Orange juice prices: The top 10 most important drivers

1) Hurricanes

Florida has been in the eye of many storms that have hit the US Gulf. In the worst case trees are uprooted and orange groves flooded, while at best nearly ripened fruit is blown off the trees. Although hurricane forecasting has come along way over the past few decades it is still very difficult to pinpoint exactly where a storm will make landfill. As such traders in orange juice futures add a premium to prices as the hurricane season commences and especially if it looks like a storm is heading towards the state. The best month for orange juice futures prices tends to be November, right at the end of the season, as uncertainty over the extent of the damage still remains unclear.

2) Frost

Damage to orange trees occurs if the temperature drops below freezing and stays there for more than four hours. Market participants realise of course that severe frost is more likely during the winter and so the price of orange juice futures going into the end of the year should be high enough to reflect the probability of a freeze during the coming season.

3) Disease

A disease called “citrus greening” has severely hampered both Brazil’s and Florida’s orange juice industry in recent years. Greening starts at the leaves and works its way through the tree like a hardening of the arteries, blocking nutrients and water. Oranges then drop off the tree unripe and unusable. Although a genetically modified version is being developed it is likely to be many years away from being introduced.  Greening — which also hurts grapefruit, limes, lemons and other citrus — has cut Florida’s output in half over the past decade, according to the U.S. Department of Agriculture.

4) Drought

Dry weather in the main orange juice producing regions of Brazil may also cause the supply of orange juice to decline. If drought occurs in this region it typically also affects other related ‘soft’ commodity prices like sugar and coffee.

5) The Brazilian Real

The largest global supplier of orange juice is Brazil (producing over 80% of the oranges for processing). When exports of Brazilian OJ are high this can cause the price of orange juice futures to fall. As such currency movements can have a disproportionate impact on how big exports of OJ are. An appreciation of the Brazilian currency, the Real, against the US dollar is likely to be positive for the price of orange juice futures since it is now more expensive to import Brazilian orange juice into the US than it was before.

6) Planting

Oranges grow on trees that require 5-15 years to mature. And so any decision by the farmer on planting does not involves long lead times, by which time the fundamentals may be very different.  This means that farmers price expectations (i.e. whether they expect high or low prices to continue sometimes 3-5 years in the future) are vitally important in determining future supply and prices

7) Inventories

Although the commodity is frozen and not very perishable, only a small amount of inventory is carried over from one year to the next. This lack of a ‘buffer’ helps to contribute to very volatile prices.

8) Consumer tastes & health concerns

Orange juice demand is being squeezed because of a shift away from sugar laden drinks and competition from other products such as flavoured water. The drop in demand can be traced back to the early 2000’s as the popularity of low carbohydrate and high protein diets surged. Changes in eating patterns has also hit demand. With fewer people eating breakfast as a sit down meal fewer families are drinking it in the morning.

9) Substitutes

The relative price of substitute products like apple juice, tomato juice and other soft drinks will influence the demand for orange juice. Meanwhile, farmers in Brazil have been tearing up orange groves and replacing them with more profitable crops such as sugar cane. As Brazilian supply becomes more important to the overall orange juice market, it is starting to become more closely related to the sugar market, which Brazil is also the major global supplier.

10) Shipping, production and logistics costs

Transportation bottlenecks when exporting orange juice out of Brazil can delay product reaching the market resulting in higher prices. Meanwhile, higher production costs (e.g. higher fuel costs) as with all farmers either result in lower margins or need to be passed onto the end consumer.

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Geopolitics and technology: An unholy union of uncertainty?

“In economics things take longer to happen than you think they will, and then they happen faster than you thought they could.”

Rudiger Dornbusch

As 2017 draws to a close geopolitics and technological innovation appear (at least on the surface) to be the main drivers of greed and fear in commodity and other financial markets. The problem for investors is that the risk underpinning both geopolitics and technological development cannot be priced up neatly.

A fragile peace?

The recent arrest of more than 200 princes, businessmen and other high ranking officials in Saudi Arabia would have been unthinkable only a month ago. Now, the inherent fragility of the Saudi political and economic system may mean that the country now implodes; or indeed the cracks may by papered over for years into the future. But how do you put a price on that risk? Just because things go quiet now does not mean that the risk has gone away.

Ruptures in the price trend for many commodities are often the result of geopolitical developments. Political scientists Ian Bremmer and Preston Keat defined geopolitics as: “The study of how geography, politics, strategy, and history combine to generate the rise and fall of great powers and wars among states.” Given its importance to the running of the modern global economy, nowhere is this more vividly observed than in the battle for energy resources and, in particular, oil.

