resort take; the pro is trying to create a marketable brand; the whale is looking for a rush; the dentist is looking for a story to take home. There’s still real money to be won at every table every night, but the meaning of that money and that gameplay isn’t what it used to be back when it was eight off-duty blackjack dealers playing poker for blood night after night. And so it is with dark investment strategies. The meaning of gamma trading has changed over the past decade in exactly the same way that the meaning of Las Vegas poker has changed. And these things never go back to the way they were.
So why does this matter?
For traders managing these derivative strategies (and the multistrats and allocators who hire them), I think this structural evolution in market participant preference functions is a big part of why these strategies aren’t working as well for you as you thought they would. It’s not quite the same classic methodological problem as (over)fitting a model to a historical data set and then inevitably suffering disappointment when you take that model outside of the sample, but it’s close. My intuition (and right now it’s only intuition) is that the changing preference functions and, to a lesser extent, the larger sums at work are confounding the expectations you’d reasonably derive from an econometric analysis of historical data. Every econometric tool in the kit has at its foundation a bedrock assumption: hold preferences constant. Once you weaken that assumption, all of your confidence measures are shot.
For everyone, trader and investor and allocator alike, the explosive growth in both the number and purpose of dark strategy implementations creates the potential for highly crowded trades that most market participants will never see developing, and even those who are immersed in this sort of thing will often miss. The mini-Flash Crash of Monday, August 24th is a great example of this, as the prior Friday saw a record imbalance of put gamma exposure in the S&P 500 versus long gamma exposure. Why did this imbalance exist? I have no idea. It’s not like there’s a fundamental “reason” for creating exposure on one of these invisible threads that you’re going to read about in Barron’s. It’s simply the aggregation of portfolio overlays by the biggest and best institutional investors in the world. But when that imbalance doesn’t get worked off on Friday, and when you have more bad news over the weekend, and when the VIX doesn’t price on Monday morning … you get the earthquake we all felt 6 weeks ago. For about 15 minutes the invisible gamma thread was cut, and everyone who was long gamma did what you always do when you’re suddenly adrift. You sell.
I can already hear the response of traditional investors: “Somebody should do something about those darn quants. Always breaking windows and making too much noise. Bunch of market hooligans, if you ask me. Fortunately I’m sitting here in my comfortable long-term perspective, and while the quants are annoying in the short-term they really don’t impact me.”
I think this sort of Statler and Waldorf attitude is a mistake for two reasons.
First, you can bet that whenever an earthquake like this happens, especially when it’s triggered by two invisible tectonic plates like put gamma and call gamma and then cascades through arcane geologies like options expiration dates and ETF pricing software, both the media and self-interested parties will begin a mad rush to find someone or something a tad bit more obvious to blame. This has to be presented in soundbite fashion, and there’s no need for a rifle when a shotgun will make more noise and scatters over more potential villains. So you end up getting every investment process that uses a computer – from high frequency trading to risk parity allocations to derivative hedges – all lumped together in one big shotgun blast. Never mind that HFT shops, for which I have no love, kept their machines running and provided liquidity into this mess throughout (and enjoyed their most profitable day in years as a result). Never mind that risk parity allocation strategies are at the complete opposite end of the fast-trading spectrum than HFTs, accounting for a few percent (at most!) of average daily trading in the afflicted securities. No, no … you use computers and math, so you must be part of the problem. This may be entertaining to the Statler and Waldorf crowd and help the CNBC ratings, but it’s the sort of easy prejudice and casual accusation that makes my skin crawl. It’s like saying that “the bankers” caused the Great Recession or that “the [insert political party here]” are evil. Give me a break.
Second, there’s absolutely a long-term impact on traditional buy-and-hold strategies from these dark strategies, because they largely determine the shape of the implied volatility curves for major indices, and those curves have never been more influential. Here’s an example of what I’m talking about showing the term structure for S&P 500 volatility prior to the October jobs report (“Last Week”), the following Monday (“Now”), and prior years as marked.
- The inverted curve of S&P 500 volatility prior to the jobs report is a tremendous signal of a potential reversal, which is exactly what we got on Friday. I don’t care what your investment time horizon is, that’s valuable information. Solid gold.
