Soybean prices: The top 10 most important drivers

1) Weather

The weather has a significant impact on crop yields and thus overall agricultural production. The wrong type of weather at the wrong time in the planting cycle, even if not prolonged or extreme, can also adversely affect the production of soybeans. The weather can also affect the shipment and logistics of transporting crops to market.

Heavy rain in the north and west-central regions of Brazil (where most of the worlds crop is grown) makes working conditions in the field more difficult and reduces the planting pace, in turn hurting the soybean output. Meanwhile, adverse weather conditions in the south of the country (where most of the crop is shipped from) could affect the supply of soybeans out of Brazil while delays could also increase wastage, further reducing supply.

2) Protein demand

The importance of China and other markets in Asia as a market for soybeans has been driven by an explosion in demand for meat as consumers switch from a diet dominated by rice to one where pork, poultry and beef play an important part. Soybean meal (a product of soybeans) is the most important protein source used to feed animals and represents about two-thirds of the total world output of protein feed-stuffs.

3) Chinese demand

China imports around 60% of global soybean imports, around 90% of its requirements. Chinese production of meat surged 250% between 1986 to 2012. However, China is unable to produce enough animal feed itself, hence the need to import soybeans from the United States and Brazil.

4) The US Dollar

Like most internationally traded commodities soybeans are priced in US dollars. At its most basic a decrease in the value of the US dollar relative to a commodity buyer’s currency means that the purchaser will need to spend less of their own currency to buy a given amount of the commodity. As the commodity becomes less expensive demand for the commodity rises, resulting in an increase in the price and vice versa.

A weaker dollar can also act as a disincentive to producers to increase output. For example, a depreciation of the US dollar against the Brazilian real can reduce profit margins for a soybean farmer in Brazil. All of the farmer’s revenues will be received in US dollars, which will now buy less real, but some proportion of the costs will be denominated in real and will remain constant (at least in the short term). Therefore, the prospect of a lower profit margin acts as an incentive to decrease the supply of soybeans.

5) Tariffs

Given soybeans position as one of the most important sources of food governments may use this as a key element of trade policy with other countries. In 2018 for example China announced that it would increase tariffs on imports of soybeans from the US. The impact of such a tariff would be to increase the price of soybeans in China, but for the price to fall in the US.

6) The price of other crops

Corn and soybean compete on multiple grounds. They compete in the cooking oil industry. They compete in the animal feed industry. They also compete in the biofuel industry. Therefore, the production of one does affect the other. In general, if the production of corn falls, soybean prices are expected to rise.

7) Stock levels

Stocks (otherwise known as inventories) act as form of buffer for both producers and consumers of soybeans. Typically, falling stock levels occur if demand increases faster than supply, resulting in higher soybean prices. Falling stock levels may, however, make the soybean market more vulnerable to an unanticipated disruption to supply or a sudden increase in demand.

8) Speculation

Hedge funds operating in agricultural markets including soybeans may result in prices moving faster than they otherwise would in the event that there is deficit or surplus in the market. Speculators play a vital role then in signalling to farmers how much they should be planting while also allowing the transfer of risk.

9) Energy costs

Higher energy costs imply higher costs of production for soybeans and higher costs of transporting soybeans to market. Energy makes up a significant part of operating costs for most crops. This is especially true when considering indirect energy expenditure on fertiliser because the production of fertiliser is extremely energy intensive, requiring large amounts of natural gas.

10) Speculation by funds

Over the short term at least the positions held by large commodity funds can have a significant impact on soybean prices. If positions in the soybean futures market become extremely overbought or oversold then, as in other commodity markets news that counters this view can result in a sharp upswing in price volatility in the market.

Previous episodes

Gold prices: The top 10 most important drivers

Silver prices: The top 10 most important drivers

Natural gas prices: The top 10 most important drivers

Copper prices: The top 10 most important drivers

Livestock prices: The top 10 most important drivers

Sugar prices: The top 10 most important drivers

Cocoa prices: The top 10 most important drivers

Palladium prices: The top 10 most important drivers

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Firming up fuzzy forecasts

What can the forecasting profession do differently? Well, like most things in life, change will only happen if you demand it. Sharp words about failed predictions are essentially forbidden where they are most needed. So for all the incentives pushing experts to pump up their predictions, there must be a countervailing incentive to tone it down. In reality, there is little accountability for predictions, and while big calls that go bad should damage the reputations of those who make them, they seldom do.

