On 2009-03-20 10:04:00, Allan Engelhardt wrote in CYBAEA Journal:
The financial crisis is all my fault. Or so David Smith from our friends REvolution seems to suggest in his post Physicists, models, and the credit crisis:
I remember working in The City in the late 90's and Wall Street in the early 00's and remarking then that just about every quant had a physics or engineering background. I met very few statisticians. Quantitative models have taken a hefty share of the blame for the credit crisis, but I wonder whether the blame lies more in their application, rather than the models themselves. Statisticians are trained on the limitations of models, and how to detect when models are breaking down, but statisticians were woefully underrepresented amongst quants. Do physicists and engineers get similar training?
I can only speak as a (ex-)physicist (I used to work on experiments at CERN) and the short answer is "yes". Of course we do get this training.
I did experimental physics. What we are good at is testing a model (or a set of competing models) against data. Physics only really moves forward when (1) we find some domain where our previous models do not work or (2) we find a simpler model that consolidates several previously separate models or extends the domain of a previous model.
The quantum properties of particles would be an example of the first and general relativity might be an example of the latter.
The caveat is that it really helps if it is a "reasonable" model we are looking at. We are not primarily trained in statistics. We are primarily trained in understanding nature: cause and effect, the scientific method, and a mathematical apparatus for communication.
A statistician is primarily trained in statistics. He will run tests on the model against data. I firmly believe that no statistician should ever be allowed out in the wild unsupervised. A physicist is trained in a world-view, if you like. He will (first) try to understand the model. He will look for internal consistency and he will compare with other models in the same or adjacent domains. He will run thought experiments. Then he might run some statistical tests against data.
I worked as a quant in a bank back in the days before "banker" became a four-letter word. We did good models. But, and this is important, the focus was on modeling the high-volume trades better. That's where the money is. If you could find an anomaly there you were set to make millions. If you could find a better model there you could make hundreds of millions.
If you could find an anomaly on the tail, you could write a research paper.
The average tenure of the people I was working with was less then three years. Nobody was interested in events that were not likely to occur within a three year time horizon. We knew and understood that the models were not valid on the tails, but there was no volume of trading on the tails so it wasn’t very interesting.
We knew that the tails didn't fit. But that was not important. Making money is the business of business.
Quoting from the article that got David Smith started:
Quants say that they should not be blamed for the actions of traders. They say they have been in the forefront of pointing out the shortcomings of modern economics.
“I regard quants to be the good guys,” said Eric R. Weinstein, a mathematical physicist who runs the Natron Group, a hedge fund in Manhattan. “We did try to warn people,” he said. “This is a crisis caused by business decisions. This isn’t the result of pointy-headed guys from fancy schools who didn’t understand volatility or correlation.”
Business decisions. But before we go completely medieval on the bankers, let us understand that society is often expected to pick up the bill for tail effects. Houses built in flood plains, hurricane prone area, or earthquake zones? DDT? Space debris? Global warming? Even famines are probably mostly preventable, but at a cost.
And the challenge with with all of this is that the cost of prevention may be bigger than the cost of fixing it. Maybe. It is hard to say: both costs are typically highly uncertain, and in the (really) long run we are all dead and extinct anyhow: if you take the long view nothing is cost-justified. This universe will end.
The question is: for all the billions of (preventable) damage caused by the current crisis and for all the personal losses (everyone without a job) and tragedies (everyone without a pension or conned by Maldoff), did the banks create net value summed over both the good and bad years? Capitalism has allowed us to grow albeit in fits and starts. The bankers made huge sums of money in the good years and not just for themselves but real money in real business and real growth and innovation. Ask the people who would not have been alive today if it was worth it: the poor who got additional aid and support (often squandered by their leaders but still) from the surplus wealth generated by all the elements of capitalism, including those bankers, and ask the people benefiting from new medicines developed by capital-intensive pharmaceutical research. Ask the people benefiting from capital investments in infrastructure projects: hospitals, hydroelectric dams, schools, etc.
We have the luxury to be angry with the bankers. I do not want to take that luxury away from us: it is wonderful that we can afford to indulge ourselves. But I would like to remind us that is is a luxury that we can afford, and that we can afford it at least in part due to those bankers.
On 2010-07-13 07:47:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
I am not sure apeescape’s ggplot2 area plot with intensity colouring is really the best way of presenting the information, but it had me intrigued enough to replicate it using base R graphics.
The key technique is to draw a gradient line which R does not support natively so we have to roll our own code for that. Unfortunately, lines(..., type="l") does not recycle the colour col= argument, so we end up with rather more loops than I thought would be necessary.
We also get a nice opportunity to use the under-appreciated read.fwf function.
Read more (~535 words).
On 2010-06-22 11:45:00, Allan Engelhardt wrote in CYBAEA Journal:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is scary.
We now re-do the analysis four years later and, just because we can, we are using the leading companies of the London stock exchange instead of the largest American companies.
The results still hold. We called it the 3/2 rule: treble the number of workers and you halve their individual productivity. Large companies with ten times the number of employees are ¼ as productive as their smaller competitors.
Employee productivity is a big issue. If all the FTSE-100 companies achieved their average profits per employee, then the index would generate almost £1 trn of additional net profits for the economy.
Read more (~245 words).
On 2010-06-22 11:20:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is mildly scary.
We revisit the analysis for the FTSE-100 constituent companies and find that the relation still holds four years later and across a continent.
Read more (~763 words, 5 comments).
On 2010-06-17 09:05:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Following on from my previous post about improving performance of R by linking with optimized linear algebra libraries, I thought it would be useful to try out the five benchmarks Revolutions Analytics have on their Revolutionary Performance pages.
Read more (~300 words, 2 comments).
On 2010-06-15 10:21:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection.
But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate.
The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.
Read more (~934 words, 1 comments).
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I don't think quants caved
I wouldn't characterize the quants as having caved or having compromised their integrity. They generally had pretty specific goals, set by their banker managers: make profit, soon. I don't blame a quant for fitting data to a recent time period in order to make short-terms gains. It's probably what I would have done too, GIVEN that the goal was short-term gain. The real problem is twofold, IMO: (1) short-term gain for each individual group within a bank does empirically does not translate into long-term gain for the bank as a whole, and (2) managers applying the results of models beyond their short-term purposes to make long-term decisions or assessments of risk. The nuances of the limitations of the models get lost once they leave the quant group. I follow up in more detail here: http://blog.revolution-computing.com/2009/03/physicists-models-and-the-credit-crisis-ctd.html
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After working with bankers, as a PhD in physics, I know how hard it is to get these guys to appreciate that you might have something useful to say since listening to you will not make money for the bank tomorrow. Often, ignoring your warnings is more profitable for them in the short term. How can you explain, however, the modellers at S&P etc who used only historical data with increasing cost of housing to value mortgage backed securities? Are they stupid? Are they fraudulent? Perhaps they can`t take the pressure of the profit incentive? I think that bankers are trained in being firm about what the bank can and cannot deliver to clients, even in the face of immense pressures. The quants are not. They caved to the pressure and compromised their integrity. We should take some responsibility. In the future quants should set firm limits on what is reasonable in complex products. Bankers should hire modellers who can stand up to them and also take heed of their opinions.