Todd's Take

One of the challenges of writing daily market comments is the urge to try to make sense out of all the market's ups and downs as if there should be some logical explanation for everything that happens. Two weeks ago, soybeans went up because of flooding concerns in Argentina. Since then, soybeans went down, partly because the sun came out and the weather stayed drier.

Sure, there was more to it than that and we at DTN try to help our customers understand all the bullish and bearish factors at play. But the truth is that, for many days, there are no obvious reasons as to why prices squiggle up or down. As I often say, markets are people and their behavior doesn't always have to make sense.

Omaha's famous investor, Warren Buffett, went one step further. His 1987 letter to Berkshire Hathaway shareholders told the story of Mr. Market, a creation of Buffett's teacher, Benjamin Graham.* Mr. Market was described as a poor, unstable fellow with incurable emotional problems. It was Graham's way of encouraging students not to be so trusting of every price move. As Graham saw it, irrational moves were investment opportunities.

On the other hand, you may have noticed that here at DTN, we pay a lot of attention to the price trend because it happens to be one of the best indicators of future prices. The soybean rally in the spring of 2016 is a good reminder of how rising prices became their own best clue at a time when many were heavily bearish about soybeans' fundamental outlook. Woe to the trader that ignores the price trend.

As I pondered the conflict of price moves being both crazy and meaningful, it dawned on me that there might be a way to separate the wheat from the chaff, so to speak -- or, in this case, separate the underlying trend from the chaotic noise. It involved the use of moving averages.

Moving averages are well-known tools in the technical trade and have a variety of uses, but for our purposes, I had a simple idea in mind. Since a moving average represents a compilation of prices over the last X number of days and shows calmer, more deliberate movements, why not simply note the slope of the moving average as being indicative of the trend?

I had to specifically define the slope, so I put a 10-day moving average on the moving average being tested. That's right, a moving average on a moving average to tell us whether the original moving average was rising or falling.

Measuring noise came from recording the distance that prices traded either above or below the moving average in percentage terms. With DTN's ProphetX software on one side and my Excel spreadsheet on the other, I crunched the numbers.

Using continuous data for January soybeans from 1995 to the end of 2016, I tested the following condition: Theoretically, buy one contract of January soybeans when its moving average turned higher and hold the contract until the average turned lower.

Once we started this hypothetical exercise, we were always in the market, either long or short. I initially tested the idea with 10-day, 25-day and 50-day moving averages. Before I give the results, it is important to note that the point was not to create a trading method. The aim here is to see if the slope of a moving average can prove itself as a valid indicator of trend, and if so, find out what period worked best.

Both the 10-day and the 25-day averages proved useful over the past 22 years, but the 25-day had the upper hand with a hypothetical profit of $72,025 on 183 trades versus $44,750 on 325 trades for the 10-day average (no slippage or commissions were included). The slope of the 50-day average proved too slow to keep up with changing prices and showed a hypothetical loss of $48,300 on 132 trades.

The other benefit of this research came from measuring the noise. When the distance of January soybean prices was measured in terms of percent above or below the 25-day average, the result turned out to be remarkably consistent.

In what looks like an electrocardiogram of the market's beating heart, price deviation from the 25-day average averaged roughly zero with a standard deviation of 4% above or below the moving average. Two standard deviations, which by definition are reached only 5% of the time in a normal distribution, meant that it was uncommon for prices to trade outside of an 8% boundary around the 25-day average.

Using 2016 as a recent example of how this research helps, January soybeans' 25-day moving average turned higher in mid-March and lower in mid-July -- two very good signals that producers could have benefited from. There were only nine daily closes outside of the plus or minus 8% boundary all year and, true to form, prices did not stay outside the range for long.

Here at the end of January, we are in a time of year when prices are more known for noise than trend. The slope of the 25-day average is pointed higher, and January soybeans at $10.12 on Monday morning are near the middle of their 8% boundaries, sitting at $9.29 to $10.90.

Fundamentally, the outlook for soybeans has plenty of bearish risk with Brazil's approaching harvest and possible trade wars brewing with China and Mexico. No one knows yet how the year will play out, but I for one, will be paying attention to the market's trend and find it useful to have a tool for keeping an eye on the noise level.

* Warren Buffett's explanation of Mr. Market found at: http://www.berkshirehathaway.com/…

Todd Hultman can be reached at Todd.Hultman@dtn.com

Follow Todd Hultman on Twitter @ToddHultman1

(AG/BAS)

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