At any given time, the price of a real commodity (such as the meats or grains) depends upon numerous independent variables. Cocoa prices are a product of the weather in Africa, the cost of shipping, government policies, the availability of substitute crops, pests, consumption levels, etc. Essentially, all of the various factors can be grouped as those affecting supply or those affecting demand. In normal times, price remains relatively stable as the supply and demand factors cause price equilibrium.
Periodically, an extremely unusual and unexpected event will occur. A hurricane or brutal freeze hits Florida, wiping out the orange juice crop. A war in Kuwait cripples the supply of oil. Prices soar. Swine flu hits Mexico, causing demand for hogs to irrationally evaporate. Prices drop dramatically.
If you’re on the right side of these shocks, you can profit immensely. Hog futures crashed from a high of 88.975¢ per lb. on Aug. 8, 2008, to 53.90¢ on Nov. 5, 2008. This is a 35¢ plunge in less than 90 days — a loss of 39% of value, or roughly 1% every two days. One contract of lean hogs is 40,000 pounds; hence, every penny price change represents a gain or loss of $400. A speculator who went short at the high and covered at the low would have earned $14,030 gross, or about $160 per day. Given the extremely low initial margin for hogs, this is an astronomical annualized return and something traders dream about.
Of course, it is easy to discern these points in hindsight. Wishing to know about them in advance causes us to fantasize about crystal balls. As technical traders, we try every possible method to find the philosopher’s stone, that mystical key to unlock the secrets of future price action. However, all of our neat statistical tools have little basis as determinants of future price activity.
A statistic, such as a moving average, is nothing more than a mathematical transformation of data points. A five-day moving average is just a method of representing five data points in a single number. A regression line is the line that best fits historical data; it reduces the total squared distance from each point to the line. There is no magic about it. Statisticians and technicians believe price will continue to fall in some reasonable range around the regression line. However, statisticians will caution you that such belief only holds over a short time period and assumes no fundamental underlying changes in the
That is the key. We know that life does include fundamental changes in variables. Traders see this all the time. Yet, prices of the real commodities eventually deal with the outrageous event. After some period of reaction to the price, the factors of supply and demand inevitably resume their inexorable forward motion and price eventually returns to stability.
Looking at the data in “Extreme hogs” the average price is 59.17¢. The net change totals 423.48 gross points, or an average swing of 38.5 points between peaks and valleys in an average of 13 months. However, the averages do us little good, and the data demonstrate the difficulty with cycles based upon fixed time or fixed price.
TRADING OFF SHOCKS
Periodic shocks to price are reliable indicators of trend reversal. For example, in July 2002, hog prices were trading around 55¢, right near the average price. Only one month later they plunged to close at 30¢, a 36% drop. By May 2003, prices had climbed back to the average of 59¢. In economic theory, it makes sense that a huge price drop will cause a tightening of supply that will eventually drive prices back up.
Indeed, price shocks are excellent trading tools for real commodities. The assumptions of this approach are:
1) price tends to move sideways much of the time;
2) periodically price shocks to the upside;
3) this will be followed by a gradual drop in price;
4) periodically the price will “shock” to the downside as a balance.
It is less frequent, but possible, to have a repeat shock in the same direction without the intervening opposite shock. But, in general, price acts like a pendulum swinging back and forth, and occasionally wildly out of control.
Our goal: develop reasonable trading rules for each commodity to mathematically, and mechanically, evaluate the shock. It’s also important not to enter the market prior to the completion of the shock to avoid getting caught in the maelstrom.
Starting with live cattle, we can develop and introduce simple and reliable trading rules that can be adapted to other commodities. The simpler a system, the easier it is to implement and more robust it will be.
Begin by recording the monthly closing price for the nearest futures contract. Then calculate the three-month moving average of momentum price change statistic, expressed as a percentage:
For live cattle, include indicator bands of plus or minus 4.10%. If the three-month moving average of momentum registers outside such a band, it will provide a trade warning indicative of a price shock. As long as price continues to follow this band, we sit on the sidelines. Once it reverses, indicating the shock has subsided, a trade is signaled.
