Managing Farm Risk Like a Professional

By Dylan Sather, 03.27.2017

Since these tools aren’t widely used by laypeople yet, there isn’t a lot of public data on how well these models perform for commodity crops. In the post, The Chances of Sub-$3 Corn (And How to Reduce Them), we described how these models work in theory; in this post we’ll examine how well they can actually predict the future.

To help you understand how all this works, let’s start with a poker analogy. If someone told you the chance of drawing a three-of-a-kind was about 2%, how would you verify that? You could play thousands of hands with your friends, counting the times a three-of-a-kind was drawn, and you’d get a number close to 2%. Sounds easy! It turns out we can calculate the odds of $4 corn in much the same way: for each day of market data, we make predictions on the final price of corn and compare that to the *actual* final price.

Unfortunately, doing this isn’t as easy as playing a night of poker. With poker, the deck never changes. There’s one fundamental difference with crop prices: since the price of corn changes every day, the odds of $4 corn change with it. To deal with this, we’ll change our focus from a specific price to a specific **risk level**: in other words, we’ll answer the question, “when our model says the chances of something is 10%, how often does that actually happen?”. The chart below can help you visualize how these predictions look over time.

The blue line – the “Predicted 10% Risk” – shows one of our price predictions over time. If the final price is above that range close to 10% of the time, we’re getting the outcome we predicted. It’s like we’re drawing as many three-of-a-kinds as we expected we’d get.

But this chart shows us only one year of predictions. It’s like trying to guess our odds from a few hands of poker – we might not draw a single three-of-a-kind and think our odds are 0%. We need to validate this over many years. Over the long term, if the final price is above the 10% risk line close to 10% of the time, this suggests the model is pretty accurate.

We ran the same analysis for every corn future from 2001 to 2014 – a full 14 years of trading data. Just like we did for the 10% risk level, we assessed our accuracy for other risk levels, too. How often were prices above our predicted 5% risk level? 6.3% of the time. What about our predicted 20% risk level? Prices ended up above it 20.2% of the time. How well did the model fare for corn across all years of data? **Surprisingly well**. We summarize the accuracy of our model in Figure 2.

Predicted Risk Level | Measured Outcome |
---|---|

5% | 6.3% |

10% | 11.3% |

20% | 20.2% |

80% | 84.3% |

95% | 97.3% |

99% | 99.7% |

The folks over at Farmdoc have turned this model into a tool that answers our original question: what are the odds you’ll have a profitable season? Enter a price and you’ll see the chances that corn will end below it. If you can assess your chances of a profit, you can make more informed decisions on crop insurance, hedging, and other mechanisms to limit risk. Learn the 3 Practical Farm Risk Management Strategies from our previous blog.

Use these tools to make more informed risk management decisions – plan your farm like a trader would.