- Today’s volatility term structure indicates to me that mega-allocators are slightly less confident in the ability of the Powers That Be to hold things together in the long run than they were in October 2013 or 2014, but not dramatically less confident. The faith in central banks to save the day seems largely undiminished, despite all the Fed dithering and despite the breaking of the China growth story. What’s dramatic is the flatness of the curve the Monday after the jobs report, which suggests a generic expectation of more short-term shocks. Of course, that also provides lots of room (and profits) to sell the front end of the volatility curve and drive the S&P 500 up, which is exactly what’s happened over the past week. Why is this important for long-term investors? Because if you were wondering if the market rally since the October jobs report indicated that anything had changed on a fundamental level, here’s your answer. No.
- In exactly the same way that no US Treasury investor or allocator makes any sort of decision without taking a look at the UST term structure, I don’t think any major equity allocator is unaware of this SPX term structure. Yes, it’s something of a self-fulfilling prophecy or a house of mirrors or a feedback loop (choose your own analogy), as it’s these same mega-allocators that are establishing the volatility term structure in the first place, but that doesn’t make its influence any less real. If you’re considering any sort of adjustment to your traditional stock portfolio (and I don’t care how long you say your long-term perspective is … if you’re invested in public markets you’re always thinking about making a change), you should be looking at these volatility term structures, too. At the very least you should understand what these curves mean.
I suppose that’s the big message in this note, that you’re doing yourself a disservice if you don’t have a basic working knowledge of what, say, a volatility surface means. I’m not saying that we all have to become volatility traders to survive in the market jungle today, any more than we all have to become game theorists to avoid being the sucker at the Fed’s communication policy table. And if you want to remove yourself as much as possible from the machines, then find a niche in the public markets where dark strategies have little sway. Muni bonds, say, or MLPs. The machines will find you eventually, but for now you’re safe. But if you’re a traditional investor whose sandbox includes big markets like the S&P 500, then you’re only disadvantaging yourself by ignoring this stuff.
Ignorance is not bliss, and I say that with great empathy for Cypher’s exhaustion after 9 years on the Matrix battlefield. After all, we’ve now endured more than 9 years on the ZIRP battlefield. Nor am I suggesting that anyone fight the Fed, much less fight the machine intelligences that dominate market structure and its invisible threads. Not only will you lose both fights, but neither is an adversary that deserves “fighting”. At the same time, though, I also think it’s crazy to ignore or blindly trust the Fed and the machine intelligences. The only way I know to maintain that independence of thought, to reject the Cypher that lives in all of us … is to identify the invisible threads that enmesh us, some woven by machines and some by politicians, and start disentangling ourselves. That’s what Epsilon Theory is all about, and I hope you find it useful.
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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)
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.
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.
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When I look over my shoulder
What do you think I see?
Some other cat lookin’ over
His shoulder at me.
– Donovan, “Season of the Witch” (1966)
|Josh Leonard:||I see why you like this video camera so much.|
|Heather Donahue:||You do?|
|Josh Leonard:||It’s not quite reality. It’s like a totally filtered reality. It’s like you can pretend everything’s not quite the way it is.|
– “The Blair Witch Project” (1999)
Over the past two months, more than 90 Wall Street Journal articles have used the word “glitch”. A few choice selections below:
Bank of New York Mellon Corp.’s chief executive warned clients that his firm wouldn’t be able to solve all pricing problems caused by a computer glitch before markets open Monday.
– “BNY Mellon Races to Fix Pricing Glitches Before Markets Open Monday”, August 30, 2015
A computer glitch is preventing hundreds of mutual and exchange-traded funds from providing investors with the values of their holdings, complicating trading in some of the most widely held investments.
– “A New Computer Glitch is Rocking the Mutual Fund Industry”, August 26, 2015
Bank says data loss was due to software glitch.
– “Deutsche Bank Didn’t Archive Chats Used by Some Employees Tied to Libor Probe”, July 30, 2015
NYSE explanation confirms software glitch as cause, following initial fears of a cyberattack.
– “NYSE Says Wednesday Outage Caused by Software Update”, July 10, 2015
Some TD Ameritrade Holding Corp. customers experienced delays in placing orders Friday morning due to a software glitch, the brokerage said..
– “TD Ameritrade Experienced Order Routing, Messaging Problems”, July 10, 2015
Thousands of investors with stop-loss orders on their ETFs saw those positions crushed in the first 30 minutes of trading last Monday, August 24th. Seeing a price blow right through your stop is perhaps the worst experience in all of investing because it seems like such a betrayal. “Hey, isn’t this what a smart investor is supposed to do? What do you mean there was no liquidity at my stop? What do you mean I got filled $5 below my stop? Wait… now the price is back above my stop! Is this for real?” Welcome to the Big Leagues of Investing Pain.
