The column’s title is misleading: Johnson focuses his frustrations only on economists – not economics. Importantly, it is the application of the science, not the science itself, which seems to have caused Johnson's concern.
Indeed the purity of all mathematical sciences can be spoiled by its application. Johnson comments that he “[fails] to see the point of professional economists,” that economists “pronounce on capitalism for a living, yet do not participate in private enterprise, which is its underlying engine.” He ends off his piece by prescribing “[the] best move for the world’s economists would be to each start their own business. Then they would experience at first hand the challenges of capitalism on the front line.”
To be fair, the direct application of economic theory was never intended to satisfy the depths of the dynamic puzzle we put before them, a puzzle for which the answer lay not in the data but in the incentives. [1]
We cannot pretend not to have known that economic models work best in reductionist environments, and that the introduction of complications (like off-balance sheet derivatives) tend to reduce the effectiveness of economic models. Conceptually, once models start to consider too many inter-related variables, or degrees of freedom as statisticians call them, they become so rich and sensitive that no empirical observation can either support or refute them. And so any failures of economists to spot the housing bubble or predict the credit crisis, as Johnson mentions, become our failures too. We would have done better to equip our economists (or academics) with the tools necessary to perform the “down and dirty” analyses that take into account the complex and changing nature of our economy. [2]
Seeing no reason why they ought to have succeeded, we’re perhaps a little more forgiving (than Mr. Johnson) of economists’ shortcomings. But we share his concerns that mathematical sciences are being too directly applied, that the practices and the incentives are being largely ignored.
On Endlessly Assuming “Mean Reversion”
Rather, we ought to encourage our researchers to go into the proverbial field – and to learn to think, and study dynamics, differently.
We can no longer allow ourselves to be informed purely by static analyses of historical data and trends, without seeking a keener appreciation for the underlying dynamics at play. The lazy assumption of mean reversion is simply an assumption, not a rule. When the fundamentals are out of whack – and a direct analysis of data alone cannot tell you that – the market can and will act very differently from a mean-reverting economic model.
Thinking Differently
Given that many market participants have emotions (one could argue that computer algorithms are to an extent emotionless), the tendency for panic or at least the capacity for panic ought to make the direct application of mean reversion models less appealing – and their results less informative, predictive or meaningful.
Ask not “is this a buying opportunity” based on a simple historical trend. But what are the underlying fundamentals? If the game changed based on underlying issues, have they been resolved? Or were they underestimated or overestimated. If the latter is determined after sufficient exploration, one could recommend a "buy." If the former, initiate a "sell." To do otherwise - to simply present a graph and suggest an idea, is folly – it's simply a guess.
Mean reverting economic forecast models continue to be constructed to this day without the thought necessary to support their assumptions (despite the realization that we’re in a very different world).
The outputs, unfortunately, are never better than the inputs.
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[1] See our earlier commentary “The Data Reside in the Field”
[2] In light of this fact, it is perhaps troublesome that when questioned by JP Morgan CEO Jamie Dimon as to the extent of the government's investigation of the effect of its banking regulations, Bernanke purportedly responded "has anybody done a comprehensive analysis of the impact on -- on credit? I can't pretend that anybody really has," ... "You know, it's -- it's just too complicated. We don't really have the quantitative tools to do that." Source
1 comment:
This study adds to the literature on mean aversion and mean reversion in housing prices. In contrast with the previous related literature, the persistence and reversion characteristics are studied by computing variance ratios using Kim's (2006) Wild bootstrapping and by investigating horizons up to 10 years. The variance ratios clearly indicate that housing prices do not follow random walk in any of the 15 Finnish cities included in the analysis. Instead, momentum in housing price growth is longlasting and considerable in size. Since the eventual reversion is substantially weaker than the initial mean aversion, housing is notably riskier asset in the long term than suggested by variances computed from quarterly or annual price movements. The results also show that the momentum and reversion patterns may substantially vary between regional housing markets. These differences influence the optimal housing portfolio allocation and highlight one more reason why it is complicated to use country level housing price data when analyzing the optimal portfolio allocation or housing price dynamics.
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