We like to get quirky on Friday afternoons, so here’s an item about gullibility and financial crises.
It’s hardly controversial to assert that mass psychology plays a lead role in the formation of asset bubbles — but would it help if we had the tools to actually measure collective irrationality?
From the abstract to a new paper by Andrew Odlyzko, professor of maths at the University of Minnesota (HT Kedrosky):
Gullibility is the principal cause of bubbles. Investors and the general public get snared by a “beautiful illusion” and throw caution to the wind. Attempts to identify and control bubbles are complicated by the fact that the authorities who might naturally be expected to take action have often (especially in recent years) been among the most gullible, and were cheerleaders for the exuberant behavior. Hence what is needed is an objective measure of gullibility.
This paper argues that it should be possible to develop such a measure. Examples demonstrate, contrary to the efficient market dogma, that in some manias, even top business and technology leaders fall prey to collective hallucinations and become irrational in objective terms. During the Internet bubble, for example, large classes of them first became unable to comprehend compound interest, and then lost even the ability to do simple arithmetic, to the point of not being able to distinguish 2 from 10. This phenomenon, together with advances in analysis of social networks and related areas, points to possible ways to develop objective and quantitative tools for measuring gullibility and other aspects of human behavior implicated in bubbles. It cannot be expected to infallibly detect all destructive bubbles, and may trigger false alarms, but it ought to alert observers to periods where collective investment behavior is becoming irrational.
The proposed gullibility index might help in developing realistic economic models. It should also assist in illuminating and guiding decision–making.
And here are his preliminary ideas for actually constructing the gullibility index:
Can one construct an objective measure of gullibility? Modern research offers hope. Asking financial regulators what is 2 + 2 is clearly not going to be productive, but more subtle approaches could bear fruit. Occasional surveys of investors appear to indicate that in bubble times their expectations for returns from stock investors soar to the 20 percent per year level, as opposed to something like 10 percent during more sober times. Such surveys should be conducted on a monthly basis, distinguishing between various categories of people, such as general public, private investors, investment managers, regulators, etc.
Even more promising might be systematic search for cases of innumeracy at various levels. There is certainly plenty of it at all times, but, as shown by the telecom bubble, it seems to grow in frequency and seriousness in boom times. Modern technologies (semantic web, Wolfram Alpha, …) appear to provide promising approaches to detecting such phenomena. It should be possible to devise automated systems that would have detected, from publicly available information, that Internet traffic growth estimates were important in decision–making during the telecom bubble, and that there were striking disparities in what different groups assumed, from 2x through 4x to 10x per year. That should have rung alarm bells for any sensible person.
Other approaches might also be fruitful. We could monitor the frequency with which Nigerian 419 scam attempts succeed (and try to correlate it with the degree of bubble thinking, as well as geography, socioeconomic status, etc. of the victims). We could even attempt active experiments with sending out 419 scam e–mail messages, extending the work that has been done (University of Exeter, 2009). How does the rate of response vary with the amount of money being mentioned, and the plausibility of the story in the message? The approaches that are already being used to investigate spread of gossip could be extended to test susceptibility to “beautiful illusions” such as that “Internet traffic doubling every 100 days.” Some work on measuring the impact of electronic message board rumors on stock prices has already been done (cf., Bettman, et al., 2010), and could be extended. We could try passing out tales of various degrees of credibility, combined with various degrees of material rewards, to see how people react (and how that changes, depending on the presence or absence of a boom mentality). The degree to which mutually contradictory gossips coexist in the network would also provide a measure of “information viscosity.” These are all just some simple preliminary ideas, but they do suggest that one might be able to construct a scientifically sound measure of gullibility and other aspects of human behavior that are implicated in bubbles.
Odlyzko focuses part of the paper on the notion that too much discussion goes towards ideas that try to protect us from the fallout of bubbles, and not enough towards actually detecting the mass irrationality to begin with.
All very interesting, but for such an index to be useful in practice (assuming it actually works), it seems that at least one condition would have to hold.
Which is that the traders (or homebuyers, or whoever is the gullible party) inflating the bubble would change their behaviour if they knew that prices were out of whack with the fundamentals.
Recent work by experimental economists, which Virginia Postrel wrote about in December 2008, found good reason to doubt this — to believe instead that asset bubbles continue to inflate because participants will keep trading not on the fundamentals, but on what will happen next based on what they think other participants are doing.
Or as Keynes once wrote in his famous beauty contest analogy:
Or, to change the metaphor slightly, professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole; so that each competitor has to pick, not those faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point of view. It is not a case of choosing those which, to the best of one’s judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practise the fourth, fifth and higher degrees.
A gullibility index would certainly be fun to have around, but regulators should probably stay more focused on mitigating wider systemic fallout from bubble-driven crises than on stopping them altogether.
Because if you think we’ll ever succeed in preventing crises from happening in the first place, well, maybe you should have your own gullibility indexed.
Have a wonderful weekend.
Related links:
Bubbles, gullibility, and other challenges – UIC
Pop Psychology – The Atlantic