From the December 01, 2009 issue of Futures Magazine • Subscribe!

Shocking profits in new markets


History has ample evidence of crude oil shocks, which also tend to be a recession predictor and have a utility beyond a mere trading vehicle. Spikes in oil correlated with nine out of the 10 recessions since World War II and they are a leading indicator for peaks in unemployment.

In October 1988, prices were at $11 per barrel and jumped to $22 in December 1989, just about 8% per month. In 1998, prices were at a low of less than $11 in June. They increased through September 2000 to more than $35 per barrel, 218% in just over two years, an average of 8% monthly. In 2006, oil prices rose from $58 to $77 in roughly seven months, only a 5% increase per month and beneath the level of a historic recession shock. In 2007, oil hit a bottom of $49.90 on Jan. 31, 2007. On Jan. 4, 2008, crude hit $100 per barrel, a monthly increase of 8.3%, which is the excessive run to predict a recession start. Price declined 14% over the next 21 days. Oil topped in July 2008, just less than $148 per barrel, then crashed to $33 per barrel in less than six months and then bounced to $80, a typical oil shock followed by a crash and a rebound.

Again, begin with the simple cattle model, using band triggers of 9.4% sell and -7.5% buy. Starting in January 1984, the basic model produces 11 wins and two losses for a respectable gross gain of 193.39 points in 21 years. A one-year momentum model with a retracement buy trigger at -35% and a sell level of 80% has just six trades, all profitable, for 328.77 points (see “Momentum in crude”).

Admittedly, it takes 23 years to make 328 points, but that is still an average of $14,295 per year. Even allowing $500 slippage per contract, the model realizes roughly $324,000 net profit with little drawdown.


Lumber is another commodity that’s highly correlated with the business cycle. Applying the cattle model to lumber using bands of plus or minus 8.4% produced a gross point gain of 926.10 points from January 1993 through June 2009, roughly $101,000 from nine winning trades. Using the hog model with optimized retracement triggers for shorts of 55% and longs of –32.75% produced four winning trades for 1,431.80 points, or $157,000 with less drawdown. Adding the extra trading contract with a $150 profit target boosted the gross gain to 2,051.10 points, about $225,000 (see “Lumbering profits”). Both long contracts are open with a closing price of $188.00 on July 24, 2009. The profit target is $297 for the trading contract.

Lumber is an excellent predictor of the business cycle. The January 1997 sale coincided with the high GDP reading of 4.5% and the September 2000 low preceded the official end of the recession. Similarly, the 2004 sales coincided with the high gross domestic product reading of 3.6% and the February 2009 low may be an early sign of the end of the 2008 recession.

Soybeans are attractive, with active 5,000 or 1,000 bushel contracts. The basic cattle model produces 2,648 points of gain with five profitable trades from April 1987 to June 2009. Bands are set at 9.7% and -8.4%. The hog model does better using penetration bands. A long trade is signaled when the statistic penetrates -31.5%; a short trade when the statistic penetrates 70.25%. However, unlike the other commodities we have modeled, do not re-enter the trade on the same side if the statistic trades beneath the band and then above it again. Instead, put on a subsequent position only if the statistic penetrates -50% for longs, or 100% for shorts, indicating a massive blowout. Aggressive traders can place a duplicate extra trading contract at the initial signal and exit on a profit target of 100 points. As a result, we gain 3,654 gross points (see “Sweet on beans”). The remaining long bean contract is open and profitable.

It is clear from the above examples that price shocks make excellent reversal points for the market and a model can be constructed to profit from these on a long-term basis. Consider testing other commodities for similar profitability. Although we have examined only monthly data, it is possible similar methodology could be applied to a five-minute chart for the day-trader seeking to take advantage of sudden aberrations in price.

Arthur M. Field has a Ph.D. in management science from Clemson and a J.D. degree from Rutgers. He is a former commodity broker and was co-director of Fidelity’s Pacific Fund and an in-house commodity fund. He wrote “Mastering the Business Cycle and the Markets.”

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