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IntradaytradingimpactofU.S.economicnewsontheEUR/USD
BachelorThesisforObtainingtheDegree
BachelorofScience
InternationalManagement
SubmittedtoSilviaBressan
ThomasJandejsek
1221004
Vienna,15.06.2016
2
Affidavit
IherebyaffirmthatthisBachelor’sThesisrepresentsmyownwrittenworkandthatI
haveusednosourcesandaidsotherthanthoseindicated.Allpassagesquotedfrom
publicationsorparaphrasedfromthesesourcesareproperlycitedandattributed.
The thesiswasnot submitted in the sameor ina substantially similar version,not
evenpartially,toanotherexaminationboardandwasnotpublishedelsewhere.
15.06.2016
Date Signature
3
Abstract
The objective of this bachelor thesis is the analysis of the impact resulting from
updates in U.S. economic indicator news on the intraday EUR/USD currency pair
throughout the last 9 years. Preliminary the resultsof the “Non-farmPayroll”, the
“Core Durable GoodsOrder” and the “Core Consumer Price Index”will be closely
examined on forming price patters which may help forecast the future price
movements which can be leveraged through the binary options investment tool.
Thereby, the thesis interprets and discusses these patterns via descriptive and
explanatoryanalysiswiththeemphasisonwhetherthesethreeeconomicindicators
could be used for forecasting intraday changes in currency prices. In addition,
simplifiedmodels,suchastheabsoluteaveragepricechangeperminute,wereable
tocontributetothe forecastingmodelsandtheirprobability formaximizingprofit.
Fromtheanalysisitcouldbeproventhattheeffectslaidoutintheresearchmodel
werepartly significant andwereable to generate forecastmodels that couldbeat
thestandard50/50percentbinaryprobabilityoutcome.
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TableofContents
Affidavit.................................................................................................................2
Abstract.................................................................................................................3
TableofContents...................................................................................................4
ListofTables..........................................................................................................6
ListofFigures.........................................................................................................8
ListofAbbreviations...............................................................................................9
I. Introduction..................................................................................................10
II. TheoreticalFramework:EfficientMarketTheoryandNewsEventAnalysis,
Real-TimeExchangeRates,MacroeconomicIndicatorsandBinaryOptions..........11
II.1 EfficientMarketTheoryandNewsEventAnalysis.....................................11
II.2 BinaryOptions............................................................................................14
II.3 MacroeconomicIndicators.........................................................................18
II.3.1 Non-farmPayroll................................................................................19
II.3.2 CoreDurableGoodsOrders...............................................................21
II.3.3 CoreConsumerPriceIndex................................................................22
II.4 Foreignexchangeratedata........................................................................23
III. Researchmethodsection..........................................................................26
III.1 Researchmodeleffects...............................................................................28
III.1.1 Surpriseeffect....................................................................................28
III.1.2 Growtheffect.....................................................................................29
III.1.3 Revisioneffect....................................................................................29
III.1.4 Asymmetricsurpriseeffect................................................................30
III.2 DescriptiveAnalysis....................................................................................31
III.2.1 Measureofeffectdirection................................................................32
III.2.2 AbsoluteAveragePriceChange..........................................................33
III.3 ExplanatoryAnalysis...................................................................................33
IV. Analysis.....................................................................................................36
IV.1 DescriptiveAnalysis....................................................................................37
IV.1.1 Non-farmPayroll................................................................................37
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IV.1.2 CoreConsumerPriceIndex................................................................39
IV.1.3 CoreDurableGoodsOrders...............................................................40
IV.2 Measureofeffectdirection........................................................................42
IV.2.1 Non-farmPayroll................................................................................42
IV.2.2 CoreConsumerPriceIndex................................................................43
IV.2.3 CoreDurableGoodsOrders...............................................................44
IV.3 Absoluteaveragepricechange..................................................................44
IV.4 Explanatoryanalysis...................................................................................46
IV.4.1 Non-farmPayroll–10-minuteforecast..............................................46
IV.4.2 Non-farmPayroll–20-minuteforecast..............................................49
IV.4.3 CoreConsumerPriceIndex–10-minuteforecast..............................51
IV.4.4 CoreConsumerPriceIndex–20-minuteforecast..............................53
IV.4.5 CoreDurableGoodsOrders–10-minuteforecast.............................55
IV.4.6 CoreDurableGoodsOrders–20-minuteforecast.............................57
IV.5 Forecastingmodelaccuracy.......................................................................59
V. Conclusion....................................................................................................60
Bibliography.........................................................................................................64
Appendices..........................................................................................................66
APPENDIXA:DescriptiveStatistics.........................................................................66
APPENDIXB:Measureofeffectdirection...............................................................71
APPENDIXC:AbsoluteAveragePriceChangeperminute......................................73
APPENDIXD:ExplanatoryAnalysis–SPSSoutput..................................................74
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ListofTables
Table 1 … Non-farm Payroll 10-minute coefficients (Surprise, Growth, Revision,
AsymmetricSurprise).........................................................................................46
Table2…Non-farmPayroll10-minutecoefficients(Surprise,Growth,Revision).....47
Table3…Non-farmPayroll10-minutecoefficients(Surprise,Growth)....................47
Table4…Non-farmPayroll10-minutemodelsummary...........................................48
Table5…Non-farmPayroll10-minuteANOVAtest).................................................48
Table 6 … Non-farm Payroll 20-minute coefficients (Surprise, Growth, Revision,
AsymmetricSurprise).........................................................................................49
Table7…Non-farmPayroll 20-minute coefficients (Surprise, Revision,Asymmetric
Surprise).............................................................................................................49
Table8…Non-farmPayroll10-minutecoefficients(Surprise,Revision)...................50
Table9…Non-farmPayroll20-minuteModelSummary...........................................50
Table10…Non-farmPayroll20-minuteANOVAtest................................................51
Table 11 … Core Consumer Price Index 10-minute Coefficients (Surprise, Growth,
Revision,AsymmetricSurprise)..........................................................................51
Table 12 … Core Consumer Price Index 10-minute Coefficients (Surprise, Growth,
Revision).............................................................................................................52
Table13…CoreConsumerPriceIndex10-minuteModelSummary.........................52
Table14…CoreConsumerPriceIndex10-minuteANOVAtest................................53
Table 15 … Core Consumer Price Index 20-minute Coefficients (Surprise, Growth,
Revision,AsymmetricSurprise)..........................................................................53
Table 16 … Core Consumer Price Index 20-minute Coefficients (Surprise, Revision,
AsymmetricSurprise).........................................................................................54
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Table17…CoreConsumerPriceIndex20-minuteModelSummary.........................54
Table18…CoreConsumerPriceIndex20-minuteANOVAtest................................55
Table 19 … Core Curable Goods Orders 10-minute Coefficients (Surprise, Growth,
Revision,AsymmetricSurprise)..........................................................................55
Table 20 … Core Curable Goods Orders 10-minute Coefficients (Surprise, Growth,
AsymmetricSurprise).........................................................................................56
Table21…CoreCurableGoodsOrders10-minuteModelSummary........................56
Table22…CoreCurableGoodsOrders10-minuteANOVAtest................................57
Table 23 … Core Curable Goods Orders 20-minute Coefficients (Surprise, Growth,
Revision,AsymmetricSurprise)..........................................................................57
Table 24 … Core Curable Goods Orders 20-minute Coefficients (Surprise, Growth,
AsymmetricSurprise).........................................................................................57
Table25…CoreCurableGoodsOrders20-minuteModelSummary........................58
Table26…CoreCurableGoodsOrders20-minuteANOVAtest................................58
Table27…Modelaccuracysummary........................................................................59
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ListofFigures
Figure1…Researchmodelincludingalleffects........................................................27
Figure2…Absoluteaveragepricechangeperminute..............................................44
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ListofAbbreviations
NFP=Non-farmPayroll
CPI=ConsumerPriceIndex
C-CPI=CoreConsumerPriceIndex
C-DGO=CoreDurableGodsOrders
OTC=OvertheCounter
ITM=Inthemoney
OTM=Outofthemoney
SEC=SecuritiesandExchangeCommission
BLS=BureauofLaborStatistics
CES=CurrentEmploymentStatistics
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I. Introduction
For many years the price discovery process of foreign exchange rates has been
subjecttomanydiscussionsamongprofessionalsandscholars.Variousmodelsand
theories have been developed in order to explain underlying dynamics that may
possess the ability to result in a competitive advantage for trading. The theory
describingtheforeignexchangemarket largelydistinguishesbetweenthetechnical
andthefundamentalanalysis.Bothofthesetheoriesaimtopredictthefutureprice
of the currency pairs. The technical analysis focuses only on different charting
techniques and indicators, the fundamental analysis aims to analyze the overall
economicfoundationtopredictthefuturepricesettingofexchangerates.Thereby,
the process of interpreting news events in termsof representing a potential price
variable has been a widely discussed and researched topic of the fundamental
analysis.
BaseduponFama’sEfficientMarkettheoryderivedinthe1970s,manyresearchers
includingFlemingandRemolona(1997),Gilliametal.(n.d.)startedtoanalyzenews
analysistechniquesinthebondmarket.Elaboratingontheimplicationsfoundinthe
bond market, Andersen at al. (2002), Carlson & Lo (2003), Dominguez (1999),
Chaboud et al. (2004), Almeida, Goodhart & Payne (1997) and Ederington & Lee
(1995) expanded the research in the foreign exchange market. These scholars
researching the foreign exchangemarket were also able to prove the link among
news surprises, the price changes, the volatility and the volume. Even though
researcherswereable toestablisha linkbetween theeventand thepricechange,
thereisnosuchthingasaforecastingmodelhelpingtocalculatethemagnitudeof
futurepricechangeafterthepublication.
Closing this gap in the literature, the researchmodel presented in this thesis will
makeseveredemandsonanticipatingthepublicationimpactmeasuredintheprice
change using nine years of consecutive 1-minute intra-day data for the EUR/USD
foreign exchange rate. Thereby the U.S. macroeconomic indicators including the
Non-farmPayroll(NFP),theCoreConsumerPriceIndex(C-CPI)andtheCoreDurable
GoodsOrders(C-DGO)willbeanalyzed.Applyingstandarddescriptivemeasuresand
regression analysis, various factors such as the surprise effect measured by the
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differencebetweentheforecastandtheactualresult,thegrowtheffect,therevision
effectandtheeffectmeasuringtheimportanceofapositiveornegativepublication
will be statistically tested for impacting the magnitude of the price change.
Throughout the thesis special attentionwill begeared towards theprofitabilityon
usingbinaryoptionsas thepreferred investment tool for trading foreignexchange
rates.Insummary,thisthesisstressesthefollowingresearchquestion:Inwhatway
do the factors ofmacroeconomic indicator news releases impact the price setting
processoftheEUR/USDandhowthesecorrelationscanbeusedtoleverageprofitin
tradingforeignexchangeratesviabinaryoptions.
The following thesis is separated into 4 main sections, each contributing to the
research question of howmacroeconomic indicator news publications impact the
EUR/USD currency pair. Section II of this thesis will introduce themajor variables
and theories, which are subsequently incorporated into the research model
described in section III. Section IVwill present the empirical findingsderived from
the research model and section V will conclude the thesis and present ideas for
furtherresearch.
II. Theoretical Framework: EfficientMarket Theory and
News Event Analysis, Real-Time Exchange Rates,
MacroeconomicIndicatorsandBinaryOptions
Throughoutthisthesis,thetopicsofnews-eventanalysis,real-timeexchangerates,
macroeconomic indicatorsandbinaryoptionsareunderextensiveuse. Inorder to
beabletounderstandthedynamicsoftheresearchmodeloutlinedinsectionIII,itis
vitaltodiscussthevarioustopics inmoredetail. Inthissection,specialattentionis
drawntotheliteraturefoundonthemaintopicsguidingtheresearchmodel.
II.1 EfficientMarketTheoryandNewsEventAnalysis
News impacting foreign exchange rates can be traced back to the well-known
efficientmarket theory.According to Fama (1969), anefficientmarketdescribesa
situation in which prices ‘fully reflect all available information at any given time’
(p.383).Followingthisdoctrinewithrespecttonewsreleases,onlythedivergenceof
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the forecasts to the actual publicized figures, also called the surprise, can make
prices change, because every other information is already reflected in the current
price. Many scholars focused on this derivate theory for several years after the
publication of Fama’s concept trying to prove the existence of such a positive
relationshipbetweennewssurprisesandpriceadaptionprocesses.
Muchresearchhasbeenconductedontheimpactofnewsintheassetmarket,for
analyzing the company’s share prices reacting to specific news. Fleming and
Remolona(1997)foundthatbondpricesreacttothepublicationofmacroeconomic
announcements that are regularly released at certain established times. Thereby,
they largelyreliedon18differentmacroeconomicpublicationsandconcludedthat
certainannouncementsnotonlyhavehigherimpactsthanothers,butalsothatthe
bond market’s reaction correlates with the magnitude of the announcement
surprise.
Gilliam et. al. (n.d.) expanded the research by examining how linguistic analysis
techniques of non-numeric news releases could improve the accuracy of financial
prediction models in the stock market. Their approach is based on a text
interpretationanalysis,whichscreensnewsreleasesfor‘good’and‘bad’vocabulary
in order to draw conclusion on the publication. Throughout the years, there has
been a lot of attention on the assetmarket by evaluating the impact of news, by
leavingtheforeignexchangemarketratherundiscovered.
However, based upon the efficient market theory, Andersen et al. (2002) have
studiedtheasymmetricpricepatternslinkedtothenewsannouncementsurprisesin
the high-frequency foreign exchangemarket. They argued that only unanticipated
shocks in the fundamentals have an effect on the price and that negative news
releaseshaveagreaterimpactthanpositiveones.Otherscholars,involvingCarlson
& Lo (2003), Dominguez (1999), andmanymore, could also prove that there is a
positiverelationshipbetweenpriceactionandnewspublications.