What happened last Monday morning, when Apple was down 11% and the VIX couldn’t be priced and the CNBC anchors looked like they were going to vomit, was not a glitch. Yes, a flawed SunGard pricing platform was part of the proximate cause, but the structural problem here – and the reason this sort of dislocation WILL happen again, soon and more severely – is that a vast crowd of market participants – let’s call them Investors – are making a classic mistake. It’s what a statistics professor would call a “category error”, and it’s a heartbreaker.
Moreover, there’s a slightly less vast crowd of market participants – let’s call them Market Makers and The Sell Side – who are only too happy to perpetuate and encourage this category error. Not for nothing, but Virtu and Volant and other HFT “liquidity providers” had their most profitable day last Monday since … well, since the Flash Crash of 2010. So if you’re a Market Maker or you’re on The Sell Side or you’re one of their media apologists, you call last week’s price dislocations a “glitch” and misdirect everyone’s attention to total red herrings like supposed forced liquidations of risk parity strategies. Wash, rinse, repeat.
The category error made by most Investors today, from your retired father-in-law to the largest sovereign wealth fund, is to confuse an allocation for an investment. If you treat an allocation like an investment… if you think about buying and selling an ETF in the same way that you think about buying and selling stock in a real-life company with real-life cash flows… you’re making the same mistake that currency traders made earlier this year with the Swiss Franc (read “Ghost in the Machine” for more). You’re making a category error, and one day – maybe last Monday or maybe next Monday – that mistake will come back to haunt you.
The simple fact is that there’s precious little investing in markets today – understood as buying a fractional ownership position in the real-life cash flows of a real-life company – a casualty of policy-driven markets where real-life fundamentals mean next to nothing for market returns. Instead, it’s all portfolio positioning, all allocation, all the time. But most Investors still maintain the pleasant illusion that what they’re doing is some form of stock-picking, some form of their traditional understanding of what it means to be an Investor. It’s the story they tell themselves and each other to get through the day, and the people who hold the media cameras and microphones are only too happy to perpetuate this particular form of filtered reality.
Now there’s absolutely nothing wrong with allocating rather than investing. In fact, as my partners Lee Partridge and Rusty Guinn never tire of saying, smart allocation is going to be responsible for the vast majority of public market portfolio returns over time for almost all investors. But that’s not the mythology that exists around markets. You don’t read Barron’s profiles about Great Allocators. No, you read about Great Investors, heroically making their stock-picking way in a sea of troubles. It’s 99% stochastics and probability distributions – really, it is – but since when did that make a myth less influential? So we gladly pay outrageous fees to the Great Investors who walk among us, even if most of us will never enjoy the outsized returns that won their reputations. So we search and search for the next Great Investor, even if the number of Great Investors in the world is exactly what enough random rolls of the dice would produce with Ordinary Investors. So we all aspire to be Great Investors, even if almost all of what we do – like buying an ETF – is allocating rather than investing.
The key letter in an ETF is the F. It’s a Fund, with exactly the same meaning of the word as applied to a mutual fund. It’s an allocation to a basket of securities with some sort of common attribute or factor that you want represented in your overall portfolio, not a fractional piece of an asset that you want to directly own. Yes, unlike a mutual fund you CAN buy and sell an ETF just like a single name stock, but that doesn’t mean you SHOULD. Like so many things in our modern world, the exchange traded nature of the ETF is a benefit for the few (Market Makers and The Sell Side) that has been sold falsely as a benefit for the many (Investors). It’s not a benefit for Investors. On the contrary, it’s a detriment. Investors who would never in a million years consider trading in and out of a mutual fund do it all the time with an exchange traded fund, and as a result their thoughtful ETF allocation becomes just another chip in the stock market casino. This isn’t a feature. It’s a bug.
What we saw last Monday morning was a specific manifestation of the behavioral fallacy of a category error, one that cost a lot of Investors a lot of money. Investors routinely put stop-loss orders on their ETFs. Why? Because… you know, this is what Great Investors do. They let their winners run and they limit their losses. Everyone knows this. It’s part of our accepted mythology, the Common Knowledge of investing. But here’s the truth. If you’re an Investor with a capital I (as opposed to a Trader with a capital T), there’s no good reason to put a stop-loss on an ETF or any other allocation instrument. I know. Crazy. And I’m sure I’ll get 100 irate unsubscribe notices from true-believing Investors for this heresy. So be it.
