Accelerated Times

I think I should take a different tack, as the response to my last post has been somewhat underwhelming, ranging from Nicole’s “well you’re obviously some kind of communist” (I’m paraphrasing), to Amir’s perfectly reasonable “hmm… I think there might be something beneficial to this stuff”.

What can’t be disagreed on is that quantitive trading strategies have a significant impact on the operation of the financial markets, and that the operation of the financial markets has a signficant impact on all of us. My focus in all this comes from my 3 main interests:

1) Accelerated Computing. I work in this field, and I see a lot of interest in orienting the industry towards serving the needs of quantatitive finance.

2) FInance and Economics. I’m not a qualified expert in this field, but I’ve had professional exposure. I’ve implemented some quantitive finance algorithms, and would say that I understand them and their weaknesses fairly well (e.g. assumptions based on historical volatilities in options pricing). In my spare time I read a lot on economics and finance.

3) Pseudoscience. I’m interested in how strongly otherwise rational people can hold beliefs that defy logic and scientific evidence. Examples include homeopathy. Homeopathy is implausible to the point of impossiblity in its proposed mechanism of action. More importantly, scientific evidence does not exist to suggest that it works beyond placebo. Proponents of homeopathy provide anecdotal accounts accompanied by logical fallacies (I took homeopathic remedies, and got better, therefore homeopathy works).

To prove that quantitative hedge funds have merit, they need to be investigated. This investigation should take place on two levels:

Level 1: Do these funds on average actually make money for the people that invest in them? Saying “people wouldn’t invest in them if they didn’t” is not a valid argument to say that they do.

Level 2: Is the effect on the wider economy of unrestricted quantitative trading beneficial, neutral or negative? This one’s a lot harder to quantify, but it doesn’t mean we shouldn’t try.

A good article on the state of play in quantitative finance can be found here.

So now I’ll pick out some things from the article and comment on them, asking some questions I would sorely love to know the answer to:

The article discusses how quantatitive hedge funds have losing lots of money since the credit markets blew up. Their models didn’t see it coming. This on its own doesn’t prove that there is no value in the quant strategies of course, if it’s balanced out by the good years, then all is fine.

In discussing the turmoil, Marek Fludzinski says:

“We had layers of option strategies on top of our stat arb strategies to protect them from this catastrophic risk.”

Question: Do these strategies take into account the ability of the counterparties to pay out on these options in the event of catastrophe? If the “catastrophe” you’re insuring makes your counterparty insolvent, then you’re not insured against the risk.

The article suggested suggested a reason for the decline in quant hedge fund performance:

Too much money trying to exploit the same market inefficiencies accounts for some of the problem. HFR estimates that investors had $225 billion in quant hedge funds at the end of last year, more than the entire hedge fund industry had in early 1996. Overcrowding can be especially trying for quants, whose portfolios typically hold thousands of positions, making overlap among managers inevitable.

Question: When devising their quant strategies, to what extent do algorithm developers consider the role that quantitative trading activity plays in market behavior?

Thales’s 35-person research team, which includes ten Ph.D.s, is looking for ways to forecast investor behavior on different timescales. (In total, the firm employs about 50 people.) Although Thales focuses most of its effort on improving current trading systems and strategies, Fludzinski says the firm devotes as much as 20 percent of its resources to finding a unifying theory of markets. Like physics, he says, finance lends itself to mathematical models based on observations. Through experiments that replicate what they see in the natural world, physicists have described its underlying principles.

Question: Is there any rational basis for supposing that the markets (made up almost entirely of entities attempting to predict the behavior of the markets) should have behavior that can be described by theories like those in the physical world?

“Maybe we need to build a computer simulation that has 50 million people, with complicated rules for each,” Fludzinski says. “It’s very difficult to explain why people behave irrationally.”

Question: Is it reasonable to expect to be able to construct a model of the markets that is anything other than full-scale (i.e. containing a perfect model of every single actor in the market?) If such a model existed, would it be possible to intervene in the markets without changing the course of the market evolution predicted by the model? How can such models deal with unpredicted events with the ability to affect markets? e.g., earthquakes, terrorist attacks, conflict, and other such occurences.

As he seeks to right his faltering hedge fund, Thales’s Fludzinski could stand to reap such rewards sooner rather than later. But he’s not losing sight of his quest to explain why markets do what they do — a goal that may require a leap of imagination. Just as quantum mechanics and the theory of relativity shook up the world of physics, Fludzinski says, “finance needs a similar out-of-the-box insight — something that changes the way we look at things.”

Question: Is it a logical absurdity to suppose the existence of laws that predict price movement in markets?

Lo says he’s not surprised that statistical arbitrage and other quant strategies got slammed last summer. “Quant funds by their nature are going to be invested in mostly liquid strategies, and so when we have massive liquidations, they’re going to be first in line to take their licks,” he explains.

To protect themselves, Lo says, quant funds must understand their exposure to future sell-offs. That means paying close attention to the relationship between liquidity and their strategies’ expected returns. “We can’t forecast with any degree of accuracy when the next LTCM is going to hit,” Lo says. “But we can tell when certain market conditions are ripe for a dislocation and gradually try to adjust our risk exposures to take that into account.”

Lo believes that forecasting such disruptions will remain a problem — at least until hedge fund firms surrender more-detailed information. “The hope is that if we can get more data for both investors and managers to use, we will be able to avoid this kind of mad rush to the exits in the future,” he says. — N.R.

Question: If hedge funds could detect an upcoming sell-off, and therefore sell off before the crash, would this not simply move the sell off further forward in time?

One thing I’m in no doubt of is that there are lots of “clever” people in this field, but what these guys are looking for seems implausible and illogical, and I’m still waiting to see concrete data that shows the benefit of quantitative hedge funds, firstly to the investors, and secondly to the economy as a whole. Given the 2% off the top and 20% of profits that is taken from investors money and given to the quants, I am in no doubt that it’s economically beneficial to the quants.