However,Chaboudetal.(2004)concludedthattheeffectofnewssurprisesonthe
exchange rate occur quickly, impacting not only the price, but also the trading
volume and the volatility. Almeida, Goodhart and Payne (1997) reported on the
high-frequency reaction of the DEM/USD exchange rate within the first fifteen
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minutes after unexpected macroeconomic news elements. They highlight that
indicators such as the Payroll Employment figures, CPI, unemployment rates and
DurableGoodsOrdersareallmajormacroeconomicnewshavingsignificanteffect.
Furthermore, according to Chaboud et al. (2004), it is worthmentioning that the
higher trading volume impacts the foreign exchange rate for several hours even
aftertheannouncement.Subsequently,itisimportanttoknowthatspeedandtime
are two major variables that have to be taken into account when trading news
events.ThisurgencyforspeedwasmadeclearbyEderingtonandLee(1995)stating
thatscheduledmacroeconomicnewsreleasesadjustforeignexchangepriceswithin
10 seconds after publication and that major price changes are largely completed
afterthefirst40seconds.Therefore,itisvitaltoknowuntilwhenacertaintradecan
beenteredand forhow longacertainpositioncanbeheld inorder toexploit the
maximumleverageofanewssurpriseimpact.
The magnitude of price fluctuation is especially interesting for any trader for
calculating the underlying risk of entering a trade right after an indicator got
published.It is importanttoknowbyhowmuchacertaincurrencypair isprobably
goingtoaccelerate,especiallyfortraderswhodecidetotradeactivelyandnotviaan
algorithm.Thisistrue,duetothefactthatusingtoday’stechnology,algorithmscan
enter tradeswithinmilliseconds,wherebyanactive traderneedsa fewsecondsto
analyzethenewsdataandexecutethetrade.Thistimelostbyactivelytrading,may
alreadyresultinthatthetradermaylosemostofthepricemovement.Therefore,it
is necessary to roughly anticipate by howmuch a currency pair is going to react
basedonthepublicizedindicatorvariables.
Throughout this thesis specialattentionwillbegeared towards theprofitabilityon
usingbinaryoptionsas thepreferred investment tool for trading foreignexchange
rates.Abinaryoptionisafinancialinstrument,forwhichtheprofitgenerationonly
depends on correctly choosing the direction of the future price and not the
magnitude of price change. This type of investment tool will be chosen over the
straightforwardforeignexchangeratetradingduetothefactthatinordertoexploit
price changes deriving from news releases would require ultra-high frequency
trading software. By answering the research question it should be possible to
forecastthefuturepricechangeintheaccordingdirection,whichwilldeterminethe
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time point until when the risk for the trade will be acceptable for earning the
maximumprofit.Therefore, itwillbeeasier toexploitprofitsusing the forecasting
model, which will be created through the process of answering the research
question.
II.2 BinaryOptions
Thescopeofthisthesis istousethebinaryoption investmenttoolforexploitinga
possible forecastingmodel generated through the analysis of past EUR/USD data.
According to theSecuritiesandExchangeCommissionof theUnitedStates (2013),
binary options differ inmany essentialways from standard options. By entering a
trade, the binary options trader will be granted with the right to buy or sell the
underlying asset. This typeofoptionmerelydependson theoutcomeof a yes/no
proposition, hence the name binary option. The trader can choose among
commodities, indices, stocks and currencies for buying theoption for. Throughout
thisthesis,thestrategyusedfortradingbinaryoptionswillbebasedoncurrencies.
Atagivenspot-ratethetraderdecidesiftheanalyzedunderlyingassetwillincrease
or decrease in the quoted price throughout a certain timeframe. If the trader
anticipatestheassetpricetodecreaseinthefuturethenhewilloptfora‘put’andif
heanticipatestheassetpricetoincreaseinthefuturethenhewilloptfora‘call’.In
thecasethatthetradercorrectlyguessedthedirectionoftheunderlyingasset-price,
apre-determinedprofitwillbepaid.Thissituationisalsocalledtobe‘inthemoney’.
Theprofitpayouttypicallyrangesbetween72percentand82percentdependingon
the asset. However, if the trader analyzed the market wrongly, the whole
investmentfortheoptionwillbelost,whichwillbecalledtobe‘outofthemoney’.
This is the main reason why binary options are perceived to be a ‘high-yield
investmentandahigh-riskinvestmentatthesametime’(Planetoption,n.d.,p6).
In comparison to a standard option, the profitability does not depend on the
percentageincrease,butonlyontherightdecisionofthe‘call’or‘put’option.Given
this all-or-nothingpayout structure, in the literaturebinaryoptions are also called
‘fixed-return options’ or ‘all-or-nothing options’ (Securities Exchange Commission,
2013). Furthermore, unlike other types of options, when entering a trade by
investing throughabinaryoption, the traderwill not be grantedwith the right to
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purchase or sell the underlying asset. However, similar to the foreign exchange
rates,binaryoptionscanonlybetradedoverthecounter,which isalsocalledOTC
market.
Dependingon theunderlyingasset the trader canchoose to tradedifferentexpiry
times,which is the futurepoint in timethat is thereference forpricecomparison.
Furthermore,itisworthmentioningthatnotallassetsareavailablethroughoutthe
wholeweek. Stocks from American companies, for example, are only available to
trade during the American trading session from 1:00 pm to 9:00 pm Central
EuropeanTime.Nevertheless,currenciesaretraded24hoursadayfromMondayto
Friday,meaning that during this time a binary option trade can be done at every
single second. Due to the fact that this thesis focuses on trading currencies,
additionalinformationwillbeprovidedontradingcurrenciesviabinaryoptions.
Accordingto‘BancDeBinary’(n.d.),whichisoneofthemostfavoredbrokersfrom
thebeginningonwhenbinaryoptionsbecamepopularin2008,thisinvestmenttool
waspreferreddue to the low-risk, thehigh rewards, butmostly due to the short-
term investment frame,whichprovides the traderwith an instant feedbackof his
tradingstrategy.Therefore,thestandardbinaryoptionhasanexpirationtimeof10-
minute intervals starting for example from 06:30 to 06:40, whereby next trading
intervalwould last from06:40to06:50andsoon.However, it isworthmentioning
that throughout this 10-minute trading interval trades cannot be entered for the
whole 10 minutes. The first 5 minutes of a trading interval are foreseen for the
trades and the last 5minutes are blocked for entering, hence the name ‘Lockout
period’.Therefore,tradescanonlybeenteredthroughoutthefirst5minutesofthe
chosen 10-minute trade interval. If a trader enters the trade after the 5-minute
period,thetradeisconsideredtobeinthelockoutperiodandautomaticallyexpires
in the next trading period,which leads to a higher risk, because the timeframe is
muchlonger,whichaddsuncertainty.
Therefore, it is important to know when you enter a trade, because due to this
lockoutperiodtime,astandardbinaryoptioncanrangefrom5.01minutesto14.99
minutes,whichmakesalotofdifferencewhenacertaintradingstrategyisfollowed.
Depending on the broker different time intervals for trading binary options are
available.Usually,evenbytradingthestandardbinaryoptionthetradercanchoose
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to expire the trade a few 10-minute periods in advance. For example, when it is
06:23theEUR/USDbinaryoptioncanbechosentoexpireatthe03:30standard10-
minuteperiod,butalsoat03:40,03:50,04:10andsoon.Furthermore,theend-of-
the-day expiry interval, also called the EOD, at 8:10 pm. is listed throughout the
trading day and can be chosen if the trading strategy gives the right signals.
However, it is worthmentioning that the trading interval largely depends on the
underlying asset and the global trading session. For example, during the Asian
tradingsessiontherearetypically30-minutetimeintervals.Themarketsandassets
whicharemorefrequentlytradedtypicallyhaveshortertrading-intervals.Thisisthe
reasonwhycurrenciesarethemostpreferredunderlyingasset totrade,dueto its
hightradingvolumeandshorttimeintervals.
As already mentioned, many traders prefer the quick response for their trading
strategy due to the short trading intervals, many brokers added the 60-second
binaryoption. Thisparticular investment tool applies the same ITMandOTMpre-
arrangedprofit payout structure just for a 60-second time interval. Therefore, if a
traderbelievesthatacertainunderlyingassetwilldecreasethroughoutthenext60
secondshewill tradeaput. These60-secondbinaryoptions canbeenteredevery
singlesecond,meaningthattherearenopre-set intervalsfortrading.Additionally,
somebrokersevenofferother short time trading intervals, including30-, 90-, and
120-secondbinaryoptions.
Even though binary optionswere initially preferred for their short intervals, there
wasagrowingdemandforlongertimeintervalsaccordingto‘BancDeBinary’(n.d.).
Thereby, a new binary options mode called the long-term binary option was
introduced, offering the trader to extend their strategies to longer time frames.
Long-termtradescanbeenteredforexpiringonthenextday,thedayafteroruntil
theendofthetradingweekatapre-determinedtime.Thiswidertimeframeallows
atradertotakeamuchbiggerviewoftheglobalmarketsintoaccount.Allinall,asit
already could have been seen, the very diverse wants and needs of the traders
inspiredbrokerstointroduceavarietyofbinaryoptionmodifications.Dependingon
the binary option modification, the timeframe and the minimum required
investment capital for a single trade can vary. The minimum
investmentcapitallargelydependsontheselectedbroker.Themostfamousbinary
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option brokers let traders already invest into the standard binary option with an
investmentcapitalof1.00€.Therefore,evenwithaverylimitedinvestmentbudget,
traderscanbequicklyandeasilyengagedwiththerealmarket.
Oneofthemajoradvantagesthatthis investmenttoolprovides isthatthereisnot
much investment capital and registration effort needed to initially fund a trading
accountandgetstarted.Unlike for the foreignexchangeratemarket,whichsolely
depends on the percentage gain, the binary optionsmarket is preferred for small
tradingaccountsdue tohigherprofitmargins,whichmakes tradingbinaryoptions
perfect forbeginnersandaggressivetraders.Anotheressentialdifferencebetween
binary options and traditional foreign exchange trading is that no spreads are
attachedtobinaryoptions.Normally,bytradingforex,thespreadissubtractedfrom
theprofit,leavingthetraderwithasmallermargin.
Binaryoptionscanbetradedsimilarlyasforeignexchangeratesbasedontechnical
and fundamental analysis.When trading technical analysis the trader analyses the
chart of the respective underlying asset and bases his decisions on previous
patterns. As Lo, Mamaysky andWang (2000) found out in their paper about the
foundationof the technicalanalysis,unlike the fundamentalanalysis, the technical
analysiswasratherundiscovereduntil recently,dueto itshighlysubjectivenature.
Thereby,LoandMacKinlay(1999)wereabletoproofthatindeedpastpricepatterns
may be used to forecast future returns to some degree.Many price patterns and
indicators are applied to use the leverage of self-fulfilling hypotheses, by other
traders trading the same universally taught price patterns. Throughout the last
coupleofyears,thetechnicalanalysisenjoyedasteadyincreaseinusage,duetoits
easyapplicationandnoneedofcostlymarketdata.
Even though most Forex traders prefer technical analysis, ‘Banc De Binary’ (n.d.)
encourages binary option traders to leverage the advantages of fundamental
analysis.According toBauman (1996), fundamental analysis refers the valueof an
underlyingassetdueto its inferredvalueaccordingtotheassetsfoundation.Fora
company listedon the stockmarket the fundamentalanalysisassesses thevarious
company activities, its financial statements and all information impacting the
companyitselforitsindustry.Similarly,whenanalyzingcurrencies,tradersfocuson
thecountriesfundamentalsandallthenewsimpactingthem.Asalreadyestablished
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throughout the introduction, news releases impact the foreign exchange market
resulting inshort-termpricechanges.Therefore,thefundamentalanalysis includes
the assessment of the various news releases and their impact of the underlying
asset.Thisisthereasonwhyfundamentalanalysisandspecificallynewspublications
are ideal for trading binary options. Depending on the outcome of the news the
trader can easily leverage the short-term price change in his favor by entering a
trade according to the direction of the price change. Ederington and Lee (1995)
discoveredthatcurrenciesthatareimpactedbynewsjumpfromtheoldequilibrium
pricetothenewequilibrium,byescalatingquicklyandretracingbackneartotheold
price. This explainswhy binary options dominate regular foreign exchange trades,
duetothefactthatmostoftheprofitpercentagegeneratedinthefirstfewseconds
arelostagainbytherebound.Binaryoptionsdonotdependonthepercentagegain
in the certain direction, but only on the fact that the expiry rate is accordingly
different to the spot ratebyentering the trade. This advantage is the reasonwhy
manybinaryoptiontradersrelyonfundamentalanalysis.
AccordingtotheSEC(2013),mostofthebinaryoptionsmarketisprocessedthrough
internet-basedtradingplatformsactingasabroker.Duetothefactthatthebinary
options market is not subject to the regulations and supervision by the U.S.
regulators like the SEC, binary options are often perceived as being unsafe and
fraudulent. Furthermore, the simple process of only choosing the direction of the
quotationmakes this investment tool verypopular,explaining the rapidgrowthof
binary options in the recent years. In addition, more and more binary options
brokers cooperate with regulatory supervision departments in order to promote
transparency.
II.3 MacroeconomicIndicators
Throughoutthisthesis,themainpurposeistolinkthemacroeconomicnewsimpacts
fromtheNon-farmPayroll,theCoreDurableGoodsOrdersandtheCoreConsumer
PriceIndextotherealpricechangesontheEUR/USDcurrencypair.Eachindicatoris
publishedfromadifferentsourcesincethereexistmanyfinancialserviceproviders
thatgatherthesepublisheddataandcondensethelengthypublicationsintousable
formats.Themostfamousdataproviderisperceivedtobe‘TradingFactory’,which
isoneofthemostwidelyusedfinancialserviceproviderintheForexmarket.Forex
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Factory provides the trader with a so-called ‘Economic Calendar’. This economic
calendar lists every single numeric data that got released and ranks it on its
significanceforthecurrencypair.Additionally,thetradercanaccessaquickreview
of every indicator and its past history. In addition to the historic data, also the
forecast for the new publication can be seen, which gives the trader already an
expectationoftheoutcome.Intheverymomentatwhichtheofficialdataprovider
publishes the results of the indicator, these will be transferred to the economic
calendarofForexFactoryandarereadytosee.