Think of it this way… what is the meaning of an allocation? Answer: it’s a return stream with a certain set of qualities that for whatever reason – maybe diversification, maybe sheer greed, maybe something else – you believe that your portfolio should possess. Now ask yourself this: what does price have to do with this meaning of an allocation? Answer: very little, at least in and of itself. Are those return stream qualities that you prize in your portfolio significantly altered just because the per-share price of a representation of this return stream is now just below some arbitrary price line that you set? Of course not. More generally, those return stream qualities can only be understood… should only be understood… in the context of what else is in your portfolio. I’m not saying that the price of this desired return stream means nothing. I’m saying that it means nothing in and of itself. An allocation has contingent meaning, not absolute meaning, and it should be evaluated on its relative merits, including price. There’s nothing contingent about a stop-loss order. It’s entirely specific to that security… I want it at this price and I don’t want it at that price, and that’s not the right way to think about an allocation.
One of my very first Epsilon Theory notes, “The Tao of Portfolio Management,” was on this distinction between investing (what I called stock-picking in that note) and allocation (what I called top-down portfolio construction), and the ecological fallacy that drives category errors and a whole host of other market mistakes. It wasn’t a particularly popular note then, and this note probably won’t be, either. But I think it’s one of the most important things I’ve got to say.
Why do I think it’s important? Because this category error goes way beyond whether or not you put stop-loss orders on ETFs. It enshrines myopic price considerations as the end-all and be-all for portfolio allocation decisions, and it accelerates the casino-fication of modern capital markets, both of which I think are absolute tragedies. For Investors, anyway. It’s a wash for Traders… just gives them a bigger playground. And it’s the gift that keeps on giving for Market Makers and The Sell Side.
Why do I think it’s important? Because there are so many Investors making this category error and they are going to continue to be, at best, scared out of their minds and, at worst, totally run over by the Traders who are dominating these casino games. This isn’t the time or the place to dive into gamma trading or volatility skew hedges or liquidity replenishment points. But let me say this. If you don’t already understand what, say, a gamma hedge is, then you have ZERO chance of successfully trading your portfolio in reaction to the daily “news”. You’re going to be whipsawed mercilessly by these Hollow Markets, especially now that the Fed and the PBOC are playing a giant game of Chicken and are no longer working in unison to pump up global asset prices.
One of the best pieces of advice I ever got as an Investor was to take what the market gives you. Right now the market isn’t giving us much, at least not the sort of stock-picking opportunities that most Investors want. Or think they want. That’s okay. This, too, shall pass. Eventually. Maybe. But what’s not okay is to confuse what the market IS giving us, which is the opportunity to make long-term portfolio allocation decisions, for the sort of active trading opportunity that fits our market mythology. It’s easy to confuse the two, particularly when there are powerful interests that profit from the confusion and the mythology. Market Makers and The Sell Side want to speed us up, both in the pace of our decision making and in the securities we use to implement those decisions, and if anything goes awry … well, it must have been a glitch. In truth, it’s time to slow down, both in our process and in the nature of the securities we buy and sell. And you might want to turn off the TV while you’re at it.
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|Jesse:||And why’d you go and tell her I was selling you weed?|
|Walt:||Because somehow it seemed preferable to admitting that I cook crystal meth and killed a man.|
– “Breaking Bad” (2008)
|Meadow:||Are you in the Mafia?|
|Tony:||Am I in the what?|
|Meadow:||Whatever you want to call it. Organized crime.|
|Tony:||Who told you that?|
|Meadow:||Dad, I’ve lived in the house all my life. I’ve seen the police come with warrants. I’ve seen you going out at three in the morning.|
|Tony:||So you never seen Doc Cusamano going out at three in the morning on a call?|
|Meadow:||Did the Cusamano kids ever find $50,000 in Krugerrands and a .45 automatic while they were hunting for Easter eggs?|
|Tony:||I’m in the waste business. Everybody immediately assumes you’re mobbed up. It’s a stereotype. And it’s offensive. And you’re the last person I would want to perpetuate it. There is no mafia.|
|Tony:||[pause] Alright…look, Mead, you’re a grown woman, almost. Some of my money…comes from illegal gambling and whatnot. How does that make you feel?|
|Meadow:||At least you don’t keep denying it, like Mom.|
– “The Sopranos” (1999)
|Jane:||You’re lying! Now I know why Ed’s been calling every half hour. You’ve been back on the case, haven’t you?|
|Frank:||No! I swear it’s another woman!|
– “Naked Gun 33 1/3: The Final Insult” (1994)
Always confess to a small crime if you want to hide the big stuff. I remember reading this in a Robert Heinlein sci-fi novel when I was a kid, and it’s stuck with me ever since. Once you start looking for this trope you see it everywhere, and even if it goes a little over the top at times in scripted media (anyone remember the “24” season where Jack Bauer tortures his own brother, who gives up a partial truth to hide their father’s role as an arch-villain of treason?), I’m always on the look-out for it in the Narrative construction of our unscripted investment news media.
