4 1.077I -0.31 -0.42 -088 -235 -160 034 2.20 1.43 0.52 0.99 0.07 0.0172 4.10 -342 -156 ~-043 -0.78 -1.38 -I.12 -0.06 0.47 057 = 3.23 8.587300 -25.18 -1667 ~13.26 -I1.15 6.23 25.10 15.97 16.73 3.93 -0.42 -1.85 0.57
74 ~3.16 -187  -3.20 -810 -9.14 -881 1.80 655 5.63 1183 610 2.3775 7.98 1.69 0.19 1.20 -2.71 -285 1.22 5.25 2.23 ~155 ~-545 -7.20
76 734-700 ~760 -7.58 -426 374 7.14 3.53 5.90 286 4.36 6.2677 1.25 0.41 748 «17.60 16.24 7.35 -6.01 -989 -10.31 ~9.96 -5.76 ~5.9078 -665 -741 -051 137) 3.15 1.25. -0.73 -0.78 1.20 3.53 2.64 2.94
79 -0.10 3.44 3.78 210 080 383 232 -083 -090 -373 -492 -5.8280 ~0.94 -1.24 4.79 -7.60 -7.11 -648 1.39 2.01 5.98 7.36 10.40 4.0381 10.25 7.30 5.54 7.93 475 067 0.84 -3.45 6.58 -7.19 -8.96 -11.0982 6.26 5.31 349 610 5.80 2.37 0.66 -3.69 -6.54 -8.14 -5.53  -61183 ~8.32  -7.86 -7.86 4.92 ~7.08 ~9.78 -459 12.52 1619 10.66 7.38 3.6784 791 4.09 9.25 9.08 1464 7.31 -4.92 ~7.00 ~944 -9.09 -9.29 -125385 6.79 5.38 5.31 5.87 2.00 207 -O41 -4.72 -4.73 -5.67 -6.62 ~5.2786 2.82 0.16 0.52 -042 081 0.09 -016 -248 -1.26 -0.96 1.00 —-O.1187 446 -5.68 -5.37  -246 281 3.28 -0.55 -252 0.12 1.90 4.74 8.1888 = -12.45 ~1292 -1234 -834 -236 1832 1291 9.32 8.59 1.93 -1.75 -0.93
89 11.07 675 8.28 3.23 250 1.71 0.36 ~-5.5) -6.03 -7.82 ~6.76 -7.7890 1.40 0.24 2.39 3.24 490 Lil 089 0.39 0.68 -1.09 -666 -7.1791 4.33 442 4.56 473 149 -0.15 4.66 -0.49 146 -385 -547 -6.3692 1,32 2.32 4.03 1.15 478 602 -i.51 -4.59 -3.99 -5.52 -273  -1.27
93 6.34 -7.79 -6.09 -482 ~3.49 -3.33 12.00 6.95 1.70 -1.91 5.07 8.03Avg -0.94 -1.29 -0.42 016 125 206 1.44 0.75 0.20 -0.92 -0.96 -1.38
Mdn = -0.63 016 -0.15 ~0.07 O81 I.Il 0.66 _-0.26 0.47 -0.96 -1.75  -O.11
 
One practical note is needed here. Although there may appear to be a statistical benefit to sell-ing heating oil in the later part of winter, there is also extreme risk. Prices jumped 40% fromMarch to April 1982, and 20% during the same period in 1983. We must also consider that theGulf War, which pushed prices higher from August 1990, conformed by chance to the seasonalPattern; in reality, it could have happened any time during the year. These special situationsdo not always cause prices to rise. In 1986, Saudi Arabia decided to dump oil in a politicallymotivated effort to drive prices down, successfully pushing crude oil under $10 per barrel.
Years with Similar Characteristics
Seasonal studies often yield results that are not as clear as desirable, and these results maybe rejected because of the obvious lack of consistency. Often, this is caused by a few yearsthat conflict with the normal seasonal patterns due to special events. David Handmakershows that separating the data into analogous years can give strikingly better results.”
7 David Handmaker, “Low-Frequency Filters in Seasonal Analysis,” journal of Futures Markets, Vol. 1, No. 3 JohnWiley & Sons, New York, 1981).178 SEASONALITY
 
