contract prices to news of the discovery of a Canadian cow with madcow disease. Other markets were also impacted. The Canadian dollarinitially fell against the U.S. dollar before ending the trading day threequarters of a cent higher. At one point, McDonald’s stock fell 8%.13It is interesting to consider the implicit trading theses marketparticipants were operating under. Figure 5.3 depicts the reaction ofthe price of McDonald’s stock to news of the discovery of BSE in aCanadian cow. Presumably, the stock prices of McDonald’s and other(burger-oriented) fast-food restaurants fell because of concerns thatU.S. beef consumption would drop substantially as a result of the discovery of mad cow disease in a Canadian cow. (Whether 8% is anappropriate amount is questionable.) The overall stock market—asmeasured by the Dow Jones Industrial Average—did not move muchthat day with the index closing at 8491.36, down 2.03 points from theprevious day. The presumed reasoning for the report to affect theCanadian dollar is likely the following.Canadian beef exports are likely to decline as foreign governments ban the importation of Canadian beef after the Canadian government’s confirmation of the diagnosis of a Canadian cow with madcow disease. These restrictions are likely to last for an unknownlength of time leading to fewer exports of Canadian beef and lessdemand for the Canadian dollar. Lower demand for the Canadiandollar would cause its foreign exchange value to fall.
78.000
5/20/03Discovery of Mad Cowdisease
77.00076.000
5/20/03date.open 73.700date.high 74.125date.low 72.400date.close 72.400
75.00074.00073.00072.00071.00070.00069.00068.000
CHAPTER 5 • WEATHER AND NATURAL DISASTERS
79.000
67.00066.0004/1/03
5/1/03
6/1/03
7/1/03
Reaction of CME front month live cattle futures prices to discovery of mad cow disease in Canada. Daily trading range of CME live cattle futures prices fromApril 1, 2003 to July 31, 2003.
FIGURE 5.2
Mad cow disease and cattle futures prices.
143
144
24.0023.0022.005/20/03Discovery of Mad Cowdisease
21.0020.0019.0018.0017.0016.00
5/20/03date.open 18.15date.high 18.50date.low 16.58date.close 16.95
15.0014.00
5/1/03
6/1/03
7/1/03
Reaction of McDonald's stock price to discovery of mad cow disease in Canada. Daily trading range of McDonald's stock price from April 1, 2003 to July 31, 2003.
FIGURE 5.3
McDonald’s stock takes a hit.
TRADING CATALYSTS
13.004/1/03
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145
Once again, traders must assess the magnitude and the durationof any impact. Although the Canadian dollar fell initially, the impactof the discovery of mad cow disease on the foreign exchange marketwas short-lived and the loonie—the slang term traders use for theCanadian dollar—closed higher on the day. This was not the case forMcDonald’s stock even though Canada was not a major beef supplierto McDonald’s restaurants.One problem with most contemporary news accounts is thatinsufficient attention is paid to the timing of market responses. Moreover, the impact of a trading catalyst on minor markets is oftenignored. Fortunately, a fascinating academic study by ProfessorsYiuman Tse and James Hackard examined this particular episode anda related subsequent diagnosis of a U.S. cow with mad cow disease inthe U.S. in detail.14 Professors Tse and Hackard paid particularlyclose attention to the sequencing of the response of various marketsto the news.As Professors Tse and Hackard point out, information on theprospective announcement reached live cattle futures traders at11:10 A.M., or more than an hour before Canadian authorities madetheir announcement at 12:15 P.M. The source of the advance information was a newsletter on the cattle industry e-mailed to subscribers.However, there is reason to believe that the information was disseminated even earlier to some traders as live cattle futures pricesplunged to their daily limit at 10:48 A.M. and the decline was attributed to the news about mad cow disease in a Canadian cow. Thedecline in live cattle futures prices appears to suggest the probabletrading thesis that beef consumption in the U.S. would fall sharply inthe wake of the announcement.This episode also highlights the difficulty of trying to determinethe correct interpretation to assign to a given piece of news. An alternative interpretation of the announcement is that the discovery ofBSE in foreign cattle would adversely affect the supply of foreign(i.e., Canadian) beef thus aiding U.S. beef prices, which the live cattle
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futures contract tracks. Indeed, Professors Tse and Hackard observethat live cattle futures prices rose later in the week in part for thisreason.The impact of the announcement extended beyond the live cattlefutures market and impacted a number of other commodity futuresmarkets. These include feeder cattle futures, pork belly futures, andlean hog futures, all traded on the CME; and soybean futures, soybean meal futures, corn and wheat futures, traded on the ChicagoBoard of Trade (CBOT). One interesting finding that Professors Tseand Hackard report is that the timing of the price moves was notsimultaneous. The selloff in feeder cattle continued for 14 minutesafter the selloff in live cattle futures ends because the price limit forfeeder cattle was not reached until