Submitted by Edward Harrison of the site Credit Writedowns.
During the last generation, the economics profession has veered toward a ‘science’ model of economics and finance. The intellectual underpinnings for this development began with the Efficient Market Hypothesis (EMH) and has continued in no small measure due to what is often termed ‘University of Chicago School Economics.’ If you are looking for a good read on what is wrong with the EMH view of the world, you should get ready for Justin Fox’s “The Myth of the Rational Market” which is coming to a bookstore near you.
My own view is that many economists today are really frustrated scientists looking to ply their science and math craft in economics. In reality, economics is a social science with large influences from psychology and the scientific view ignores this. However, the fact that psychology plays a large role in economics is something that is increasingly appreciated, as the Nobel Prize received by Daniel Kahneman attests.
So, I am not going to discuss EMH or rational markets. Rather I want to delve into the psychology of economic forecasting and why economists act as they do. Late last month, I posted an article with an attached video in which Marc Faber made the very astute comment, “it’s very tough for a forecaster who was ultra-bearish to stay bearish, because if he’s wrong he has a reputational risk.” What I believe Faber was saying is this: an economist who is proved wrong is an economist who loses credibility. This statement is at the heart of economic forecasting.
What Faber is giving voice to is the very real concern that any economic forecaster feels in making a prediction. If one is proved right, then plaudits will follow. If one gets it wrong, the Bronx cheer is what you are likely to get. This is true for macroeconomists as much as for Wall Street analysts. I will give you two examples from Wall Street to illustrate my point.
Henry Blodget: Amazon to $400
In October of 1998, Blodget predicted that Amazon’s stock would soar to $400 a share. At the time, he was a little known analyst at Oppenheimer, the same company for which Meredith Whitney worked until recently. His Amazon prediction propelled Blodget to a much higher status and attracted the attention of Merrill Lynch, the bulge bracket firm to which he moved for a huge salary. Clearly, making a bold call that comes true is a boon to a market forecaster.
Arjun Murti: Oil to $100 and then $200
Back in 2007, Arjun Murti, an oil analyst at Goldman Sachs, made a bold call that oil could rise to $100 a barrel in a ‘super-spike.’ I stress the fact that he said could because he was not predicting $100 a barrel per se, but rather he was making an analysis about the factors which could create a spike in oil prices. When oil did in fact rise to $100, many were shocked and Murti looked to be a prophet. Then he penned a piece which said the super spike could take oil to $150-$200. When oil peaked at $147 a barrel and subsequently collapsed down to $33, Murti was widely vilified in the media.
In fact, if you look for his name in a search engine, you will find all manner of references to his $200 oil call as a wrong prediction that was the height of hubris. However, if you read the above linked Bloomberg article, you can see he never said oil would rise to $200 a barrel any more than he said oil will rise to $100 a barrel. In fact, he gave a range from $150-$200 which was arguably met when oil rose to $147 a barrel. Clearly, making a bold call that is ‘proved’ false is detrimental to the reputation of a market forecaster.
So, in retrospect, Marc Faber was making a statement about Nouriel Roubini, dubbed by the media as ‘Dr. Doom,’ that one can easily see has having relevance in the Arjun Murti case. The question is what impact these facts have on how forecasters act. I would argue that it constrains their forecasting more than is readily apparent, especially due to ‘personality factors’ in the forecasting community.
The first outcome of this asymmetric treatment of bold calls gone wrong and ones proved right is what is known as herding. This is a phenomenon known to be at work in bubbles and was popularised in a 19th century book called “The Madness of Crowds” by Charles Mackay. More recently, herding has been seen amongst fund managers judged according to an index benchmark and relative fund performance. But, it is also evident in how forecasters make predictions as well. No one wants to go out on a limb with a bold call only to see this prediction proved wrong. If one fails, it is better to fail conventionally. The necessary corollary of that statement is this: market forecasters and analysts play it safe by making sure their forecasts are not often far from the consensus forecast. Think of the consensus forecast as an anchor which restricts the outlook of any individual forecaster afraid of failing unconventionally.
In Roubini’s case – and this logic also applies to media darlings like Meredith Whitney – it does NOT pay to up the ante. What Faber is saying is that they have already benefitted from the bold and unconventional contrarian market call they initially made. There is little payoff and much risk from continuing on that path. A bearish analyst who misses the turn gets the stick. Just ask the original Dr. Doom, Henry Kaufman.
Personality Factors: think Mr. Spock
There is another overlooked part of forecasting which contributes to the herding of analysts. I would call this personality factor, the ‘Mr. Spock Syndrome.’ Let me explain.
In the early 1990s when I entered the Foreign Service, we were all given a personality test called the Myers-Briggs Type Indicator (you can take the test here). This test is designed to give individuals a general sense of their own particular personality proclivities and modus operandi. While the test has generated some criticism for not having enough real world statistical validation, it has been adopted by a wide range of human resource departments worldwide.
Now, when I took this test, I had no idea what the MBTI was. So, I found it quite interesting to hear what it was designed to achieve. What was more interesting was how unevenly distributed different personality types are across the population. Of the four types, two make up as much as 80-85 percent of the population, whereas the other two make up as little as 15-20 percent.
When we were asked to raise our hands and self-identify after we received the test results, two thirds of the classroom identified themselves as NTs – otherwise known as rationals (I am an NT as well). Mr. Spock, the character from Star Trek, best exemplifies the exaggerated two-dimensional version of an hyper-rational.
Given the fact that rationals make up 5-10 percent of the population, it is very unlikely that two-thirds of my thirty-odd Foreign Service colleagues were NTs by random chance. More likely is that we self-selected based on the fit between our personality and the job and based on self-selection (i.e. NT Diplomats unconsciously picking other NTs).
In economic analysis much the same dynamic is at play – rationals are a natural fit for the role of stock analyst or economic forecaster. I guarantee you that you would see an equally disproportionate number of rationals in those positions were you to administer a global poll of economic forecasters (which makes me wonder if the whole ‘rational economic agent’ meme in economics is just a projection onto the broader population?).
Mr Spock doesn’t like being wrong
So, what are the personality characteristics of an NT? Opinionated and arrogant are two things that come to mind. But, that’s being negative. There are many positive ones like pragmatic, even-tempered, inventive. One interesting characteristic is that rationals do not like to be wrong. It is like a blow to a rational’s sense of self to proved wrong. So, more than other personality types, rationals take episodes to heart like the one I described with Arjun Murti. While this might tend to make one more meticulous and precise, I believe it also makes one more cautious. Take a look at this post and you will see that I, as an NT, have unconsciously filled my article with qualifiers like “might’ and ‘could’ or ‘tend to.’ I didn’t realize this until I read the last sentence. But, clearly I am doing the same thing I am accusing other forecasters of doing: qualifying my statements in order to make it easier to weasel out of a bad call.
The easiest way to weasel out of a bad call, however, is to vote with the consensus, otherwise known as herding. Outliers are punished if they are wrong. Now I know rationals tend to be very independent minded and are, therefore, more prone to be contrarian, but I also think that the rewards and incentives in forecasting are skewed toward consensus. In my opinion, this is another reason why momentum is such a force in markets – no one is willing to stick out his neck.
I hope you find this post entertaining. I look forward to your comments – positive or negative.