For example, crop production is primarily determined by weather. Poor weather willcause sharp rallies during the growing season, whereas good weather results in a dull,sideways market. Bad weather develops slowly. A drought is not caused by the first hot daybut prolonged days of sunshine and no rain. Similarly, delayed planting due to wet fieldsor a late winter will set the stage for an underdeveloped crop. A trader can see the charac-teristics of a weather market in time to act on it.
In Figure 7-74, the seasonal corn pattern has been separated into those years withgood weather and those with bad weather. In a later study by ContiCommodity,® soybeanseasonality was separated into bud/ and bear years (Figure 7-76). Bull years include all badweather years but also years with such incidents as the 1973 Soviet grain sale. Becauseother events are not confined to the growing months, they may distort rather than clarifythe patterns. Bear would represent mostly good weather patterns; however, markets thatdecline steadily during the year following a bull market would also be included.
During the bad weather years, the corn chart shows a prolonged rise during the pri-mary growing months, May through August. The study of bull years for soybeans shows alonger sustained rise from the beginning of the new crop in October into the followingJune. In the case of good weather and bear markets, corn shows a rise from May to June,some sideways hesitation from June to July (possibly the last anticipation of bad weather),then a return to previous price levels. For bear years soybeans have steady declinesthrough April and then a short rally through the summer.
The corn analysis shows a critical point around July 1, when the market decides thatthe weather is not a factor. The soybean analysis is completely different showing nearly theopposite patterns for bull and bear markets. Because the resulting pattern of combiningboth bull and bear markets looks very similar to the bull pattern, the magnitude of the bullmoves must overwhelm the overall picture. In fact, the magnitude of the bull moves areoften three to four times greater than the price change in bear markets.
Updating Bull, Bear, and Nonseasonal Market Patterns
Both the Handmaker and ContiCommodity studies ended in the early 1980s and while grainfundamentals have essentially remained the same, the use of more data and recent years canonly help understand seasonality better. Using the soybean average prices expressed as apercentage of average annual price in Table 7-9, we separated the years from 1969 to 1993into bull and bear categories, where bull years (and bear years) had significantly higher (orlower) prices at the end of the year than at the beginning. Table 7-10, parts (a) and (6), whichalso give the prices as a percent of the average annual price, show that there were about thesame number of years in each category. The averages at the bottom of those tables indicatethat both bull and bear years had a price swing of about 10% over the year.
Bull and bear years are nearly opposite in pattern, as seen in Figure 7-8, and cross atabout the same price in June. This makes the seasonal pattern using all years peak in June.As interesting as it is to know that there are an equal number of years.going the oppositedirections within a seasonal pattern, the bull and bear tables were created after the factand can not be traded 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 soybeanswas higher during the harvest months of September and October than during the previ-ous summer months.
One important quality of crops is that each year we begin again; a shortage one yeardoes not mean a shortage the next year. This is seen in the chart of nonseasonal years in Fig-ure 7-8. After a nonseasonal year, prices rally early, nervous about planting and shortages,
® ContiCommodity, Seasonality in Agricultural Futures Markets (ContiCommodity Services, Chicago, 1983).SEASONAL FILTERS- 179ee
——FIGURE 7-7 (a) Corn price seasonality separated into all years, years with good weather, andyears with bad weather. (b) Soybeans (10 years) shown with separate bull years (5 years) andbear years (5 years).
 
415
    
No
Years with good weather
All years
120
100
80
 
60 “-Bear years
 
120)Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec (an)
(0)
 
but the reality of a good crop and normal demand drive prices steadily lower during the sec-ond half of the year. The pattern is typically seasonal and very well defined by large swings.
Seasonality for Trading
Whatever method is used to find the seasonal pattern, it is equally important to apply itproperly.when trading. Select a market that has a dominant seasonal pattern. The cropsalways qualify, with the exception of potatoes, which are fully stored and do not react tothe harvest depression. Livestock tends to be more complex than other commodities180 SEASONALITY
 
 
TABLE 7-10 Bull, Bear, and Nonseasonal Market Patterns
 
(a) Bull years begin at relatively low levels, peak during the summer but remain high at the end of the year. (b) Bearyears decline steadily after April and do not reflect the concerns 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 back to what we expect of normal seasonal patterns.
 