then. Traders apparently hypothesized that pork consumption would rise as beef consumption fell andbid up the price of lean hog futures and pork belly futures contracts.Professors Tse and Hackard report that lean hog futures prices initially rose following the news and that the high of the day for leanhogs occurred at 11:39 A.M. However, lean hog futures prices subsequently fell and closed lower for the day. Pork belly futures ralliedafter the news on mad cow disease came out and reached a high atthe same time as lean hog futures prices did; but unlike lean hogfutures, contracts managed to close up for the day.Grain futures prices were also affected by the news of the discovery of a Canadian cow with mad cow disease. Grain futures pricesrose in response to the news but at different times. Corn and wheatfutures seemed to have reacted at 11:16 A.M., whereas soybean andsoybean meal futures didn’t rally until the 12:15 P.M. announcement.All of the grains gave up some of their gains (i.e., they overreacted)and closed higher for the day except for corn, which closedunchanged. Professors Tse and Hackard suggest that the differentialreaction between corn and the other grains “…is indicative of herdbehavior on the part of corn futures traders being sucked along by themomentum of the other grain traders.”15
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Professors Tse and Hackard also report that the stock prices ofthe various burger-oriented fast-food companies reacted to the newsat different times, ranging from 11:37 A.M. to 11:43 A.M. They argue:“The lag between livestock futures, grain futures, and stock pricemovements indicates a spillover effect from the futures market to thestock market, which is consistent with cascading information andherd behavior as defined in the relevant literature.”16After surveying the information, the empirical evidence on thereaction of these and other markets to news of the discovery of aCanadian cow with mad cow disease, Professors Tse and Hackardconclude the following.We find that related livestock and grain futures and stockprices do not quickly and accurately process information, butfollow a pattern of herd behavior. This behavior is evidencedby the uncertainty about the impact of the announcement onthe value of certain securities, and the composition of information entering the markets immediately following the initialannouncement. These uncertainties are the aspects of herdbehavior that Avery and Zemsky (1998) cite as the conditionsnecessary for herd behavior to lead to mispricing.17The differential reactions that the Tse and Hackard article document are commonly observed in commodity and financial markets tounscheduled news. This creates opportunities for traders who did notforesee the occurrence of the trading catalyst but discern quickly thelikely interpretation most traders will assign to a given catalyst. Evenso, there is still considerable risk that the effect may be transitory.
Potential Epidemics and Pandemics: SARSContagious human diseases represent another type of natural disasterthat can become a trading catalyst. The scale of such disasters is oftencompounded by political decisions to minimize the extent of theproblem or conceal the existence of the problem. Such attempts also
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increase the power of such diseases to act as a trading catalyst andincrease the breadth of assets affected. One example in this regard isthe identification of a strain of atypical pneumonia, later namedsevere acute respiratory syndrome, or SARS, in early 2003.The disease may have originated in Guangdong province insouthern China. The disease had a number of pneumonia-like symptoms and was called atypical pneumonia. SARS first came to publicattention on February 10, 2003 when Chinese authorities in theprovince of Guangdong in southern China announced that 305people had become sick from a pneumonia-like illness and fivepeople had died from it. Additional information about the diseasebecame slowly available. However, there were plenty of disconcertingrumors about the magnitude and severity of the problem.The disease later spread to Hong Kong when a doctor who hadtreated patients with SARS visited Hong Kong. Several individualswith whom the doctor had come into contact contracted the disease. Anumber of medical personnel in Hong Kong later contracted the disease (sometimes with fatal results) when treating patients with SARS.Diseases transmitted through the air pose a special risk in thatthey may be more easily contracted than water-borne diseases. Theease of international air travel—and with it a means of transmittingthe virus to new populations in distant lands—heightened concernsabout the spread of SARS. The discovery of SARS cases in Singapore,Canada, and elsewhere in mainland China reinforced the view of theease of transmission of this potentially deadly disease.The relatively high fatality rate associated with the disease andthe threat of an epidemic or global pandemic increased fears further.Reports that the disease was being understated by health authoritiesin China added to concerns. The spread of the disease to Canada alsoincreased concern about the impact of the disease. Yet, for whateverreason, these concerns were not immediately reflected in financialmarket prices.