If geopolitics plays such a big role in seismic moves in the oil market and many other commodities, then if geopolitical shifts can be forecasted with any accuracy this must give forecasters an edge, right? The Good Judgment Project, set up by Phillip Tetlock, set out to answer the first part of this question. They explored the profile of the best out of hundreds of forecasters who made over 150,000 predictions on roughly 200 events during a two year period. Forecasters were asked a multitude of questions, such as: Will the United Nations General Assembly recognise a Palestinian state by September 30th 2011? Will Bashar al-Assad remain president of Syria through to January 31st 2012? The researchers found that forecasters can be good at spotting changes, but only over long timescales.

The problem with geopolitical events is that they tend to be binary outcomes (although clearly not always). They either happen in the future or they don’t. This contrasts with what we might term “market” or “economic” risks which are more dynamic. There are three main problems with binary outcomes: first, they offer little information advantage for investors to play with; second, they are hard to predict and, third, they offer few easily identifiable markets that might benefit from a particular outcome.

Even if you have fantastic foresight about how a geopolitical event is likely to develop, the next problem is decoding what the impact is likely to be on a range of different markets. All too often pundits focus on the immediate effect; for example, based on whichever candidate wins an election. However, they forget to draw the dots as to how the “narrative” could change once the geopolitical uncertainty of the political event falls away.

Even if you could correctly forecast that the regime of a particular oil producing nation would be toppled within a given year, you wouldn’t be able to know the exact path that oil prices would go as a result. You could at least add a risk premium to your forecast, but even that might not be correct. It is after all the risk of a sharp spike in prices that gets people’s attention.

Batteries now included?

Take technology. How do you price up the likelihood of further innovations in shale extraction technology both in the US and elsewhere in the world? Or how do you price up the likelihood that the recently launched Tesla truck will usurp conventional combustion engine powered vehicles and so put a dent in transport demand for oil?

Implicit in any forecast of commodity prices is an assumption of how technology could evolve and how its adoption will affect commodity prices. Commodity prices provide the incentive for new technology, yet also influence commodity production and consumption. Innovations, once introduced, may lead to higher yields from agriculture, more oil being extracted from offshore wells and deeper mines to extract more metals and minerals – all of which could eventually lead to rising commodity supplies.

High commodity prices may also lead to innovation on the demand side too. High energy prices, for example, may discourage consumers from using a particular energy inefficient product. This acts as an incentive for companies to redesign their products to become more energy efficient and less resource intensive. However, just because a technology might appear to be negative for demand doesn’t mean it has to be bad for prices, at least not in the short to medium term. For example, if oil producers are worried about the growth in electric vehicles they may decide to postpone large scale, multi-decade, multi-billion dollar investments. If they get it wrong and electric vehicles don’t take off as fast as they expect, then oil prices may rise sharply if there isn’t enough supply to meet demand.

And remember, don’t forget about rebound effects. If an innovation results in an energy intensive product (transportation for example) becoming cheaper or more accessible consumers are likely to want to consume more of it. Every improvement in technology has a rebound effect.

It’s the uncertainty over how current technology can be utilised and how technology could evolve that makes forecasting so difficult. Technological developments of all sorts involve a large dose of serendipity. The philosopher Karl Popper perhaps best describes the struggle to anticipate future innovations: “The course of human history is strongly influenced by the growth of human knowledge.” Popper also wrote:

But it’s impossible to “predict”, by rational or scientific methods, the future growth of our scientific knowledge because doing so would require us to know that future knowledge and, if we did, it would be present knowledge, not future knowledge.

Yet to forecast the price of oil, lead or cobalt into the next decade we need to make some assumption about how technology will make it easier to extract these commodities and how technology will change the demand for these commodities. Note that no one predicted the invention of pig iron or imagined how it would affect the nickel market, neither did anyone anticipate the introduction of hydraulic fracking and how it would turn the market for oil on its head.

Just noise?

Yet, a question is whether it has it always been such? Times are always uncertain and is now really any different? As can be heard almost any time someone frets about the future, there is the issue of hindsight bias. Someone says, “Things are uncertain, not like it was in the past.” The first part of that statement is accurate; the second is not. The future is always uncertain, whether it is the future we face right now or the future that people faced a century ago.

