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|Subject: The Siren Song of “Past Performance”||Date: 2/9/2013 2:46 PM|
|Author: globalist2013||Number: 34768 of 35801|
Weekends are when I do a lot of my heavy-duty researching. While I was looking for something else, I stumbled across an earlier post of mine that I’m going to re-print in its entirely, because it makes general points about investing that are worth repeating. The context that gave rise to the post doesn’t matter, nor the fact that it is addressed to a specific individual. The mistaken beliefs he expresses are common.
I do appreciate that "past performance is not a guarantee of future results." That said, in the absence of reliable fortune tellers, historical performance info will have to do.
The problem is more complicated than that. In their 2006 paper, “A focus on the exceptions that proves the rule”, Talbeb and Mandelbrot lay out a readable case that:
Despite the shortcomings of the bell curve, reliance on it is accelerating, and widening the gap between reality and standard tools of measurement. The consensus seems to be that any number is better than no number – even if it is wrong. Finance academia is too entrenched in the paradigm to stop calling it “an acceptable approximation”. http://www.ft.com/cms/s/2/5372968a-ba82-11da-980d-0000779e23......
That statement is only a small part of their paper, but it’s one that is relevant here. If one’s intent to determine the min, max, and mean of human heights or weights, then past performance is an excellent guide. After taking a hundred or a thousand measurements, you won’t likely be surprised by the next one. But if you are trying to estimate min, max, and mean for wealth distribution, and Bill Gates happens to be the 1,001st person you encounter, all your previous measurements become meaningless. Same-same with fund performance, stock performance, bond default-rates, or any other “measurable” in the financial world. Past performance is a good guide in the physical world, but not in the financial world. The mistake that believers in Modern Portfolio Theory make (which doesn’t merely assume the usefulness of the historical record but worships it) is to apply Gaussian assumptions to phenomena that don’t distribute normally. They always make the pro form disclaimer that "Past performance... but they don't believe it and they argue, as you mistakenly did, that "it will have to do."
Well, "No", it doesn't have to do, nor can it. As T&M argue, … while weight, height and calorie consumption are Gaussian, wealth is not. Nor are income, market returns, size of hedge funds, returns in the financial markets, number of deaths in wars or casualties in terrorist attacks. Almost all man-made variables are wild. Furthermore, physical science continues to discover more and more examples of wild uncertainty, such as the intensity of earthquakes, hurricanes, or tsunamis.
Economic life displays numerous examples of wild uncertainty. For example, during the 1920s, the German currency moved from three to a dollar to 4bn to the dollar in a few years. And veteran currency traders still remember when, as late as the 1990s, short-term interest rates jumped by several thousand per cent.
We live in a world of extreme concentration where the winner takes all. Consider, for example, how Google grabs much of internet traffic, how Microsoft represents the bulk of PC software sales, how 1 per cent of the US population earns close to 90 times the bottom 20 per cent or how half the capitalization of the market (at least 10,000 listed companies) is concentrated in less than 100 corporations.
Taken together, these facts should be enough to demonstrate that it is the so-called “outlier” and not the regular that we need to model. For instance, a very small number of days accounts for the bulk of the stock market changes: just ten trading days represent 63 per cent of the returns of the past 50 years.
If this epistemology is accepted --and it doesn’t have to be, as the CD crowd doesn’t-- what are the consequences? “Your biggest draw-draw is yet to come.” Worse, that draw-down might be a 20-25 sigma event that your model assured you was impossible. However, as we saw in 2007, 2008, and again in 2009, multiple instances of those supposedly “outlier” events happened, for which Scott Patterson’s book, The Quants, is as good a chronology as any.
Or in another paper, “On Robustness and Fragility”, Tabeb puts the matter this way. “You can measure a table, but you can’t measure risk.” This doesn’t mean that nothing can be done. Risk has to be managed, and he makes suggestions in yet another paper, “Finiteness of Variance is Irrelevant in the Practice of Quantitative Finance” that amount to the need to chop left-hand tails. http:/