The problem in mass Narrative construction is not (or at least is very rarely) an issue of intentional misdirection through selective confession. But you don’t need intentionality for this dynamic to take root and misdirect all the same. Much more commonly, it’s the spreading of an easy to understand revelation of old fashioned greed that generates such a sense of outrage among all of us that regulators and policy makers mobilize to “crack down” on a few obvious bad guys while leaving the underlying flawed system intact. The result is that the flawed system often gets a new lease on life, as both the popular and regulatory attitude becomes “Oh … well, I guess so long as you’re not doing THAT, then I suppose we’ve got nothing to worry about.”
Case in point: the record $20 million fine levied by the SEC last week on ITG for its egregious wrongdoing in management of its trading dark pool. I can say that this was egregious wrongdoing without any fear of contradiction because – in sharp contrast to almost every settlement you’ve ever seen with the SEC, where the defendant “neither admits nor denies” anything – ITG was forced to confess as part of the settlement. You can read the SEC press release here, you can read the Bloomberg take here, and you can read the Wall Street Journal take here and here. As with most things market structure-related, I’ve learned a ton about this case from Sal Arnuk and Joe Saluzzi at Themis Trading, who put out a daily note on market structure that I think is very useful.
What did ITG do? They blatantly traded against the interests of their own clients, by peeking into their order book to buy and sell stock in other venues for their own account a few milliseconds ahead of their client orders. It’s pretty much a textbook case of front running, only in a modern context of dark pools and multiple electronic trading venues. This predatory HFT program traded 1.3 billion shares and (per Arnuk and Saluzzi’s calculations) impacted the pricing and execution of about 130 billion shares by a few pennies per share. That’s billion with a B. My favorite factoid from the SEC docs: ITG ranked their clients by how easy they were to trade against, and – surprise! – tried to do more business with the suckers. Oh, and here’s another shocker – the suckers were always the sell-side; ITG would turn off the program when it faced its buy-side clients. To be clear, this wasn’t a “rogue” operation at ITG, but something that was explicitly approved by their Board … twice.
My concern is NOT that what ITG did is rampant in the trading world. I doubt that any other dark pool operator or independent execution trader is cheating their clients in such an overt, really almost caricaturish fashion. My concern is in the grey area between cheating and edge. My concern is that our market structure is fundamentally flawed – or at least contains unanticipated and uncompensated risks – and that an honest discussion of those flaws will be shunted to the side in favor of easy regulatory posturing against those darn evil-doers. My strong hunch is that a regulatory and media focus on obvious front running will lock in the current market structure, although equally bad for investors would be some sort of witch hunt against all dark pools and all electronic trading venues.
Whichever way it goes, though, the ultimate result will be the same – an accelerated victory of the big bank trading groups over the high-tech trading firms for control of market flow data. HFT liquidity providers and quant-oriented execution shops are technology companies disguised as financial intermediaries. They hijacked the market infrastructure in the aftermath of the Great Recession, stealing it away from under the noses of the big financial firms who had come to see control over market structure as their birthright, and they had a good run. But now the big boys want their market infrastructure back, and they’re going to get it.