The answer for investors then may be to unplug from the noise and just ask themselves two questions: first, where are we in the cycle?; and second, what lessons from history can we draw for what might come next?

Related article: Narrative economics

Related article: Batteries now included: You’ll meet a bad fate if you extrapolate

Related article: What lessons does rhodium have for commodity investors?

Related article: Where do we stand in the commodity cycle?

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Just how accurate are oil price predictions?

“Economists don’t forecast because they know, they forecast because they’re asked.”

K. Galbraith, economist

The Wall Street Journal (WSJ) polls institutions every month on a range of economic variables including inflation, unemployment and West Texas Intermediate (WTI) crude oil prices. Each month, the survey asks for predictions for the forthcoming June and December. For the sake of consistency, I have reviewed the accuracy of forecasts made both six and 12 months prior to June and December each year. I reviewed surveys from mid-2007 to the end of 2016 and so this covered booms and busts, financial crises and quantitative easing, the Arab Spring and the shale revolution.

By means of a disclaimer, this is not an exhaustive study. By definition, it only covered a ten year period, and there is no guarantee that forecasters that were correct during this boom and bust period will be any more or less successful in future periods. It also says nothing about how well those same institutions did trying to predict other commodity prices including metal and agricultural prices. Finally, it only covers those forecasters that the WSJ surveyed – there may have been others who were more or less accurate in their predictions.

Using this data (available to view at, I tried to answer the following three questions: were the forecasts correct?; were the predictions valuable?, and, third, was there a forecaster that you could have followed that would have led to a better overall result than taking the consensus? Let’s discuss each of these points in more detail.

First, were the forecasts right? The answer was clearly no. The average consensus forecast (ie, the average of the commodity price predictions) for WTI crude oil was off by 27% when forecasting six months out. Oil price forecasts looking twelve months out were only slightly worse, off by an average of 30%. Another way of looking at it is that only in three of the nineteen periods reviewed was the consensus six month forecast within 5% of the actual result.

Producers, manufacturers, investors and traders are making billions of worth of investment based on the outlook for commodity prices. If these forecasts are awry on as short term a time period as twelve months then how can they have confidence making decisions over much longer time periods?

Second, did any of the forecasts spot the major changes in the direction of the oil price over the past decade? In June 2008, WTI crude was trading at approximately $135 per barrel. The consensus prediction for December 2008 was just under $112 per barrel and $101 per barrel twelve months ahead. The reality was somewhat different. The financial crisis hit and with it the oil price was hit too. WTI crude prices fell to $41 per barrel in late December 2008, only rebounding to $70 per barrel in mid-2009. Almost all forecasters polled in mid-2008 saw prices falling over the next twelve months, but no one saw the scale of the collapse. The closest six month forecast, although over 50% higher than the outcome, came from Parsec Financial Management!

It was a similar story in trying to call the rebound in prices. Remember that oil and other commodities rebounded in 2009 as quantitative easing helped support prices. Back in December 2008, however, the consensus prediction for June 2009 was for prices to stay low, only nudging up from the current levels of the time. This time the consensus was over 30% too low. Only three forecasts called the market within 5%: Societe Generale, Barclays and the Economic and Revenue Forecast Council.

Over the next few years, oil prices traded in a gradually narrowing range between $70 and $110 per barrel. Sure enough, the consensus and individual forecasts, increasingly anchored against recent prices, turned out to be broadly correct – well at least within a range of 5–15%. Like many forecasters, these economists were driving with their eyes fixed on the rear view mirror, enabling them to tell us where things were but not where they were going. This bears out the old adage that “it’s difficult to make accurate predictions, especially with regard to the future.” The corollary is also true: predicting the past is a snap.

If we fast forward to June 2014, oil prices were trading at approximately $105 per barrel, having peaked at just over $112 per barrel ten months earlier. The consensus forecast was for oil prices to fall from those levels to below $99 per barrel in December 2014. In reality, the consensus proved too optimistic by 85%. All of the forecasters were over 65% too optimistic, apart from one – Parsec Financial Management had predicted oil prices to be in the late $60s per barrel range in December 2014, only 24% too high.

Does that mean that Parsec Financial Management have superior insight? Well, not quite. A look back through earlier forecasts reveals that they were consistently bearish all the way back to early 2010, calling for oil prices to stay around $50–70 per barrel, even though oil prices kept on rising. This is what’s known as the “stopped watch” method of prediction. If you keep on saying something extreme will happen and it eventually does then you are feted as a guru when, in reality, you were lucky (eventually) with the timing.

