Rise of the Machines

“Where are the tumbrils?” asks my friend Adam Smith. If, like me, you have no idea what is a tumbril, it is a type of horse cart used during the French Revolution to transport condemned prisoners to the guillotine for beheading. What Adam wonders is how we can get so deep into such a hellacious financial crisis without finding at least a few bad guys to behead?

It’s a good question.

In one sense there simply have to be bankers or money managers of some sort who have benefitted greatly from the financial discomfort of the rest of us. On the other hand it is difficult to find many such people. Maybe they are hiding. I know I would.

I’m beginning to think there aren’t as many devils as one might suspect in this passion play. There are a few devils, sure, but also a lot of innocent dopes who may have made the situation much worse while not even making very much money from our pain.

A lot of it comes down to a probable misapplication of technology, something that hasn’t been discussed much in the coverage of this financial crisis. We talk for seconds at a time about the confounding complexity of derivative securities then quickly go on to something more understandable.

Is technology our friend in this mess or our enemy?

If we’ve got a tumbril idling in the driveway and really need to find people for beheading, there are still plenty of successful hedge fund managers to choose from. According to Institutional Investor’s Alpha Magazine, which comes out today, the top 25 hedge fund managers were paid a total of $11.6 billion last year, which isn’t bad for the worst financial year since the Great Depression. Most of those managers correctly foresaw the market fall and found ways to benefit while making as much as $3.7 billion for the year.I could live on that. Heck, I could live on the interest on the interest on that.

But most hedge fund managers DIDN’T make money in 2008 – a very bad year for their industry overall. I have an interesting take on how some of that could have happened.

Let’s start by looking back to the dot-com era, which also happened to be the era of the day trader. Remember them? A successful day trader in the late 1990s could gain a following over Internet chat then use that following to make money by becoming an alpha trader. He’d say “I’m selling this” or “I’m buying that” and copycat day traders would do the same. If enough of them acted they could influence the price down or up and – since the leader was leading – he could almost always liquidate his position with a profit. The quickest of his acolytes would make profits, too. Those who didn’t profit weren’t seen as exposing the inherent flaws of this system, they were just viewed as too slow.

To a certain extent, the heirs of day trading have taken the lessons of that earlier era and applied them with devastating effect in the Twitter Age.

If a bunch of wealthy traders get together at Starbucks and agree to short-sell a company or a financial instrument, driving down that price ideally to the point where it never recovers, well that’s against the law. But with trading automation and the Internet as a platform it is possible to accomplish this same end WITHOUT it being explicitly illegal. It is even possible that the perps don’t know the level of damage they are inflicting, though I doubt that’s true. The trick is to avoid communication. If there is no communication between traders there is no chain of causation, no conspiracy, just an unhappy accident.

Where the alpha day traders of the 1990s were squeezing nickels out of penny stocks and settling-up at the end of every day, trading automation makes it possible today for Wall Street to make bigger and longer bets against much larger targets, with the perfect trade being one that leads to the quick and certain death of its victim.

The ideal short sale, you see, is one that never has to be covered because the company or financial instrument being shorted goes to a value of zero. That’s WAY more profitable than making a few cents or a buck or two here or there before covering that short. It’s much better to go for the headshot.

But as I say, that’s also illegal.

The market hasn’t worried about this much because the SEC hasn’t worried about anything in many years. And the task is viewed as requiring so much financial muscle that it was considered impossible to keep such a huge conspiracy quiet. You can’t take down a Bear Stearns, for example, without a LOT of help. And you can’t get a lot of help without two-way communication, or so the regulators – stupid regulators – thought.

Remember government regulation is by definition reactionary. The regulators have to observe bad behavior before they can move to control or prohibit it.

What ISN’T illegal is for a trader to essentially train the market and then rely on a conditioned response on the part of other traders or trading programs to achieve his deadly end.

Think of this in terms of physics. Force equals mass times acceleration. In order to have the greatest economic impact on the market you need to concentrate your efforts on a single target. It is much more lucrative to bet that a single company will die, for example, rather than that a market sector will rise or fall. So choose a target, finding leverage on that target, if possible. Apply mass by getting (or attracting – more to come on that) a large number of traders and their capital to your side. Then use acceleration by acting as quickly and as uniformly as possible, ideally within seconds. The effect can’t be anything but devastating.

Remember the story of George Soros and the Bank of England. Soros quickly made $1.1 billion in 1992 by selling the Pound and finally forcing the Bank of England to devalue that currency, thus lowering Soros’s cost of covering his shorts and generating a huge profit. Soros’s success in 1992 came from believing the Bank of England when it said it would defend the pound at any cost. Well the cost was $1.1 billion, thank you, transferred into George Soros’s bank account.