(a) Soybean Bullish Years (Prices Higher at the End of the Year)
 
 
 
 
 
Year Jan Feb Mar Apr = May = Jun Jul Aug Sep Oct Nov Dec70 -1.83  -1.62  -16l -125 -104 O11 15! 633 028 1.82 224 1.0772 4.10  -342 -156 -0.43 -0.78 -1.38 -I.12 -0.06 047 057 3.23 8.5873 25.18 16.67 -13.26 -II.15 623 25.10 15.97 16.73 3.93 ~042 -i185 0.5774 -3.16 -1.87 -3.20 -810 -9.14 -881 180 655 563 1183 610 2.3776 -7.34 -7.00 -760 -758 -4.26 3.74 7.14 3.53 5.90 286 436 6.2678 -665 -7.41 -0.51 1.370 3.15 1.25 -0.73 -0.78 1.20 353 264 2.9480 + -0.94 -1.24 -479 -760 -7.11 -648 1.39 201 5.98 7.36 1040 1.0383 -8.32 -786 -786 -4.92 -7.08 -9.78 -4.59 12.52 16.19 1066 738 3.6787 446 -568 -537 -246 281 3.28 -055 -252 O12 1.90 474 81888 -1245 -12.92 -12.34 -8.34 -236 1832 12.91 932 859 1.93 -1.75 -0.9393 6.34 -7.79 -6.09 482 -3.49 -3.33 12.00 695 1.70 ~I.91 5.07 8.03Average -7.34 -6.68 -5.84 -5.03 -210 200 416 4.96 455 365 387 3.80(b) Soybean Bearish Years (Prices Lower at the End of the Year)
Year jon Feb Mar Apr = May — jun jul Aug Sep Oct Nov Dec
75 . 798 169 0.19 120 -271 -285 1.22 5.25 2.23 -1.55 -5.45  -7.2077 ~1.25 041 748 17.60 1624 7.35 -6.01 -989 ~10.31 -9.96 -5.76 -5.9079 -0.10 344 3.78 210 080 383 232 -083 -0.90 -373 -492 -5.828I 10.25 7.30 554 7.93 475 067 0.84 -345 -658 -7.19 -8.96 -11.0982 626 531 349 610 580 237 066 -3.69 -654 -814 -5.53  -6.1184 791 4.09 9.25 9.08 14.64 7.31 -4.92 -7.00 -9.44 -9.09 -9.29 -12.5385 679 538 5.31 587 2.00 207 -041 -4.72 -4.73 -5.67 -662 -5.2786 282 0.16 052 -042 08! 0.09 -0.16 -248 -1.26 -0.96 1.00 -O.1189 11.07 675 828 3.23 250 L7P 036 -S51 -603 -782 -676 -7.7890 1.10 021 239 3.24 490 I. 0.89 = 0.39 0.68 -1.09 666 -7.179 433 442 456 4.73 1.49 -0.15 4.66 -0.49 146 -3.85 -547 -6.3692 1.32232 4.03 LIS 478 602 -I.51 -459  -3.99 -5.52 -273 -1.27Average 4.87 3.46 457 5.15 467 246 -0.95 -3.08 -3.78 -538 -559 -638
(©) Soybean Nonseasonal (Prices Higher during the Previous September-October than Summer)
Year Jan Feb Mar Apr May = jun jul Aug Sep Oct Nov Dec7I 0.3! -042 -088 -235 -1.60 034 220 1.43 0.52 0.99 0.07 0.0173 25.18 -16.67 -13.26 -II.1S 623 25.10 15.97 16.73 3.93 ~0.42 -1.85 0.5775 7.98 1.69 0.19 1.20 -271 -285 1.22 5.25 2.23 ~155 -545 -7.2077 -1.25 041 748 1760 16.24 7.35 -6.01 -9.89 -10.31 ~9.96 -5.76 -5.9079 —0.10 3.44 3.78 2.10 080 383 232 -083 -0.90 ~3.73 -4.92 -5.828I 10.25 7.30 5.54 7.93 475 0.67 0.84 -345 -658 ~7.19 -8.96 -11.0984 791 4.09 9.25 9.08 1464 7.31 -4.92 -7.00 -9.44 ~9.09 -9.29 -1253Average -0.10 -0.02 1.73 349° 548 5.96 166 032 -2.94 -442 -5.17  -5.99SEASONAL FILTERS 181
 
