Feb

9

 It seems that to be a trader these days you must have a PhD in Math. Actually how much math does a trader need?

I've been struggling with math since childhood, and I'm taking statistics personal classes right now. Recently Victor told me that I would only have had to count Galton's way and that would be enough. It seems to be easy to immerse yourself in deep math and forget about what the markets really are.

I know this lecture is from last year, but Soros has a good point when saying that the statistic models available fail to consider the role of uncertainty. When you are too much leveraged and the event goes too many standard deviations, you know you’re in trouble. Take a look at:

George Soros at MIT and

Quantitative Trading, an excellent blog on quants by Ernest Chan. His latest post:

This Quebec pension fund lost some $25 billion due to non-bank asset-backed commercial paper (ABCP). Their Value-at-Risk (VaR) model did not take into account liquidity risk. As usual, the quants got the blame. But can someone tell me a better way to value risk than to run historical simulations? Can we really build risk models on disasters we have not seen before and cannot imagine will happen?

The replies to the post were most illuminating:

quarterback said…

"Nassim Taleb's ”Fooled by Randomness” is a must read. Simply we don't live in a Gaussian world.”

Paul Teetor said…

"The NY Times recently quoted Taleb as saying, “VaR is like an airbag that works all the time, except when you have an accident.” I think that’s a perfect characterization.Can we prepare for what we have not seen? The folks in the insurance business have faced this problem for centuries. Some actuaries use Extreme Value Theory, and I’ve often wondered if the finance world needs to look more closely at that. Are the quants to blame for VaR’s short-comings? Sort of. I ran the VaR reports for previous employer — who got wiped out. In retrospect, I should have been telling everyone, “These numbers do not mean what you think they mean.” That was my error.”

Bill Rafter replies:

Dr RafterYou do not need an advanced degree in mathematics to be a successful trader. What you need firstly is the ability to think for yourself and secondly the ability to do research in a scientific manner. Regarding statistics, what you need to know is that one event is not significant, and that your highest return will not be your average return. Those are common mistakes of novices.

Most “quants” are employed in “risk” work. That is, they are given tasks such as, “assuming you have a certain profit, how do you protect it?” Or, “given a certain alpha, how do you make it portable?” Perhaps the best-known quant made his money by finding an anomaly in the contract specs of a certain futures market and then exploiting that. Those points suggest that your efforts at profitable trading should not be concentrated in the risk area. Instead they should be directed towards generating that profit or alpha that the supervisors assume is inherently there.

Let me pose a scenario: Suppose you had perfect knowledge that the broad market was going to rise, what stocks would you buy? The standard philosophy is that you would buy the ones with the highest beta, because they should go up the most. For the most part you would be disappointed, because those with the highest beta (i.e. past beta) would be the most volatile, as past volatility is equated with subsequent bearish performance. Don’t take my word for it; there are lots of academic articles saying the same thing.

Also — and this is important — would you rather trade an efficient market or an inefficient one? Obviously the latter, so do not waste your time with the efficient ones. Oh, but the risk guys love the efficient markets. Yeah, but the risk guys do not make money.

Let me give you our experience. We screen for risk early in our selection process, and never look at it again. We never use stops, which we feel simply provide certainty in losses. Except in the odd times when we are in bills, we never buy one asset because we cannot predict one asset well. On the contrary, our success rate in predicting baskets of assets is better, so we stick to that. Of course they are baskets where the early screening was on a risk-adjusted basis, but with no other attention being paid to risk. I am not suggesting that ours is the only way or the best way; only that it is a profitable method that does not hold the risk mavens up as gods.

Dr. Rafter is President of Mathematical Investment Decisions, a quantitative research consultancy


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