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Paul Wilmott Wants To Retrain and Reform Wall Street's Quants
theodp writes "What if an aeronautics engineer couldn't reconcile his elegant design for a state-of-the-art jumbo jet with Newton's second law of motion and decided to tweak the equation to fit his design? In a way, Newsweek reports, this is what's happened in quantitative finance, which is in desperate need of reform. And 49-year-old Oxford-trained mathematician Paul Wilmott — arguably the most influential quant today — thinks he knows where to start. With his CQF program, Wilmott is out to save the quants from themselves and the rest of us from their future destruction. 'We need to get back to testing models rather than revering them,' says Wilmott. 'That's hard work, but this idea that there are these great principles governing finance and that correlations can just be plucked out of the air is totally false.'"
How about bringing their pay down in line with the pay of others (engineers and scientists) that do analysis of a similar level of difficulty? This is just a guess, but it would seem increased pay attracts people who want to make more money, not those that are genuinely interested in solving the problems in a field.
I'm a long way from New York, so someone correct me if I'm wrong[*], but I've always understood the problem to lie more with the people feeding data into the equations, rather than with the equations themselves.
Now, I accept that risk calculations consisted of a great deal of voodoo because, as Taleb tells us, they tended to ignore 'Black Swan' events (where the 1 in a million catastrophe wasn't going to happen just yet) and saw patterns where only chaos existed, but as I understand it, the core of the problem was simple greed: money-hungry mortgage and securities dealers deliberately feeding bad data into the system.
So-called quants may be decidedly imperfect, but if someone's willing to game the system to make a buck, nothing the quant does can stop it.
If Wilmott doesn't have an answer to that, I fear that his efforts will only obscure the real problem.
Crumb's Corollary: Never bring a knife to a bun fight.
...you always reach a point where there's this one number that is completely made up...
***Try a sensitivity analysis using Monte Carlo techniques. That sounds hard but it isn't. Take the parameter that you have doubts about and give it a distribution (Gaussian or rectangular or something) with the mean at your best guess and the std deviation chosen to be big enough to cover the range it might reasonably vary over.
***Use Gaussian if you have an idea of what the parameter probably is but aren't exactly sure, rectangular if you really have no idea. A rectangular distribution says "I have no idea, the parameter could be anywhere within this particular range."
***Then run your analysis a million times with the parameter selected randomly from it's distribution for each run.
***That gives you a stochastic dataset of results. You can run simple stats on that data set to find the mean and std dev of the result value. You will then know how sensitive your results are to your poorly understood input parameter. If your 2-sigma output tells you the expected rate of return on a particular investment varies between -50% to +50% then you will know your model is pretty much useless and you will be doing a better job than the vast majority of professional analysts.
****Monte Carlo is great for those of us who don't care to learn the arcane minutiae of stat math. If you have a working model it takes an hour or two to extend it so you get stochastic results. Note that it's no harder to give a distribution to all your input parameters not just one. In which case you will be doing the kind of work that people who make 500 grand a year do.
Equine Mammals Are Considerably Smaller
Mathematical models always only work in a certain range As Newtonian mechanics well for smaller velocities and macroscopic bodies it has to be replaced for large velocities or in smaller scales. Exponential growth laws have to be replaced by logistic growth. etc Models are especially popular in probability theory. The text mentions Gaussian Copula function, the "rocket fuel" for collateralized debt obligation, which is cited as one of the reasons for the finance disaster. See "The formula that killed Wall street".
The reason why the quants ignore Black Swan events is that they are not financially impacted by them to any real extent. They make their living from making small amounts of money using lots and lots of leverage. But I prefer Buffett's metaphor for this sort of practice: picking up nickels in front of a bulldozer.
As long as "quants" can pick up "nickels" in front of a bulldozer for a few years, they can retire and never have to work again, even if their parent companies (and the companies they borrow from) go bankrupt. Those "nickels" are many millions, their percentage of those "nickels" are still high enough to retire on. Of course, they risk billions in the process.
I suspect the only way to really curb the practice would be to either limit amounts of leverage or cause complete bankruptcy/imprisonment/physical harm somehow to those responsible when the bulldozer (the black swan) eventually comes along. Of course, these laws can't really be applied to those responsible for the GFC. Laws can and probably will be created, and then after a few generations those laws will be repealed as the creation of a few old fuddy duddies who didn't understand whatever "new economy" comes along, and the cycle will repeat.
If I have seen further it is by stealing the Intellectual Property of giants.
Wall Street Is just Las Vegas with better clothes. All the day traders and other 'quick money" guys have rendered the idea of having an actual investment in a company because you believe they are going to do well in the future and desiring to be a part of that passe. They have also trained corporations to "damn everything but the quarterly reports!" causing long term damage and even failure to a company in return for short term profits.
We need to get the day traders out, and the investors back in. perhaps by setting up a tax than penalizes anyone who buys stocks for very quick turnovers and rewards those that hold onto a stock for a set period. Because real long term growth of a company takes investment. Investment and the building of infrastructure, training of employees, construction of new buildings, etc and all of these things cost. In the current Wall Street model such investments show up on the quarterly report and torpedo the stock. We should legalize gambling for those that want to take a shot at the quick cash and leave investing in the growth of businesses to investors that are willing to look at the long term picture, not simply the quarterly report.
ACs don't waste your time replying, your posts are never seen by me.
I think there is a misperception that quants just run wild with models with catastrophic results and that they are naive when it comes to practical matters. However, quants are also taught about "model risk" to include things like: wrong assumptions, poor estimation of parameters, errors in discretization, etc. Let's also not forget the positive social value of financial innovation. It helps you borrow at lower rates, pay less for insurance, etc. There were a few problems that led to this financial crisis and I think quants played a relatiely minor role.