As much as you or I like to think of ourselves as forward-looking, we are all backward-looking and we update our perception of the world only gradually. Known as the recency bias, forecasters often give greater weight to very recent events in their forecast and let what could just be random events colour their perception of how the future will evolve.
In benign market conditions we should see institutional inertia among professional forecasters – only updating their view of the world slowly and iteratively, not wanting to appear too far from the pack or consensus. The exception to this only appearing when markets reach a peak or a trough. Then investment banks and commentators, etc, all seem to want to come up with an even more extreme prediction of where prices could go.
Researchers at the European University Viadrina Frankfurt (EUVF) analysed over 20,000 individual forecasts of nine different metal prices over different forecasting horizons between 1995 and 2011. Instead of finding the institutional inertia and forecasting herding that we might expect, they found strong evidence of “anti-herding”.
So why might some forecasters want to stray from the herd? It all comes down to incentives. Understanding them will enable you to more accurately judge forecasts on their merits.
The incentive to herd or stay away from the consensus depends upon the mix of clients (both existing and prospective) of the bank, institution or even sole pundit. Think about who buys forecasts. There are two groups of buyers. The first are those that buy forecasts regularly, perhaps as part of a subscription to a company’s analysis or for free as clients of an investment bank, for example. In the case of commodity price forecasts, examples of frequent buyers of commodity forecasts might include an oil company or a manufacturing company that regularly buys a certain small range of commodities. Given they are long-time consumers, they may have based their decision on how accurate a forecaster was over several forecasting periods.
In contrast to the regular buyer, there are also onetime or irregular consumers of commodity price predictions. This second group of buyer is more likely to be swayed by the commodity forecaster that was most accurate in the past year or so, or has been the most vocal about his or her success. This is rational from the irregular buyer’s point of view in that perhaps movements in the price of copper or another commodity only have a minor impact on their business, or maybe they only need to buy or take a view on a commodity infrequently. Either way, the cost/benefit of monitoring whether the commodity forecast they are buying has been accurate in the longer term is much higher than in the first group of buyers.
If the second group of buyers dominates (the infrequent consumer), forecasters have a strong incentive to differentiate their forecasts from the predictions of others by making extreme (or non-consensus) predictions. Even though an extreme forecast may have a small probability of being accurate, the expected payoff of such a forecast can be high, since the number of other pundits making the same extreme prediction is likely to be small. Should they be successful in their prediction, then the forecaster can capture the attention and the wallets of the infrequent consumer of forecasts.
In contrast, if a forecaster publishes a less extreme forecast, one close to the consensus forecast, then by definition there is a high probability that other forecasters will make similar forecasts. If this is the case then even if a forecaster’s price prediction is spot on, then the impact on his income and reputation will be minimal. The infrequent buyer will ask, “why pay for a forecast from an average forecaster?”
Incentives, being as they are, set up a paradox. For just as commodity prices work on reflexivity, the same could be true of the commodity forecaster. While a single or a string of successful predictions will bolster a forecaster’s reputation, this may result in future forecasts being much less extreme in order to protect their reputation. When a person or a company has their name on a forecast that may also alter the incentives; for example, a commodity research firm with a relatively low profile (perhaps it is just moving into the area) would be rational to make a wild forecast, drawing big attention from the media. In contrast, firms with a strong reputation are likely to make much more conservative forecasts, not wanting to stray too far away from the consensus.
Career concerns can also play a part too in forecasting. Just as at the level of the firm (whether a bank, consultancy or something else), you might think there is the temptation for an analyst to produce a bold prediction. If the analyst makes an “outlier” forecast that turns out to be spot on, this is likely to capture a lot of attention in the financial media, raising the prospect of the analyst being recruited by a rival firm touting a bigger salary and an even bigger bonus. However, set against this is the risk of being fired (or at least having a few rungs taken from under the career progression of a young analyst) for a bad call.
