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Forecasting commodity prices

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.

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Forecasting commodity prices

  1. 1. Crude Forecasts Predictions, Pundits & Profits In The Commodity Casino
  2. 2. “Among all forms of mistake, prophesy is the most gratuitous.” George Eliot
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 12. Uncertainty over how current technology can be utilised and how technology could evolve makes forecasting very difficult. Problem #3 Technological change
  13. 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. 14. “No one ever made a decision because of a number. They need a story.” Daniel Kahneman
  15. 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. 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. 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. 18. “Whenever you find yourself on the side of the majority, it is time to pause and reflect.” Mark Twain
  19. 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. 20. Find out more in "Crude Forecasts: Predictions, Pundits & Profits In The Commodity Casino" Available from Amazon and all other good online bookstores