A lot of HFT critics are crowing over the ITG confession. You see! HFT is front running, plain and simple! Told you! And HFT defenders are largely silent because … well, you can’t defend the indefensible. I’m in the anti-HFT camp (see “The Adaptive Genius of Rigged Markets”), but I’m not crowing. If history is any guide at all, the existence of a clearly identifiable small-v villain will forestall the unmasking of what I believe is a Big Bad … the subterranean influence, bordering on control, of human investment behaviors by firms controlling advanced inference machines (see “Troy Will Burn – the Big Deal about Big Data”). Market infrastructure is only the first battleground in this war, but it’s a critical one. If even more advanced non-human intelligences owned by even more powerful institutions are allowed even more unmonitored and unregulated access to even more massive order books, this first battle is even more lost than it already is. But that’s exactly where I think we’re headed.
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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)
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
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
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!
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.
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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|Neil McCauley:||We want to hurt no one! We’re here for the bank’s money, not your money. Your money is insured by the federal government, you’re not gonna lose a dime. Think of your families, don’t risk your life. Don’t try and be a hero!|
|Butch Cassidy:||What happened to the old bank? It was beautiful.|
|Guard:||People kept robbing it.|
|Butch Cassidy:||Small price to pay for beauty.|
“Butch Cassidy and the Sundance Kid” (1969)
|John McClane:||Why’d you have to nuke the whole building, Hans?|
|Hans Gruber:||Well, when you steal $600, you can just disappear. When you steal $600 million, they will find you, unless they think you’re already dead.|
“Die Hard” (1988)
“The Town” (2010)
“Point Break” (1991)
“The Dark Knight” (2008)
|Cobb:||What is the most resilient parasite? Bacteria? A virus? An intestinal worm? An idea. Resilient…highly contagious. Once an idea has taken hold of the brain it’s almost impossible to eradicate.|
– “Inception” (2010)
|Jimmy Dell:||I think you’ll find that if what you’ve done for them is as valuable as you say it is, if they are indebted to you morally but not legally, my experience is that they will give you nothing, and they will begin to act cruelly toward you.|
|Jimmy Dell:||To suppress their guilt.|
– “The Spanish Prisoner” (1997)
“Is it true that you shouted at Professor Umbridge?”
“You called her a liar?”
“You told her He Who Must Not Be Named is back?”
“Have a biscuit, Potter.”
– J.K. Rowling, “Harry Potter and the Order of the Phoenix” (2003)
I hold it that a little rebellion now and then is a good thing, and as necessary in the political world as storms in the physical.
– Thomas Jefferson (1743 – 1826)
I really can’t think about kissing when I’ve got a rebellion to incite.
– Suzanne Collins, “Catching Fire” (2009)
In the day we sweat it out on the streets of a runway American dream.
At night we ride through the mansions of glory in suicide machines.
Sprung from cages out on Highway 9,
Chrome wheeled, fuel injected, and steppin’ out over the line.
– Bruce Springsteen, “Born To Run” (1975)
So few want to be rebels anymore. And out of those few, most, like myself, scare easily.
– Ray Bradbury (1920 – 2012)
Every act of rebellion expresses a nostalgia for innocence and an appeal to the essence of being.
– Albert Camus, “The Rebel: An Essay on Man in Revolt” (1951)
I used rebellion as a way to hide out. We use criticism as fake participation.
– Chuck Palahniuk, “Choke” (2001)
One of the first Epsilon Theory notes I wrote, and the one that really put this effort on the map, was about the modern meaning of gold. “How Gold Lost Its Luster” argued that gold today is not a currency or some sort of store of value; instead, it is an effective insurance policy against central bank error. That’s an Important Thing, just not as important as it used to be or as its more ardent proponents would have you believe. Today’s note is about the meaning of Bitcoin. Not its technical construction or its formal market interactions, but the behavioral WHY that gives Bitcoin its ultimate value. I caught a lot of flak for “How Gold Lost Its Luster”, and I expect some multiple of that for this note. So be it. The core tenet of Epsilon Theory is to call things by their proper names, even if that’s not the best way to make friends here in the Golden Age of the Central Banker.
Like gold, Bitcoin is neither a currency nor a store of value. Bitcoin is the cautious expression of a rebellious identity. Using Bitcoin is an effete act of rebellion, a weak signifier of resistance like wearing a hoodie or getting a tattoo that’s well covered by your work clothes. Bitcoin is fashion, more than a fad but less than lasting. Now fashion can be lucrative and fashion can be fun. Fashion is one of those intersections of art and commerce that I personally find fascinating (go ahead, quiz me on “Project Runway”). But fashion is not an Important Thing. Sorry, but it’s not.