Based on the published data the trader can react accordingly by trading the right
strategy.Forthisdecision-step,thethesiswillprovidethetraderwiththeabilityto
calculatetheanticipatedthefuturepricechange.Therefore,theresearchmodelwill
be applied to every single indicator leading to an indicator specific forecasting
model. However, in order to properly interpret the result derived through the
researchmodel,itisnecessarytostudythethreeindicatorsinmoredetail.
II.3.1 Non-farmPayroll
TheU.S.BureauofLaborStatistics,shortBLS,publishestwomonthlysurveys,which
bothhave the targetofgivingaclearerpictureof thecurrent labormarket.These
two surveys are the Current Population Survey and the Current Employment
Statistics, which is also called the Non-Farm Payroll. According to the Bureau of
Labor Statistics (2016), the payroll survey gives a ‘reliable gauge of the monthly
change in nonfarm payroll employment’ (p. 1), whereby the household survey
depicts employment including agriculture and self-employed labor forces. During
this thesis, the focus is set on the Non-farm Payroll, due to the fact that this
indicator is oneof themost influential indicators that get publishedon amonthly
basis.ThissignificanceissupportedbymanyscholarsincludingAndersen,Bollerslev,
Diebold and Vega (2005) from the National Bureau of Economic Research in
Massachusetts, stating that the Non-farm Payroll impact resulted in one of the
highest coefficientof determination, togetherwith theCPI and theDurable goods
orders, by comparing all indicators for their contemporaneous news response.
Therefore, the Non-farm Payroll creates a very favorable trading environment, in
that the publication of the news nearly guarantees a price move that can be
exploited.
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The Bureau of Labor Statistics (2016) defines the ‘Payroll Survey (CES)’ as an
indicator operating in the research universe that is concerning the nonfarmwage
and salary jobs. Thereby, they survey approximately 146.000 businesses and
government agencies accounting up to 623.000 individualworksites on amonthly
basis. Throughout the survey, themain focus isbasedon theobjective to retrieve
data about the overall employment, hours-worked, and earnings categorized by
industry and geographic region. However, only the paid jobs during the reference
periodarecountedintothesample.
Usually, the Non-farm Payroll results get published on the firstmonth’s Friday at
8:30EST. Thenumerical result is depicted in thousands,meaning that apublished
resultof250kactuallyisanactualincreaseof250.000employees.Accordingtothe
BureauofLaborStatistics(2016),thepayrollsurveyissubjecttosamplingerrorsdue
tothefactofthatthesamplesizeissolarge.Therefore,certainbenchmarkrevisions
arenecessaryafter the initialpublication inorder tocorrectsampleerrors.As it is
outlinedintheresearchmodeltheserevisionsaretobetestedofhavinganimpact
onthetrader’snewssurpriseperception.
Haltom,MitchellandTallman(2005)stressthatthepayrollindicatorisperceivedto
beanindicatorofmajorimportancebyprovidingpeoplewithaholisticviewofthe
labor market characteristics and the economy. Furthermore, they analyzed the
above-mentionedbenchmarkrevisionthattheBureauofLaborStatisticspublishes,
which is a ‘comprehensive revision adjusting the monthly payroll estimates to
universe counts of employment derived from unemployment insurance statistics’
(p.3).Dependingontheimpactoftherevisionpublication,thischangeisperceived
toalter the futureobservationof thepayroll time-series. Throughout the studyof
Haltom,MitchellandTallman(2005)theyputtheirattentiontowardstheweakness
laidoutbyKitchen(2003)thatthepayroll-indicatorissubjecttopotentialbias.This
bias is perceived to disrupt the significance of the monthly-published Non-farm
Payrollindicatoriftheyaresubjecttodrasticbenchmarkchanges.
It isworthnoting that if the researchmodel is able topredict the impactofNon-
farmPayroll revisionson theprice-changingpatternof theEUR/USD,past revision
time-series can be exploited to gain an advantage. Based on the information
providedbyHaltom,MitchellandTallman(2005)apositiveserialcorrelationof0.21
21
in the benchmark changes, allows making predictions about the size and the
directionoffuturerevisions.Ultimatelythismeansthatbyanalyzingthewholepast
time-series it is partially possible to explain the ‘variation of the log of the
benchmark payroll employment series in addition to the other explanatory series’
(Haltom,Mitchell&Tallman,2005,p.13).Therefore,futureresearchcanbuildupon
this premise in order to improve the research model outlined in this thesis, by
includingthisbenchmarkrevisionforecast.
II.3.2 CoreDurableGoodsOrders
AccordingtoRyanBarnes (n.d.)publishing for Investopedia,awell-knownfinancial
advisoryservice,theCoreDurableGoodsOrdersrepresentthe“newordersforU.S.
Core Durable Goods, which are the total durable goods orders excluding
transportation equipment”. Thereby, the durable goods are perceived as higher-
pricedgoods thatdonotwearout immediatelyandhavea longer lifespan.Classic
examples for durable goods are automobiles, planes, military equipment, trains,
industrial machinery, information technology equipment and much more. As
reportedbyBarnes(opt.cit.),transportationequipmentispurposelyexcludedinthe
CoreDurableGoodsOrderindicatorbecauseofthehighpricesofaircraftsandother
transportationequipment.Thesehighpricesmayskewordistortthecurrenttrend
ofthemonthlyresult,iflargequantitiesoftransportationequipmentarebought.
TheCoreDurableGoodsOrdersreport ispublishedonamonthlybasisbytheU.S.
CensusBureauusuallyaroundthe20thofthemonth.SimilartotheNon-farmPayroll
theCoreDurableGoodsOrdersarepublishedat8:30EST.Thereby,morethan4000
manufacturers in over 85 industries represent the observed samplemirroring the
wholeU.S.economy(Barnes,n.d.). Theresult ispublishedasapercentagechange
fromthepreviousmonth,givingaquickoverviewofthecurrentbusinessdemand.
The whole ‘Advance Report on Durable Goods Manufacturer’s Shipments,
Inventories and Orders’ additionally provides a detailed list of total numbers and
percentchangesof thevarioussectorsandthereviseddatafrompreviousmonths
(Stoica,2016).
The Core Durable Goods Order is a very favorable trading event because the
indicatorprovidesaclearbreakdownoftheindustriesandanadditionallyadjusted
22
valueoftherawdata.Thistrader-friendlyenvironmentresults inahighnumberof
trading volume, creating an ideal chance to exploit the resulting price jumps.
However, Barnes (n.d.) stresses that the Core Durable Goods Order brings some
weaknesses, which have to be taken into account. First of all, the survey outline
does not take a statistical standard deviation into account in order to measure
errors.Second,theindicatorisperceivedtobevolatile,meaningthatthepublished
result may distort the real trend. However, it is worth mentioning that the Core
Durable Goods Order already reduces this volatility by excluding transportation
equipment,whichstrengthensthesignificanceofthepublishedresult.
II.3.3 CoreConsumerPriceIndex
Ingeneral,apriceindexisameasuringtoolthatdepictsthechangeofpresetprices
over a certain time. With respect to the Consumer Price Index, this price index
measures the percentage change of prices of goods and services, which are
consumed by urban households on a monthly base. The Consumer Price Index
Manual, published by the International Labor Office in collaboration with the
International Monetary Fund, the Organization for Economic Co-Operation and
Development, the Eurostat, the United Nations and the World Bank (2004),
mentionsthatmostCPIsarecalculatedonaweightedaveragepercentagechangeof
a preset- basket of consumer products and services. The weights assigned to the
certain categories depend on the relative importance in the current household
consumption,wherebythesignificanceofthereliabilityontheCPIdependsonthe
appropriateweightsetting.
HoweverduetovolatilepricechangesinthefoodandenergysectortheU.S.Bureau
ofLaborStatisticspublishesaspecialindexreportexcludingthesetwosectorsfora
more reliable result, the Core Consumer Price Index or CPI-U (CPI for all Urban
Consumers).AccordingtoPeachandAlvarez(1996)policymakers,financialmarkets
and the Bureau of Labor statistics regard the Core Consumer Price Index as a key
inflation indicator by reducing the skew, which could result from the dramatic
swings corresponding to unusual shifts in ‘weather and other unforeseen events’
(p.1).Furthermore,theypointedoutthatadditionalproductssuchasusedcarsgot
excludedthroughthetimeinordertomakeresultsmoreaccurate indepictingthe
realeconomicconditions.
23
The International Labor Office (2014), stresses that the Consumer price index is
favoredbymanytradersasaninvestmentindicatorinthattheCPIispublishedona
monthly base, quickly available and usually not revised. Therefore, the monthly
release is a famous and awaited trading event by attracting a lot of publicity and
trading volume. Similar to the other two macroeconomic indicators, the Core
ConsumerPriceIndexisreleasedonamid-monthlybaseat08:30ESTresultingina
tradingfriendlyenvironmentgivingopportunitytoexploittheresultingpricejump.
II.4 Foreignexchangeratedata
The raw 1-minute EUR/USD foreign exchange rates were obtained from the free
historical forex data provider called ‘HistData.com’. HistData.com is a platform
initialized by a groupof traders and strategy developerswho seek using historical
forex data for developing their own strategies. According to the data provider,
historicalexchange ratesareespecially important for traderswhowish todevelop
newtradingstrategiesandbacktest tradingsystems.The fulldatabase forawhole
yearconsistsofroughly370.000quoteslabeledwithauniquetimestamp.Intotal,
this thesis analyses all indicator publications from January 2007 until December
2015,summinguptonineyearsoffulldata-points.Thereby,everysingle1-minute
EUR/USDquotedisplaysthecorrespondingopeningpriceandclosingprice,aswell
asthemaximumamplitude,alsocalledthehigh,andtheminimumamplitude,called
thelow.
However,throughoutthisthesisonlyalimitednumberofquotationswillbeneeded
inorder tobeable toproperlyasses the intra-dayeffectof indicatorpublications.
Specifically, only the immediate 20minutes after the indicator publicationwill be
subject to the analysis due to the fact that these first 20 minutes are the most
importantonesintradingbinaryoptionsasanintra-dayactivity.Thisfactalsoholds
trueforthegeneralforeignexchangeratetradingontheOTCmarketasEderington
and Lee (1995), Chaboud et al. (2004), Almeida, Goodhart and Payne (1997) and
manyotherscholarsconcludedthatthemajorpricechangewillbecompletedafter
thefirstminutesofpublishingtheindicator.
Nevertheless,primarily focusingon tradingbinaryoptions, it isvital toanalyze the
first10minutesafter thepublication inorder toassess thevalidity toenteraput-
24
/calloptionrightafterthepublication.Duetothefactthatactivehumantradersare
not able to enter trades within milliseconds, because they have to assess the
outcome of the publication and subsequently enter the trade on the brokerage
platform, these active traders may already lose a significant amount of the price
jumpuntiltheyareabletoenterthetrade.AsEderingtonandLee(1995)reportedin
their paper, themajority of the price changewill be done after the first 10 to 40
secondsrightafterthenewsrelease.Therefore,asoutlinedintheresearchproblem,
thisthesiswillelaborateontheprobabilityofprofitingfromabinaryoptionstrade
evenafterthefirstpricechange,byweighingthepriceadaptationmagnitudeofthe
currency pair according to the minutes past the release. This weighing process
dependingonthenewseffectwillpredicttheaccordingpricechangethroughoutthe
nextfewminutesinordertoassesstheriskofenteringatradeevenafewseconds,
orminutesafterthepublicationofthemacroeconomicindicator.
Furthermore,notonlythefirstten1-minutequotesdirectlyafterthereleasewillbe
taken into account but also the price exactly 20minutes after the publication. As
previouslymentionedtradingnewsreleasesusingbinaryoptionswill letthetrader
buyingoptionsfor10-minutetimeintervals.Accordingtoinsiderinformationfroma
senior accountmanager at the BancDe Binary, professional binary option traders
usually trade the 10-minute and the 20-minute option after a news release. This
tradingpatternstemsfromtheprobabilityof‘beinginthemoney’basedontypical
previous price changes, increasing the chance of profiting. Therefore, the closing
priceofthe20th-minuteafterthereleasewillbeanother importantquotethathas
tobetakenintotheanalysis.
Asmentioned,thewholedatasetprovidedby‘HistData.com’providestheopening,
closing,highandlowquotationforeverysingleminute.Inordertodepictthereality
ascloselyaspossible,itisimportanttoassigntherightpricestothedifferentminute
quotations. The EUR/USDprice used as a basis for the change in pricewill be the
openingquotationatthepublicationminute.Thesubsequent1-minutepricesfrom
thefirstuntiltheninthminutewillbecalculatedasameanofthehighandthelow
fromthatparticularminute.Themeanwilldepictthesimplifiedreality,asatrader
willenterthroughoutaminuteandnotattheveryopeningorclosingprice.The10th-
and20th-minuteafterthepublicationwillbemeasuredontheclosingprice,dueto
25
the fact that this price will be to one that assesses a trade either to be ‘ITM’ or
‘OTM’.
AmajorprerequisiteforminingtherightEUR/USDquotationsistospecificallyknow
at which time the various indicators are published. As already described earlier,
dependingontheindicatorandthemonthofpublication,thereleasetimeoverthe
wintermonths, usually takeplaceduring 08:30 EST and the release timeover the
summermonths takeplaceat07:30EST.Themonths fromNovember toFebruary
areperceivedtobethewintermonths,wherebythemonthsfromMarchtoOctober
are the summer months. The one-hour time difference mentioned before stems
from thedaylight saving time.However, it is important to note that the EUR/USD
foreignexchangeratetimeseriesaswellastheindicatorpublicationtimesareboth
without the daylight saving time. This information is vital for mining the correct
quotesbothdatabaseshavetobematchedinthesettingscorrectly.
