Thousands of “quants” armed with PhDs
Let me start this discussion with a claim and a disclaimer. Years ago I obtained a university degree in Math and Physics. I have forgotten a lot of what I learned. But, the idea of the strengths and weaknesses of mathematical thinking have stayed with me.
Let me quote something I read recently. Where it comes from and who wrote it is irrelevant. To my way of thinking it captures one of the great conceits of the investment industry.
“Today, there are tens of thousands of “quants” armed with PhDs combing through global financial markets. These Soros “wannabes” have translated their insights into algorithms, which now account for over 50% of trading on U.S. stock exchanges.”
It is understandable that mathematicians should think they have an advantage in the stock market. Economics, business and the stock market are dominated by ‘facts’ and ‘data’. They are capable of being organized and analyzed using math.
By ‘using math’ I include all math and statistics. I include algo trading, modelling such as Value at Risk (VAR) models, correlation, standard deviation and so on. The whole kettle of fish.
It is understandable of math whizzes that they feel they can conquer stock markets with the principles and tools of science. But, the stock market cannot be tamed by facts and data alone, if at all.
Franky, I am perfectly happy with the thought that so much trading takes place based on the algorithms of an army of quants. If they are misguided that is their problem. They seem to proceed on the assumption that gathering and analyzing masses of data will lead to good decision making. It strikes me that this can often lead to a false sense of confidence in the decision being made.
Robert Hagstrom quotes writer and philosopher G. K. Chesterton:
“The real trouble with this world of ours is …. [that] it looks just a little more mathematical and regular than it is; its exactitude is obvious, but its inexactitude is hidden; its wildness lies in wait.”
Readers may remember or have read about Long Term Capital Management (LTCM). It was a huge hedge fund with $126 billion in assets. It almost collapsed in late 1998. It had to be bailed out by a consortium of Wall Street banks in order to prevent systemic contagion that would have set off a global financial crisis.
Niall Ferguson explains: “What had happened? Why was Soros so right and the giant brains at Long-Term so wrong? Part of the problem was precisely that LTCM’s extraterrestrial founders had come back down to Planet Earth with a bang. Remember the assumptions underlying the Black-Scholes formula? Markets are efficient, meaning that the movement of stock prices cannot be predicted; they are continuous, frictionless and completely liquid; and returns on stocks follow the normal, bell-curve distribution.” (Ferguson, 2008)p.327. Ferguson adds: “Their mathematical models said there was next to no risk involved”
David Shirreff author of ‘Dealing with Financial Risk’ and former Economist writer published a paper on the debacle and wrote “Despite the presence of Nobel laureates closely identified with option theory it seems LTCM relied too much on theoretical market-risk models and not enough on stress-testing, gap risk and liquidity risk. See here
In my view the collapse of LTCM was due to the conceit of PhD mathematicians.
There is a marvelous quote that makes you stop and think. Einstein is widely believed to have said: “Not everything that can be counted counts, and not everything that counts can be counted.” It seems however that the author was a Professor of Sociology named William Bruce Cameron who wrote these words in the 1960s.
There are two ideas captured in the Cameron quote. Let’s look at them one at a time.
Not everything that can be counted counts
There is a lot that can be counted. In today’s world, there are innumerable statistics about everything under the sun. The world of finance is filled with things that ‘can be counted’ and are counted. That does not mean that they ‘count’.
There are masses of data and spreadsheets calculations with sensitivity analyses and back testing that give the impression of usefulness and reliability.
But, much of the counting is of things that are not relevant or meaningful; i.e. they don’t count. This is a problem for unwary investors.
Charlie Munger has weighed in on both parts of the Cameron problem on the Farnham Street blog:
On March 10, 2015, he was talking about ‘Overweighing what can be counted’.
Munger wrote: “A special version of this “man with a hammer syndrome” is terrible, not only in economics but practically everywhere else, including business. It’s really terrible in business. You’ve got a complex system and it spews out a lot of wonderful numbers that enable you to measure some factors. But there are other factors that are terribly important, [yet] there’s no precise numbering you can put to these factors. You know they’re important, but you don’t have the numbers. Well practically everybody (1) overweighs the stuff that can be numbered, because it yields to the statistical techniques they’re taught in academia, and (2) doesn’t mix in the hard-to-measure stuff that may be more important. That is a mistake I’ve tried all my life to avoid, and I have no regrets for having done that.”
Because of the plethora of information at our finger tips through the magic of the internet people tend to overestimate its usefulness to make decisions. This is because we are overloaded by information but much of it is irrelevant, distracting and misleading.
Not everything that counts can be counted
The second part of the quote is just as important. Many of the things that count can’t be counted.
Bernstein references Keynes views written in 1937: “By ‘uncertain’ knowledge… I do not mean merely to distinguish what is known for certain from what is only probable. The game of roulette is not subject, in this sense, to uncertainty… the sense in which I am using the term is that in which the prospect of a European war is uncertain, or the price of copper and the rate of interest twenty years hence, or the obsolescence of a new invention…About these matters, there is no scientific basis on which to form any calculable probability whatever. We simply do not know!” (Bernstein, 1996)p.229.
Math, including statistics, are useful tools. They need to be used intelligently and we need to fully understand their limitations. Math and algorithms will give precise answers. And therein lies the problem. Buffett quotes with approval Keynes observation that: “I would rather be vaguely right than precisely wrong.”
Graham remarked in a speech in 1974 that investing did not require genius: “What it needs is, first, reasonably good intelligence; second, sound principles of operation; third, and most important, firmness of character.”
Much of what we face as investors is decision making under uncertainty. We can be vaguely right and have success in investing. There is no need to despair. The best way for individual investors to proceed is using sound Principles of Operation. These are introduced in the Motherlode in Part 4: Principles of Operation. The essential principles are describes briefly in Chapter 27. Sound Principles of Operation. There you will find cross references to the main text of the Motherlode where each of the Principles of Operation are discussed in full.
Want to dig deeper into the principles behind successful investing?
Click here for the Motherlode – introduction
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