Weighing the value of carbon price predictions

Power generators and major industrial emitters rely on carbon price forecasts to understand whether it is worth pursuing investments that could decarbonise their businesses.

If carbon forecasts prove to be too high then investors may pay too high a price, bringing forward investments when other opportunities could have made better returns on capital. On the flipside if carbon forecasts prove to be too low then investments may be delayed, and obligated emitters may find themselves paying a high price to meet future compliance needs.

Carbon price forecasts also influence the actions of individual investors. Bullish predictions may encourage investors to buy carbon futures in anticipation of higher prices. For example, in late 2021 a number of analysts were making very bullish calls as to how high carbon prices could go.

That begs the question: How accurate are carbon price forecasts?

Asset management group DWS sought to provide an answer by comparing analyst forecasts made at the start of each calendar year between 2010 and 2021 and comparing their predictions with the outturn at end of the year. According to DWS carbon price predictions looking out 12 months typically have an average absolute error rate of 35.5%.

DWS then compared carbon analyst prediction performance against gold, copper, corn, Brent crude and US natural gas forecasts. No surprise that high price volatility tends to increase the likelihood that the forecasting error rate is also high. That being said, carbon prices exhibit similar levels of volatility to US natural gas prices, yet carbon’s forecast error rate is almost double that of US natural gas.

In 2017 I published my second book Crude Forecasts: Predictions, Pundits & Profits in the Commodity Casino. After reviewing forecasts between mid-2007 and 2016 I found that the average 6-month consensus forecast for WTI crude oil had an error rate of 27%. Oil price forecasts looking twelve months out were only slightly worse, off by an average of 30%.¹

Incentive structures, operating at both the firm and the individual analyst level are often dismissed and discounted by ‘consumers’ of forecasts. In my book I argue that incentives are the true driver of forecasting performance. As Warren Buffet says, “Forecasts usually tell us more of the forecaster than the future.”

Incentive #1: Safety in numbers

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 early 2000’s 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.

Incentive #2: Anti-herding

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”.