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  • Marc

Financial Experts and their inflated Egos

Most readers will have noticed that a branch of Economics called “Behavioural Economics” entered the realm of scientific research in the mid to late 1970s. In fact, Professors Kahnemann and Tversky wrote one of the earliest research papers on the topic in a 1979 Econometrica publication, titled “Prospect Theory: An Analysis of Decision Under Risk”, where they challenged the neo-classical assumption of perfectly rational market participants (the good old super-rational “homo economicus”) who all make uniformly perfect rational decisions in markets with perfect information. Out of these early insights, the branch of “Behavioural Economics” and its sub-field of “Behavioural Finance” was born, where traditional economists and psychologists worked together in tandem to analyse actual market behaviour of actual market participants rather than merely relying on the neo-classical assumption of perfectly rational human beings to derive at economic insights. This branch of research has truly grown out of its infancy, manifested by the fact that the 2017 Nobel Laureate for Economics is none other than Professor Richard Thaler, one of the most prominent proponents of Behavioural Economics.

While we had touched upon one source of cognitive bias as part of earlier discussions in our blog in an article from February 2017 and in an article from April 2017, especially overconfidence as a source of cognitive and emotional bias reigns supreme as prime impediment to rational decision making, both for the average investor but even more so for self-professed business gurus and glorified business analysts.

In a ground-breaking June 2010 study performed by the Leon Kozminski Centre for Market Psychology, Professors Tadeusz Tyszka and Piotr Zielonka published their findings in an article called “Expert Judgments: Financial Analysts vs. Weather Forecasters”. In their study, Tyszka and Zielonka asked two groups of experts to "predict corresponding events” (the value of the Stock Exchange Index and the average temperature of the next month). In this instance, “corresponding events” have the same level of underlying statistical uncertainty and are equally hard to predict.

They found that although both groups of experts showed the above mentioned overconfidence effect, this effect was significantly higher among financial analysts compared to weather forecasters. In the group of financial analysts one-third of the participants succeeded in their forecast, while the predictions of the weather forecasters were twice as accurate with a success rate of 67%. Further analysis showed that – perversely – the group of experts with the highest self-professed confidence estimate of 80% or higher prediction accuracy were exactly the group of experts with the lowest prediction accuracy and all hailed from the realms of finance analysis experts. The insights of this study was exactly in line with the fact that in November 2007, economists in the Survey of Professional Economics Forecasters -- examining some 45,000 economic-data series collectively -- foresaw less than 0.2% probability of an economic meltdown as severe as the one that would begin one month later during the Great Financial Crisis of 2008/2009.

At first it seems to be counter-intuitive that the most confident experts are the group with the least reliable forecasting ability. The reason why weather forecasters succeeded on a significantly better scale compared to Economics expert forecasters seems to be that long ago, weather forecasters collectively came to accept the imperfections in their knowledge and in their weather models. That helped them understand that even the most sophisticated computers, combing through seemingly limitless data, are painfully ill equipped to predict something as dynamic as the weather. And, as it turns out, the Economy shows an even higher level of dynamics compared to today’s widely accepted weather forecasting models. So as fields like economics began relying more on Big Data, meteorologists recognized that the amount of data on its own will never be enough to increase prediction accuracy.

Weather forecasters seem to have an awareness of uncertainty about the natural world in general and the weather in particular that causes these experts to manifest a lower overconfidence effect than experts from the field of Economics. "Paradoxically," Tyszka and Zielonka discovered, “financial analysts, having less precise knowledge than the weather forecasters about the underlying system, can be more self-assured."

That self-assurance, according to John Nofsinger, associate professor of finance at Washington State University, a specialist in behavioural finance, and the author of The Psychology of Investing, "causes people to overestimate their knowledge, underestimate risks, and exaggerate their ability to control events."

"In fact, studies have shown that the ratio of confidence to accuracy is an inverse one -- that is, a lesser confidence level tends to correlate with a higher degree of accuracy," John Nofsinger posits in his excellent book.


As investors, we are all well advised to take the humble stance of the Weather Forecasters and accept the fact that we really don’t know for certain. We are much better advised to accept the insights of Economics Nobel Laureates, including those from Behavioural Economics, eat humble-pie in the knowledge that our collective crystal balls are all extremely foggy, and stay the course in an evidence-based, low-cost index fund approach based on the ideas of Nobel Laureates, as presented in previous blog posts titled “The Science of Investing I to IV”. That way, we will never run the risk of succumbing to over-confidence and subsequently trying to outfox the market by market timing or stock picking with detrimental effects to our retirement pots.

Sources: John F. Nofsinger; “The Psychology of Investing”, 2001, Routledge Publishing

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