On Wall Street the Soros story is always told with admiration because he beat the bank and won the game. At the Bank of England they probably look at it somewhat differently. What Soros did, though, was identify the algorithm that governed the behavior of the Bank of England. Then he found a way to take advantage of that algorithm and the Bank’s unwillingness to change or adapt. From today’s view what Soros did was hack the Bank of England.

That was 17 years ago. The average workstation running on Wall Street today has 1000 times the power of its 1992 counterpart. Trading data is today available vastly quicker and in vastly greater volumes than before. It’s time to think about program trading.

We don’t hear much anymore about program trading, which was something that seemed to play a big role in the 1987 stock market crash. Computers back then were for the first time managing lots of money automatically and it took awhile to see the dark side of that – massive trades that were un-commanded and unexpected and only acted to hurt the market. And those trades were very crude with only a dozen or so firms even capable of making them. This was before Soros when computers were FIVE THOUSAND TIMES less powerful than they are today. So we learned from 1987 to put some wait states in the code, to turn off the programs under certain rules conditions, and program trading hasn’t been much of a problems since.

Or so we thought.

Think about piranha fish. These little guys with their big teeth travel in large schools. They kill and eat their prey, which can be as large as cattle drinking in the river. Piranha, too, take advantage of force times acceleration. The trick is getting a lot of fish – hundreds of fish – to attack at exactly the same time.

How do they do it? How do the piranha know to attack? They don’t wait to bump into a cow leg under water. They don’t sniff for the smell of blood in water. Both of those responses are too slow and would lead to too many victims escaping. Force equals mass times acceleration, remember? And besides, piranhas have tiny little brains to go with their big teeth, so don’t look for any insight there. These are just violent little eating machines.

Piranhas hunt as a school and take all their cues from the fish beside them. Only one fish has to smell blood or bump into some food for the entire school to reflexively attack.

Now we’re back on Wall Street in today’s era of hedge funds and genetic trading algorithms. At any given moment in the market there is more than a $1 trillion in cash that can be brought to bear in seconds by computers that are functioning essentially like piranhas. The cash isn’t held in a few funds or hidden behind some mainframe interface – it is held by hundreds of workstations each operating independently yet as part of a global economic system – conscious or not.

These trading workstations are running in hundreds of offices, all scanning the same data. They have learned over time that certain signals lead to certain outcomes. They may be following an alpha trader but they don’t have to because at some point the market signal, itself, is going to be too strong to ignore.

Here it comes. An alpha trader makes a bold move against a firm or, more likely, against one or more of that firm’s financial products. Say the firm is big stupid AIG, an insurance company, and the instrument is a credit default swap sold by AIG.

Though AIG seems to have forgotten or ignores it, Credit Default Swaps act like insurance and are treated by the market like insurance, but they technically AREN’T insurance. They are ultra-hyper-purified demonic risk and nothing else. That’s because CDS’s are not regulated (they are in fact IMMUNE to regulation – funny that), they can be shorted without having to EVER actually own the underlying security (naked shorts of CDS’s are perfectly legal), because they don’t have to be owned the volume available to be shorted isn’t limited, and – here’s the best one of all – there’s no requirement that the trader have any causal, custodial, or familial relationship with the covered debt. In other words, while most credit default swaps are intended to hedge debt defaults, they don’t have to be. It’s like buying a life insurance policy on the guy down the hall because you hear him coughing at night. His death is meaningless to you so buying the policy is just a gamble, not insurance.

Here’s how it works in practice. The alpha trader senses, guesses, or maybe just wishes for weakness on the part of AIG and its particular CDS issue, so he shorts that mother. The signal from that short (it is big and aggressive, having as much force as possible) is detected by 500 trading workstations running genetic algorithms – workstations that are not regulated in any sense whatsoever. AIG’s CDS begins to glow in front of 500 junior traders. Some programs kick-in automatically and sell, too. The CDS glows even brighter and begins to throb as if its heart was beating. Traders pile-on like piranhas, sensing opportunity, smelling blood, until the CDS is oversold to nothing, until it is dead.

What we’ve accomplished here, through the miracle of synthetic derivatives, is buying a $1 billion insurance policy on a $10 million asset.

It isn’t investing, isn’t even trading, it’s just betting.

Nobody started it. Or at least it is impossible to figure out who started it. No one trader could have saved the issue by staying out of the fray (doing so would only have cost easy profit). There was no meeting at Starbucks. Yet the final result was just as certain.

The problem with this scenario is that conditions – primarily technology – have changed enough to allow what were always parasites to become true predators. Parasites need a healthy host to maintain their lifestyle. If they eat too much the host dies and the parasites die with it. But predators just find something else to kill and eat when all of one prey is gone.

In this case that prey is the American mortgage market, which is a fair proxy for the American economy.

Better make that two tumbrils.

Thomas Heiman, PhD
Info Systems Eng, Sr
The MITRE Corporation | Center for Enterprise Modernization