Indy Telecom & Industrial Media

I’ve been looking for various tools for Quantitative Analysis in finance. I stumbled upon QuantLib.

It’s an Open Source tool written in C++, but it is written with an open object model such that it is has been ported to work with several other languages.

If you are interested, you can download QuantLib and play with some of their examples.

Post-August 2007, everyone seems to be saying that the age of quants and quant-based hedge funds are over and other such dire doomsday predictions. Even the bigwigs lost a good bit of money and people are probably more than a little wary of quant-based hedge funds at the moment.

But here is the question — was it really quantitative trading that did them in or was it the fact that they were leveraged beyond redemption?

Sure, part of the quantitative strategy involved some rather convoluted leveraging, but I think that the important thing to keep in mind is that a lot of the ones that failed were ones that were heavily-leveraged. Similarly, there were a lot of quant-based hedge funds that were not as heavily leveraged that did rather well.

While it may take a while for the landscape to clear out and settle, it is worth considering that quant-based hedge funds may not necessarily be a bad thing. But leveraging, as we’ve seen, can even bring down the greats.

Thoughts?

The MIT Technology review has an interesting two part write-up on the quant meltdown from this August.

Extremely interesting and a must-read.

Taleb defines a Black Swan as something that is an extremely rare event. When 9/11 happened, that was a Black Swan — a ten sigma or worse. When LTCM collapsed and the feds had to engineer a bailout, that was a Black Swan.

Now, the way quant hedge funds have operated is that when things went wrong for one firm, the odds were that its effected would be diluted by better performance from others. Usually, the odds of a lot of firms being affected by the same problem are rather low and consequently, their effects on the economy would be limited as well.

HFN asks an interesting question in this regard — what are the chances of several hedge funds blowing up in this regard?

Is it me or does it seem like there is something to be said about Taleb’s rants against the traditional practices of Wall Street traders and progressive betting in Blackjack?

The fact that progressions cannot overcome expectation is also rather interesting, given the way some institutions work.

While normal distributions are nice and wonderful, they aren’t really feasible in the world of finance. This is because the market can be so volatile and fickle that variances become meaningless.

So, enter fat-tailed distributions. These are distributions where events deviate significantly from the mean in comparison to normal distributions. What this means in terms of the stock market is that assets and investments are prone to jumps (in either directions).

On this topic, Paul Kedrosky links to an interesting presentation on Fat Tailed distributions by Northfield, the guys that make analytical and investment software.

It’s quite interesting and talks about some work that’s being done in this area.

On a related note, another area of application for Fat tailed distributions is the CRM arena. Contact-centers and self-service applications (i.e. IVRs) receive millions of calls a day, and there is just as much variation in C-sats, agent performance, AHT and so on.

It would be interesting to apply some of these tools and techniques to call-center analytics and see how well those work.