Archive for October, 2008

To regulate, or not to regulate

Friday, October 31st, 2008

The surreal scenes in Washington continue.

Last week saw Alan Greenspan, who spent a good portion of his time as chairman of the Federal Reserve fighting any attempt at market regulation, getting grilled by a congressional committee and apparently admitting that:

he had put too much faith in the self-correcting power of free markets

Indeed, he went as far as to say that:

Those of us who have looked to the self-interest of lending institutions to protect shareholders’ equity, myself included, are in a state of shocked disbelief

However, it is not clear that this shocked disbelief has actually has much effect on his world view since he went on to say that:

Whatever regulatory changes are made, they will pale in comparison to the change already evident in today’s markets

Let me get this right. He used to think that the market was smart enough that it didn’t need any regulation, and now that belief has been shown to be false, he thinks that the market is smart enough that it doesn’t need any regulation. H’mmm.

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A scientist walks into a bar…

Wednesday, October 29th, 2008

The Economist reports on a paper that claims that

headline-grabbing scientific reports are the most likely to turn out to be wrong.

Given that the paper that includes this claim has done a pretty good job of grabbing headlines itself, should we expect that it will turn out to be wrong?

More seriously, the paper suggests that phenomena such as the winner’s curse may be having a deleterious effect on the publishing of research results.

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Bio-inspired algorithmic trading

Sunday, October 26th, 2008

Recently, I had an argument on a mathematics blog with a group of mathematicians ignorant of the major influence of biological and economic ideas in contemporary computer science, and refusing to believe it when pointed out to them.  (I wonder if refusing to learn new stuff qualifies you for special treatment in the Academic Dogs’ Home?) Well, here’s another instance:  the use of metaphors from [tag]immunology[/tag] for the design of [tag]automated trading algorithms[/tag], specifically for the identification of [tag]mis-priced options[/tag]:

“Essentially AISs [[tag]Artificial Immune Systems[/tag]] do a good job of figuring out what is ‘itself’ or ‘normal’ and what is ‘non-self’, ‘alien’ or ‘abnormal’ in a system. Antigens are components that find pathogens (antigens find patterns that are normal to them, the non-normal things then are pathogens).

Some ~2 or 3 years ago, MIT’s Journal of Evolutionary Computation had a ’special issue’ on AISs. There were several interesting publications in that particular issue, and it led me on a minor reading splurge regarding AISs. At the time, I had been heavily focused on evolutionary and other bio-inspired algorithms and their applications for automated trading systems. There are a ton of algorithms in this class, many of them have mappings to various sub-problems in automated trading.

As far as I can find, there is no work out there currently that applies AISs to finding mis-priced options, and it seems quite a good fit. Searching over a sea of options to sort out which one seem to be aberrations may be a good match. AISs are relatively efficient algorithms compared to alternatives. Interestingly – it would not even necessarily involve your own option pricing model – you would be basically letting the AIS sort out what seem to be ‘normal’ pricings and what seem to stand out as odd.“

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A revolutionary Marxist government in Washington

Wednesday, October 15th, 2008

Institutions seeking to hedge very uncertain investments often use something called [tag]The-End-of-The-World Trade[/tag].   As we noted earlier, one operational definition of such a catastrophic event reported by Donald MacKenzie was ‘a revolutionary Marxist government in Washington’.   Given the recent decision by the Bush administration, following the lead of Governments in Europe, to undertake the nationalization of the means of exchange, can we conclude that we have now reached that position?

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Modeling Markets

Monday, October 6th, 2008

With the [tag]global financial crisis[/tag] and the failure of existing regulatory models, more attention will be paid to alternative viewpoints about [tag]economic theory[/tag] and alternative ways of doing economics.   The IHT has just carried an article by econophysicist Mark Buchanan about [tag]computer simulation models[/tag] in economics.  

It is unfortunate that this article confirms our opinions of the mainstream economics profession:

“Certainly, markets have internal dynamics. They’re self-propelling systems driven in large part by what investors believe other investors believe; participants trade on rumors and gossip, on fears and expectations, and traders speak for good reason of the market’s optimism or pessimism.

Really understanding what’s going on means going beyond [tag]equilibrium thinking[/tag] and getting some insight into the underlying ecology of beliefs and expectations, perceptions and misperceptions, that drive market swings. Surprisingly, very few economists have actually tried to do this, although that’s now changing – if slowly – through the efforts of pioneers who are building computer models able to mimic market dynamics by simulating their workings from the bottom up.”

. . .

Sadly, the academic economics profession remains reluctant to embrace this new computational approach. Something of the attitude of economic traditionalists spilled out a number of years ago at a conference where economists and physicists met to discuss new approaches to economics. As one physicist who was there tells me, a prominent economist objected that the use of computational models amounted to “cheating” or “peeping behind the curtain,” and that respectable economics had to be pursued through the proof of infallible mathematical theorems.

If we’re really going to avoid crises, we’re going to need something more imaginative, starting with a more open-minded attitude to how science can help us understand how markets really work. With simulations, we can discover relationships that the unaided human mind would never grasp.”

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