One of the major concerns with quantitative investing is that the “black box” running the portfolio suddenly goes Skynet and destroys the portfolio. It raises an interesting distinction between “quantitative investing” as I intend it and as it is often perceived. For many, the word “quantitative” in relation to investing suggests two potentially dangerous elements:
- A very complex investment methodology, the understanding of which is beyond the minds of mere mortals, requiring a computer to calculate its outputs, ala Long-Term Capital Management.
- Automated trading, where the black box interacts directly with the market, without human supervision, which some have suggested caused the “program trading” crash of 1987.
The Wall Street Journal has a florid article, The Minds Behind the Meltdown, by Scott Patterson, which speaks directly to these two fears in the context of the recent credit crisis:
PDT, one of the most secretive quant funds around, was now a global powerhouse, with offices in London and Tokyo and about $6 billion in assets (the amount could change daily depending on how much money Morgan funneled its way). It was a well-oiled machine that did little but print money, day after day.
Then it achieved sentience (which, adds Wikipedia, is not to be confused with sapience).
That week, however, PDT wouldn’t print money—it would destroy it like an industrial shredder.
The unusual behavior of stocks that PDT tracked had begun sometime in mid-July and had gotten worse in the first days of August. The previous Friday, about half a dozen of the biggest gainers on the Nasdaq were stocks that PDT had sold short, expecting them to decline, and several of the biggest losers were stocks PDT had bought, expecting them to rise. It was Bizarro World for quants. Up was down, down was up. The models were operating in reverse.
The models were operating in reverse. How do we know? Instead of making money, they started losing money.
The market moves PDT and other quant funds started to see early that week defied logic. The fine-tuned models, the bell curves and random walks, the calibrated correlations—all the math and science that had propelled the quants to the pinnacle of Wall Street—couldn’t capture what was happening.
The market, under the thrall of sentient black boxes, defies logic, fine-tuned models, bell curves, random walks and calibrated correlations, leaving only fresh Talebian epistemic humility in its wake.
The result was a catastrophic domino effect. The rapid selling scrambled the models that quants used to buy and sell stocks, forcing them to unload their own holdings. By early August, the selling had taken on a life of its own, leading to billions in losses.
The selling had taken on a life of its own. Even the selling turned sentient (or sapient).
It was utter chaos driven by pure fear. Nothing like it had ever been seen before. This wasn’t supposed to happen!
Nothing like it had ever happened before. Of course the “program trading / portfolio insurance” crash of 1987 would be a total mystery to a newly sentient black box, given that it happened a full 20 years earlier.
As conditions spun out of control, Mr. Muller was updating Morgan’s top brass. He wanted to know how much damage was acceptable. But his chiefs wouldn’t give him a number. They didn’t understand all of the nuts and bolts of how PDT worked. Mr. Muller had kept its positions and strategy so secret over the years that few people in the firm had any inkling about how PDT made money. They knew it was profitable almost all the time. That was all that mattered.
The models were so complicated, and so secret, not even the guys running them knew know they worked.
That Wednesday, what had started as a series of bizarre, unexplainable glitches in quant models turned into a catastrophic meltdown the likes of which had never been seen before in the history of financial markets. Nearly every single quantitative strategy, thought to be the most sophisticated investing ideas in the world, was shredded to pieces, leading to billions in losses. It was deleveraging gone supernova.
The bizarre computer glitches, still unexplained, led to big losses, rendering billions of dollars into nothing but dust and Talebian epistemic humility.
Sounds pretty scary. Putting aside for one moment the melodrama of the article, it does highlight the potential problems for a quantitative investment strategy. It also presents some pretty obvious solutions, which I believe are as follows:
- The investment strategy should be reasonably tractable and the model should be simple. This avoids the problem of not understanding the “nuts and bolts” of the fund and should reduce the “bizarre, unexplainable glitches”.
- The strategy should be robust. If it can’t survive a crisis the magnitude of at least 1987, or 2007-2009, it’s not robust. The problem is probably too much leverage, either in the fund or baked into the security. What’s the “right” amount of debt? Little to none. What’s that mean? As Charlie Munger might say, “What don’t you understand about ‘no debt’?”
- To the extent that it is possible to do so, a human should enter the trades. Of course, human entry of trades is not the panacea for all our ills. It creates a new problem: Fat fingers are responsible for plenty of trades with too many or too few zeros.
Perhaps the best solution is a healthy skepticism about the model’s output, which is why simplicity and tractability are so important.
[…] closely to Montier’s, O’Shaughnessy’s and Philip Tetlock’s definition than Scott Patterson’s. A good process-driven approach to long-only equity investment should and does provide a very good […]
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[…] 28, 2010 by greenbackd In “Black box” blues I argued that automated trading was a potentially dangerous element to include in a quantitative […]
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“The strategy should be robust.”
Kind of an oxymoron. If the models have repeatedly blown up in spectacular fashion and aren’t deemed to fail the robust test until after the the latest new twist, what indication is their that anyone can determine when a model is robust until after all the data is in. Not the old data, the new data that sends the next quant model crashing and burning.
That I think is the crux of the problem with models. You don’t know what you don’t know and that makes it inherently tough to design a robust model.
Second, it doesn’t make much difference who places trades if the model they are basing their activity are junk.
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It’s not an oxymoron, but I take your point. We’re talking about the Talebian “epistemic arrogance” that I refer to obliquely in the post. I have described my definition of a “robust” model in other places on this site. Two strategies that I believe qualify as robust – i.e. unlikely to render a permanent loss of capital – are Graham net current asset value and low price-to-book value (although book value is my preferred metric, earnings, sales, or anything in LSV’s Contrarian Investment paper should also qualify). I think there is sufficient logic behind those strategies and evidence – both academic and practical – that they work over the long term to rely on them, no?
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My take on robust was in the context of the higher order math implied by the black box and quant investing. I don’t see the relationship between net nets and quant models.
Rephrased, no quant model can be robust, ergo an oxymoron. And…I agree that net nets and low P/B are robust strategies. I don’t really look at them as complex enough to connotate a model.
Net nets are simple man’s investing which may be why they have little appeal for most investors. The complex derivatives (quant models) and hedge fund strategies are much more glamorous and making them much more functional as a tool to fleece the unsuspecting out of their cash.
There is no reward for needless complexity. That message resonates little on Wall Street. For me I’ll gladly take the easy road. Nets nets when you can get ’em and plenty of hard assets.
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I am enjoying this series of posts on quantitative investing. James Montier says that it’s difficult to beat a quantitative strategy (as you have pointed out previously), but it’s tough to sit by and let a model pick a portfolio of stocks out. The Little Book that Beats the Market has a similar conclusion (follow the model), but that most people can’t do it for an extended period of time, especially when the model is losing money or underperforming the market.
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