This presentation will help you make better investment decisions in commodity markets. Find out which pundits and forecasters really know what they are talking about and track them. Understand the factors you can use to hold the ‘experts’ to account. It tells the story of how dairy farmers in New Zealand, petrochemical companies in the US and miners in Canada have been affected by overly optimistic views on future commodity prices. This isn’t just a story of investors losing their money, but loss of communities and livelihoods and even whole economies usurped by just the expectation of a commodity boom. Central to this is the power of the forecast in driving decision-making. All to often investors and executives outsource vital thinking to others they perceive have some edge in predicting prices, without really stopping and asking why, how and what if? Do you rely on commodity price forecasts for your business or investments? Are you finding it ever more difficult to find out who is the voice of reason? Does relying on no one but yourself to research markets scare you? In Crude Forecasts: Predictions, Pundits and Profits in the Commodity Casino, economist Peter Sainsbury shows how you can take back control. In these pages you’ll discover: * Why incentives tell you everything about financial market pundits * What warning signs to watch out for * How to be a more sophisticated consumer of financial media * What you can do to avoid your business, industry or country becoming a commodity “white elephant”. * Why MiFID 2 will increase demand for transparency and evidence based forecasting. Demand forecasts if you have to, but please demand better forecasts.

1. Crude Forecasts
Predictions, Pundits & Profits In The
Commodity Casino

2. “Among all forms of mistake,
prophesy is the most gratuitous.”
George Eliot

3. The average consensus oil price forecast
was wrong by 27% - looking forward just
6 months*
* 2007 to 2016
Commodity price forecasts
are not very good

4. The outlook for commodity
prices is more than just of
academic interest
To find out more about predicting
commodity markets buy the book.
Click on the book cover.

5. White elephant #1
Farmers in New Zealand,
encouraged by forecasts of high
dairy prices expanded their
herds in order to meet the
apparent insatiable demand
from China.
However, boom quickly turned
to bust, decimating
communities

6. White elephant #2
Taken in by forecasts of that
high oil prices, and low gas
prices would continue,
petrochemical companies
invested $160 billion in new
capacity.
Much of this new capacity may
never be needed.

7. White elephant #3
As technology, geopolitics, and
economics collide, commodities
are a challenging place to invest.
One example from recent history
is rare earth metals. Investors
lost billions on forecasts that
stratospheric price action would
continue.

8. “It’s frightening to think that you
might not know something, but
more frightening to think that, by
and large, the world is run by
people who have faith that they
know exactly what’s going on.”
Amos Tversky

9. 0
5
10
15
20
1 3 5 7 9 11 13 15 17 19 21 23 25
Turning points are hard to predict

10. Positive and negative
feedback loops make
the analysis of
commodity markets
and the ability to
forecast prices much
more challenging.
Problem #1 Non-linearity

11. Economies are subject
to cycles - the short-term
business cycle and
longer-term commodity
and currency cycles -
that are very difficult to
forecast.
Problem #2 Economics and politics

12. Uncertainty over how
current technology can
be utilised and how
technology could
evolve makes
forecasting very
difficult.
Problem #3 Technological change

13. Data on commodity
demand and supply is
opaque and frequently
revised. Information
vacuum provides a
context for compelling
narratives to spread.
Problem #4 Poor data quality

14. “No one ever made a decision
because of a number. They need a
story.”
Daniel Kahneman

15. Most forecasters predict
a future quite like the
recent past. Forecasters
are over reliant on recent
information while real
“sea changes” are
extremely difficult to
foretell.
Anchoring bias

16. Confirmation bias
We tend to only listen to
information that confirms our
preconceptions. Forecasters
and investors tend to seek
out information that confirms
our own worldview and
reject or ignore any dis-
confirming evidence.

17. Theory induced blindness
Scepticism about the use of
out-dated forecasting models
does not come easily,
especially if you were
responsible for building the
model.

18. “Whenever you find yourself on the
side of the majority, it is time to
pause and reflect.”
Mark Twain

19. #1 Firm up fuzzy forecasts and track them
#2 Scenario plan instead
#3 Do your own research
#4 Better data
#5 Smarter media consumption
#6 Stop forecasting
#7 Know the knowable
There is another way...

20. Find out more in
"Crude Forecasts:
Predictions, Pundits
& Profits In The
Commodity Casino"
Available from
Amazon and all other
good online
bookstores