How to Manage Risk in Crop Marketing When Black Swan Events Happen
This crop year has witnessed an unprecedented number of rare occurrences, from trade wars to historic flooding to a global pandemic. Economists will often label such truly exceptional, once-in-a-lifetime market events as black swans.
What makes black swans especially challenging? For one, they are nearly impossible to predict. Imagine yourself six months ago trying to forecast a global pandemic that would cause the widespread health and economic consequences we’ve seen with COVID-19.
But they also tend to distort markets to such a wide degree that the altered landscape seems surreal, and as human beings we expect a return to normal. It is precisely this “waiting for normal” mentality that can impact our decision-making ability.
Crop marketing is challenging for many farmers, even under the best of circumstances.
Determining how much grain to sell—and when to sell it—is generally not an easy decision. You can read charts, scour the news and listen to pundits, and at the end of each day you may forgo a decision or tentatively agree to sell if the market offers you slightly less or more than you anticipated.
But what unfortunately tends to play out is this: The pricing decisions on which you pass today are forced upon you later when loan payments, mounting bills and storage constraints demand—and all too often in a lower price environment than before.
When faced with commodity price uncertainties, we believe one of the best approaches to smooth out the gyrations of markets and contain some of the emotional aspects is a disciplined, consistent marketing strategy over the course of the season.
But while that approach seems prudent in theory, how do you actually implement it on your farm? After all, it is impractical to make sales every day—or even every week. And even if you adopt a monthly or quarterly sales strategy, are there some time periods when it is historically better or worse to sell, implying you should adjust how much you sell?
This is the key element of FBN HedgeCommand, which presents each farmer with a customized pricing strategy over the marketing year that dynamically responds to changing market conditions.
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What all goes into FBN HedgeCommand?
Two factors inform HedgeCommand’s recommendations. It begins by taking into account a historical understanding of market trends and probabilistic moves at different times of the year. Then, it considers your existing contracts, trades, input costs and crop insurance coverage to help guide your selling decisions going forward.
Have crop insurance that offers risk protection tied to futures prices? That’s factored in because insurance acts as a hedge against market losses. Use futures and options? That, too, goes into consideration before HedgeCommand generates a tailored sales roadmap specific to your operation.
But HedgeCommand is not a static roadmap from year to year. It has built in dynamic responses to market moves in critical ways.
First, because HedgeCommand uses historical data to generate recommendations, it makes economic judgments to speed up sales at price levels that reflect a higher margin as well as scale back sales when potential gains are lower.
Second, FBN analysts are continuously feeding their real-time model assessments about odds and probabilities about price direction to either ramp up or slow down the sales pace.
HedgeCommand and the self-driving car
To understand the smart-assistant style of a marketing program like HedgeCommand, let’s turn to another innovation backed by data and models: the self-driving car.
Self-driving cars are informed not by man-made rules about how fast or slow to drive but instead by millions upon millions of real-world driver observations. It is these observations on a deep array of road conditions, driving environments and speeds that feed into models to fine tune the optimal driving response.
Does a self-driving car guarantee that you will get from point A to point B in the fastest time possible? No. That’s because there are inherent risks of speed. But likewise it also knows to manage speed to a point that assures a reasonable driving time. So it inherently balances the risk and reward of speed versus a crash.
In a similar fashion, HedgeCommand’s data-driven, model-verified algorithm guides you on a path that keeps you out of the market lows of the year, but that also means your final crop price will be below the year’s high. Like the self-driving car, it is balancing risk and reward to keep you at a comfortable and safe marketing environment.
How well does HedgeCommand work with black swan events?
So how has HedgeCommand performed in 2019? We applied our algorithm to FBN’s “model farm,” a hypothetical farm where we use FBN’s HedgeCommand system to make simulated paper trades throughout the season. This year was a good litmus test for our smart-marketing tool as it attempts to effectively navigate key events that impact corn and soy markets differently over the marketing year.
Before we dig into the results, let’s discuss the trajectory of the markets over the past year:
What is clear about the table above that narrates the price trajectory in the charts below is how different these two markets behaved over the course of the same year.
In corn, there were periods of widespread price stability surrounded by intense rallies and steep declines. But the price trajectory was much more erratic in beans, with sharp swings from highs to lows that tended to persist throughout the marketing year.
The charts above illustrate the marketing decisions made by FBN’s “model farm” as informed by HedgeCommand and our own FBN market bias.
A few events are worth pointing out:
First, the summer rally for corn was a point where the FBN model farm did not take much marketing action as the farm was sold at early prices around $4. At the time, there was a great deal of uncertainty concerning acreage and yields and a quick erosion from $4.60 to $3.60 left many unable to pull the trigger. Once the production picture became clearer, the FBN model farm continued to add to sales around $3.90.
Second, the last material sale of soybeans for the FBN model farm was above $9.50 as the U.S.-China trade deal euphoria wore off in January.
But as you’ll see in the table that follows, price readings ($3.18 for corn; $8.34 for soybeans) as of May 21, 2020, are well below where trading happened earlier in the season. Farmers holding on to sizable unsold crops face a challenging environment to get back to even the lower 25% of where the market traded for much of the season.
Meanwhile using our model farm and simulated trades, you’re able to see how HedgeCommand can help farmers weather downside risk through a prudent data-backed selling strategy over the course of the season:
Again, just as the self-driving car won’t get you to your destination in the fastest time possible, HedgeCommand does not focus exclusively on selling at the highest crop prices of the year. The risk in that approach is that if you hold out for better prices for too long, and you might ultimately find yourself selling the bulk of your grain at the market lows—an outcome you will definitely want to avoid.
Instead, HedgeCommand is able to achieve better than the daily average, and that makes a big impact when you’re working with volatile markets—and a particularly big impact when faced with the black swans we’ve seen over the past year.
Although you’ll never be able to completely eliminate risk, by following HedgeCommand’s recommendations you’re able to remain on the safest route to farm profitability.
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HYPOTHETICAL PERFORMANCE RESULTS HAVE MANY INHERENT LIMITATIONS, SOME OF WHICH ARE DESCRIBED BELOW. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN. IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN HYPOTHETICAL PERFORMANCE RESULTS AND THE ACTUAL RESULTS SUBSEQUENTLY ACHIEVED BY ANY PARTICULAR TRADING PROGRAM.
ONE OF THE LIMITATIONS OF HYPOTHETICAL PERFORMANCE RESULTS IS THAT THEY ARE GENERALLY PREPARED WITH THE BENEFIT OF HINDSIGHT. IN ADDITION, HYPOTHETICAL TRADING DOES NOT INVOLVE FINANCIAL RISK, AND NO HYPOTHETICAL TRADING RECORD CAN COMPLETELY ACCOUNT FOR THE IMPACT OF FINANCIAL RISK IN ACTUAL TRADING. FOR EXAMPLE, THE ABILITY TO WITHSTAND LOSSES OR ADHERE TO A PARTICULAR TRADING PROGRAM IN SPITE OF TRADING LOSSES ARE MATERIAL POINTS WHICH CAN ALSO ADVERSELY AFFECT ACTUAL TRADING RESULTS. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF ANY SPECIFIC TRADING PROGRAM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF HYPOTHETICAL PERFORMANCE RESULTS AND ALL OF WHICH CAN ADVERSELY AFFECT ACTUAL TRADING RESULTS.
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