ummer 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 jon Feb. Mar = Apr_—s May Jun Jul, © Aug Sep-Oct Dec70 -1.83 162-161-125 -1.04 O11 151 033 0.28 1.82 1.072 410-342 -156 043 -0.78 -1.38 -I.12 -0.06 047 057 8582B 25.18 —1667 13.26 -I1.15 623 25.10 15.97 16.73 3.93 -0.42 05774 3.16 -1.87 8.10 -9.14 -881 180 655 5.63 11.83 2.377 -734  -7.00 “758 -426 374 7.14 353 5.90 286 6.2678 6.65 137° 315 1.25 -0.73 -0.78 1.20 3.53 2.94go 0.94 -7.60 -7.11 -648 1.39 201 5.98 7.36 1.0383 8.32 492 -7.08 -9.78 459 1252 16.19 10.66 3.6787 446 -5.68 -246 281 328 -0.55 0.12 1.90 8.1888 -1245 -12.92 8.34 -2.36 1832 1291 B59 1.93 0.9393 6.34 -7.79 482 -3.33 12.00 1,70 -1.91 8.03Average -7.34 6.68 5.03 2.00 4.16 455 3.65 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. Now7S 798 169 O19 1.20 -271 -285 122 525 223 -1.55 -5.457 -125 O41 748 1760 1624 7.35 ~6.01 -1031 -9.96 -5.7679 -0.10 344 378 210 080 383 232 -083 -0.90 -373 49281 10.25 730 554 7.93 475 067 084 -345 -658 -7.19 -8.9682 626 531 349 610 580 237 066 -369 -654 -8.14 -5.5384 791 409 925 9.08 1464 7.31 4.92 -7.00 -944 -9.09 -9.298S 679 538 531 S87 200 207 -O41 -4.72 4.73 -S67 -6.6286 282 0.16 052 042 081 0.09 -0.16 -248 -1.26 -0.96 1.0089 11.07 675 828 323 250 L7f 036 -5.51 -6.03 -782 -6.7690 1.10 021 239 324 490 Lil 089 039 0.68 -1.09 6.661 433 442 456 473 149 -O.1S 466 -049 146 -3.85 -5.47
92 132 232 403 LIS 478 602 -ISt -459 3.99 ~552 -273Average 4.87 346 457 5.15 467 246 -0.95 -3.08 -3.78 -5.38 -5.59
(6) Soybean Nonseasonal (Prices Higher during the Previous September-October than Summer)Yeor jan Feb. = Mar = Apr_—Ss May fun Jul 9 Aug. «Sep = Oct. «Nov Dec
 
 
7 O31 042 088 -235 -160 0.34 220 143 052 0.99 0.07 0.0173 25.18 1667 -13.26 -IL.I5 623 25.10 15.97 16.73 3.93 -0.4275 7:98 1.69 O19 1.20 -2.71 -285 1.22 5.25 2.23 -1.557 -125 O41 748 17.60 1624 7.35 -6.01 -9.89 —10.31 -9.9679 0.10 344 3.78 210 080 3.83 232 -083 -0.90 -3.738l 10.25 7.30 5.54 793 475 067 0.84 -345 -658 -7.1984 791 409 925 9.08 14.64 7.31 4.92 -7.00 -9.44 -9.09
Average  -0.10 5.96 6 60.32 -2.94 4.42
nei nae nO eae
      
 
 
SEASONAL FILTERS™ 181
 
FIGURE 7-8 Soybean seasonality, a comparison of filtered years.
Soybean seasonality 1969-1993Comparison of bull and bear years
4
2b
oT
mak
mah
6b
8 po
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
 
 
 
Percentage price change from average
 
 
 
—#Allyears —e- 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 thatmay 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 agricultural 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 of
.the 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-of-the-year liquidation and midwinter rally.Coffee and juice. No common moves in the three studies .
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 7-10a. By selecting those trades _common 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 the
SEASONAL FILTERS — 183
 
TABLE 7-11 Results of Seasonal StudiesJF FM MA AM MJ jj fA AS SO ON ND Df
 
 
  
 
 
  
 
 
 
Corn24 years* 5 16 34 5 18-53-85 -13 5 2110 years* 22 -23 230° 77 45 [4 -75 -13 64 1846 years? -6 3.0 23038 -I5 32 -67 -37 9 54 15%e 58 67 5046 50 54 88 638376 52 80 45 (59 62 63 82 60 «8270Soybeans24 years* 74 75 84 95 64 -7 13 -258 -75 42 58 3lOyearst = -2.3 2.1 23:7 «126 «200 147 -I17 -149 467 -98 -89 2142 years*§ 3 Wl 8 8 3 -I 5 20 8 10 3 2ee 67 50 67 50 42 38 58 83 63 7S 67 79wt 62 53ST) SEC AT 64 74 64 76 7270Cattle- -A4 425 years* -4A 6 A -t A 4 310 years? 14 20 8 18 4 -12 4-4 =7  -13 350 years## 1120 13 4-1 7 3 4 ~9%e 64 60 48 44 56 60 56 44 76 72“Kt 52 70 59 52 52 59 55 42 60 62Orange Juice=. = = 7lOyears*# +160 = <3 “9-3 a 2 120-2300 =310 yen! “19° 24 11 Oo 16 18 23 2 0 0 -47= 2 - “434 years? 2714 4 -8 =I 1 3 2 2-13me 78 45067 450 45 56 67 45° 45 «56 (56mF 61 64 50 50 65 66 8 61 406762.Coffee-. - -S5 422 years* -2 -5  -4 Ao AT 3 A 3. A!8 years! ~65 76 50 -3 33 -115 -20 43 -86 47 2253 years! ° 2-4 1 4 ta 2 9 -10 —-4 -2a 4l 50 SO 5946 46 55 440 64 «S077% 56 43530 5048 56 56 SI 59 S449
 
*Grushcow and Smith (change in price).*ContiCommodity (% change in seasonal factor),
Bernstein (% change in seasonal factor). -SApproximate values.
% refers co 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
aTABLE 7-12 (Continued)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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TABLE 7-12 (Continued)
 
TABLE 7-12 (Continued)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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