Earlier in May I had the opportunity to be interviewed by former hedge fund manager Mike Alkin. One of the things we discussed was the power of incentives, how they can crop up in unexpected places and the reason investors in commodities and indeed all financial markets need to be wary of them, especially when considering advice on where markets will go in the future. You can listen to me talking to Mike from 58.44. In this article I expand upon my interview with Mike.
“Never, ever, think about something else when you should be thinking about the power of incentives.”
– Charlie Munger
You will often see reports on financial TV, social media and the press pick up on a story that one or another famous investor or investment bank is bullish on this or bearish on that. Should you follow them blindly and invest? No, absolutely not. You don’t know what level of risk they are taking to enter that position. Have they hedged it and how? Do they even have any “skin-in-the-game” anyway and are following up their forecast with a position in the market? The best investors should also be able to change their minds if the evidence no longer supports their original hypothesis, and the same can be said for institutions that publish price forecasts. However, if the bank changes its mind will it get picked up by the press, or even get published? It might do, but it might not.
Think about why the financial media publish forecasts next time you see them on TV or on the internet. Are they to help inform those not fortunate enough to be able to afford buying their forecasts off the shelf? Are they meant as “entertainment”, as a way of gathering publicity to sell other “services”? Or are they setting up the sucker to invest in a particular commodity market, just as the forecaster has pulled out of the market or, even worse, is on the other side of the trade?
Various consultancies also operate in different commodity markets. Often they might be involved in collecting prices for opaque commodity markets, while using their experience and understanding of the underlying physical market to provide forecasts. Others might operate outside of commodity markets altogether, relying on their macroeconomic skills and models to provide forecasts.
At first glance these commodity forecasters might be seen as being the most independent from the actual market, since they have much less incentive to tailor the forecast to suit their client base or a position that they hold. That isn’t necessarily true however and ultimately depends on which of its client (buyers or sellers) have the most money to spend. Mining companies and other commodity producers often commission consultancies to provide ‘independent’ commodity forecasts to go into their prospectuses. On the other hand if buyers hold the purse strings in a particular commodity market then, incentives being what they are forecasts and analysis may be well be tailored to what that client wants.
Miners, commodity trading houses and others often release their own forecasts of where they expect one or a range of commodity prices to be in the near or long-term future. 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 hanging on every word for clues as to how underlying physical demand and supply is likely to evolve. On the flip side, it’s difficult to argue that they are an unbiased prediction of commodity prices.
Forecasters are exposed to other incentives that may influence their behaviour. 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? According to the EUVF paper 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 or for free as clients of an investment bank. 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?”
Herding behaviour isn’t specific to explaining how the commodity forecasting firm appears to the outside world, it can also affect internal incentives. Career concerns can also play a part too. 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 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.
The final type of herding is known as investigative herding. Investigative herding arises when investors trade similarly by reacting to the arrival of a commonly observed information signal. Analysts have an incentive to investigate a piece of information or a market that he knows other analysts may also investigate and trade in. From the point of view of getting a return on the forecast, there is no incentive to build a position in a particular market if other investors won’t join on the same side and push the price in the direction of the forecast. Illiquid markets tend to be less well served precisely because it is not worthwhile for banks and other financial institutions to trade them. It follows that the reputational risk of making forecasts about illiquid and volatile commodity markets is much higher.
Incentives, they’re everywhere if you know where to look. Ignore them at your own risk.