8/6/2019 Why Seasonal Patterns Work

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An excerpt from

Trade Your Way To Financial Freedom

by Van K. Tharp

http://www.mrci.com/

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HOT!

InventoryBuilding

Why Seasonals Work

The following was written by MRCIs Editor JerryToepke for Dr. Van K. Tharps new book, Trade YourWay to Financial Freedom, published by McGraw-Hill.We hope it helps explain the concept behind seasonalresearch and how it is derived, some of its strengths and

weaknesses, and how it can be used and/or incorporatedinto various styles of trading.

The seasonal approach to markets is designed toanticipate future price movement rather thanconstantly react to an endless stream of oftencontradictory news. Although numerous factors affectthe markets, certain conditions and events recur atannual intervals. Perhaps the most obvious is the annualcycle of weather from warm to cold and back to warm.

However, the calendar also marks the annualpassing of important events, such as the due date forU.S. income taxes every April 15th. Such annual events

create yearly cycles in supply and demand. Enormoussupplies of grain at harvest dwindle throughout the year.Demand for heating oil typically rises as cold weatherapproaches but subsides as inventory is filled. Monetaryliquidity may decline as taxes are paid but rise as theFederal Reserve recirculates funds.

Natural Market Rhythms

These annual cycles in supply and demand giverise to seasonal price phenomena to greater or lesserdegree and in more or less timely manner. An annualpattern of changing conditions, then, may cause a moreor less well-defined annual pattern of price responses.

Thus, seasonality may be defined as a markets naturalrhythm, an established tendency for prices to move inthe same direction at a similar time every year. As such,it becomes a valid principle subject to objective analysisin any market.

In a market strongly influenced by annual cycles,seasonal price movement may become more than just aneffect of seasonal cause. It can become so ingrained asto become nearly a fundamental condition in its ownright almost as if the market had a memory of itsown. Why? Once consumer and producers fall into apattern, they tend to rely on it, almost to the point ofbecoming dependent on it. Vested interests then

maintain it.Pattern implies a degree of predictability. Future

prices move when anticipating change and adjust whenthat change is realized. When those changes are annualin nature, a recurring cycle of anticipation/realizationevolves. This recurring phenomenon is intrinsic tothe seasonal approach in trading, for it is designed toanticipate, enter, and capture recurrent trends as theyemerge and exit as they are realized.

The first step, of course, is to find a marketsseasonal price pattern. In the past, weekly or monthly

high and low prices were used to construct relativelycrude studies. Such analysis might suggest, for instance,that cattle prices in April were higher than in March67% of the time and higher than in May 80% of thetime. Computers, however, can now derive a daily

seasonal pattern of price behavior from a compositeof daily price activity over several years. Properlyconstructed, such a pattern provides historicalperspective on the markets annual price cycle.

Basic Pattern Dynamics

Consider the following seasonal pattern thatevolved for January Heating Oil. Demand, and thereforeprices, are typically low during July often the hottestmonth of the year. As the industry begins anticipatingcooler weather, the market finds increasing demand forfuture inventory exerting upward pressure on prices.Finally, the rally in prices tends to climax even beforethe onset of the coldest weather as anticipated demand isrealized, refineries gear up to meet that demand, and themarket begins to focus instead on inventory liquidation.

The other primary petroleum product encounters adifferent, albeit still weather-driven, cycle of demand asexhibited in the seasonal pattern for August Gasoline.

Annual Price Cycles

CycleComponent

Seasonal PatternCharacteristic

FundamentalCondition

Bottom Seasonal Low Greatest Supply/ Least Demand

Ascent Seasonal Rally Increasing Demand/ Decreasing Supply

Peak Seasonal High Greatest Demand/

Least Supply

Descent Seasonal Decline Decreasing Demand/ Inreasing Supply

Jan Heating Oil(NYM) 15 Year Seasonal(86-00)

Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

25

50

75

100

MAY 2000- MOORE RESEARCH CENTER, INC.

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InventoryBuilding

Memorial

Day

End ofU.S. Tax Year

QuarterlyTreasury

Refunding

FNDUSH

QuarterlyTreasury

Refunding

QuarterlyTreasury

Refunding

FNDUSZ

FNDUSM

QuarterlyTreasury

Refunding

FNDUSU

April 15

FNDSF

FNDSK

FNDSN

FNDSQ

"FebruaryBreak" Harvest

Crop Being "Made"

U.S. PlantingFNDSH

FrostRally

Prices tend to be lower during the poorer drivingconditions of winter. However, as the industry beginsto anticipate the summer driving season, demand forfuture inventory increases and exerts upward pressureon prices. By the official opening of the driving season(Memorial Day) refineries then have enough incentiveto meet that demand.

Seasonal Pegs

Seasonal patterns derived from daily prices rarelyappear as perfect cycles. Even in patterns with distinctseasonal highs and lows, seasonal trends in between aresometimes subject to various, even conflicting forcesbefore they are fully realized. A seasonal decline maytypically be punctuated by brief rallies. For example,even though cattle prices have usually declined fromMarch/April into June/July, they have exhibited a strongtendency to rally in early May as retail grocery outletsinventory beef for Memorial Day barbecues. Soybeanprices tend to decline from June/July into Octobers

harvest, but by Labor Day the market has usuallyanticipated a frost scare.

Conversely, a seasonal rally may typically bepunctuated by brief dips. For example, uptrends areregularly interrupted by bouts of artificial sellingpressure associated with First Notice Day for nearbycontracts. Such liquidation to avoid delivery can offeropportunities to take profits and/or to enter or reestablishpositions.