Given how pervasive and influential commodity price predictions are, there is a surprising lack of data into how accurate the forecasts have been and which forecasters have the best track record. According to Philip Tetlock and Dan Gardner – authors of “Superforecasting: The Art and Science of Prediction” – there is a lack of accountability when it comes to financial forecasts. “Every day, the news media deliver forecasts without reporting, or even asking, how good the forecasters who made the forecasts really are,” say Tetlock and Gardner. They continue:

Every day, corporations and governments pay for forecasts that may be prescient or worthless or something in between. And every day, all of us – leaders of nations, corporate executives, investors and voters – make critical decisions on the basis of forecasts whose quality is unknown.

Governments, businesses, investors and individuals don’t demand evidence of accuracy before deciding whether to accept and act on a prediction. Forecasts are routinely made but the results are almost never tracked. As noted earlier, prominent forecasters build reputations not because of their accuracy but because of their skill at telling a compelling story with conviction.

Predict if you want, and rely on predictions if you really need to, but keep a tally of the predictions. Although my review of oil price forecasts over the past ten years was relatively easy to carry out, many of the predictions that pundits make are much more difficult to gauge. Part of the problem is the fuzzy language in which many predictions are often expressed, making it difficult to tell if the forecast was right or wrong even after the event.

Karl Popper famously made the observation that the usefulness of a prediction was related to its potential for falsification. “It will rain in London in the future” is a statement that is 100% accurate, but useless when it comes to telling us which day we should carry an umbrella. A statement that it will rain at 10.30am tomorrow is much more useful; it will rain or it will not. If it doesn’t, then we can examine what assumptions were used in the forecast that turned out to be false.

Forecasts are often expressed using ambiguous words like probable, possible and risk, for which there are no agreed definitions, making it impossible to score them afterward. The US Intelligence Community has famously struggled with the lack of precision in the meaning of words that are commonly used to express likelihood and chance since the 1960s. Sherman Kent, often described as the “father of intelligence analysis”, was a CIA analyst that recognised the problem of using imprecise statements of uncertainty. Particularly, Kent was jolted by how policymakers interpreted the phrase “serious possibility” in a national estimate about the odds of a Soviet attack on Yugoslavia in 1951. After asking around, he found that some thought this meant a 20% chance of attack, while others ascribed an 80% chance to the phrase. Most people were somewhere in the middle.

Investor and author Michael Maubossin recently posted a survey to gauge how people view probabilistic language. Take ‘real possibility’ as an example. To some people this meant a probability of just 20%, but to others it meant an 80% likelihood. Next time you hear someone say there is a ‘real possibility’ of this or that happening to a particular market you know they are just trying to make a forecast that no one can hold them to account for.

Remember, hits and misses don’t come with labels. It’s often a matter of perception whether a forecast is deemed to be a hit or a miss, which makes language important. The more ambiguous the wording is, the more a pundit’s prediction can be stretched. And since we want hits, that’s the direction in which things will tend to stretch. As Dan Gardener describes in his book “Future Babble”:

When the notoriously vague Oracle of Delphi was asked by King Croesus of Lydia whether he should attack the Persian Empire, the oracle is said to have responded that if he did he would destroy a great empire. Encouraged, the king attacked and lost.

The same confusing probabilistic terminology are used by pundits, and are then often also used by many in the financial media to imply something is much more likely to happen than it actually is. We saw an example of that in a previous chapter when Goldman Sachs suggested that because of limited spare capacity oil prices “could lead to $150-$200 a barrel oil prices” – many in the financial media interpreted “Could” as “Will”.

In other cases, relatively specific forecasts are matched with an unspecific time frame, which also makes it difficult to score them for accuracy. There is a maxim among professional analysts that cynically confirms the problem: “always predict a price, or a time frame, but never both”. However, in recent years, some commodity market forecasters have been pushed to quantify their forecasts by making specific price predictions over specified time horizons. Many have also embraced uncertainty by offering forecasts in the form of a probability distribution, rather than a point estimate, which is a much more useful and realistic way to think about the future. Commodity market forecasters are catching up with weather forecasters and the US Intelligence Community in trying to estimate the likelihood of a whole range of outcomes, not just the central one.