 
FIGURE 7-8 Soybean seasonality, a comparison of filtered years.
 
Soybean seasonality 1969-1993Comparison of bull and bear years
 
 
Percentage price change from average
 
 
 
ght
 
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
—- Allyears —@®— Bear —O- Bull | —<+ Nonseasonal
 
because of the joint dependency on both feedgrain prices as well as their own produc-tion and consumption factors. Both crops and livestock have primarily supply-orientedseasonal trends, depressed at harvest and highest during the growing months—ordepressed in the fall when the livestock supply becomes greatest due to liquidationbefore winter. Metals patterns are caused by demand, because they have no depen-dence on weather and production is usually constant. The summer months still seethe highest prices due to increased consumption—copper in housing and silver inphotography.
Select a delivery month most likely to reflect the pattern to be traded. If a sharp rise insoybeans 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 newcrop. In more extreme cases, February pork bellies do not reflect events occurring in theprevious July or August contracts.
Exceptions occur in seasonal patterns as they do in any model. There are events that.may overwhelm normal seasonality such as runaway interest rates or inflation, rapidchange in the U.S. dollar, or Soviet grain embargoes. Even though the seasonal variationstill exists, the size of the price change resulting from these other factors is much greaterand tends to obscure the seasonality. It is usually easy to recognize a nonseasonal pat-tern—prices simply go steadily up when they normally go down, or down when they areexpected to go up. From 1972-1974, corn prices increased steadily with only small adjust-ments in September and October. Once this variation occurs, it may be wise to wait untilanother season has reestablished the normal pattern,
The systems and methods that follow are based entirely on seasonal patterns and maybe used alone or to filter other methods.182 SEASONALITY
 
Seasonal Studies and Key Dates
Most agricultzral commodities exhibit traditional, reliable price moves at one or moreperiods throughout the year. The grains grown in the Northern Hemisphere have a highlikelihood of a rally during the late spring and early summer, when the chance of droughtwill have the greatest effect on yield. When prices show the normal harvest lows, followedby a modest rise and sideways pattern throughout the winter, the potential is good for arally during the early growing season. Because prices start at relatively low levels, the riskof holding a long position is small. Once prices have moved higher, there is rarely a seasonin which a short sale of corn, soybeans, cotton, or sugar will not net a good profit withinthe two months before harvest begins.
Seasonal studies are intended to provide information on when the largest move willoccur. The following studies—(1) Grushcow and Smith (1980), (2) ContiCommodity(1983), and (3) Bernstein (1986)—will be used to compare the seasonality in some ofthe more active commodities. A summary of the results is shown in Table 7-11. Eachstudy offers a different perspective on seasonality. Grushcow and Smith analyze bothcash and individual futures markets over a fairly long period and present complete sta-tistics; ContiCommodity used mostly the past 10 years, but included a unique volatilityanalysis; Bernstein, the most recently published study (Seasonal Concepts in FuturesTrading), gave the most complete background on calculations, including separate stud-ies of bullish and bearish years and an exceptionally long time period for cash marketanalysis.
The number of years in the seasonal analysis counts heavily in determining the normalpatterns. As Table 7-11 shows, the ContiCommodity results, based on a maximum of 10years, is often quite different from the other two studies. For trading safety, it would be bestto select those patterns that have proved reliable over many years; however, because incon-sistency in the past 10 years cannot be ignored, a trader must be able to identify nonsea-sonal, bullish, or bearish patterns.
The periods that seem consistent throughout all studies are:
Corn and soybeans. September and October show major harvest pressure.Cattle. An end-ofthe-year liquidation and midwinter rally.Coffee and juice. No common moves in the three studies. s
Some commodities are clearly more seasonally consistent than others. Both the coffeeand orange juice markets were expected to show patterns that reflect a rise as the possi-bilities of a freeze increase. It must be that the normal seasonality of these products is dis-torted by the inconsistent and highly volatile periods that follow a freeze. These marketswould be candidates for an analogous years study in which you compare only those yearswith common factors.
The three studies shown here, as well as most others, include recommended tradesbased on key dates, which reflect the patterns in Table 7-10¢. By selecting those tradescommon to all of them, you have found those that are most reliable, In a summary by Bern-stein, which catalogues commodities by those months with the highest reliability, the agri-cultural products are clearly the most seasonal. The only nonagricultural market thatshows any consistent seasonality is copper. Although there may be interesting argumentsfor the forces of demand on silver, currencies, and financial markets, their inconsistency isapparent and they are not candidates for seasonal trading.
Seasonal Calendar
Because Bernstein’s work covers the cash markets over an extremely long period, it mustbe considered the most reliable source of basic seasonal patterns. Table 7-12 is part of theSEASONAL FILTERS 183so
_—_——TABLE 7-1! Results of Seasonal Studies
JF FM MA AM MJ JJ fA AS SO ON ND DY
 