Intotal,thefirst10minutesafterthepublicationandthe20th-minutepricewillbe
the targeted quotes out of the whole sample. This will leave a total of eleven 1-
minutequotespersingletradingevent.Coveringnineyearsofforeignexchangerate
dataand12publicationsperyear,thiswillleadtoatotalof108tradingeventsper
indicator.Multiplyingthatnumberby3indicators, leadstoanintermediarysumof
324 trading events. However, there is one overlap resulting from two indicators
publishedon the sameday,whichwill reduce the total numberof tradingevents,
revising the absolute number to 323. Throughout the analysis, for every single
trading event, eleven 1-minute EUR/USD quotes have to be gathered in order to
properlybuildup thedatabase for the researchmodel. Thiswill sumup to a final
datasetcovering3553foreignexchangeratequotationsrepresentingthebasedata-
set,whichwillbeusedforallanalysesthroughoutthisthesis.Inthenextsectionof
the thesis, the focus will be on the development of the research model and the
analytical tools thatwillbeused inorder togain informationoutof thebasedata
set.
26
III. Researchmethodsection
In this section of the thesis, the research method will be outlined in detail and
subsequently discussed. The collection of the data, the choice of design, the
strengthsandweaknesseswillbe subject toanalysis.Amajor focuswillbeputon
setting up the research hypotheses, which will be subject to test in the research
analysis. These hypotheses describe all major dynamics the model tries to cover.
Furthermore,thelimitationsofthedescribedmodelwillbelaidoutinordertoshow
restrictionsandgiveideasforfurtherresearch.
The research model applied to the analysis of this thesis will be based upon the
premises of different independent variables impacting the depended variable.
Regression analysis and standard descriptivemeasureswill be applied in order to
calculate the importance of the various independent variables. Having applied
regression analysis, it will be possible to forecast the price change due to the
independent variables described by the research model. This forecasted price
change will give the trader an indication of how much room there will be for a
profitabletrade.
The research model will be based on the application of an economic indicator
provider like Forex Factory in combination to themathematicalmodel outlined in
this thesis. Before the indicator is published the traderwill load themathematical
modelinamathematical-softwaresuchasMicrosoftExcelandtransfertheavailable
valuesbeforetheactualpublicationintothemodel.Thesepre-knownvaluesarethe
previousmonth’s result and the forecast for the publication. Due to the fact that
timeplays a vital role in order todecrease the risk ofmissing theprice jump, the
traderhastobereadytojustplug inthepublishedvalueandexecutethetradeas
quicklyaspossible.
27
EUR/USDPriceat
timet
EUR/USDPriceat
timet+1
1.2.
3.
4.
Theabove-outlinedresearchmodeldescribesthevariousvariablesthatcan impact
theEUR/USDforeignexchangeratewhenan indicatorgetspublished.Therebythe
modelcanbeeasilydividedintotheindependentandthedependentvariables.The
independentvariables involveall thenumeric indicatorvariables.The forecast, the
previousandtherevisionarethe figures thatareknown inadvance,meaningthat
thetraderalreadypossessesthisdatabeforethepublication.Theactualpublication
figureisthemostimportantindependentvariablebybeingthebasevaluetoallthe
other variables. These underlying dynamics stemming from the independent
variables will trigger an impact that will be released to the EUR/USD foreign
exchange rate, the dependent variable, at time ‘t’, which is the publication time.
Dependingontheoutcomeoftheindicatorpublicationtheimpactonthecurrency
priceattime‘t’willbeeitherpositiveornegative,resultinginadifferentpricelevel
PreviousForecast
Revision
Actual
Pricechange
impactedby
theindicator
positive/negative
Figure1…Researchmodelincludingalleffects
28
at time ‘t+1’. This anticipated price changewill be subject to the central research
question,byanalyzingthemagnitudeofthebreakout.
Based upon the research model outlined in the above section the following
underlyingdynamicshavetobediscussedinmoredetailforfullyunderstandingthe
impactsthatcanleadtothepricechange.
III.1 Researchmodeleffects
III.1.1 Surpriseeffect
Thismajoreffectdescribestheoften-discussednewssurprisethatleadstoquickand
violentpricechanges.Thereby,thedifferencebetweentheforecastedfigureandthe
actualpublicationfigurewillbetheso-calledsurprise.Dependingonthemagnitude
of thedifference theprice should react in theaccordingdirection. For example, if
the forecasted figure differs greatly from the actual one, then the impactmay be
more significant than if the difference is smaller. Based on this example it can be
seenthattheoverallgoalofanalyzingthesurpriseeffectistoassignthemagnitude
ofthepricechangetothesizeofthesurprise.However,itisworthmentioningthat
asAndersenatal.(2002)describedinthisstudy,thatnegativenewssurprisesimpact
the foreign exchange rate in amore severe way than positive surprises do. This
asymmetricsurpriseeffectwillbeexplainedandappliedinaseparatepointlater.
Thehypothesis testedwith the researchmodelwill test if thedifferencebetween
forecasted and actual indicator figure has an impact on the EUR/USD foreign
exchangerate?
H0:There isno impactontheEUR/USDexchangerateresulting fromthe
surprise effect, measuring the difference between expected and actual
indicatorrelease.
H1:There isan impacton theEUR/USDexchangerate resulting fromthe
surprise effect, measuring the difference between expected and actual
indicatorrelease.
29
III.1.2 Growtheffect
Similartothefirsteffect,thedifferencebetweenthepreviousandtheactualfigure,
called the growth effect, also presents a valuable measure for the price change
forecast. Thereby, the difference of the previous figure and the actual
macroeconomicindicatorfigureoutlinesthegrowthfromoneperiodtoanother.For
example,ifthepreviousfiguredeviatesalotfromtheactualone,theimpactonthe
pricemaybemoresignificantthanifthereisnodifference.Thiseffectalsoexplains
a kind of surprise effect but rather limits its importance to the prognosis of the
growth of the indicator. The difference between the previous and the actual
publicizedfigurecanalsobeinterpretedasatrendsignal,showingthetraderifthe
indicatorincreasesordecreasesfromoneperiodtoanother.
The hypothesis testing the significance of the growth effect measuring difference
betweenpreviousandactualindicatorfigureisstructuredasfollowing:
H0: There is no impact on the EUR/USD exchange rate resulting from thegrowth effect, measuring the difference between revised and actualindicatorrelease.H1: There is an impact on the EUR/USD exchange rate resulting from the
growth effect, measuring the difference between revised and actual
indicatorrelease.
III.1.3 Revisioneffect
Due to the fact themost numericmacroeconomic indicators get revised after the
initialpublicationdue tomeasurementerrors, this revisioneffecthas tobe tested
for its significance. This revision is done by the publisher due to measurement
inaccuraciesthatareunavoidableatthefirstcountormeasurement.Dependingon
the indicator these revisions happen more or less often. However, it is worth
mentioningthattheserevisionscanbedoneinvariousstepsandatdifferenttimes
untilthenextpublishingperiodstarts.Therefore,itisimportanttoknowifarevision
has an impact on the trader’s perception of the news release, which will
subsequently impact theprice creation. For example, if it is common for a certain
indicator that the results are up-revised then this estimated revision of future
publicationscouldhaveanimpact.
30
Therefore,therevisionofeverysinglepublicationwillbetestedforhavinganimpact
on the price percentage change. This will allow to elaborate on the hypothesis
outlining the question if the difference between the previous and the revised
indicatorvaluehasastrengtheningorweakeningeffectonthepricechangeimpact?
H0: The revision effect described by the difference between the previousand the revised indicator value has no effect on the EUR/USD foreignexchangerate.
H1: The revision effect described by the difference between the previous
and the revised indicator value has an effect on the EUR/USD foreign
exchangerate.
III.1.4 Asymmetricsurpriseeffect
Andersenatal.(2002)pointedoutthattheimportanceoftheasymmetricsurprise,
measuring how a negative or a positive news release result can have different
impactson theprice change.Thiseffect isperceived tobeasymmetricdue to the
factthatnegativepublicationsurprisesresultinamuchmoresignificantimpactthan
positive results do. Therefore, it is vital to test and also include this asymmetric
price change pattern into the research model for increasing the accuracy of the
forecast. Comparing both positive and negative publication surprises, namely the
difference between the forecasted and the actual result, will make it possible to
elaboratefurtheronthemagnitudeoftheimpact.Ifthehypothesiscanbevalidated
that negative results impact the foreign exchange rate more than positive one,
regressionanalysiswillhavethegoaltodeclaretheunderlyingsignificance.
Theaccordinghypothesiswillbebasedonthepremiseswhetherornotanegative
resulthasmoreimpactthanapositiveone?
H0: The asymmetric surprise effect shows no significant impact on the
EUR/USDexchangerate.
H1: The asymmetric surprise effect shows a significant impact on the
EUR/USDexchangerate.
31
Allthehypothesesdevelopedintheprocessofsettinguptheresearchmodelarein
linewith the aim to contribute valuable input to the overall research problem. In
ordertomeasuretheimpactandthevalidityofthevariouseffects,itisnecessaryto
discuss all the analysis tools and techniques which will be used throughout this
thesis.
III.2 DescriptiveAnalysis
Applyingstandarddescriptivemeasuressuchasthemean,median,mode,minimum,
maximumandthestandarddeviationitwillbepossibletogetaclearpictureofthe
various indicators. This analysis will give the researcher and the trader a much
deeperunderstandingoftheEUR/USDforeignexchangeratebehaviorbasedonthe
individualeffects. Furthermore, theanalysiswill givean ideaof theaverage result
that should be expected if a new indicator gets published based on the past
descriptive.However, in order to apply the descriptive analysis for all four effects
thedatapointsfromtheindicatorsandforeignexchangerateshavetobeseparated
andsortedbyindicatorandeffect.Thiswillbedoneinaneasythree-stepplan:first,
theeffectsforallpublicationeventswillbecalculated,second,alldatapointswillbe
mergedwiththecalculatedeffectvalueandthird,alldatapointsalreadysortedby
indicator and effect will be divided into positive and negative effect results. If all
threestepsareappliedtothemaindataset,thedescriptiveanalysiscanbestarted.
In thebeginning, each indicator has its own spreadsheet, due to the fact that the
three indicators are published at different time points during the month and
therefore have different starting currency prices to compare to. Therefore, each
indicatorhavingitsownspreadsheet,startswithdifferent108dataentryrows,each
rowforone indicatorpublication.Asdescribedbefore,thefirststep is tocalculate
the four effect values for each row. For the first three effects the mathematical
procedure is a straightforward subtraction of the values described in the effect,
however the asymmetric surprise has to be discussed a bit closer. Concerning the
asymmetric surprise effect, the goal of the analysis is to test if negative indicator
publications have a more significant impact on the foreign exchange rate than
positiveonesdo.Therefore,wheneverthedifferencebetweentheforecastandthe
actualoutcomeisnegativetheasymmetriceffectvaluewillbeassignedwitha“0”.
However, if the difference is positive the effect value will be labeled with a “1”.
32
Based on this dummy variable it will be possible to perform a simple descriptive
analysistestingthedesiredeffectvalidity.
After theeffect valueshavebeen calculated the four individual effectshave tobe
separatedandforeacheffectthe10-minuteandthe20-minutepricedeltahastobe
allocated.Inthelaststepbeforethedatacanbeanalyzed,thesub-datasethastobe
separated by positive and negative outcomes. Solely for the Core Consumer Price
Indextherewillbeathirdoptionofhavinganeutraleffectoutcome,becausethere
areasignificantnumberofneutraleffectoutcomesincomparisontotheothertwo
indicators. Furthermore, it has to be mentioned that the 108th data row of the
revision sub-data sethas tobe removeddue to the fact that the revisioneffect is
missing the newest data input. If all the sub-data sets are split into the possible
outcomes,itisnowpossibletoapplyallstandarddescriptiveanalysismeasures.
Byapplyingthemean,mode,median,maximum,minimumandstandarddeviation
forallpositive,negativeandneutralsub-datasets,itwillbepossibletomakesome
conclusions about the complexion of the effect. In addition to the standard
descriptivemeasures, therewill be a ‘measure of effect direction’ included in the
nextpart,whichmeasures theprobabilityofgettinganegative/positive10-or20-
minutedeltaiftheeffectoutcomeisnegative/positive.Thishelpsthetraderinthe
decisionifacertainindicatoroutcomewillresultinapositiveornegativebasedon
the outcome of the effect and what price change he can expect to happen on
average.
III.2.1 Measureofeffectdirection
The ‘measure of effect direction’ examines if a certain effect outcome can impact
theforeignexchangerateseverelyenoughtoresultinapricechange,whichisinthe
same direction of the effect outcome. The analysis is based on a simple count,
wherebystartingfromthealreadystructuredsub-setsfromthestandarddescriptive
analysis,forallpositiveeffectoutcomesthepositivepricechangesatthe10-minute
markandthe20-minutemarkwillbecountedandbenchmarkedtothesamplesize.
Doingthisforthenegativeeffectoutcomesaswell,theresultoftheanalysiswillbe
apercentage,whichistheopportunitythatdescribesthechanceofthepricechange
goingintothesamedirectionastheeffectoutcome.
33
Thisinformationwillbeespeciallyimportantforabinaryoptionstrader,becausethe
profitability of the trade is based on knowing the direction of the future price.
However, it isworthmentioning that thismeasurement tool doesnot includeany
informationaboutthemagnitudeofthepricechange,whichmeansthatthereonly
mightbeaslightpricechange intoacertaindirection,which isalreadymissedout
after a short period. Therefore, this analysis cannot be taken as a single decision
instrument,butratherasanadditionaltool.
III.2.2 AbsoluteAveragePriceChange
In addition to the standard descriptive analysis of the researched effects, the
absoluteaveragepricechangeperminuteafterpublicationwillbeanalyzedinorder
toprovide the traderwithanoverview towhatextent thepublicationonaverage
impactstheforeignexchangerate.Thisabsoluteaveragepricechangeis important
duetothefactthatthetraderhastoknowwhenthebiggestimpactonthecurrency
isfinished.ReferenceismadetothepaperfromAlmeida,Goodhart&Payne(1997),
whoconcluded that themajorprice changewouldbedonewithin the first fifteen
minutes. However, Ederington and Lee (1995) argued that the major price jump
already occurs during the first 10 to 40 seconds after the publication. Therefore,
within the research conducted for this bachelor thesis, a simplified check for the
reactiontimewillbeappliedthroughtheabsoluteaveragepricechangeperminute.
Subsequently, a trader could argue that if the analysis shows that after the first 2
minutesthemajorpricejumpiscompleted,thenitwouldnotbewisetoenterthe
trade anymore. However, it has to bementioned that this test is a simplification,
because all thedeltaswill be averagedand the time frame is rather largebyonly
havingaccessto1-minutedata.