As for the blockchain technology that underpins Bitcoin and is trumpeted as both an Important Thing and the Next Big Thing in every venture capital conference of the past two years, it’s a modern twist on the “technology” of the letter of credit. Color me unimpressed.
Strong words. Let’s dig in.
Bitcoin’s greatest attribute – its independence from every manner of organized social control – is also its fatal flaw. Bitcoin is a bearer bond. We all know what a bearer bond is, because we’ve all watched heist movies like “Heat” and “Die Hard”. Bearer bonds are the MacGuffin of choice for so many screenwriters because they side step all of those annoying plot questions when it comes to the logistics of stealing cash ($600 million in $100 dollar bills weighs more than 6 tons) or fencing stolen goods. By definition, there’s no registered owner of a bearer bond. If you possess it, you can trade it for value without your trading partner worrying about whether or not you are the “rightful” owner.
Bearer bonds have a very similar legal foundation to a bank letter of credit, where the bank will release the contracted funds to anyone who presents the documents required by the letter of credit, regardless of whether or not there was fraud or theft associated with the underlying real-world transaction or sales contract. This so-called “abstraction principle”, where the bank is only responsible for validating the documents defined in the letter itself and has no responsibility for validating the underlying transaction, is what makes a letter of credit work. The abstraction principle limits the liability of the letter issuer when faced with an unscrupulous beneficiary (the person receiving cash from the issuer) and places that liability squarely on the applicant (the person giving cash to the issuer in exchange for the letter). For those who are interested in such things, the abstraction principle is a fundamental concept in German common law and has lots of interesting twists and implications. I can just imagine some clever merchant guild master of the Hanseatic League coming up with this idea in the 13th century and transforming international commerce for the next 1,000 years.
The abstraction principle is Bitcoin’s fatal flaw. If I possess the private key associated with a Bitcoin address, then I can trade that Bitcoin with any counterparty for value without the counterparty worrying about whether or not I am the “rightful” owner of the Bitcoin. The private key is the only “document” required to satisfy the abstraction principle at the legal heart of Bitcoin, and so long as that document is not forged (which is what blockchain is very good at preventing) then the Bitcoin issuer has absolutely zero liability to any party in a Bitcoin transaction, including the “rightful” owner of the Bitcoin. ALL of the liability associated with unscrupulous presentation of the documents associated with a beneficiary claim on a Bitcoin credit rests with me, the rightful owner of that Bitcoin. I have absolutely zero recourse if my private key is lost or stolen. I am, to use the technical texting acronym, SOL.
As you might imagine, banks don’t go out of their way to inform you of the liability assignments associated with the abstraction principle, and neither do Bitcoin service providers. It’s not that they hide any of this, but they also know full well that the legal principles surrounding letters of credit and Bitcoin are entirely foreign to our common experience. They know full well that our behavioral expectations in this regard are almost entirely determined by our experience with credit cards and cash accounts – two bank-issued products underpinned by radically different legal principles.
Credit card issuers have made a simple deal with the US government. Bank issuers can charge outrageous fees and rates of interest on their revolving loans, but they do NOT enjoy the protection of the abstraction principle on the underlying transactions made with these loans. If someone steals my credit card, then my maximum liability is $50. Period. The bank will undoubtedly try to shift a portion of the liability onto the merchant accepting the stolen “document”, particularly with a card-not-present transaction, but it is illegal for the bank to push more than $50 of the liability onto me, no matter how careless or stupid I was in losing the keys to my revolving credit account. This is why credit card issuers are so quick to freeze your account when you go on vacation and start charging in person (card present) in a new locale. They couldn’t care less about “looking out for you”. It’s entirely an effort to limit their liability at the expense of your convenience.
It’s a little more complicated when it comes to your cash accounts, because any nation’s currency is, in effect, a form of bearer bond. Neither the cash in my wallet nor the cash in my checking account is registered to me, and whoever possesses those physical cash bills can trade them for value without the transaction counterparty worrying about whether or not the possessor was the rightful owner of those bills. That’s not to say there are no limitations or liabilities associated with cash acceptance by a transaction counterparty – this is the entire purpose of anti-money laundering (AML) regulations and other capital controls – but on a fundamental level the abstraction principle is in effect here, as the currency issuer bears zero liability if my “documents” (the cash bills) are lost or stolen.