Therefore, a seasonal pattern constructedfrom daily prices can depict not only the four majorcomponents of seasonal price movement, but alsoespecially reliable segments of larger seasonal trends.Recognizing fundamental events that coincide withthese punctuations can provide even greater confidencein the pattern.

Consider the seasonal price pattern that hasevolved for September 30-Year Treasury Bonds. TheU.S. Federal governments fiscal year begins October 1,

perhaps increasing liquidity and easing borrowingdemands somewhat (even if only for accountingpurposes). Is it merely coincidental that the tendencyfor bond prices to rise from then tends to culminatewith personal income tax liability for the calendar year?

Does the seasonal decline into April/May reflect amarket anticipating tighter monetary liquidity as taxesare paid? Notice the final sharp decline beginning surprise! April 15th, the final date for payment ofU.S. income taxes. Does liquidity tend to increasesharply after June 1 because the Federal Reserve finallyrecirculates funds?

Take a close look at the typical market activitysurrounding December 1, March 1, June 1, andSeptember 1 dates of first delivery against ChicagoBoard of Trade quarterly futures contracts on debtinstruments. Finally, notice the distinct dips during thefirst and second week of the second month in eachquarter November, February, May, and August.Bond traders know that prices tend to decline into thesecond day of a quarterly Treasury refunding at which time the market gets a better sense of thethree-day auctions coverage.

Consider the pattern for November Soybeans as ithas evolved in the 20 years since Brazil became a majorproducer with a crop cycle exactly opposite that in theNorthern Hemisphere. Notice the tendency for prices towork sideways to lower into the February Break asU.S. producers market their recent harvest and Brazilscrop develops rapidly. By the time initial notices ofdelivery against March contracts are posted, thefundamental dynamics for a spring rally are in place the Brazilian crop is made (realized), the pressure of

Aug Unleaded Gas(NYM) 14 Year Seasonal(86-99)

Jan Feb Mar Apr May Jun Jul

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25

50

75

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Sep 30-Year T-Bonds(CBOT) 15 Year Seasonal(85-99)

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

0

25

50

75

100

Nov Soybeans(CBOT) 15 Year Seasonal(85-99)

Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov

0

25

50

75

100

MAY 2000 - MOORE RESEARCH CENTER, INC.

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U.S. producer selling has climaxed, the marketanticipates the return of demand as cheaper rivertransportation becomes more available, and the marketbegins focusing attention on providing both an incentivefor U.S. acreage and a premium for weather risks.

By mid-May, however, the amount of prime U.S.acreage available in the Midwest for soybeans is mostlydetermined and planting gets underway. At the sametime, Brazil begins marketing its recent harvest. Theavailability of these supplies and the potential of the new

U.S. crop typically combine to exert downward pressureon market prices. The minor peaks in late June andmid-July denote tendencies for crop scares to occur.

By mid-August, the new U.S. crop is made(realized), and futures can sometimes establish an earlyseasonal low. However, prices more often declinefurther into Octobers harvest low but only afterrallying into September, perhaps on commercial demandfor the first new-crop soybeans and/or concerns over anearly crop-damaging frost. Notice also the minorpunctuations (declines and rallies) associated with FirstNotice Day for July, August, September, and November

contracts.Inherent Strengths/Weaknesses

Such trading patterns do not repeat without fail.The seasonal methodology, as does any other, has it owninherent limitations. Of immediate practical concern totraders may be issues of timing and contraseasonal pricemovement. Fundamentals, both daily and longer term,inevitably ebb and flow. For instance, some summersare hotter and dryer and at more critical times thanothers. Even trends of exceptional seasonal consistencyare best traded with common sense, a simple technicalindicator, and/or a basic familiarity with current

fundamentals to enhance selectivity and timing ofentry/exit.How large must a valid statistical sample be?

Generally, more is better. For some uses, however,modern history may be more practical. For example,Brazils ascent as a major soybean producer in 1980 wasa major factor in the nearly 180-degree reversal in that

markets trading pattern from the 1970s. Conversely,relying solely on disinflationary patterns prevalent in1981-1999 could be detrimental in any new inflationaryenvironment.

During such historic transitions in underlyingfundamentals, trading patterns will evolve. Analyzingcash markets can perhaps help neutralize such effects,but certain patterns specific to futures (such as thosethat are delivery- or expiration-driven) can get lost intranslation. Thus, both sample size and the sample itself

must be appropriate for its intended use. These may bedetermined arbitrarily, but best by a user fully cognizantof the consequences of that choice.

Related issues involve projecting into the futurewith statistics, which confirm the past but do not predictin and of themselves. The Super Bowl winner/stockmarket direction phenomenon is an example ofstatistical coincidence: no cause-and-effect relationshipexists. However, it does raise a valid issue. Whencomputers mechanically sift only raw data, whatdiscoveries are truly relevant? Does the simple, isolatedfact that a pattern has repeated in 14 out of the last 15

years make it necessarily valid?Nevertheless ...

Certainly, patterns driven by known fundamentalsinspire more confidence; but to know all relevantfundamentals in every market is impractical. Properlyconstructed seasonal patterns may typically help onefind trends that have recurred in the same directionduring the same period of time most years with a highdegree of past reliability. Finding a cluster of suchhistorically reliable trends, with similar entry and/or exitdates, not only reduces the odds of statistical aberrationbut also implies recurring fundamental conditions that

presumably will exist again in the future and affect themarket to one degree or another in a more or less timelymanner.

A seasonal pattern merely depicts the well-wornpath a market itself has tended to follow. It is a marketsown consistency which provides the foundation for whyseasonals work.

MAY 2000 - MOORE RESEARCH CENTER, INC.

http://www.mrci.com/