To herd or not to herd: How skewed incentives result in biased forecasts

First published on ChAI

“Forecasts usually tell us more of the forecaster than the future.” – Warren Buffet

Commodity market forecasters, like others who offer their views on the outlook for other financial markets are subject to several specific behavioural biases. These include confirmation bias (seeking out information that confirms our own worldview and reject or ignore any disconfirming evidence), recency bias (expecting the future to look very similar to the recent past), and theory-induced blindness (factors important in driving historical past are assumed to have the same weighting in the future). These and many other emotional and cognitive biases can result in poor predictions, influenced only by the individual biases of the forecaster.

The incentive structures, operating at both the firm and the individual analyst level are often mistakenly dismissed and discounted by ‘consumers’ of forecasts. In this article I outline under what conditions forecasters seek safety in numbers or stray from the herd, how career incentives affect the boldness of analyst predictions, the marketing value of forecasts, and finally the relationship between ‘skin-in-the-game’ and wishful thinking.

Safety in numbers

“Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally.” – John Maynard Keynes

As commodity prices soared late in the first decade of the 21st Century the investment plans of major commodity companies were increasingly based on the assumption that high prices would be sustained indefinitely. When prices have been high and rising for some time, it becomes an entrenched assumption that these high prices will persist for the foreseeable future.

In market conditions such as these there is an institutional inertia among forecasters. Analyst’s update their view of the world slowly and iteratively, not wanting to appear too far from the pack or consensus.

The exception to this appears to be when markets reach a peak or a trough. Then investment banks and commentators, etc, all want to come up with an even more extreme prediction of where prices could go – they seek safety in bullish or bearish sentiment. For example, towards the peak in the commodity super-cycle, forecasters came up with ever more bullish projections of how high prices could go. This was mirrored in early 2016 as forecasters sought safety in ever more bearish projections for crude oil prices.

Diversity of opinion often breaks down when beliefs (often also reflected in commodity prices) become too stretched. Seasoned investors might take a back seat while novices push prices to more extreme levels. When this happens, there is no countervailing force to cancel out the irrationality of one individual or group.


“Potentially profitable, non-consensus forecasts are very hard to believe in and act on for the simple reason that they are so far from conventional wisdom.” – Howard Marks

Researchers at the European University Viadrina Frankfurt (EUVF) analysed over 20,000 forecasts of nine different metal prices over different forecasting horizons, during the fifteen years 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; and the incentive to herd or stay away depend upon the mix of clients, both existing and prospective. Think about who buys commodity 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 for example. In contrast to the regular buyer, there are also onetime or irregular consumers of commodity price predictions.

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, the second group of buyers 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 forecasters 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 budget of the infrequent consumer of forecasts.

In contrast, if a forecaster publishes a less extreme forecast, one close to the consensus forecast, then 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 the impact on his income and reputation will be minimal. The infrequent buyer will ask, “why pay for a forecast from an average 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 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 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 risk and analyst incentives

“Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally.” – John Maynard Keynes

Herding behaviour isn’t specific to explaining how the forecasting firm appears to the outside world, it can also affect internal incentives. You might think that 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, it 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 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 (i.e., 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).

The researchers found 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.

Forecasts as marketing

“There are three key biases in financial forecasting. Economists never forecast recessions, equity strategists are always bullish, and bond strategists are always bearish.” – Albert Edwards

Investment banks, other financial institutions and consultancies frequently publish their views on the future path of various commodity markets. There are two main issues that investors need to consider when verifying the objectivity behind these forecasts:

First, there’s a potential conflict of interest, i.e., the providers may benefit from those same clients acting upon their predictions. For example, a bullish commodity market outlook will benefit other parts of the bank with an interest in investors “buying in” to a growth story. This could involve a miner looking to an investment bank to secure more funding from the capital markets.

Second, ‘left-field’ forecasts are often used to garner media attention. Not necessarily for the accuracy of the prediction, but instead to market other services or products. As we’ve seen earlier, non-consensus forecasts, especially ones that are proved correct often mean that the analyst, and the firm they work for can dine out on that reputation for years, no matter the accuracy of their previous predictions.

Skin-in-the-game and wishful thinking

“Don’t tell me what you think, tell me what you have in your portfolio.” – Nassim Nicholas Taleb

Miners, commodity trading houses and other firms involved with the physical buying and selling of commodities also opine on the outlook for commodity markets. In the case of a mining company, these forecasts might be released around the same time as annual reports detailing the company’s activities are published, or when they are trying to raise funding for new investments. These forecasts can be said to have “skin-in-the-game”, with many commodity investors looking for clues as to how underlying physical demand and supply are likely to evolve. On the flip side, it’s difficult to argue that they are an unbiased prediction of commodity prices.

It’s not unreasonable to assume that a buyer (perhaps a large airline wanting to buy aluminium) or a seller (a miner/smelter of industrial metals) of commodities must have a much stronger incentive to have an accurate view of where a commodity market is heading. However, that’s not what the evidence finds. Wishful thinking is a powerful factor in adversely affecting forecasting ability.

The economist Guy Mayraz conducted a simple experiment at Oxford University’s Centre for Experimental Social Science to test how wishful thinking affected the accuracy of predictions. Mayraz ran sessions in which the participants were shown ninety days of historical wheat price data and were then asked to predict the price of wheat on the one-hundredth day. Besides being paid a bonus for accurate forecasts, half the experimental subjects were told that they were “bakers” who would profit if the price of wheat fell, and the other half were told they were “farmers” who would make money if the price of wheat rose.

Logically, in the study a “farmer” should make the same forecast as a “baker” since the forecast does not change the outcome, and both are paid for accuracy. However, that’s not what Mayraz found. Instead, nearly two-thirds of “farmers” predicted higher-than-average prices and nearly two-thirds of “bakers” predicted lower-than-average prices. Even when the scale of the bonus was increased, Mayraz found no significant increase in forecast accuracy. It seems that wishful thinking can get you into a lot of trouble.

What this means for commodity market participants

Forecasters are typically too influenced by emotional and cognitive biases, and too busy looking across at their competitors to provide an unbiased forecast of the future. This should be a concern for physical buyers and sellers of commodities seeking robust projections upon which to make trading and investment decisions.

Incentive structures, operating at both the firm and the individual analyst level are often discounted by most consumers of forecasts, but they really shouldn’t be. In this article I have demonstrated how both external and internal competition affects the boldness of analyst predictions, how forecasts are often misused as a form of marketing for other services, and show ‘skin-in-the-game’ can influence price forecasts. As Charlie Munger is oft quoted as saying, “Show me the incentives and I’ll show you the outcome.”

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