Predictions are most useful when they anticipate change. If you predict that something will stay the same and it doesn’t change, that prediction is unlikely to earn you much money or wow clients with your predictive abilities. However, predicting change can be very profitable for investors, while timing hedging strategies can be a welcome boost to both producers and manufacturers.

Third, was there a forecaster that you could have followed that would have led to a better overall result than taking the consensus? Of those 26 institutions that contributed prices for at least 14 of the 19 forecast periods and during the key turning points in the market identified above, three forecasters achieved a better than average result than the consensus when looking over a period of six months. These were: JP Morgan (26% forecast error, 4 correct calls); Comerica Bank (25%, 2) and The Conference Board (25%, 3). The most accurate institution achieved a two-percentage point improvement on the consensus, but still had an average six-month forecasting error of well over 20%. The research sample also includes Goldman Sachs, often famed for its supposed commodity prediction ability. How did they do? They were an average of 36% off with one correct call.

“The lucky idiot”

Nevertheless, the ability to predict, over very short periods says little about the quality of a forecast. As with investment success in the stock market, or anywhere else, it’s very difficult to measure success in retrospect or a priori. If someone you follow correctly predicts the price of oil, it looks good on paper, but what you can’t gleam from the magic number is what the risk was that it didn’t happen. To what extent was the forecaster a “lucky idiot”, as the investor Nassim Nicholas Taleb would call them?

That a forecast for future oil prices turns out right doesn’t mean it was bound to happen. Howard Marks, manager of Oaktree Capital, uses the example of the weatherman to explain:

He says there’s a 70 percent chance of rain tomorrow. It rains; was he right or wrong? Or it doesn’t rain; was he right or wrong? It’s impossible to assess the accuracy of probability estimates other than 0 and 100 except over a very large number of trials.

But the climate and the weather are never the same on any one day. A meteorologist can look back at similar patterns and infer what is likely to happen tomorrow. Commodity forecasters can do a similar exercise too, but it will never be the same. The world is always changing.

Risk exists only in the future, and it’s impossible to know for sure what it holds. No ambiguity is evident when we view the past. Only the things that happened, happened. That definiteness, however, doesn’t mean the process that creates outcomes is clear-cut and dependable. Many things could have happened, and the fact that only one happened devalues the variability that existed.

Remember, predictions are made with foresight, but tested with hindsight. It is easy to look back at a sequence of events that led to a forecast turning out correct and to lead the pundit, and anyone who had seen that forecast, to say: “I knew it would, it was obvious it would turn out that way.” The hindsight bias, as it’s known, prevents the forecaster and the consumer of that forecaster from reviewing whether it was correct because the pundit judged the risks correctly, or whether the pundit was just a “lucky idiot”.

The pundit (or lucky idiot) might be infamous for making one big call. But is that enough? Given enough events, even a monkey can make the right prediction eventually. Does that mean that the monkey is endowed with magical powers of insight about the future? Sadly no. For purely statistical reasons, outstanding performances tend to be followed by something less impressive. This is because most performances involve some randomness. On any given day, the worst observed outcomes will be incompetents having an unlucky day, and the best observed outcomes will be stars having a lucky day. Observe the same group on another day and, because luck rarely lasts, the former outliers will not be quite as bad, or as good, as they first seemed.

Regression to the mean, as it’s known, probably explains why many winners subsequently disappoint. And the disappointment will be spectacular if some people are taking bigger risks than others. The most impressive performance may combine skill with luck. In a financial market — or a casino — the easiest way to become an outlier is to make a big bet.

While randomness can explain much, hubris may also play a role. The economists Ulrike Malmendier of University of California, Berkeley and Geoffrey Tate of University of California, Los Angeles examined what happened to companies whose chief executives won accolades such as Forbes’s “Best Performing CEO” or BusinessWeek’s “Best Manager”. They picked a statistical control group of near-winners who might have been expected to win an award, but did not.

Like the near-winners, the winners ran large and profitable companies. However, those companies run by the winners did far worse in the three years following the award, lagging behind the near-winners by approximately 20%. The prizewinning CEOs, nevertheless, enjoyed millions of dollars more in pay. They were also more likely to write books, accept seats on other corporate boards and improve their golf handicap.

This article is based on a chapter from the book, Crude Forecasts: Predictions, Pundits & Profits in the Commodity Casino. Buy it to find out more about how you can make better predictions of future commodity markets and hold others to account.

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