The eMini Me Crash?, or Did a Butterfly Flap its Wings in Chicago?

Last week I conjectured that a likely starting place for the May 6 crash-boom was the index futures market. The WSJ ran an article today that claims a 50,000 contract options trade (ironically from the firm associated with Nassim Taleb, of Black Swan fame) touched off the dive. Eric Noll, an Executive VP at NASDAQ, has submitted testimony to the House Financial Services Committee that also fingers the eMini:

Second, the Chicago Mercantile Exchange was beginning to experience unusual trading activity in the “E-Mini June” at the same time equities markets were experiencing heavy trading in highly correlated equities. As you can see in Figure 1, the E-Mini began experiencing heavy volumes and prices begin sinking rapidly at 2:42, just before equities prices sink rapidly. At 2:45:30, E-Mini trading becomes so volatile that the Chicago Mercantile Exchange triggered an automatic 5-second trading halt in E-Mini futures. The price of the E-Mini future immediately leveled off and began to climb rapidly. Equities followed that pattern shortly afterwards.

The time lines between the stories don’t exactly line up: the 50K options trade (puts, presumably) was a little after 2:15, but the big action in the eMini occurred at about 2:42.

Felix Salmon is skeptical about the Black Swan link to this particular black swan: “Lots of options trades of that size take place every day, and just because this one happened just before the market fell doesn’t mean it was the cause of the crash.” Fair enough: 50K cars is not a big deal on most days. But “every day” and “May 6th” are not the same thing. That’s kind of the point: a confluence of events particular to that date likely made the ordinary exceptional, and perhaps exceptionally destabilizing.

Put differently, the market went non-linear–chaotic–in that short period of time. A key feature of non-linear systems is that small perturbations can have big, disproportionate impacts. The butterfly flapping its wings and all that. Or a black swan.

In these sorts of situations, chains of usually ordinary events can feed back on one another with destabilizing consequences. Here’s one scenario.

The market was very turbulent due to the chaos in Greece and Europe. (“Chaos” in the ordinary sense of the word, not the mathematical/scientific sense.) Market makers had backed off in the face of this risk. Markets were not as deep as usual. An order comes in. Maybe several in a row. The price starts to break, and then the feedbacks kick in. Options hedgers–including, perhaps, Barclays and others hedging the exposure from the options it had bought from Taleb’s fund–start selling as part of a dynamic hedging strategy. That’s one positive feedback. Some stops get triggered. More positive feedback. Prices get forced down some more. Given the background volatility, in light of the big price move, market makers human and electronic begin to fade big time. Liquidity dries up as a consequence, meaning that even small orders move prices a lot. With dynamic option hedges and stops going on automatic, the process feeds on itself. Through correlation algos and program trading systems, the price move in the futures pits is communicated to the cash markets. And off we go.

My sense remains that a market-wide sell-off most likely started in a market index-based contract, not in a single stock, or even a handful of stocks. Options-based stories, like that in the WSJ, make particular sense, because dynamic hedging can create the positive feedback that leads to non-linear responses to individually small shocks.

But the eMini story alone is not enough. Like Salmon says, the market deals with that every day. But an order or orders that would have been no big deal on ordinary days could have been a very big deal if liquidity was already low due to the uncertainty emanating from across the Atlantic.

It would be VERY interesting to see the evolution of the S&P futures and futures options order books on the 6th. Part of the scenario I sketched out is that the books weren’t as deep as usual as market makers (whether HFT types, or guys sitting at their TT screens) had pulled back due to the uncertainty surrounding Europe. (It would be interesting to look at other order books too, to see if the decline in liquidity was general.)

And if you want a very amazing demonstration of how liquidity had fled the market, you HAVE TO listen to this play-by-play of action in the eMini pit on the 6th. One of the most incredible things I’ve ever heard. I mean, you HAVE TO listen. (Man, it takes me back.)

At times, the market was quoted 10 wide, bid 1060 at 1070. Normally this is a one-tick market; the bid-offer is a quarter point. So the bid-ask was 40 times larger than usual. That’s an illiquid market. One can only imagine what other markets, normally less liquid than the eMini, were like.

The other fascinating thing about the recording is the incredibly rapid price rebound, with the price jumping by a point at a time in seconds–again, this in a market that usually moves in quarter points.

And note that this was an old school floor market, not some computerized market untouched by human hands.

The WSJ article also discusses the rebound, and supports my conjecture from the 6th: some of the demonized computerized traders played the role of the cavalry, and charged in to save the day by snapping up bargains:

Around 3 p.m., the selling pressure abated. Just as swiftly as the market fell, it recovered ground. One factor behind the swift recovery, traders say, were funds that use computers and formulas to sniff out bargains in the market. These funds swooped in on hundreds of cheap stocks, helping push the market higher.

In ‘87, corporate stock repurchases brought the market from the depths. I hypothesize that value-based quantitative strategies did it last Thursday.

There is a lot of data to be analyzed before a verdict should be rendered. We are blessed, though, by a superabundance of such data. An abundance that is the direct result of the computerization of the markets. In reconstructing the ‘87 Crash, it’s impossible to know what was in the order decks of the locals. It’s also impossible to sequence events in time with any accuracy. In reconstructing this one, the order books are computerized and time stamped to the fraction of a second. It will be a massive data processing task, but it’s just that: the data are there to be processed.

In the meantime, withhold rushes to judgment about HFT or algos or computers or whatever. Take statements like Noll’s with a grain of salt. No doubt the exchanges will all go around singing a Chuck Berry tune:

It wasn’t me, Sheriff; Uh huh, Sheriff, it wasn’t me
Ah! It must have been some other body, uh uh, Sheriff, it wasn’t me

all the while pointing fingers at other exchanges and algos. Let folks analyze the data, particularly the evolution of the order books and the flow of orders into those books. The answer will come in time, and with more precision that has been possible in previous crashes.

About Craig Pirrong 223 Articles

Affiliation: University of Houston

Dr Pirrong is Professor of Finance, and Energy Markets Director for the Global Energy Management Institute at the Bauer College of Business of the University of Houston. He was previously Watson Family Professor of Commodity and Financial Risk Management at Oklahoma State University, and a faculty member at the University of Michigan, the University of Chicago, and Washington University.

Professor Pirrong's research focuses on the organization of financial exchanges, derivatives clearing, competition between exchanges, commodity markets, derivatives market manipulation, the relation between market fundamentals and commodity price dynamics, and the implications of this relation for the pricing of commodity derivatives. He has published 30 articles in professional publications, is the author of three books, and has consulted widely, primarily on commodity and market manipulation-related issues.

He holds a Ph.D. in business economics from the University of Chicago.

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