Or at least that’s the way it was back when Butch Cassidy and the Sundance Kid were out robbing banks. Whether Butch and Sundance stole cash or gold nuggets from the vault made absolutely no difference to the owners of that cash or gold. Whatever you had on deposit with the local bank was almost always uninsured, recoverable only if you put together a posse and got your money back from Butch and Sundance directly. Some banks maintained “blanket bonds” that would insure accounts from fraud and theft, but far more often these provisions were honored only in the breach.
That all changed with the Banking Act of 1933 (establishment of the FDIC and deposit insurance), the Banking Act of 1935 (essentially all banks under FDIC jurisdiction), and the Federal Deposit Insurance Act of 1950 (codification of our current system). Now obviously these laws and the entire notion of deposit insurance came out of the massive spate of bank failures associated with the Great Depression, not because we were overrun with bank robbers, and even today the FDIC does not directly insure deposits against theft and fraud. But with the FDI Act of 1950, the FDIC was empowered to require regulated banks (which means essentially all US banks) to maintain sufficient blanket bond coverage to make cash account holders whole (up to FDIC limits) for almost any source of loss. More importantly, for the past 60+ years the FDIC and every other organ of government has promoted the idea that, without exception, no one can lose a dime in an FDIC-insured account. Our government has well and truly become an insurance company with an army attached, to use the phrase popularized by Paul Krugman, and nowhere is this set of behavioral expectations more solidly ensconced than in the cash deposits of US banks. It’s no wonder we all have a soft spot in our hearts for the plucky thieves in bank heist movies. Whatever they’re stealing, it’s no skin off our collective noses.
There’s one more piece of legislation relevant to our story, and that’s the Tax Equity and Fiscal Responsibility Act of 1982. This was the final nail in the coffin for the issuance of corporate and municipal bearer bonds in the US, as it eliminated the corporate issuer’s tax deduction for interest payments and the muni purchaser’s federal tax exemption for interest received. Both tax advantages were preserved for registered bonds, of course. I say of course because the US government, regardless of political party (the 1982 Act was in Reagan’s first term), has been trying to eliminate bearer bonds for a looooong time. Why? Because the US government believes that bearer bonds are at best a gift for criminal enterprises and at worst actively subversive. Whether this belief is right or wrong (and I think it’s mostly right), the notion that the US government will do anything to help a modern twist on the bearer bond under ANY circumstances is absolutely ludicrous.
So where does that leave us? It leaves us with an extremely elegant credit instrument that is almost immune to forgery or government registration, but because of this immunity it is permanently trapped by the abstraction principle within the world of bearer bonds and letters of credit. As such, Bitcoin is the apple of every criminal’s eye. Every modern day Butch and Sundance, every Neil McCauley, every Hans Gruber is trying to steal your private key. Some will succeed through violence and intimidation. More will succeed with words rather than guns, using what cybersecurity experts call social engineering. If you’ve never seen David Mamet’s “The Spanish Prisoner”, now would be a good time.
And because Bitcoin is hated by governments, it’s all on you to maintain the security of your private key. There is no insurance here, either directly through deposit insurance or indirectly through a blanket bond required of federally regulated banks. There is no “forgot your password?” button to push here, no regulatory or enforcement agency that will vouchsafe a service provider.
For some, the constant liability risk generated by the abstraction principle is – as Butch Cassidy said – a small price to pay for beauty. But for anyone with a serious amount of money who’s not in a criminal enterprise, this is an intolerably risky legal no-man’s land. Look … there are good reasons why bearer bonds have gone the way of the dodo. Are they illegal? No. Do they have an insanely poor risk/reward profile as a central part of any investment portfolio? Yes.
So why is Bitcoin popular, at least on its appropriately small scale? Because it IS beautiful in its technological conception and execution. Because it IS independent from and mildly threatening to the Powers That Be. Because it IS associated (albeit indirectly and at a safe distance) with criminal venues like Silk Road. Bitcoin projects an identity of technological sophistication, bad boy savvy, and a healthy suspicion of Big Government in a safe, palatable manner. That’s an identity that many people (including me) find attractive and would like to take on. It’s an identity that mainstream corporations that sell to those people, like Dell and Microsoft, would like to take on. Bitcoin is a fashion statement. I don’t say that to be pejorative. I say that as high praise. It’s a brilliant marriage of art and commerce, and that’s a lot. Unfortunately that’s not enough for some.
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