III.3 ExplanatoryAnalysis
Havinganalyzedthevariousdynamicsoftheresearchmodel,itispossibletobuilda
mathematical forecasting model based on multi-linear regression describing all
interlinks of the variables. Thereby calculating the price change is the desired
outcomeofthemathematicalmodel.
∆𝑃 = 𝑓(𝐼𝑎𝑡, 𝐼𝑝𝑡, 𝐼𝑎t-1, 𝐼𝑓𝑡, 𝐼./)Formula1
34
AsitcanberetrievedfromFormula1thechangeinpriceisafunctionoftheactual
indicator figure at time t (Iat), the previous indicator figure at time t (Ipt), the
forecastedindicatorfigureattimet(Ift),theactualindicatorfigureattime‘t-1’(Iat-1).
Alltheselistedvariablesareindependentandareessentialcomponentstobuildthe
mathematicalinterrelationshipsofthefourdifferenteffectsmentionedbefore.
∆𝑃 = 𝑃 + ∝∗ 𝐼𝑎𝑡 − 𝐼𝑓𝑡 + 𝛽 ∗ 𝐼𝑎𝑡 − 𝐼𝑝𝑡 + 𝛾 ∗ 𝐼𝑝𝑡 − 𝐼𝑎𝑡 − 1 +
𝛿 ∗ 𝐼./ ) Formula2
The above-mentioned Formula 2 captures all the interrelation effects of the
independentvariableshavinganimpactonthepricechange,thedependedvariable.
Theseveralcoefficientsbuiltintotheformulaexpressthemagnitudeoftheeffects.
In order to derive these coefficients, all the effects will be tested via regression
analysis, which will result in a measure that expresses the magnitude of the
independentvariableonthepricechange.Thenumericvaluesofthecoefficientswill
bederivedfromtheoutputtableresultingfromtheSPSSregressionanalysis.
The first effect explaining the difference between the forecasted and the actual
value,alsocalledsurpriseeffect,iscalculatedbysubtractingIftfromIat.Thegrowth
effecttakingintoaccountthedifferencebetweenthepreviousandtheactualvalue
is derived through the subtraction of Ipt from Iat. The divergence from the actual
publishednumbertothe‘previous’figurefromthenexttime,explainstherevision
effect, by subtracting Iat-1 from Ipt). The last and fourth effect describing the
asymmetricsurpriseeffectwillbecalculatedbythedummyvariableIAS.Thisdummy
variablewill be assignedwith a ‘0’ is thedifferencebetween the forecast and the
actualvalueisnegative,andwitha‘1’ifthedifferenceispositive.
Once thecoefficientsarederived through thepractical researchanalysis, itwillbe
possibletopluginthevariablesforeveryfuturepublicationinordertocalculatethe
anticipatedpricechangebasedonthehistoricvalues.Thisprice-changeforecastwill
beprovidedforallofthethreeindicatorsanalyzedinthisthesis.Thereby,itisworth
mentioningthateverysingleindicatorwillhavedifferentcoefficients,strengthening
or weakening the various interrelationship effects, due to the fact that some
indicators impact the foreign exchange market more than others. Based on the
forecast models, the forecast accuracy will be calculated by comparing the
35
forecasted values with the actual values of the time series. Doing this it will be
possible to see if the various forecastmodels are able to develop accurate values
andtherightpricechangedirection.However,theprescribedmathematicalmodelis
solelyapplicabletotheEUR/USDforeignexchangerate,becausethecoefficientsare
basedonthepastEUR/USDcurrencyvalues.Furtherresearchmayapplythesame
structure andmethodology toother foreignexchange rates inorder tobe able to
derivethecoefficients for thatgivencurrency.Therebyonlythecoefficientsof the
underlying effects that are outlined in this thesis will change, leaving the several
effectdynamicsbeingthesame.
This researchmodel is based on the premise that historic patterns of changes in
price are repeated in the future. Therefore, it is vital that the effect dynamics
governing the price adjustment stay constant over time. This premise builds upon
the continuation-rule, which states that the past will be true for the future. This
same rule applies to the majority of the financial models and implications.
Furthermore, this model is based on the pre-requisite that the trader is trading
activelyandnotthroughanalgorithm,duetotheneedforpersonal interpretation
asasafeguard.Tradingactivelyautomaticallyimpliesthatthetraderhastoanalyze
thepublishedindicatorresultsasfastaspossibletoincreasetheriskofmissingthe
pricespike.
The research model developed throughout this thesis has the clear advantage of
beingbasedonaverydetailedandbigdatasample.Previousworks in the fieldof
news effects on foreign exchange rates cover rather small data samples, ranging
fromweekstoonlyafewyears.Duetothefactthatthisresearchcoversanine-year
sample of data on the high frequency scale, the interpretation of the results is
increased in validity. Furthermore, the EUR/USD exchange rate is analyzed on a
minute-to-minute scale, which is much more detailed than most of the available
researchliterature.
The researchmodel outlined throughout thiswork tries to take asmany variables
andeffects thatmay impact the foreignexchange rate intoaccount.Nevertheless,
the model will be a simplification of the reality, meaning that there are several
uncertaintiesandlimitationsimplicittothismodel.Thereby,themodelisnotableto
catchtheoverallpictureofthemarketandthehappeningsaroundtheworld.Ifthe
36
overallmarket is in a recession thepublished resultsofmacroeconomic indicators
mayhaveadifferentimpactthanundernormalconditions.Thesameappliesifthe
overall economy is enjoying a recovery or a boom. Therefore, it is important to
analyzetheeconomiccycleandconstantlytakingthecurrentstateoftheeconomy
into consideration when interpreting the results of macroeconomic indicators.
Additionally, it has to bementioned that the forecastmodels derived throughout
this thesis have to be continuously updated by including the newest publication
valuesinordertobeabletocatchcertaintrendsandlong-termchanges.
Furthermore, the model does not take other indicators into account that are
published at the same time, which may add uncertainty to the validity of the
researchmodel. In the U.S. and other countries, it is common that indicators are
publishedatthesametimewhenthemarketopens.Thismayleadtothesituation
thatatacertaintimemorethanoneindicatorwillbepublished.Ifsuchasituation
appears, it may happen that the results of the other indicators overrule,
strengthening or weakening the impact of the indicator observed in this thesis.
Depending on the number andwhat kind of indicators are published at the same
time, thesemay skew the accuracy of the price changes derived from themodel.
However, due to time and resource limitations, it was not possible to cover the
effectofotherindicatorspublishedatthesametime,whichindicatesaprosperous
ideaforfurtherresearch.
Likewise, the researchmodel presented in this thesis does not cover nun-numeric
news releases. Similar to indicator publications, also non-numeric news releases
impacttheforeignexchangemarket,invariousways.Therefore,nottakingthiskind
of market information into account, the model will be exposed to uncertainty. A
similar approach as outlined by Gilliam et al.(n.d.) could be followed in order to
interpret non-numeric news releases through algorithms in order to reduce
uncertaintyinthefuture.
IV. Analysis
Throughout this part of the bachelor thesis, all the results of the descriptive and
explanatory analysis will be presented in a systematic and detailed manner.
37
Therefore,thischapterwillbestructured inthesamewayastheresearchmethod
section was, by starting with the descriptive analysis and finishing with the
explanatory analysis. All tables and spreadsheet discussed throughout the analysis
canbe found in theappendixof the thesis. In theendof this chapter it shouldbe
clearwhattheresultsoftheresearchoutlinedinthisbachelorthesisare.
IV.1 DescriptiveAnalysis
Theresultsofthedescriptiveanalysiswillbeseparatedbytheindicatorinorderto
provideameaningful overview. Furthermore,withineach indicator all foureffects
will be discussed separately in order to notmix up any results. The spreadsheets
withtheresultsofthedescriptiveanalysis,whichwillbereferencedthroughoutthe
analysis,canbefoundinAPPENDIXAofthebachelorthesis.
IV.1.1 Non-farmPayroll
Withreferencetotheeffectmeasuringtheimpactofthesurpriseeffect,theoverall
picture of the descriptive analysis shows that the effect tends to strengthen the
positiveeffectoutcomes.Additionally, themeanof thepositiveoutcomesshowsa
significantdifferencetothenegativeoutcomebyresultinginapricechangebeing5
to 7 times bigger on average. However, on general it can be seen that negative
effect outcomes also result in negative price changes over time and the positive
onesviceversa.Nevertheless, itcanbedetectedthatthepositiveeffectoutcomes
impactthepriceinacertainwaythatitalsofluctuatesmore,whichcanbeseenby
comparing the minimum and maximum values. All other descriptive measures
supporttheinterpretationthatthepositiveoutcomesareexceedingtheimportance
ofnegativeones.
Thesecondeffectmeasuringtheimpactofthedifferencebetweenthepreviousand
the actual value, also called growth effect, shows a very similar result as the first
effectdidwithonemajordifference.Analyzingthemean,itisclearthattheforthe
negative effect outcomes the average price change on the 20-minute mark even
turn positive, meaning that the effect influences the currency pair in the wrong
direction. Similar to the surpriseeffect thepositiveoutcomesonaverage result in
wayhigherpricechangesthanthenegativeresultsdo.However,withregardstothe
38
volatility it can be detected that the negative effect outcomes tend to result in
higherpricevolatilityincomparisontothepositiveones.
Withregardtotheeffectanalyzingtheimpactoftherevision,itisinterestingtosee
thatonaverageeventhenegativeoutcomesresultinpositivepricechangesatboth
timeframes,whichevenincreaseovertime.Thisobservationiscompletelydifferent
to the effects that were analyzed before. Besides that, the mean shows a clear
growth pattern, meaning that the effect outcome influences the pricemore over
time. Apart from that, the other descriptive measures are not showing any
anomalies.
Thefourtheffectmeasuringtheasymmetricsurpriseeffectshowsanaverageresult
thatiscontrarytothestatementsthatcanbefoundinthepapersofAndersenatal.
(2002), Carlson & Lo (2003) and Dominguez (1999). They stated that on average
negativesurprisesresultinhigherpricechangesthanpositiveonesdo,whichcould
not be supported by the descriptive analysis done for the Non-farm Payroll.
Furthermore, it is worth mentioning that the price change during the first 10
minutes isbigger than theafter20minutes,whichcanbeseen,bycomparing the
mean values at the respective time marks. Additionally, even though the price
change for thenegativeeffect results isonaveragenegative forboth timepoints,
after the first 10 minutes the price change decreases again. Besides the median
beingzeroforbothnegativeeffectoutcometimeframes,allothermeasuresdon’t
showdeviations.
Overall,analyzingthedescriptiveresultsfromtheNon-farmPayroll indicator itcan
be observed that there is an overall trend that positive effect outcomes generally
result in higher and more positive price changes than the negative ones do.
Additionally,itcanbesaidthatpositiveeffectoutcomesaremorereliable,because
the negative ones may change direction and even turn positive over time. With
regardtothevolatility,therecannotbeaunifiedanswerhowtheindicatorbehaves
accordingtocertaineffectoutcomes,becauseitvariesfromeffecttoeffect.Besides
that, there are no significant deviations in the other descriptivemeasures such as
themaximum,minimum,median,mode,varianceandstandarddeviation.
39
IV.1.2 CoreConsumerPriceIndex
AnalyzingthedescriptivemeasuresoftheCoreConsumerPriceIndexwasdifferent
fromtheothertwomeasures inthattheneutraleffectoutcomewasaddedtothe
analysis.Asitcouldbeseenfromtheanalysisaboutonethirdoftheeffectoutcomes
wereneutral,whichmadeclearthatthisthirdoptionhastobeanalyzedseparately.
Furthermore,ithastobementionedthatincomparisontotheNon-farmPayrollthe
CoreConsumerPriceIndexismeasuredinpercentinsteadofabsolutevalues.
Withregardtothesurpriseeffect,thereisanequaldistributionofpositive,negative
and neutral effect outcomes. However, comparing the mean of all three effect
outcomes, different results canbe found. Both theneutral and thepositive effect
outcomes show a clear indication for a positive average price changes over time.
Thereby,thenegativeeffectoutcomesareimpactingtheexchangerateinawaythat
for both time frames the price changes are positive. Furthermore, the negative
effectoutcomeleadstopriceeffectatthe20-minutemarkthatisaboutdoublethe
price change of the other two effect outcomes. Analyzing the minimum and the
maximum it can be seen that the positive effect outcome results in the highest
volatility,whichcanalsobeconfirmedbythestandarddeviation.
Theeffectmeasuringthedifferencebetweenthepreviousandtheactualvalue,also
called the growth effect, results in 48 out of 108 cases in a neutral outcome and
represents the biggest category of effect outcomes. The overall picture of the
average price changes is very similar to the results explained in the first effect.
However,thepositiveeffectoutcomesexceedthenegativeresultsbyamultipleof
3. This observation is reflected in the standard deviation of the positive effect
outcome by having the highest values. Additionally, it can be detected that the
negative and the positive effect outcomes result in a bigger magnitude of price
changeatthe20-minutemark.
Eventhoughtheprevioustwoeffectsshowinternallyconsistentresults,therevision
effect varies in the results. First, almost 50 percent of the effect outcomes are
neutral. Second,with reference to the negative effect outcome the average price
change is negative at the 10-minute mark and the positive effect outcome turns
negative at the 20 –minutemark. Lastly, for the neutral results the average price
40
change for both time frames indicate a clear and strong positivemagnitude. Even
thoughtheneutraleffecthasthehighestaverageprice,thepositiveeffectoutcome
hasahigherstandarddeviation.
Thefourtheffectevaluatingtheasymmetricsurpriseeffectshowsacleartendency
towardthepositiveeffectoutcomes in75outof108cases.Opposite to theresult
obtainedanalyzing theNon-farmPayroll, thenegativeeffectoutcomesonaverage
resultinahigherpricechangethanthepositiveonesonbothtimemarks.However,
it is worth mentioning that this bigger price change from the negative effect
outcome isapositivepricechange,meaning that thepricechange is in thewrong
direction.Thiseffectshowsthestrongestpositivepricechangeofthenegativeeffect
outcomes,which indicates a veryweak correlation between the price change and
theasymmetricsurpriseeffect.