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It should be mentioned that SARS broke out during a periodwhen many market participants focused on the potential of an imminent second U.S. war with Iraq. The war broke out in mid-Marchwith the invasion of Iraq by coalition forces.Unlike some natural disasters, SARS was not a one-off event.Moreover, it was a new disease whose cause was initially unknown buthad a high mortality rate. Like other natural disasters, estimates ofthe damage that SARS had on the economy were periodically revised.This meant that market participants had far greater information onthe impact of SARS on the economy as time progressed. On Monday,March 31, 2003, the Hong Kong stock market fell 2.58%.18Fear of the impact of SARS also affected the currency market bycausing the one-year forward premium to widen significantly.19 Thepremium got even larger in a few days’ time when it rose to an intraday high of 360-370 points and closed at 348-358.20 The forward premium, in this instance, can be viewed as a market measure of theprobability that the Hong Kong dollar will be devalued. SARS startedto cast a pall over the broader equity market rather than just individual sectors. Concern over the effects of SARS started to affect otherAsian currencies, stock, and fixed income markets.21 The outlook ofthe impact of SARS also started to evolve and a new trading thesistook root.22
Weather ConditionsWeather plays a major role in determining agricultural futures prices.Indeed, in many cases, weather is arguably the most importantfundamental economic factor determining agricultural commodityprices. Traders who have access to consistently more accurate weatherforecasts or obtain information on changes in weather conditionsbefore others have a decided advantage over other less well-informed
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traders. For this reason, many agricultural commodity traders employthe services of private weather forecasters.There are many examples where price changes in grain futurescontracts are attributed to the effects of changes in weather on grainproduction. The basic idea is that changes in supply (i.e., production)will cause changes in prices in the opposite direction, holdingdemand constant.Given the central role that weather conditions play in determining agricultural output, it seems that weather would be the dominantfactor in determining the price of an agricultural commodity. Thatnotion is incorrect, however.Most commodities are grown over a fairly large area and exposedto many different weather systems. This makes it difficult to assessthe influence of weather on commodity prices. In an interesting andprovocative study that was published in 1984, Professor Richard Rollof the University of California Los Angeles (UCLA) examined theinfluence of weather on frozen concentrated orange juice futuresprices. At the time of the study, orange juice futures prices weredetermined based on orange production in central Florida. This represented a fairly limited area from a weather perspective. Roll foundthat changes in weather conditions accounted for only a small fractionof the total variation in frozen concentrated orange juice futuresprices. Put differently, most of the variation in orange juice futuresprices was not explained by weather news or other fundamentaleconomic factors. This suggests that there are other, potential nonfundamental factors driving orange juice futures prices, and by extension, the prices of other agricultural commodities.A drought is another natural disaster and weather condition thatcan act as a trading catalyst. The United States Geological Serviceprovides several definitions of a drought including meteorological,agricultural, and hydrologic, among others.23 Financial market
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participants typically focus on the agricultural definition of a droughtas a period where agricultural output is perceived as being potentiallyaffected by insufficient precipitation. In practice, this may mean thatmarket participants confuse a period of low rainfall with a drought.Droughts, by definition, span a period of time rather than occur at asingle point in time. This makes documenting their impact as tradingcatalysts more difficult.However, perceived droughts still serve as trading catalystsdirectly or indirectly. For instance, during the spring of 1988, belowaverage rainfall led to concern that there was a “drought” in the U.S.Midwest agricultural heartland. This led to higher grain futuresprices. Higher grain futures prices were interpreted by many marketparticipants as a harbinger of higher rates of future inflation. Higherinflation meant higher nominal interest rates and lower bond prices.As a result, bond futures prices became temporarily linked to grainfutures prices. This