In May 1994, the indicator falls to -5.1% and then continues dropping in June 1994, to -5.7% (see the top chart in “Shock therapy” ). In July 1994, it recovers to -1%. It is this recovery that triggers a buy signal. We buy one live cattle contract at the July 1994 closing price of 68.65¢ per lb. Cattle trades up to 74.27¢ as of the close of February 1995, but our indicator only reaches 2.7%, so we continue to hold long. Price falls back to 61.02¢ in May 1995, having given up 7¢ in March. Unfortunately, we watch our 6.5¢ ($2,600 profit) turn into a drawdown of $2,800, as the indicator drops to -6.3%. But in June 1995, prices firm, and the month closes at 64.35¢, with the indicator climbing back to -1.4%, causing a second buy and a long position of two contracts.
A more defensive trader could ignore the second signal, or elect to use a trailing stop to lock in profits, but this is not included in the initial model. We wish to demonstrate the simple, raw form that includes all drawdowns.
In April 1996, price falls to 57.35¢, a 9.6% drop from its prior month close, producing a significant drawdown in the two-contract position. However, price reverses and shoots straight up to close at 72.20¢ in August 1996, sending our indicator soaring to 5.5%. The following month, price continues slightly higher to 73.32¢, but the indicator falls to 3.8%, triggering a sell of longs and reversal into a short. We close out two contracts for a total profit of 13.44¢ before slippage and commission, or $5,376 in just over two years.
Our fortitude in enduring a drawdown of almost $7,300 was rewarded by a profit, and a new trade, which has no drawdown at all. We sell at the high close of 73.32¢ and watch price drop consistently through July 1998, when the close falls to 58.87¢ from 65.02¢ in June, a 9.5% drop in one month. This sends the indicator down to -5.3%, setting up a potential buy signal. The next month, prices trade sideways, closing virtually unchanged at 59¢ and causing a cover of the short and a reversal to long. This is a realized profit of 14.32¢, or gross profit of $5,728 in 23 months. The trade was profitable during all 23 months.
Good fortune smiles again. The purchase is at the bottom, and live cattle trades higher, rallying to 68.02¢ in February 1999, before pulling back to 62.62¢ in June 1999. The February close was 9% higher than January, but the moving average statistic was only 2.7% — insufficient to generate a sell warning. Hence, we remain long.
In December 2000, cattle prices soar to 77.92¢, causing the indicator to increase to 4.2%, generating a sell warning. In January 2001, prices trade sideways and close at 77.55¢, dropping the indicator to 2.3%, a sell signal. This earns 18.55¢ in 29 months.
The period from February 2002 to August 2004 is shown in the bottom chart in “Shock therapy.” The worst drawdown, 25.50, occurs in August 2008.
Admittedly, this is not a get-rich- quick methodology. However, the table “Slow profits” reflects all of the trades since 1994. The method has 10 closed trades over a 15-year period, for a total gross profit of 181.50¢, or $72,600, with 10 winners and 0 losers. The smallest gain was $4,380 in four months. The largest gain was $9,640 in one year. The average gain was 18.15¢, with a standard deviation of 5.57¢. Assuming cost for slippage, rolls and commissions of $100 per position, and counting a total of 17 actual order entries, the method still produces a net profit of $70,900. This is an average of $393 per month over 15 years after costs.
Of course, this assumes trading the nearest futures contract, and there will be the need to roll forward periodically in some of the longer-term trades. Hence, we should add a $250 per trade slippage fee to be extremely conservative. Even doing so reduces the $72,600 profit to $68,350, or $4,557 profit annually.
This kind of trading isn’t for everyone. Indeed, holding a single position for two years may be anathema to the typical trader’s personality. But a method with such a high success rate, even with an admittedly small sample size, is worth attention, even if it requires patience. Think of it as investing, rather than trading. Of course by applying this method to a group of markets, you can see more opportunities and perhaps lower overall volatility. Next month, we’ll expand this concept to other markets.
Arthur M. Field has a Ph.D. in management science from Clemson University and a J.D. degree from Rutgers. His undergraduate degree is in statistics. He is a former commodity broker with A.G. Edwards and was co-director of Fidelity Int’l’s Pacific Fund and in-house commodity fund. He is the author of “Mastering the Business Cycle and the Markets.”