 
 
 
 
 
Corn24 years* 5 -Ll 16 3.6 3.4 5 1S 5.3 “85-13 5 2110 years* 220 ~-130 -23 +12 23 77 45 (4 75 -13  -64 1846 years* -6 2.0 3.0 29 2.3 38 -15 3.2 -67 -37 54 15we 58 58 67 92 50 46 50 54 88 63 83 7Iwe 52 60 80 72 45 59 62 63 82 60 82 70Soybeans24 years* 7A 75 8.4 95 6.4 -7 -13 9-258 -75 42 58 310 yearst -230 214 237 126 20.0 147 -I17 ~149 46.7 -98 -89 2142 years*§ 3 MM 8 8 3 -l 5 -20 -8 10 3 2Be 67 50 67 50 42d 38 58 83 63 75 67 79we 62 53 57 St 42 37 64 74 64 76 72 70Cattle25 years* -4 a & l =. 4 4 -3 -A -5 0 = 410 years* 14 2 8 18 4-12 a4 -7 -13 5-3 250 years*§ | 12 13 4 -l 7 3 4 -9 9 2 2Me 64 60 48 44 56 60 56 44 76 64 52 72wt 52 70 59 52 52 59 55 42 60 71 51 62Orange Juice10 years* -16 = ~3 -9  -9 -3 ot -.2 1.2 -23 3 4,710 yearst -19 24-11 -7 0 16 18 23 2 0 O -4734 years* 27 |4 4 3 -8 -l | 3 -2 2 = -13 “4wm 78 45 67 56 45 45 56 67 45 45 56 56vt 61 64 50 58 50 65 66 81 6l 40 67 62Coffee22 years* ~2 a -5 4 l al -7 -3 re) 5 l8 years’ 65 18 76 §0 -3 3300-115 -2.0 43 -8.6 47 2.253 years* Oo -10 2 4 | 4 18 2 19-10 -4 2Me 4l 64 50 So 59 46 46 55 4l 64 50 77we 56 58 43 53 50 48 56 56 5I 59 54 49
 
¥Grushcow and Smith (change in price).*ContiConimodity (% change in seasonal factor).
‘Bernstein (% change in seasonal factor). -SApproximate values.
% refers to the reliability of monthly seasonality.
weekly seasonal calendar, which appears in Seasonal Concepts in Futures Trading. Thenumbers that appear show those weeks with consistent historic moves. Weeks of 64% andhigher represent upward moves; weeks of 36% and lower display downward trends. Thiscalendar can be extremely useful when combined with some simple trading logic whichasks, “Is the market acting in a seasonal manner?” before the position is entered.TABLE 7-12 Seasonal Calendar
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
iels 2/8] §