became known as the beans over bonds spread, orBOB spread. This also illustrates how price changes induced by anexternal trading catalyst in one market can become an internal trading catalyst for another market. It should be pointed out that the perceived relationship between beans and bonds was short-lived.There is a famous story that may be more apocryphal than truethat relates how, during a perceived drought in the Midwest one year,the sight of a sudden rainfall sparked a selloff in grain futures priceson the Chicago Board of Trade. The selloff ended and the rallyresumed when traders found out that although it was raining inChicago, it was not raining in grain producing areas.In a related vein, poor crop production in one part of the worldmay trigger demand for similar crops elsewhere. For instance, thepoor output of Soviet agriculture in the 1970s led to massive purchasesof U.S. grain in the 1970s with an attendant huge impact on grain spotand futures prices. Of course, chronic low Soviet grain output was as
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much related to the flawed system of production known as communism as it was to weather conditions in the Soviet Union.In addition to agricultural commodities, weather conditions canaffect the demand for energy to heat buildings in the winter or coolbuildings in the summer. Weather conditions can also affect supply byaffecting production. An example of how weather can act as a tradingcatalyst for energy prices is illustrated in the following excerpt from anarticle by Masood Farivar about Tropical Storm Arlene that appearedin the Friday, June 10, 2005 issue of The Wall Street Journal.…[July NYMEX] Crude [oil] futures…shot $1.74 higher to$54.28 a barrel…[due to] Tropical Storm Arlene…The stormis expected to pass with little damage to production, but coming at a time when global capacity is stretched…had a psychological impact on prices.…Hurricane Ivan last September cost the industry some $3.5billion. “The hangover from the damage done by Ivan is stillon everybody’s mind,” said John Kilduff, senior vice presidentof risk management at brokerage Fimat Futures in New York.“We’re still in that mode where any bullish news is tremendously bullish even if it is slight.”But natural gas futures, typically more sensitive to severeweather patterns…rose only slightly. July [NYMEX] naturalgas futures…settled…up 4.5 cents…[T]raders…said therewas little concern about the storm’s potential impact.24Several points are of interest here. First, note the large reactionto weather news by crude oil futures market participants, eventhough most analysts didn’t expect any significant impact on oil production. Second, notice that natural gas futures prices did not risemuch in reaction to the same piece of news, even though natural gasproduction is arguably more sensitive to weather conditions. The
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preceding example illustrates how critical it is to determine whichmarket would be most impacted by a trading catalyst and the magnitude of the impact. A trader who bought a natural gas futures contractin response to the news of Tropical Storm Arlene would not have performed as well as a trader who bought crude oil futures. Third, noticethat the prospect of significant production damage that arose duringSeptember 2004 with Hurricane Ivan is one of the driving factorshere. This is yet another example of how past reactions can guide themarket’s response to new trading catalysts. Fourth, the excerpt illustrates how sentiment—in this case, bullish sentiment—in the marketcan exacerbate the impact of any news consistent with the sentiment.The relevant question is whether the magnitude of the reactionwas appropriate. Should crude oil prices really rise by 3% on stormwarnings when most observers do not expect that Tropical StormArlene will impact crude oil production? The reaction of crude oilprices to the news is even more puzzling when one considers that theimpact is not limited to crude oil for July delivery. Crude oil forAugust 2005 delivery rose by $1.72 per barrel, whereas crude oil forSeptember 2005, October 2005, or November 2005 delivery rose by$1.63, $1.56, and $1.55 per barrel, respectively. Should November2005 crude oil rise by $1.55 per barrel because of a tropical storm inmid-June 2005 that is not expected to affect production?25 To be sure,futures prices are linked across maturity, but truly short-term factorsshould not drive long-term prices.The impact of weather conditions on crude oil, natural gas, andgasoline prices was demonstrated to the public in the sharp priceincreases in the preceding commodities when Hurricane Katrinamade landfall in August 2005. Figure 5.4 depicts the reaction ofcrude oil futures prices to Tropical Storm Arlene in June 2005 andHurricane Katrina in August 2005.
