Research Doesn’t Tell Us
June 14, 2011
What’s wrong with all of this?
Long-lived phenomena are often the by-product of a single accidental moment in history.
Social studies began in about 1957 to call themselves “sciences” and to assume an exaggerated posture of math-based objectivity in order to lump themselves with science. At the time, the Soviet Union’s Sputnik launch had just siphoned all research funding to the physical sciences to enable the U.S. to catch up in space.
In Fama and French’s paper, their posture of objectivity even extends to not trying to interpret or explain their results. One reason, of course, could be that they can’t explain them. Another is that any explanation would be sheer speculation, and therefore doesn’t belong in a scientific paper. That too, is another way of saying they don’t have a good explanation.
There’s nothing wrong with presenting results without explaining them, and leaving it to others to try to explain them. That’s the idea of seminal research; if your findings provoke discussion and get others to interpret them, eventually leading to a new and improved theory, that’s great.
Regression formulas alone, however – even if they fit the data well – are not a theory. They are merely here patterns perceived in data, barren of explanation – and possibly accidents of randomness. Unfortunately, the results of these regressions are being treated as if they were a theory in and of themselves. Without theory, regressions have no lasting or practical implications. Without an explanation of those results, there is no reason to apply results mined from historical data to predict the future, or even to evaluate historical performance that relies on making accurate predictions.
Some people, realizing that the historical outperformance of value stocks is not enough to sell the future performance of a value stock investment approach, have tried to concoct ersatz theories to explain it; hence, for example, the “fundamental indexing” approach to value investing.
So far, there are only two potentially viable explanations for the value effect. One is the behavioral finance explanation – the “anchoring” or the “availability” heuristic, for example. All you know is that it’s a lousy stock because it’s got a low price, so you predict it will be a lousy stock and don’t buy it – keeping its price low even though its future rise will be as good as or better than that of a growth stock.
The other explanation is that small and value stocks carry risks that aren’t captured by the usual risk models. But if that is the case, why apply band-aids to the models by adding small and value factors? How enlightening is that? If something is missing from our risk models – and there is – that won’t fix it.
Exhibit A: Fama and French’s handling of the momentum effect
In fact, I think this is an example of what David Colander et al. mean in their article in the book “What Caused the Financial Crisis” (reviewed in my Advisor Perspectives article of May 24), in which they speak of a “systemic failure of the economics profession” (emphasis in original). Colander et al. fault academic economic models because they “fail to account for the actual evolution of the real-world economy” and largely crowded out research on the inherent causes of financial crises.
Does this really apply to Fama and French’s paper? Yes, I think it does. Let’s take their handling, for example, of the so-called momentum effect.
The momentum effect has been documented over short time periods, but “reversion to the mean” – the opposite of the momentum effect – has been observed. In fact, it must be observed over longer periods if there is a momentum effect over short periods.
The momentum effect says that what goes up will keep going up. Reversion to the mean says what goes up will come down. That means that over some short period momentum will not continue but will reverse. Observation of financial crises suggests that momentum is gradual and long-lived, but reversal is sharp and sudden. You won’t get good long-run performance by relying on momentum.
Where, you may wonder, do Fama and French suggest anything about the inevitable long-term failure of momentum in their paper? They merely woodenly note the momentum effect is discernable through the weak lens of linear regression – in fact a relatively unsophisticated methodology, in spite of all the babble about sophistication in the financial industry.
In my view, the Fama-French paper represents little more than a continuation of the economics profession’s “systemic failure.”
Michael Edesess is an accomplished mathematician and economist with experience in the investment, energy, environment, and sustainable development fields. He is a Visiting Fellow at the Hong Kong Advanced Institute for Cross-Disciplinary Studies, as well as a partner and chief investment officer of Denver-based Fair Advisors. In 2007, he authored a book about the investment services industry titled The Big Investment Lie, published by Berrett-Koehler.
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