All in all, the descriptive analysis of the Core Consumer Price Index showed that
neutral effect outcomes are very common and that these result in positive price
changes on average. Furthermore, it could be detected that besides the positive
effect outcomes, also the negative effect outcomes showed a clear tendency to
resultinpositivepricechangesovertime.Theotherstandarddescriptivemeasures
such as min, max, median and mode showed no severe outliers or inconsistent
resultswithinthesample.
IV.1.3 CoreDurableGoodsOrders
SimilartotheCoreConsumerPriceIndex,theCoreDurableGoodsOrdersvaluesare
an expression of the percent change. However, the average percent change is a
multiple of the one from the Core Consumer Price Index. This may also be the
reasonwhytherewereonlyveryfeweffectoutcomesthatwereneutral.Aswiththe
Non-farm Payroll these few neutral effect outcomeswill be counted as a positive
outcome.
Thefirsteffecttestingthesurpriseeffectshowsaperfectfitfortheindividualprice
changesover time in that thepositiveeffectoutcomeshaveaclearpositivemean
pricechangethatisincreasingovertime.Similar,thenegativeeffectoutcomesshow
astrongtendencytoresultinnegativepricedeltasincreasingoverthetimehorizon.
Furthermore, themagnitudeof the negative price changeoverweighs thepositive
41
values,whichstrengthensthegeneraleffectofanegativeresult.However,itcanbe
seen from the minimum and maximum values that the positive effect outcomes
resultinahighervolatility.
Withregardtothegrowtheffect,bothoutcomesresultinpricechangesaccordingto
the effect outcome. However, it is worth mentioning that the average price
differencedoesnot increase,butratherdecreasesovertimeforboththenegative
andthepositiveeffectoutcomes.Additionally,itcanbedetectedthatthestandard
deviation of the negative effect outcomes is much higher than the standard
deviation of the positive outcomes. The minimum and the maximum descriptive
measuressupporttheobservationofthenegativeeffectoutcomeresultinginmore
volatilepricechanges.However,themedianofthenegativeeffectoutcomeshows
thatobviouslymorepositivepricechangesarepresentinthesample.
Thedescriptivemeasuresoftherevisioneffectshowamixedpicture,whichisnotin
linewiththeresultsofthetwoeffectsanalyzedbefore.Forboththepositiveandthe
negative effect outcome the price change at the 10-minute mark goes into the
wrongdirection.However,atthe20-minutemarkforboththeeffectoutcomesthe
meanpricechangegoesslightlyinthecorrectdirection.Additionally,itcanbeseen
thatthepositivepricechangeofthenegativeeffectoutcomeisevenbiggerthanthe
positive price change of the positive effect outcome. The same pattern can be
detected for the negative price change, which is higher for the positive effect
outcome.Thismixedstructureissupportedbytheotherdescriptivemeasures.
Lastly, the asymmetric effect analyzed for theCoreDurableGoodsorders showed
the exact same result thatwas predicted by Andersen et al. (2002), Carlson& Lo
(2003) and Dominguez (1999). The average price change of the negative effect
outcomewashigherintherightdirectionasthepricechangeofthepositiveeffect.
Thissupportstheirassumptionthatanegativenewssurpriseresultsinhigherprice
changesthanpositiveonesdo.Nevertheless,boththenegativeandpositiveeffect
outcomes were consistent by showing negative average values over time for the
negativeeffectoutcomeandpositivevaluesforthepositiveoutcomes.
Overall, it could be seen that the effectsmeasured from the Core Durable Goods
wereconsistentfromoneeffecttoanotherwithonlyslightchanges.Thismeansthat
42
on average the trader can expect a negative price change from negative effect
outcomes and positive price difference from positive outcomes. Furthermore, the
Core Durable Goods Orders macroeconomic indicator was the only indicator
analyzed that was in line with the asymmetric surprise assumption. The other
descriptivemeasuresshowednosignificantdeviationsfromeachother.
IV.2 Measureofeffectdirection
The analysis of the measure of effect direction presents the probabilities of the
variousindicatorsandthecorrespondingfoureffects.Thisanalysiswillbestructured
similarly to the standard descriptive analysis by discussing on indicator and effect
afteranother.Thefulltableofalltheresultsandtheexactprobabilityvaluescanbe
foundinAPPENDIXB.
IV.2.1 Non-farmPayroll
Withregardtothesurpriseeffect,themeasureofeffectshowsthatifanoutcomeis
negativewhichhappenson average in 49out of 100 cases, the chanceof the10-
minute and the 20-minute price change being negative aswell is only close to 50
percent.However,theprobabilityofapositiveoutcometoresultinapositiveprice
changeexceedsatthe10-minutemarkthe50percentalreadywith62percentand
atthe20-minuntemarkevenwith71percent.
Similarly, the probability of positive growth effect outcome resulting in a positive
price change shows a 68 percent chance for both time frames. However, the
negativeoutcomesmerelypassthe50percentforthe10-minutetimeframeandare
evenbelowforthe20-minutetimeframe.
The revision effect shows a clear pattern of strengthening the effect of direction
over time. Even though at the 10-minute mark the negative effect outcome is
slightly below the 50 percent level, the probability increases over the next 10
minutes to 51 percent. The same increase pattern can be found for the positive
revisioneffectoutcomewherethepercentageincreasesfrom60to69percent.
Theasymmetricsurpriseeffectfollowsthesamestructuredescribedfortherevision
and the surprise effect, that the probabilities increase in the right direction over
43
time. However, it is worth mentioning that for the Non-farm Payroll the overall
picture clearly indicates that positive effect outcomes not only result in higher
probabilities for a positive price change, but also that the probability increases in
time.Eventhoughtheprobabilityvaluesforthenegativeeffectoutcomesarerather
low,thesameincreasepatterncouldbeproven,exceptforthegrowtheffect.
IV.2.2 CoreConsumerPriceIndex
Incomparisontotheothertwoindicatorsathirdeffectoutcomeoption,theneutral
one,wasadded to theanalysis,due to the fact thataboutone thirdof thewhole
effect outcomes are neutral. It is interesting to mention that the neutral effect
outcomesresultinthehighestlikelinesstoreceivearespectivepositivevalueabove
the50percentlevelforeachtimeframe.Thereby,thepositiveeffectoutcomesare
veryclosetothe50percentprobabilitylevelofresultinginapositivepricechangeat
both time marks. However, the negative effect outcomes are surpassing the 50
percentlikelinesslevelonlyonthe20-minutetimemark.
Withreferencetothegrowtheffect,itcanbeseenthatthepositiveeffectoutcomes
also result in the highest probabilities for the price change being in the same
direction. Thenegativeeffectoutcomeshowsavery lowprobabilityvalue for the
10-minute mark with only 39 percent. However, it can be seen that the neutral
effect outcomes rather results into negative price changes by not passing the 50
percentlevel.
The revision effect shows a mixed interpretation of the various effect outcomes.
Eventhoughtheneutraloutcomesresult ina58percentprobabilityofresulting in
positivepricechanges, thepositiveeffectoutcomesrather result inmorenegative
onesthanpositive.Withregardtothenegativeeffectoutcome,itcanbeseenthat
atthe10-minutemarkthe50percentlevelwasnotmet,butatthe20-minutemark
theeffectoutcomeshowsaveryhighprobabilitywith60percent.
With regard to the asymmetric surprise effect, it can be said that only for the
negative20-minuteandthepositive10-minutemarkthepricechangeresultsinthe
according direction. Overall, the measure of effect direction shows a very mixed
structure for the Core Consumer Price Index indictor. Nevertheless, it can be said
44
that the neutral and the positive effect outcomes mostly result in positive price
changes,wherebythenegativeoneisinconsistent.
IV.2.3 CoreDurableGoodsOrders
Withrespecttothesurpriseeffect,itcanbeseenthatthepositiveeffectoutcomes
show a clear 60 percent probability of resulting in a price change in the same
direction.However,analyzingthenegativeeffectoutcomes,ithastobementioned
thatforbothtimeframesthepercentagesarebelowthe50percentlevel,indicating
thatthereweremorepricechangesinthewrongdirection.
The samepatterncanbe found forallothereffects,howeveronly thepercentage
valueschangeabitincomparisontothesurpriseeffect.Findingthispatternisvery
interesting,because itmeansthat if thetraderfacesapositiveeffectoutcomethe
probabilityofgettingtherighttradeishigherthan50percent,wherebythenegative
resultsoverallresultinmorenegativepriceoutcomesthanpositiveones.
IV.3 Absoluteaveragepricechange
Asithasbeendescribedinthebeginningofthethesis,theactivetraderhasaclear
disadvantage over a trading algorithm that is able to execute trades within
milliseconds.Therefore,itisvitalfortheactivetradertoknowatwhichpointintime
themajorpricemovementcausedthroughanindicatorpublicationisalreadyover.
Due to the fact that this part of the analysis was not the major concern of this
bachelorthesisonlytheaveragepricechangeoverthetimewasanalyzedwhichcan
beobservedinFigure2.TheunderlyingvaluescanbeseeninAPPENDIXC.
Figure2…Absoluteaveragepricechangeperminute
0100200300400500
0 1 2 3 4 5 6 7 8 9 10 20
Absoluteaveragepric
echan
ge
Timeinterval[minutes]
Absoluteaveragepricechangeperminute
Non-farmPayroll
CoreConsumerPriceIndex
CoreDurableGoodsOrders
45
Inthechartdepictingtheabsoluteaveragepricechange,ajointwavepatterncanbe
detected that gives a better understanding of all the three indicators. The wave
pattern that can be seen has its first peak shortly after the publication, a trough
betweenminute3and5,anotherpeakatduringminute6and7,anothertroughat
minute8andalastpeakduringminute9and10.Itisworthmentioningthatthereis
aclear indication thatduring the first twominutesmostof thepricemovement is
alreadyover.Solely theNon-farmPayrollhasahighpeakoutperformingtheother
two indicators in themiddle atminute six.Due to the fact that usually trades are
enteredrightafterthepublicationit isworthmentioningthatfortheCoreDurable
GoodsOrdersandtheCoreConsumerPriceIndexitispossibletoenteratradeuntil
thesecondminutewithagoodchanceofnotmissingthemajorpricechange.With
regardtotheNon-farmPayrollthetradeshouldbeexecutedwithinthefirstminute
inordertoleveragethepricechangeahead.
It isworthnotingthatonlythefirstpartofthewavewillbe interestingforthe10-
minute trade because the second peak is shortly after the lockout period. With
regardtothe20-minutetrade,whichcouldbeentereduptothe15thminuteafter
thepublication,theanalysisshowsthatfortheCoreConsumerPriceIndexandthe
CoreDurableGoodsOrders there is notmuchprice change after the 10thminute.
Solely for the Non-farm Payroll there is another peak in average absolute price
changes,whichhastobeconsidered.Theincreaseinabsoluteaveragepricechange
canbeseenasanincreaseintradingactivityandhassubsequentlybeanalyzedifitis
positiveornegativeforplacingprofitabletrades.
Overall itcouldbedetectedthatanactivetraderhassometime,aboutoneortwo
minutes depending on the indicator, to analyze the result of the publication and
place a trade accordingly. This effect may be explained by the high popularity of
tradingtheNon-farmPayroll,wherebytheCoreConsumerPriceIndexandtheCore
DurableGoodsOrdersareabit lesspopular.Nevertheless, itcanberetrievedthat
even though a trading algorithmhas a timely advantage, an active trader can still
leveragethepricejumpresultingfromanindicatorpublicationifheentersthetrade
on average in the first minute for the Non-farm Payroll and within the first two
minutesfortheothertwoindicators.
46
IV.4 Explanatoryanalysis
Themaingoaloftheexplanatoryanalysisistodevelopaforecastingmodelforeach
indicatorbasedonthemultivariableregressionanalysisusingSPSSStatistics.Every
singleindicatorwillbeanalyzedindependentlyinordertoderivethemathematical
models for a 10-minute and 20-minute price change forecast. Throughout the
analysis,thesignificancelevelofthevarioustestedeffects,theadjustedR-squared
valueandtheANOVAtestofthemodelwillbeanalyzedinordertojudgeonthefit
ofthemodel. Inaddition, ifacertainmodel indicatesaveryweaksignificanceofa
certaineffect,theregressionanalysiswillbeperformedagain,howeverwithoutthat
certaineffect.
Ifamodelgetsadaptedonlythetableofcoefficientsofthefirstregressionanalysis
andtheadaptedtablesofthecoefficients,theANOVAtestandadjustedR-squared
valuefromthesecondanalysiswillbeincludedintheanalysis.Thefullcollectionof
tablescanbe found inAPPENDIXD. Themathematicalmodelwillbederived from
theoutputof the statistics software, thevariablecoefficientsand the interception
value.Intheendoftheexplanatoryanalysissixdifferentmathematicalmodels,two
for each indicator, will be set up in order to be able to forecast the future price
changeoftheEUR/USDforeignexchangerate.
IV.4.1 Non-farmPayroll–10-minuteforecast
Below the SPSS output of the 10-minute Non-farm Payroll indicator data can be
found described by the four effects. The output presents the coefficients and the
interceptvaluetogetherwiththeirsignificancevalues.
Table1…Non-farmPayroll10-minutecoefficients(Surprise,Growth,Revision,AsymmetricSurprise)
47
Analyzingthesignificancevalues,whichexpresstheerrorpercentage,itcanbeseen
that out of the four effects the ‘Asymmetric Surprise’ shows a very high error
expressedinahighsignificancevalue.Therefore,theregressionanalysiswillbedone
again,howeverbyexcludingtheasymmetricsurpriseeffectinordertoincreasethe
strengthofthemodel.
Table2…Non-farmPayroll10-minutecoefficients(Surprise,Growth,Revision)
Running themodelagain, it canbeseen that for theothereffects thesignificance
values decrease, which is a very good sign. By excluding the asymmetric surprise
effect,itwaspossibletoincreasetheadjustedR-squaredvaluesfrom7,5percentto
8,3,meaning thecurrentmodelbasedon the3effectsonlydescribes the forecast
much better. However, it can be seen that the significance value of the revision
effectisstillconsiderablyhighincomparisontotheothers.Therefore,themodelwill
beperformedoncemorewithouttherevisioneffect.
