-- John Kemp is a Reuters market analyst. The views
expressed are his own --
By John Kemp
LONDON, June 28 There is no evidence anyone can
successfully predict commodity prices. Most forecasts seem to be
adaptive -- reacting to past price changes -- rather than
forward-looking, and therefore miss turning points.
There is no evidence any forecasters consistently get it
more right than wrong. Forecasts can never be more than a
baseline for planning and investing surrounded by significant
Nonethless, preparing forecasts remains a useful exercise
because it forces analysts to identify factors driving prices
and how the balance might change in future. It is the process
rather than the outcome that is valuable.
While equity investors have embraced this approach --
valuing research for the supporting information and analysis of
sensitivities as much as the price call -- too much research in
commodities still emphasises point forecasts . There is
little consideration of the distribution of risks or
of factors that might drive alternative outcomes.
Enormous resources are now being poured into commodity price
forecasting but a thoughtful paper by the New York Fed casts
doubt on whether it is likely to be successful.
The challenge is to transform the purpose and presentation
of research. Rather than trying to help investors and hedgers
predict the future (which is doomed to failure) research should
help them accept and manage uncertainty in the most appropriate
In a 2010 working paper on "Commodity prices, commodity
currencies, and global economic developments" and now featured
on the New York Fed's website, Jan Groen and Paolo Pesenti ask
"how easy it to forecast commodity prices?" (www.nber.org/papers/w15743).
The authors conclude "across our range of commodity price
indices we are not able to generate out-of-sample forecasts
that, on average, are systematically more accurate than
predictions based on a random walk or autoregressive
specifications". In other words, forecasts could not beat a
prediction based on the current price or some weighted average
of past prices.
Groen and Pesenti considered four families of indices
(Reuters/Jefferies CRB index ,
Standard and Poor's GSCI
<.SPGSCITR > , Dow
Jones-UBS , and IMF Non-Fuel
Commodity Prices Index) and three ways of forecasting them.
The first approach focused on exchange rates from a small
group of commodity exporting countries which derive a
substantial portion of output and export earnings from primary
products but are too small to exert monopoly power on prices and
have free-floating exchange rates (Canada, Australia, New
Zealand, Chile and South Africa).
The second and third added a wide range of other
theoretically important factors -- including macroeconomic
variables (industrial production, business and consumer
confidence, retail sales, unemployment, core inflation, interest
rates), commodity fundamentals (inventories, production), and
the Baltic Dry Index .
Groen and Pesenti examined the out-of-sample forecasting
power of these approaches over five time horizons ranging from 1
month and 3 months to 2 years and compared them with benchmark
predictions based on a random walk or an autoregressive process.
For some approaches over some time periods the forecasts
performed a little better than predictions based on the
benchmarks, but the outperformance was fairly random. Some
approaches worked for some indices at some horizons, but not for
other indices at other time frames.
"Neither the exchange rate approach nor a broader approach
that uses information from larger data sets including both
exchange rates and other macrovariables are overwhelmingly
successful in predicting commodity price dynamics," observe the
Perhaps poor performance of exchange-rate and macroeconomic
based forecasts was due to the dominance of commodity specific
factors in determining returns? The authors therefore consider
whether including futures and forward prices for commodities,
which encapsulate expectations about specific factors, could
improve the predictions.
They warn "futures prices provide, at best, highly noisy
signals about future price spot prices" and " it is unclear
whether prices in relatively illiquid segments of the futures
market such as longer-dated contracts can be considered unbiased
and effective aggregators of information". In any event, adding
futures and forward prices into the models did not improve their
We are left with the humbling conclusion that our ability to
forecast commodity prices is no greater than our ability to
forecast other aspects of the future. It will not surprise
long-standing professionals in commodity markets; the authors
have provided statistical proof of what most market participants
and observers have long known.
For intermediaries such as dealers and even hedge funds the
most successful and stable long-term strategy has been to delink
profitability from price forecasts and directional trades and
find other sources of added-value by providing fee-earning
services such as market-making, brokerage, warehousing,
marketing and logistics.
For example, Goldman Sachs has moved into metals warehousing
with the acquisition of Metro, while Glencore has been keen to
stress its revenues are not based on directional speculation but
exploitation of arbitrages and the informational advantages and
optionality that comes with trading around a huge network of
If the market's biggest and most successful participants
cannot guarantee to make money based on (directional) price
forecasts, there is no more hope for smaller investors and
producers and consumers engaged in hedging, even if they are
armed with the best forecasts.
For commodity producers and consumers, the challenge is
instead to understand the whole range of risks they face and the
most effective way of managing them, recognising that some
strategies merely transform one risk into another, and in some
instances it may be more cost-effective to accept the risks
rather than try to offload them.
For investors, the challenge is to recognise the limit of
forecasting but extract an appropriate fee for assuming risks
other market participants do not want to shoulder, recognising
that in most cases high returns are obtaining by accepting the
possibility of large losses.
In this context, research is most effective when it helps
investors and hedgers explore the full range of factors
influencing prices and the range of possible outcomes, not just
the central tendency but the whole distribution.
Given the proved limits of price prediction, research may be
most useful when it examines in depth the full range of upside
and downside risks and expresses outcomes as probability
(Editing by Anthony Barker)