154
72.006/9/05date.open 52.51date.high 54.50date.low 52.22date.close 54.28
70.0068.0066.0064.0062.006/9/05Tropical Storm Arlene
60.00
8/29/05Hurricane Katrina
58.0056.0054.00
8/29/05date.open 68.60date.high 70.80date.low 66.30date.close 67.20
52.0050.0048.00
6/2/05
7/2/05
8/2/05
9/2/05
Reaction of NYMEX front month crude oil futures prices to Tropical Storm Arlene and Hurricane Katrina in June and August 2005. Daily trading range of NYMEXcrude oil futures prices from May 1, 2005 to September 30, 2005.
FIGURE 5.4
Crude oil prices and the weather.
TRADING CATALYSTS
46.005/2/05
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155
Other Natural DisastersThe impact of natural disasters on market prices can be broad orfairly narrow. Some examples follow.
Hurricane Andrew and Prepayment SpeedSometimes, natural disasters impact a specialized market. Forinstance, in the wake of Hurricane Andrew, which hit the Miami areain 1992, mortgage traders saw an opportunity in selling pools of highyielding mortgages from the Miami area in the belief that the mortgages on many damaged homes would be prepaid. The high interestrates on these mortgages meant that prior to the arrival of HurricaneAndrew these mortgage-backed securities were selling at a premium(i.e., above par). However, they would be redeemed early at par. Theopportunity in this case was to sell premium bonds that were going tobecome par bonds very, very soon. In this case, the hurricane damageaffected the prepayment speed of mortgages underlying mortgagebacked securities and acted as a trading catalyst.26 In this instance,the potential for market overreaction was limited because tradersknew that the bonds could not fall below par.
The Chicago FloodMost floods are classic examples of natural disasters. Sometimes, aflood is the result of, or exacerbated by, the flawed actions or inactionof man. The resultant disaster is a combination of a natural and manmade disaster. Such an example occurred on April 13, 1992 when oldfreight and coal tunnels connecting buildings in downtown Chicagoflooded as the result of bureaucratic inaction. In its own way, theflood became a trading catalyst, although not in the way one mightexpect. The flooding of an old system of underground railway tunnelsthat carried coal to office buildings in the loop caused the closure of
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both the CME and the CBOT. The closure of the CBOT’s Treasurynote and bond futures markets meant that cash market bond dealerswere not easily able to transfer their risks to other market participants. As a consequence, the bid offer spread widened. This effect(i.e., a widening of the bid offer spread) has been observed in otherinstances where the futures market has been closed while the corresponding cash market was open.
The San Francisco EarthquakeThe delayed market response to the Kobe earthquake is reminiscentof the U.S. equity market’s reaction to the San Francisco earthquake.At about 5:12 A.M. on April 18, 1906, San Francisco was awoken by amagnitude 7.8 earthquake. The San Francisco earthquake and thesubsequent fires it engendered resulted in tremendous damage to thecity of San Francisco and significant loss of life.27 In his book 101Years on Wall Street, John Dennis Brown notes that news of theearthquake did not initially impact the U.S. stock market but was theprincipal precipitant of a subsequent selloff that, over the course of acouple of months, drove the Dow down by 17%. Part of the reasonfor the protracted decline was the need of property and casualtyinsurance companies to sell stock to pay insurance claims arising fromthe earthquake.28It is difficult to determine what fraction of the 17% decline instock prices over the two-month period was due to the San Franciscoearthquake. The important point is simply that the earthquake wasregarded at the time as a trading catalyst for part of the decline.
Trading LessonsMany natural disasters are inherently unpredictable. What are thetrading lessons when natural disasters and changes in weather conditions act as trading catalysts?