Table3…Non-farmPayroll10-minutecoefficients(Surprise,Growth)
Takingouttherevisioneffectclearlyhasahugeimpactonthesignificancevalueof
thegrowtheffect,whichisevenbelowthe5percenterrorlevel.Thismeansthatthe
growtheffectissignificantandthenullhypothesisoftherevisioneffectfortheNon-
farmPayrollcanbeneglectedandthealternativehypothesisstatingthatthegrowth
48
effecthasanimpactontheEUR/USDcanberetained.Inaddition,itcanbeseenfor
thepositivecoefficientvaluesthat,boththesurpriseandthegrowtheffecthavea
positiverelationshipwiththepricechange.
Table4…Non-farmPayroll10-minutemodelsummary
Inaddition, itcanbeseenthattheadjustedR-squaredvaluecouldbeincreasedto
10,5 percent. Based upon the multi-linear regression analysis the following
mathematical forecastingmodel can be set up for predicting the 10-minute price
changefortheNon-farmPayroll:
∆9:;<=>= 429,235 + 2,599 ∗ 𝐼𝑎𝑡 − 𝐼𝑓𝑡 + 9,413 ∗ 𝐼𝑎𝑡 − 𝐼𝑝𝑡
Table5…Non-farmPayroll10-minuteANOVAtest)
The ANOVA table presented above clearly shows that there is a highly significant
relationshipbetween the10-minuteprice changeand the foureffects.All in all, it
canbe seen thatby removing the revisionand theasymmetric surpriseeffect it is
possibletoimprovetheforecastingmodelandthatthereisasignificantrelationship
betweenthetwoeffects.Byplugginginthenewlyreleasedindicatordatapublished
atthe‘ForexFactory’website,thetraderwillbepresentedwithaforecastedoverall
49
pricechangeoftheEUR/USDexchangeratethatcanbepredictedoverthefirst10
minutes.
IV.4.2 Non-farmPayroll–20-minuteforecast
Below the SPSS output of the 20-minute Non-farm Payroll indicator data can be
founddescribedbythefoureffects.Sameproceedingsastheywereappliedforthe
10-miunteforecastwillbedonehere.
Table6…Non-farmPayroll20-minutecoefficients(Surprise,Growth,Revision,AsymmetricSurprise)
Different from the 10-minute model, where the growth effect was the most
significant one, it can be seen that for the 20-minutemodel this effect has to be
removed. Therefore, the model will be run again, however without the growth
effect.
Table7…Non-farmPayroll20-minutecoefficients(Surprise,Revision,AsymmetricSurprise)
The significance values of the remaining effects can be improved as it can be
detected from the tableabove.Onceagain themodelwillbe testedagain,due to
thefactthattheasymmetricsurpriseeffecthasaveryhighsignificancevalue.
50
Table8…Non-farmPayroll10-minutecoefficients(Surprise,Revision)
Byexcluding thegrowthandasymmetric surpriseeffect the remaining twoeffects
canbeimprovedintheirsignificance.Thesurpriseeffectturnsouttobesignificant
andisevenbelowthe5percentlevel.Therefore,forthe20-minuteNon-farmPayroll
indicator, itcanbesaidthatthesurpriseeffect issignificantandthereforethenull
hypothesis can be rejected. From the negative sign in front of the revision
coefficient, it can be said that the revision effect has an inverse relationwith the
pricechange.
Based upon the multi-linear regression analysis the following mathematical
forecastingmodel canbe setup forpredicting the20-minutepricechange for the
Non-farmPayroll:
∆9:;<D>= 732,248 + 12,005 ∗ 𝐼𝑎𝑡 − 𝐼𝑓𝑡 + −8,486 ∗ 𝐼𝑝𝑡 − 𝐼𝑎𝑡
Table9…Non-farmPayroll20-minuteModelSummary
AccordingtothemodelsummaryoftheSPSSoutput,theadjustedR-Squaredvalue
canbeimprovedfrom4percentto5,2percentbytakingthetwoeffectsoutofthe
51
model. Incomparisontothe10-minutemodelthe20-minutemodelshowsalower
explainedfitoftheregressionlineandsubsequentlyalessreliableresult.
Table10…Non-farmPayroll20-minuteANOVAtest
Analyzingthep-valueoftheANOVAoutputitcanbedetectedthatthesignificance
level is only below the 10 percent and not the 5 percent level. The result of the
ANOVAtestisincommonwiththeobservationoftheadjustedR-squaredvaluethat
the10-min-modelismoreprecisethanthe20-min-model.
IV.4.3 CoreConsumerPriceIndex–10-minuteforecast
BelowtheSPSSoutputforthemulti-linearregressionanalysiscanbefoundforthe
10-minutedatabasedonallfoureffects.
Table11…CoreConsumerPrice Index10-minuteCoefficients (Surprise,Growth,Revision,AsymmetricSurprise)
The first three variables showa very good significance level,which is below the5
percent threshold. Solely the Asymmetric surprise effect depicts a large error
percentage, which is similar to the effects measured for the Non-farm Payroll.
52
Therefore,themodelwillberunagainbyexcludingtheasymmetricsurpriseeffect.
Table12…CoreConsumerPriceIndex10-minuteCoefficients(Surprise,Growth,Revision)
Byexcludingtheasymmetricsurpriseeffect,thesignificancevaluesoftheremaining
effectsturnedevenbetter.Forallthreeeffectsthenullhypothesisforthe10-minute
CoreConsumerPrice Indexmodel canbe rejected,and thealternativehypotheses
can be retained. Out of the three effects, only the surprise effect has a positive
relationshipwiththeindependentvariable.
Based on the coefficients determined through the SPSS analysis the following
forecastmodelcanbedeveloped:
∆9I<J=>= 80,805 + 119370,332 ∗ 𝐼𝑎𝑡 − 𝐼𝑓𝑡 + −18753,777 ∗ 𝐼𝑎𝑡 − 𝐼𝑝𝑡
+ −19493,987 ∗ 𝐼𝑝𝑡 − 𝐼𝑎𝑡
Table13…CoreConsumerPriceIndex10-minuteModelSummary
Analyzing the model summary output the initial adjusted R-squared value of 5,4
percentcouldbeincreasedto6percent.Inaddition,fromtheANOVAtest,wecan
53
statethatthecorrelationofthefoureffectvariables issignificant inexplainingthe
pricechangeoftheEUR/USDforeignexchangerate.
Table14…CoreConsumerPriceIndex10-minuteANOVAtest
IV.4.4 CoreConsumerPriceIndex–20-minuteforecast
Analyzingtheexplanatoryanalysisforthecoefficientsofthemulti-linearregression
analysis, it can be seen that the average significance level of the effects is very
similar to the ones of the Non-farm Payroll analysis. As it can be seen the
significancelevelsvarybetween30and46percent.
Table15…CoreConsumerPrice Index20-minuteCoefficients (Surprise,Growth,Revision,AsymmetricSurprise)
The growth effect with a 46,3 percent error value shows the highest significance
valueofall theeffects.Therefore, similar to theothermodels, theanalysiswillbe
runagainwithoutthegrowtheffect.
54
Table16…CoreConsumerPriceIndex20-minuteCoefficients(Surprise,Revision,AsymmetricSurprise)
Eventhoughthesignificancevaluesoftheremainingvalues improvedbyexcluding
thegrowtheffect,noneof theeffectsarebelowthe10percentsignificancevalue.
However,itcanbeseenthatthecoefficientshavethesamesignsastheonesfrom
the10-minute forecastingmodel.Resulting fromthecoefficientsandthe intercept
valuethefollowing20-minuteforecastingmodelcanbedeveloped:
∆9I<JD>= 575,025 + 3158,323 ∗ 𝐼𝑎𝑡 − 𝐼𝑓𝑡 + −3004,978 ∗ 𝐼𝑝𝑡 − 𝐼𝑎𝑡 − 1 + −528,708 ∗ 𝐼𝐴𝑆 )
Themodelsummarydeviatesquiteabitfromtheothermodelsummariesthathave
been calculated before. The negative adjusted R-Squared value explaining a non-
existing correlation for the model without excluding the growth effect, turned
positivetoavalueof0,4percentafterthemodelwasrunagain.Nevertheless,the
forecastingmodelforthe20–minuteCoreConsumerPriceIndexshowsaveryweak
explainedvarianceincomparisontotheotherforecastmodels.
Table17…CoreConsumerPriceIndex20-minuteModelSummary
55
TheANOVAtestsupportstheobservationoftheweakmulti-linearregressionmodel
with a significance level about 30 percent, which is higher than all the other
forecastingmodels,setupbefore.
Table18…CoreConsumerPriceIndex20-minuteANOVAtest
IV.4.5 CoreDurableGoodsOrders–10-minuteforecast
Lastly, themulti-linear regressionmodelof theCoreDurableGoodsOrderswillbe
analyzed inorder toderive the forecastingmodel.Themodel isbasedon the four
effectvariablesforwhichthecoefficientsdeterminetheirimportanceinthemodel.
Below,theSPSSoutputwillbepresentedwithallthecoefficientsandtheintercept.
Table19…CoreCurableGoodsOrders10-minuteCoefficients(Surprise,Growth,Revision,AsymmetricSurprise)
Analyzing the errormeasurements of the effects, it can be seen that the revision
effectrepresentstheweakesteffect,meaningthatitdoesnotfitthemodeltoowell.
Therefore,therevisioneffectwillbeexcludedfromtheforecastmodel.
56
Table20…CoreCurableGoodsOrders10-minuteCoefficients(Surprise,Growth,AsymmetricSurprise)
By excluding the revision effect the growth effect turned significant on the 10
percentlevel.Additionally,thetwoothereffectswereveryclosetothe10percent
level, indicatingarathergoodfitofthemodel. Incomparisontotheothermodels,
the surprise effect for this 10-minute forecast model is negative. Based on these
coefficientsthefollowingmodelcanbesetup:
∆9MNO=>= −222,190 + −101,861 ∗ 𝐼𝑎𝑡 − 𝐼𝑓𝑡 + 153,429 ∗ 𝐼𝑎𝑡 − 𝐼𝑝𝑡 + 4428,728 ∗ 𝐼𝐴𝑆 )
WithregardtotheadjustedR-Squaredvalue, itcanbeseenthat itwaspossibleto
increase theadjustedR-squaredvalue from2percent to2,9percent.Even though
therewas a slight improvement in the explained variance, the value is still rather
smallincomparisontotheothermodels.
Table21…CoreCurableGoodsOrders10-minuteModelSummary
With regard to the ANOVA test, it can be seen that the correlation between the
threeeffectsisveryclosetothe10percentsignificancelevel.Overall,itcanbeseen
thatthemodel ismediocreinforecastingthe10-minutepricechangecomparedto
theothermodels.
57
Table22…CoreCurableGoodsOrders10-minuteANOVAtest
IV.4.6 CoreDurableGoodsOrders–20-minuteforecast
Intheprocessofdevelopingtheforecastingmodel forthe20-minutepricechange
effectedbytheCoreDurableGoodsOrders,itcanbeseenthatwiththeexceptionof
therevisioneffectwithasignificancevalueof53percent,allothereffectsshowan
errorpercentageof22to30percent.
Table23…CoreCurableGoodsOrders20-minuteCoefficients(Surprise,Growth,Revision,AsymmetricSurprise)
Inordertoincreasethemodelvalidity,therevisioneffectwillbeexcludedandthe
analysiswillberunagainwithoutit.
Table24…CoreCurableGoodsOrders20-minuteCoefficients(Surprise,Growth,AsymmetricSurprise)
58
Eventhoughthesignificancevaluesoftheasymmetricsurpriseeffectdecreased,the
surprise and growth effects decreased in their importance. Therefore, the
asymmetric surprise effect seems to be themost valuable one for forecasting the
20-minutepricechangefortheCoreDurableGoodsOrders.Similartothe10-minute
model, the coefficient for the surprise effect is negative for the 20-minutemodel.
Basedonthetablepresentedabovethefollowingmodelwascreated:
∆9MNOD>= −249,329 + −74,468 ∗ 𝐼𝑎𝑡 − 𝐼𝑓𝑡 + 114,661 ∗ 𝐼𝑎𝑡 − 𝐼𝑝𝑡 + 495,245 ∗ 𝐼𝐴𝑆 )
Table25…CoreCurableGoodsOrders20-minuteModelSummary
From themodel summary, it canbe seen thatbyexcluding the revisioneffect the
negative adjusted R-squared turned positive. Even though the adjusted R-squared
value canbe improved the explained variance is considerable low.With regard to
the ANOVA test, the result shows a rather high significance value of 42 percent,
meaningthattheerrorpercentageisveryhigh.
Table26…CoreCurableGoodsOrders20-minuteANOVAtest
59
IV.5 Forecastingmodelaccuracy
After the 10- and 20-minute models have been developed for all three
macroeconomic indicators, the forecast values could be calculated and compared
with the actual data in order to show themodel fit.1 Based on the probability of
forecastingtherightpricechangedirection,furtherinformationabouttheaccuracy
ofthedevelopedforecastmodelcanbegained.Duetothefactthatpredictingthe
rightdirectionofthepricechangeisthemostvitalstepwhentradingbinaryoptions,
theprobabilityofforecastingtherightdirectionwillbeofmajorimportance.
Modelaccuracymeasureonthecorrectpricedirectionforecast 10-minutemodel 20-minutemodelNon-farmPayroll 58% 64%CoreConsumerPriceIndex 44% 50%CoreDurableGoodsOrders 54% 54%Table27…Modelaccuracysummary
Out of the six forecastingmodels, two per indicator, only 4models were able to
predict the right price change direction in more than 50 percent of the cases.
