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.

PD recently recommended two excellent books by Nassim Nicholas Taleb (homepage), the mathematician turned philosopher turned epistemologist. I am half way done with one of them, Fooled by Randomness and I have a brand new copy of The Black Swan sitting right here on my desk.

While I was reading the books, one thing that stuck me was this — within a given system, how many such quant funds (such as Taleb’s old firm, Empirica Capital LLC) does it take for Taleb’s risk-based hedge funds to succeed? Or inversely, how many is too many that would cause the system to fall down upon itself?

To find a way to answer these questions, I am interested in starting with finding out the possible success rates for these firms.

Let me explain myself. Let us assume that there are 100 quant firms that invest the way Taleb’s old firm did. This would mean that you have a hundred firms slowly bleeding to death, waiting for that one Black Swan, when things work out for them.

Now, for the sake of argument, let us assume that only half of these firms are going to succeed to some extent while the other half are going to fail.

The probability of all fifty of these firms having the exact same degree of success is quite low. In fact, it is more likely that within their given domain, these fifty firms have some sort of distribution of success-rates.

Let’s assume that two kinds of distributions are possible here — the first, obviously, is a Bell-curve, assuming a normal distribution. Going this route, we could try and apply the 68-95-99.7 rule and get the following distribution (courtesy Wikipedia):

So, let us assume that 68.2% of the quant firms are good enough for us to work with. This would leave us with:

This leaves us with about 34 firms which may perform well enough. Of course, the other interesting thing would be to see how much longer they can bleed themselves (assuming a decaying function) and seeing what percentage of these 34 companies actually “decay” themselves out. But that’s for later.

Now going back to our problem, the second distribution is more interesting. What if we had a log-normal distribution? Or better yet, let us assume a Log Levy distribution, which is more indicative of financial models (and of the stock market). This would be one where they have sharp peaks (for success) and long-tails (for the long periods of failure).

Since the stocks themselves are likely to show a Levy distribution, it would not be wrong to assume that our quants mirror this pattern.
If that is the case, our graph for a stable Levy distribution would look something like this (courtesy Wikipedia):

Now, we have a few cases to consider. If we consider then it becomes a normal distribution, which we just talked about. On the other hand, for all other values of , we could have heavy-tailed distributions.

So, here are my questions –

• What would be a logical assumption among the various values of alpha for our Levy distributions?
• What does this entail for our “functioning quant firm” numbers, i.e. where does that leave ?
• Assuming a standard decay rate for both normal and Levy distributions, which would be more effective (i.e., a better density function)?

Quite obviously, I am trying to back-track from a series of assumptions to evaluate the feasibility of having more than an optimal number of quant-based hedge funds. And I am also interested in evaluating the success and failure thresholds for such numbers.

Finally, I am looking for the Black Swan that will throw these numbers into quant hell.

(Yes, I am quite well aware that this post has a lot of inconsistencies and assumptions, and that I have completely ignored the injection of new blood into the system — quite obviously, this is an exercise in theory, so forgive me while I meander.)

Paul Kedrosky talks about an interesting NYT article on how hedge funds and private equities seem to change career options for people who might otherwise consider going for an MBA.

The interesting thing about this is the fact that I in fact do know several people who are at those cross-roads.

While being a quant-jock definitely has its advantages in terms of pay and big fat bonuses, it also tends to burn out a lot of people quite fast. On the other hand, all it takes is a few years on the market as a quant and you are set to do whatever you want a wee bit later. But does having that top B-school MBA really help you in those particular areas that you work on, as a quant? That is the more interesting question.

The other thing that came to my mind was this — to what extent does this apply to other areas, besides finance? In IT, going anything above a Solutions Architect (or its equivalent) with just a technology degree is rather hard, in most places. But are there other areas where an MBA does not particularly matter? Say, Biotech?

It would be interesting to see if there are similar patterns in other industries, besides finance.