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First, a trader need not be able to accurately forecast weatherconditions or natural disasters to trade profitably off of them. This isbecause market reactions are often delayed. The temporal delay maybe a matter of a few minutes or several days. Not all markets willrespond to the same trading catalyst simultaneously. Second, differential reactions of similar commodities to the same catalyst occurwith some frequency. This makes it important for traders to anticipate which markets will be most sensitive to a trading catalyst. Third,the severity of a natural disaster is not necessarily a good proxy for itspotential importance to market participants. The great influenza pandemic of 1918-1919 wreaked enormous damage and was responsiblefor the deaths of more than 600,000 people in the United States andbetween 20 and 30 million people worldwide. However, the marketreaction to the Spanish flu was muted. The deadliest or most destructive natural disasters need not exert the greatest impact on financialmarkets. Consequently, it is more important for traders to predictwhich commodities will react to a given trading catalyst, as well as thedirection, timing, and magnitude of the likely reaction than to be ableto forecast when a trading catalyst will occur as the result of a naturaldisaster or weather conditions.As always, forecasting the likely reaction of market prices to trading catalysts requires an understanding of the likely interpretationsthat other traders will assign to various events—that is, what are thelikely trading theses other market participants will adopt? Many ofthe trading theses are partial equilibrium models that assume fixedsupply in the case of a demand shock or fixed demand in the case of asupply shock. These theses frequently overstate the actual impact of anatural disaster on market prices. Of course, this means that theremay be trading opportunities created in any overreaction of marketprices. Traders must know the current state of market conditions andsentiment bias as market conditions and market sentiment bias mayexacerbate (or diminish) any price moves trading catalysts induce.Finally, besides serving as trading catalysts, weather conditions andnatural disasters may become trading vehicles in themselves.
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The introduction of weather derivatives and specialized online bettingservices allows individuals to trade on weather conditions. So far, thesenew vehicles have enjoyed only limited success in the marketplace.
ReferencesAgence France Press. “HK Shares Lower on Concerns Over IraqWar, SARS.” March 31, 1995.Bary, Andrew. “Trading Points.” Barron’s, August 31, 1992, p. 39.Brown, John Dennis. 101 Years on Wall Street: An Investor’sAlmanac. Englewood Cliffs, New Jersey: Prentice Hall, 1991.Dow Jones Newswires. “HK Dlr Mkts Late: HK Dlr Forwards Continue to Surge on SARS.” April 25, 2003.Dow Jones Newswires. “1-Yr HK$ Forward Premium to Spot Hits5-Mo High on SARS.” April 21, 2003.Farivar, Masood. “Oil Prices Surge as Tropical Storm ApproachesGulf.” The Wall Street Journal, June 10, 2005, p. C4.Haavardsrud, Paul and Peter Morton. “Fast-food Stocks Plummet:McDonald’s Shares Down 8%; U.S. Temporarily Bans Import ofCanadian Cattle, Sheep and Goats.” National Post, May 21, 2003.Rafferty, Kevin and Mark Milner. “European Markets in Spin fromFall-Out of Kobe Earthquake.” The Guardian, January 24, 1995.Roll, R. “Orange Juice and the Weather,” American EconomicReview. 74, December 1984, pp. 861-80.Shiller, Robert. “Exuberant Reporting: Media and Misinformation inthe Markets.” Media, Vol. 23, (1) Spring 2001.Somerville, Paul. “Kobe Earthquake: An Urban Disaster.” Eos, Vol.76, No. 6, February 7, 1995, pp. 49-51.Spicer, Andi. “Commodities-Metals: Kobe Earthquake DisasterBoosts Market Demand.” Inter Press Service, January 20, 1995.
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Torchia, Andrew. “Asian Markets Decline on Fears about SARS—Stock Prices, Currencies and Interest Rates Drop.” The Asian WallStreet Journal, April 28, 2003.Tse, Yiuman and James Hackard. “Holy Mad Cow! Facts or (Mis)perceptions: A Clinical Study.” Journal of Futures Markets, Vol. 26, No.4, April 2006, pp. 315-341.United States Geological Service. Earthquake Hazards Program Website. (http://72.14.207.104/search?q=cache:1JaDBj_xDCUJ:neic.usgs.gov/neis/eqlists/eqsmajr.htm).United States Geological Service Web site. (http://md.water.usgs.gov/drought/define.html).The Wall Street Journal, June 10, 2005, p. B6.
Endnotes1
The earthquake occurred at 7:58:53 local time at the epicenter, or 00:58:53 Universal Time. It originated when the India tectonic plate slipped below the Burmatectonic plate along 1,200 kilometers of the boundary between the