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49

rqaresent mostly good weather pattems; however, markets that decline steadily during the year following a bull marfcel would also be included.

During the bad weather years, the com chart shows a prolonged rise during the primarj growing months. May through August. The study of bull years for soybeans shows a longer sustained rise from the beginning of the new crop in October into the following June. In the case of good weather and bear markets, com shows a rise fran May to June, some sidewajs hesitation fran June to July (possibly the last anticipation of bad weather), then a retum to previous price levels. For bear years soybeans have steadj declines through April and then a short rally through the summer.

The com analysis shows a critical point around July 1, when the market decides that the weather is not a factor. The soybean analjsis is completely different showing nearly the opposite pattems for bull and bear maricets. Because the resulting pattem of combining both bull and bear markets looks very similar to the bull pattem, the magnitude of the bull moves must overwhehn the overall picture. In fact, the magnitude of the bull moves are often three to four times greater than the price change in bear markets.

Updating Bull, Bear, and Nonseasonal Maricet Pattems

Both the Handmaker and ContiCommodity studies ended in the early 1980s and while grain fundamentals have essentially remained the same, the use of more data and recent years can only help understand seasonality better. Using the soybean average prices expressed as a percentage of average annual price in Table 7-9, we separated the years from 1969 to 1993 into bull and bear categories, where bull years (and bear years) had significantly higher (or lower) prices atthe end of the year than at the beginning. Table -lO, parts (a) and(b), which also give the prices as a percent of the average annual price, show that there were about the same number of years in each category The averages at the bottom of those tables indicate that both bull and bear years had a price swing of about 10°o over the year.

Bull and bear years are nearly opposite in pattem, as seen in Figure 7-8, and cross at about the same price in June. This makes the seasonal pattem using all years peak in June. As interesting as it is to know that there are an equal number of years going the opposite directions within a seasonal pattem, the bull and bear tables were created after the fact and can not be fraded unless you know in advance that a bull or bear year will occur. Instead, it is possible to look at nonseasonal years, those in which the price of soybeans was higher during the harvest months of September and October than during the previous summer months.

One important quality of crops is that each year we begin again, a shortage one year does not mean a shortage the next year. This is seen in the chart of nonseasonal years in Figure 7-8. After a nonseasonal year, prices rally early, nervous about planting and shortages.

ContiCommodity; Seasonality in Agricultural Futures Maikets (ContiCommodity Services, Chicago, 1983).

FIGURE 7-7 (a) Corn price seasonality separated into all years, years with good weather, and years with bad weather, (b) Soybeans (10 years) shown with separate bull years (5 years) andbear years (5 years).

-1-3-- -



Jan Feb Mar Apr May June July Aug S«) Oct Nm Dec Cian) (b)

but the reahty of a good crop and normal demand drive prices steadily lower during the second half of the year. The pattern is tjpically seasonal and very well defined by laie swings.

Seasonality for Trading

Whatever method is used to find the seasonal pattem, it is equally important to apply it properly when trading. Select a martlet that has a dominant seasonal pattem. The crops alwajs qualify, with the exception of potatoes, which are fully stored and do not react to the harvest depression. Livestock tends to be more complex than other commodities 180

TABLE 7-10 Bull, Bear, and Nonseasonal Martlet Pattems

(a) Bull years begin at relatively low levels, peak during the summer but remain high at the end of the year, (b) Bear years decline steadily after April and do not reflect the concems that often drive prices higher during the summer, (c) Nonseasonal years follow years in which the harvest months of September-October posted higher prices than the summer months. These years revert bad; to what we expect of normal seasonal pattems.

(a) So>-bean Bullish Years (Prices Higher at the End of the Year)



FIGURE 7-8 Soybean seasonality, a comparison of filtered years.

because of the joint dependency on both feedgrain prices as well as their own production and consumption factors. Both crops and livestock have primarily supply oriented seasonal trends, depressed at harvest and highest during the growing months-or depressed in the fall when the livestock supply becomes greatest due to liquidation before winter. Metals pattems are caused by demand, because they have no dependence on weather and production is usually constant. The summer months atill see the highest prices due to increased consumption-copper in housing and silver in photography.

Select a delivery month most likely to reflect the pattem to be traded. If a sharp rise in soybeans is expected in July, trade the August contract. Prices of the November delivery, which will also be active, will not move as much because it is both deferred and a new crop. In more exfreme cases, February pork bellies do not reflect events occurring in the previous July or August conhcts.

Exceptions occur in seasonal pattems as they do in any model There are events that may ovemiiehn normal seasonality such as runaway interest rates or inflation, rapid change in the U.S. dollar, or Soviet grain embargoes. Even though the seasonal variation still exists, the size of the price change resulting from these other factors is much greater and tends to obscure the seasonality. It is usually easj to recognize a nonseasonal pattern-prices simply go steadily up when they normally go down, or down when they are expected to go up. From 1972-1974, corn prices increased steadily with only small adjustments in Septeni)er and October. Once this variation occurs, it may be wise to wait until another season has reestablished the normal pattem.

The sjstems and methods that follow are based entirely on seasonal pattems and may be used alone or to filter other methods.



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