Percentage forecasts are an important step forward, but the commodity market is still lagging behind in terms of measuring forecast accuracy after the event. The problem with percentage forecasts is working out whether they were accurate even in retrospect. Tetlock and Gardner call this problem “being on the wrong side of ‘maybe’”. To understand the problem, imagine a weather forecaster who says that tomorrow there is a 70% chance of rain. The forecast also implies there is a 30% chance it will not rain. If it doesn’t rain, the forecast was not necessarily wrong in a statistical sense but it is still likely to be criticised by anyone concentrating on more than just the most likely outcomes.

Although assigning probabilities to particular scenarios is an improvement, it is far from being a panacea. It can give an impression of faux certainty, that all possible outcomes are knowable in advance and have been captured by the forecast. Known as “Knightian uncertainty”, probabilities cannot be assigned to different outcomes because the existing distribution of possible outcomes is unknowable.

The danger with faux certainty is that it might lead market participants to believe something is more likely than it actually is. Consumers of forecasts might think that all of the possible outcomes have been captured by the commodity forecaster and then act based on this supposed “evidence”. Remember, pundits see uncertainty as something that threatens their reputation. Often forecasters will make assumptions in their models that lower the perception of the degree of uncertainty in future prices.

Again, the solution to firming up fuzzy forecasts is to track performance over time. This would weed out those forecasters unable to capture the range of possible outcomes accurately and the over-confident from the accurate. Meteorologists pioneered the solution to the probability forecasting problem and the solution was published by Glenn Brier of the US Weather Bureau in 1950. Brier published a careful methodology for comparing a set of forecasts expressed as probability distributions with eventual outcomes, and scoring forecasters on a standard scale from zero (complete accuracy) to 2.0 (perfect inaccuracy). The most accurate forecaster is the one whose forecast probability distributions get closest to the distribution of actual out-turns over time. If a forecaster predicts there will be a 70% chance of rain, they should be correct approximately 70% of the time.

Verifying accuracy is obviously much easier for weather forecasts, where thousands of fresh forecasts are issued every day and can be compared with thousands of outcomes. Verification is more difficult for subjects like commodity prices but, given how frequently prices are forecast, it is not impossible and would be highly desirable.

Brier scoring price forecasts could also bring important benefits for commodity markets. The aim would not be just to identify the most accurate forecasters, those most worth paying attention to, but improving the accuracy of all forecasts by subjecting them to rigorous analysis after the event. Weather forecasts have improved enormously over the last fifty years because they have been subjected to rigorous analysis. It is far less obvious that forecasts for commodity prices and other financial markets have become any better.

In “Black Box Thinking”, Matthew Syed describes how the airline industry actively promotes the sharing of mistakes and failures in order to help propel the safety of the industry forward. Other professions are not so great at looking at past mistakes, learning from them and improving their processes and models to make the future better. Syed describes how psychotherapists gauge whether their treatments are effective, not by observing the patient with objective data over a long period of time, but by observing them in the clinic. As well as being prone to all kinds of biases, both from the patient and the psychotherapist, there is no feedback on the lasting impact of the treatment and hence no opportunity to learn – from success and from failure.

There is no reason why learning from mistakes and failures should not be part of the job description of the analysts involved in commodity forecasting. Capturing data is not a problem (whether that be prices, futures market activity, etc), information on market events are well publicised (for example, changes in interest rates, currency movements or political instability). All it needs is a change in attitude.

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The biggest misconception people have about economics

That economics is all about forecasting!

Its not.

After the 2008 financial crisis, the Queen famously asked LSE academics “Why did nobody notice it?” However, she should have elaborated and asked: why have economists failed to predict every crisis in the past 150 years? With their use of complex mathematics and some of the best brains, surely they should be doing better than this?

“Nobody has a clue,” Jan Hatzius, Goldman Sachs’ chief economist, said to Nate Silver in the book “The Signal and the Noise”. “It’s hugely difficult to forecast the business cycle. Understanding an organism as complex as the economy is very hard.”

According to Hatzius, forecasters face three fundamental challenges: first, it is very hard to determine cause and effect; second, that the economy is always changing and, third, that the data that they have to work with is pretty bad.

With that in mind, always remember the following two quotes from the economist J. K. Galbraith:

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


“The only function of economic forecasting is to make astrology look respectable.”

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