To examine what the age and experience of the forecaster has on the degree of herding, research published in the RAND Journal of Economics examined over 8,000 forecasts by equity analysts between 1983 and 1996. Equity analysts should, in theory produce reliable forecasts of future earnings of the companies that they monitor, which are then used to produce recommendations on what their clients should buy. Equity analysts face their own quandary, having to balance the interests of the buy-side (ie, their clients who prefer accurate forecasts) and those on the sell-side (other parts of the same bank they work for that might value trading commissions and large initial public offerings more than the accuracy of their analysts forecasts). Note that commodity analysts may face their own conflicting internal objectives too. From trading commissions on a commodity-related exchange traded fund, to a bank’s own proprietary trading on commodities and on to gaining profitable consulting business from a highly valued client. There is more than one incentive.
What the researchers found is that younger analysts tend to herd more than their more experienced colleagues do. Less experienced analysts, meanwhile, are more heavily punished for getting their forecasts wrong and so they have every incentive to stick with the herd. In contrast, older analysts, who have presumably built up their reputations, face less risk of termination. The researchers also found that, contrary to expectations, making bold and accurate predictions does not significantly improve a young analyst’s career prospects.
There is another factor to consider when thinking about forecasts. Again it comes down to the incentives of the forecaster, but this time the inference is more insidious. The nature of forecasting may drive out those that are best equipped to produce them. Commodity price forecasts might just be a market for lemons.
This reference to lemons comes from economist George Akerlof, who published a paper in 1970 in the Quarterly Journal of Economics called “The Market for Lemons”. Within it was a simple and revolutionary idea in which he noted that markets in which buyers possess imperfect information while sellers possess a profit motive are thin, insubstantial and low quality.
Akerlof used the example of the used-car market. Suppose buyers in the used-car market value good cars – referred to as “peaches” – at $20,000, while sellers value them slightly less. A malfunctioning used car – a “lemon” – is worth only $10,000 to buyers (and, again, assume a bit less to sellers). If buyers can tell “lemons” and “peaches” apart, trade in both will flourish. In reality, buyers might struggle to tell the difference: scratches can be touched up, engine problems left undisclosed, even odometers tampered with.
To account for the risk that a car is a lemon, therefore, buyers cut their offers. They might be willing to pay, say, $15,000 for a car they perceive as having an even chance of being a “lemon” or a “peach”. But dealers who know for sure they have a “peach” will reject such an offer. As a result, the buyers face “adverse selection”: the only sellers who will be prepared to accept $15,000 will be those who know they are offloading a “lemon”.
Smart buyers can foresee this problem. With the knowledge that they will only ever be sold a “lemon”, they offer only $10,000. Sellers of “lemons” end up with the same price as they would have done were there no ambiguity. However, the “peaches” stay in the garage. This is a tragedy: there are buyers who would happily pay the asking price for a “peach”, if only they could be sure of the car’s quality. This “information asymmetry” between buyers and sellers kills the market.
In the same way as the used-car market example, one could argue that bad forecasters drive out good forecasters. The deep uncertainty that forecasting fosters may create incentives that perversely degrade the ability to offer better predictions. Many bright individuals might be deterred from working in the sector because from a career perspective the likelihood of error is so high. You could argue that there is little incentive to contribute when the exercise is seen as a dubious one. This then creates the space for people who are less afraid of such reputational costs, which in the end only results in a less critical debate.
There is also little incentive for forecasters to improve on their predictions. Rather, the incentives are geared towards exaggerating the precision of forecasts – a form of signaling in economics parlance. Just as with second-hand car dealers, the job market and many other markets, there is an asymmetry of information. In order to correct for this, dealers, job seekers and perhaps forecasters may try to signal their trustworthiness and talents by collecting awards that convey some authority – the best second-hand car dealer in the North West, for example, or the best gold price forecaster of the last quarter. Such exaggerations satiate the cognitive preferences of governments and corporations, and generate greater media attention to the forecast itself.
For much of the private sector, public forecasts are designed to maximise marketing rather than predictive accuracy. As Philip Tetlock concluded:
…the demand for accurate predictions is insatiable. Reliable suppliers are few and far between. And this gap between demand and supply creates opportunities for unscrupulous suppliers to fill the void by gulling desperate customers into thinking they are getting something no one else knows how to provide.
For every seer, there’s a sucker. Just always think about the seers incentives before following the herd.
Related article: Just how accurate are oil price predictions?