Most of the randomness we see outside of financial markets fits nicely under a bell curve. Things like flipping a coin, rolling dice, human height, and atomic motion all fall neatly under bell curves. The variation is neat and regular. Individuals are difficult to predict, but large groups will display consistent characteristics. If we roll a six-sided die 100,000 times; we'll come very close to observing 16,666.67 of each number. Of course, it won't be exact. Still, it is possible to calculate the probability of any outcome before we start rolling.

Randomness is a strange animal because exposure plays a part. If we flip a coin four times, there is a 1-in-8 chance to see all heads or all tails. Interestingly, if we repeat sets of four flips five more times, we will be more-likely-than-not to see at least one group with all heads or tails. If we continue repeating four flip sets, we will be increasingly likely to see a consistent group. Of course, we will never reach absolute certainty; however, we will get closer and closer to it. One of my high school math teachers used a colorful example to demonstrate this concept. Assume a teenage boy and teenage girl are sitting at either end of a park bench. Every few minutes, they close the distance between them by half. He would quip, "In theory, they'll never touch, but eventually, they will be close enough for practical purposes."

If we increase the number of consecutive heads or tails for which we are looking, the probabilities drop quickly. If we are looking for ten-out-of-ten flips to be the same side, the likelihood drops to 1-in-512. It's doubtful any of us has the time or desire to repeat sets of ten coin flips long enough to find a collection of all heads or all tails. The result is that low probability events lie on the edge of or beyond our experience. We generally don't see them - until we do.

Take, for example, the Thanksgiving turkey. He shows up at the farm as a baby chick in July or August. Every morning for the next 90-120 days, the farmer arrives with a bucket of feed. For the turkey, his morning routine is always enjoyable. Every morning his breakfast appears; he eats, then spends the rest of his day lazing about the yard. The turkey grows comfortable with mornings, even looks forward to them. Then, one November morning, the farmer arrives, and things are about to change. Instead of a bucket of grain, the farmer is holding a butcher knife. The turkey is about to experience a low-probability, high-consequence event.

Just because we have not yet experienced a particular event does not mean that event is impossible or even unlikely. Like the coin flip example in the second paragraph, we become likely, nearly guaranteed, to see low-probability outcomes when given enough repetitions. Standard risk models take into account probability, exposure, and consequences. Quality risk management should address each of these factors. Ignoring high-consequence events simply because they seem unlikely is a recipe for disaster.

Market returns don't always fit nicely under a bell curve. Worse, we can't know the distribution of those returns until after-the-fact. Markets are much more difficult to predict than sets of coin flips. That knowledge is part of the foundation on which we build our risk management strategies. Our flagship program for grain and soybean producers, Quartzite Precision Marketing (QPM), doesn't try to predict markets. Since we can't know the probabilities ahead of time, we work with our QPM clients to manage exposure and consequences. We are experts in measuring and managing risk. Contact us today for more information.