Sterling Terrell

Heart attacks are hard to diagnose.  Or, at least that is what I have been told.

Conventional wisdom would say that when trying to predict a heart attack, like most things, the more information the better.

What is the patient’s age, weight, height, race, medical history, current medications, test results, etc?

Sometimes, however, the more information we have, the more we get distracted – and we end up making poorer decisions.

Statistician, Nate Silver calls this overload of information, the “noise.”  He makes the point that in today’s world of big data, the difficult task is to separate out the “signal” from the “noise.”  Basically:  What information matters?  And what information doesn’t?

Silver’s latest book is aptly titled:  The Signal and The Noise:  Why So Many Predictions Fail – but Some Don’t.

This overload of information is also the case with predicting heart attacks.  Writer Malcolm Gladwell tells the story of Cook County Hospital in his bestselling book:  Blink: The Power of Thinking Without Thinking.

During a Q&A on this issue, Gladwell had this to say:

One of the stories I tell in Blink is about the Emergency Room doctors at Cook County Hospital in Chicago. That’s the big public hospital in Chicago, and a few years ago they changed the way they diagnosed heart attacks. They instructed their doctors to gather less information on their patients: they encouraged them to zero in on just a few critical pieces of information about patients suffering from chest pain–like blood pressure and the ECG–while ignoring everything else, like the patient’s age and weight and medical history. And what happened? Cook County is now one of the best places in the United States at diagnosing chest pain.

So what do predicting cotton prices and predicting heart attacks have in common?

Exactly what I have already implied:  More information is not always better.

For example, what information should I be looking at and studying to predict, or explain, cotton prices?

• Stock/Use ratios by country
• Production by country
• Consumption by country
• Beginning stocks by country
• Import/Export by country
• Government interventions by country
• Volume/open interest/cash sales
• Weather
• Alternative prices
• Inflation
• Land valuations
• Exchange rates
• Shipping rates
• Energy costs
• Etc.

That list looks like a good place to start.

At the same time, the list above also looks like an awful lot of information to get in front of in order to get an edge on the direction of prices.

If I was asked to build a model of cotton prices, I would no doubt use the data above, as well as anything else obvious that I found descriptive.

But is there a simpler way? A way that does not give most of us a headache?

Is there a variable that anyone can easily follow that would give us insight into all of the available information on cotton?  A variable that allows us to tune out the noise, and focus on what matters?

In fact, there is such a variable:  It’s the price!

Every variable listed above, current and expected, as well as other variables we have not even thought of are factors that are influencing and constantly setting the current, and futures, price of cotton.

By using the price of cotton to forecast the price of cotton – a simple prediction rule might be:

• If cotton prices are moving higher – we predict higher prices.
• If cotton prices are moving lower – we predict lower prices.

Traders call this prediction method: “Trend-following.” Trend-following trading, however, is not prediction at all though.

Like the name says:  It’s following.

In this method you are simply saying:  “I have no predictions or ideas or preconceptions about where the market is going.  But wherever it goes, I will follow it.”

Just to show the idea, let’s make a few imaginary predictions (post-hoc) using only the price of cotton as our indicator.

If the green line (the 15 day moving average of prices) is above the red line (the 30 day moving average of prices), we predict that prices will go higher.

If the green line is below the red line, we predict that prices will go lower.

For a few more examples, what about corn, and wheat?

Source: barchart.com

Source: barchart.com

Over the last year, if you followed a trend-following rule, like the one above, you probably would not be wrong very often – and when you were wrong, you would not be wrong by much.

Trying to stay informed is great, but trend-following is immensely easier than stressing ourselves about the onslaught of information out there every day that so easily overwhelms us.

Good-grief, you would have begun predicting lower oil prices beginning in August of 2014 (below).

Trend-following trader, billionaire, and owner of the Boston Red Sox, John Henry had this to say:

I don’t believe that I am the only person who cannot predict future prices. No one consistently can predict anything, especially investors. Prices, not investors, predict the future. Despite this, investors hope or believe that they can predict the future, or someone else can. A lot of them look to you to predict what the next macroeconomic cycle will be. We rely on the fact that other investors are convinced that they can predict the future, and I believe that’s where our profits come from. I believe it’s that simple… when I was designing what turned out to be a trend following system…[that] approach–a mechanical and mathematical system–has not really changed at all. Yet the system continues to be successful today, even though there has been virtually no change to it over the last 18 years.

I’ll take his word for it.

And you should too.

[Note:  The connection between trend-following trading and the book “Blink,” by Malcolm Gladwell was first introduced to me by author Michael Covel in Episode 44 of his popular podcast, available on iTunes.]

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