Thereby, only the Core Consumer Price Index was not able to generate enough
correctpricedirectionforecasts,eventhoughthe10-minutemodelwasperceivedto
have the most significant effects. The forecast models for the Non-farm payroll
indicatorwereabletogenerateresultsthathavea58percentprobabilityforthe10-
minutemodelanda64percentprobabilityforthe20-minutemodeltopredictthe
right price direction. Also for the Core Durable Goods Order, the forecastmodels
wereabletopredict in54percentofthecasestherightpricechangedirectionfor
bothmodels.SolelythemodelsfortheCoreConsumerPriceIndexwerenotableto
overcomethe50percentlevelwith45percentforthe10-minuteand50percentfor
the20-minutemodel.Therefore,onlytheCoreConsumerPrice Indexmodelswere
notabletobeatthe50/50percentportabilityofabinaryoutcome.Overallitcanbe
seen that4outof the6 forecastingmodelswereable todevelop forecasts,which
1TheMAPEfortheforecastmodelswhereasfollowing:Non-farmPayrollindicator:280%forthe10-minutemodeland307%forthe20-minutemodel;CoreConsumerPriceIndex:4814%forthe10-minutemodeland154%forthe20-minutemodel;CoreDurableGoodsOrders:114%forthe10-minutemodeland150%forthe20-minutemodel.Even though the MAPE is skewed by the limitations and weaknesses of the model, it was calculated for thecompletenessoftheanalysis.InordertogivemorecredibilitytotheMAPEalltheotherindicatorpublicationsthatarehappeningatthesametimewouldhavetobecalculatedintothemodel.AlthoughsomeofthemodelscreatedacceptableMAPEvalues,morecredibilityisgiventotheprobabilitypredictingtherightpricechangedirection,duetothelimitationsmentionedabove.
60
giveatradingedgetothetrader,meaningthatusingthemodel,hecanoutperform
asimpleguessingtechnique.
V. Conclusion
In this last sectionof thebachelor thesis, all the results from the various analyses
willbebroughttogetherinordertogiveaclearpictureofeveryindicatoronitsown.
Thereby,thefindingswillbelinkedtotheresearchquestionandpresentedinaway
thatthetraderwillgainthemostuseoutof it inordertoobtainanedgeoverthe
market. The indicators will be consecutively summarized followed by a general
summary of the research model used throughout the thesis. Nevertheless, it is
important to keep inmind that all the forecastingmodels and information gained
through the analysis are subject to the limitations outlined in the research
methodology.
TheNon-farmPayroll
The descriptive analysis revealed that the revision effect resulted in the wrong
average price change for the negative effect outcomes and that the asymmetric
surpriseeffectwasnotabletoprovethesenegativeeffectoutcomesresultinhigher
pricechanges.Theseeffectflawswereprovenintheexplanatoryanalysis,whereby
excludingtherevisionandtheasymmetricsurpriseeffectfromthe10-minutemodel
itwas possible to increase the accuracy of the forecasts by improving themodel.
Also for the 20-minutemodel the asymmetric surprise effect had to be removed.
Additionally, the growth effect was excluded in order to improve the model
accuracy.Theweaksignificanceof thegrowtheffectcanalreadybeseenfromthe
resultsofthedescriptiveanalysis.Inthedescriptiveanalysisforthe20-minuteprice
change, the negative growth effect outcomes resulted in a positive price change,
whichillustratesaweakrelationship.
Themathematical forecastingmodels based on themulti-regression analysis both
showthattheyareabletoproduceaforecastthatprovidesthetraderanedgeover
themarket. In addition, it can be proven that the growth effect has a significant
impactonthe10-minutepricechangeandthatthesurpriseeffect issignificantfor
the20-minutepricechange.However,itcanbeproventhatonaveragethepositive
61
effect outcomes have a much higher probability to result in the according price
change direction. Nevertheless, it has to bementioned that the absolute average
pricechangeperminuteresultedintheconclusionthatthetraderhastoenterthe
tradeduringthefirstminuteinordertotakeprofitsfromthepricechangeresulting
fromtheindicatorpublication.
CoreConsumerPriceIndex
The insights gained from the descriptive analysis show that mostly every single
effect outcome resulted in a positive price change on average. Solely the revision
effect was able to generate a negative 10-minute price change for the negative
effect outcome and a negative 20-minute price change for the positive effect
outcome.Itcanbeproventhatneutraleffectoutcomes,whichhappenaboutathird
ofthetime,areresultinginastrongerpositivepricechangeonaverage.
Additionally, itcanbeseenfromtheexplanatoryanalysisthatthegrowth,revision
and asymmetric surprise effect show an inverse relationship. Furthermore, by
excluding the asymmetric surprise from the 10-minute forecast model and the
growtheffectfromthe20-minutemodelitwaspossibletoincreasetheaccuracyof
theforecast.Itisworthmentioningthatforthe10-minutemulti-regressionanalysis
models notonly the surprise and the growtheffect, but also the revisioneffect is
highlysignificantatthe5percentthreshold.Therefore,itisallowedtorejectthenull
hypothesis stating that these effects do not have any significant impact on the
EUR/USD price change. Even though this forecasting model showed the most
significant effect variables from all the other models developed throughout this
analysis,themodelonlyreacheda6percentexplainedvarianceandwasnotableto
forecasttherightpricedirectioninmorethan50percentofthepasttimeseries.
Similarly, the 20-minutemodelwas only able to predict exactly 50 percent of the
right price direction, meaning that the model is not better than a simple 50/50
guess. The weakness of the model might be explained by the fact that other
indicatorspublishedatthesametime,mighthaveahigherimpactontheexchange
rate. Nevertheless, if the trader decides to trade on the insights gained from the
descriptiveanalysisandithastobementionedthatunlikefortheNon-farmPayroll,
fortradingtheCoreDurableGoodsOrdersitispossibletoenterthetradeduringthe
first2minutesinordertocatchthepricejump.
62
CoreDurableGoodsOrders
From the descriptive analysis, it can be seen that the surprise, growth and
asymmetric surprise effect resulted in a correlation expected from their effect
outcomes.Thismeansthatifthetraderfacesanegativeeffectoutcomeforacertain
indicatorpublicationhe canestimate thatonaverage thiswill result in anegative
pricechangeandviceversaforthepositiveeffectoutcomes.Onlytherevisioneffect
showed mixed results which were not in line with the expectations. This weak
relationshipcanalsobedetectedbytheexplanatoryanalysis,wheretheexclusionof
therevisioneffectleadstoanoverallincreaseinthevalidityofthemodel.Itisworth
mentioningthattheonlyfortheCoreDurableGoodsOrdersitwaspossibletoproof
the asymmetric surprise effectmentionedbyAndersenet al. (2002), Carlson& Lo
(2003)andDominguez(1999)
With regard to themulti-regressionanalysis, it canbe concluded that the surprise
effect showsan inverse relationship forbothmodels.Even though theadjustedR-
squared values and the relationship among the effect variables are rather weak,
bothforecastmodelsareabletogenerateaforecastthatisabletobeatthesimple
50/50 binary guess. Additionally, it can be seen that the positive effect outcomes
haveamuchhigherprobabilitytoresultinapricechangeintheaccordingdirection.
Thereby, it is importanttomentionthatthenegativeeffectoutcomesarenotable
tosurpassthe50percentprobabilityofpredictingtherightpricedirection.Basedon
the mathematical forecast model and the descriptive analysis the trader can
leveragetheinsightsduringthefirsttwominutesafterthepublicationforbeingpart
ofthepricejumpasdescribedintheaverageabsolutepricechangeperminute.
Eventhoughtherearelimitationsforthemodelsdevelopedthroughoutthisthesis,
furtherresearchmayworkona forecastmodel thatconsiderstheother indicators
published at the same time, which are distorting the impact of the analyzed
indicators.Additionally, future researchmay consider to elaborateon the forecast
model by using more advanced forecasting techniques. Future research may
elaborateonthesameresearchmethodologyoutlinedduringthisthesisbyapplying
themodeltootherforeignexchangerates.
63
Overall, itcanbeproventhatwiththeexceptionoftheasymmetricsurpriseeffect,
every other effect is at least significant for one of the forecasting models.
Additionally, it is possible to develop four forecastmodels, two for the Non-farm
Payrolland two for theCoreDurableGoodsOrders,whichareable tooutperform
the simple binarymarket probability. As it can be derived from the analysis it is
important that the active trader executes the tradewithin the first or the second
minuteinordertoleveragetheinformationgainedthroughthisthesis.Therefore,it
hastobestressedthatthemodel laidoutandanalyzedthroughoutthisthesiswas
able to find and prove that the effects resulting from macroeconomic indicators
have a significant impact in explaining theprice changeof the EUR/USDexchange
rateandthatthetradercanleveragethisinformation.
64
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66
Appendices
APPENDIXA:DescriptiveStatistics
Non-farmPayroll
MeasuredEffect Outcome Measure Effect
Δp10min
Δp20min
Surprise Negative Mean -77 -197 -155totaln=108 n=53 Median -65 0 0 Mode -72 500 100 StandardDeviation 83 2253 2962 Min -4 5900 7600 Max -558 -7500 -8600 Positive Mean 65 990 1473 n=55 Median 60 200 580 Mode 0 -100 600 StandardDeviation 53 2554 3105 Min 0 -3600 -4300 Max 196 8140 9750
Growth Negative Mean -53 -233 85totaln=108 n=58 Median -42,5 -35 15 Mode -4 500 100 StandardDeviation 43 2258 3247 Min -2 5900 9600 Max -213 -7500 -8600 Positive Mean 45 1150 1358 n=50 Median 46 265 490 Mode 48 300 600 StandardDeviation 35 2525 2871 Min 0 -3600 -2900 Max 175 8140 9750
Revision Negative Mean -43 99 282totaln=107 n=49 Median -27 0 -10 Mode -1 500 -500 StandardDeviation 76 2417 3153 Min -1 8140 9750 Max -537 -7500 -6980
67
Positive Mean 51 547 871 n=58 Median 36,5 190 315 Mode 24 300 600 StandardDeviation 51 2366 2961 Min 0 -5900 -8600 Max 231 7320 9600
AsymmetricSurprise Negative Mean 0 -197 -155totaln=108 n=53 Median 0 0 0 Mode 0 500 100 StandardDeviation 0 2253 2962 Min 0 5900 7600 Max 0 -7500 -8600 Positive Mean 1 990 1473 n=55 Median 1 200 580 Mode 1 -100 600 StandardDeviation 0 2554 3105 Min 1 -3600 -4300 Max 1 8140 9750
CoreConsumerPriceIndex
MeasuredEffect Outcome Measure Effect
Δp10min
Δp20min
Surprise Negative Mean -0,1 139 329totaln=108 n=33 Median -0,1 100 -30 Mode -0,1 500 -200 StandardDeviation 0,048 816 1049 Min -0,1 1960 2400 Max -0,3 -1480 -2230 Positive Mean 0,1 179 184 n=37 Median 0,1 -10 -40 Mode 0,1 100 -260 StandardDeviation 0,040 1069 1467 Min 0,1 -1900 -3040 Max 0,2 2500 5710 Neutral Mean 0 57 105
68
n=38 Median 0 0 -10 Mode 0 -370 1780 StandardDeviation 0 792 1478 Min 0 -1300 -3390 Max 0 2600 4300
Growth Negative Mean -0,1 139 172totaln=108 n=33 Median -0,1 100 130 Mode -0,1 -30 -200 StandardDeviation 0,044 811 1322 Min -0,1 1960 2400 Max -0,3 -1900 -3390 Positive Mean 0,1 243,3 515,2 n=27 Median 0,1 0 -100 Mode 0,1 -400 -260 StandardDeviation 0,019 1040 1571 Min 0,1 -1800 -1000 Max 0,2 2200 5710 Neutral Mean 0 46 43 n=48 Median 0 -5 -100 Mode 0 800 -900 StandardDeviation 0 877 1225 Min 0 -1480 -3040 Max 0 2600 2800
Revision Negative Mean -0,1 -77 89totaln=107 n=25 Median -0,1 0 -100 Mode -0,1 500 -100 StandardDeviation 0,02 797 1227 Min -0,1 2000 4300 Max -0,2 -1480 -2230
Positive Mean0,1125 39 -182
n=32 Median 0,1 -15 -160 Mode 0,1 -500 -200 StandardDeviation 0,034 930 1616 Min 0,1 -1900 -3390 Max 0,2 2500 5710 Neutral Mean 0 249 473
69
n=50 Median 0 65 315 Mode 0 800 600 StandardDeviation 0 901 1167 Min 0 -1800 -2570 Max 0 2600 2800
AsymmetricSurprise Negative Mean 0 139 329totaln=108 n=33 Median 0 100 -30 Mode 0 500 -200 StandardDeviation 0 816 1049 Min 0 1960 2400 Max 0 -1480 -2230 Positive Mean 1 117 144 n=75 Median 1 0 -40 Mode 1 -370 -200 StandardDeviation 0 935 1464 Min 1 -1900 -3390 Max 1 2600 5710
CoreDurableGoodsOrders
MeasuredEffect Outcome Measure Effect
Δp10min Δp20min
Surprise Negative Mean -2,14 -169 -213totaln=108 n=53 Median -1,8 0 80 Mode -4 -400 300 StandardDeviation 1,7 965 1235 Min -0,1 1600 2900 Max -6,6 -2800 -3900 Positive Mean 1,90 99 179 n=55 Median 1,3 200 230 Mode 0,8 200 1100 StandardDeviation 1,9 894 1289 Min 9,8 1900 5710 Max 0 -4000 -4000
Growth Negative Mean -1,23 -130 -92totaln=108 n=63 Median -1,1 130 100 Mode -0,4 500 300
70
StandardDeviation 1,0 1106 1466 Min -0,1 1900 5710 Max -4,4 -4000 -4000 Positive Mean 1,18 104 98 n=45 Median 1 200 100 Mode 0 200 1100 StandardDeviation 1,2 607 943 Min 5,9 1300 2200 Max 0 -1200 -2800
Revision Negative Mean -1,30 38 -66totaln=107 n=48 Median -1 210 200 Mode -1 200 200 StandardDeviation 1,2 887 1343 Min -0,1 1600 2900 Max -5,1 -2400 -3900 Positive Mean 1,46 -88 19 n=60 Median 1,2 100 60 Mode 1,5 200 300 StandardDeviation 1,3 983 1231 Min 5,7 1900 5710 Max 0 -4000 -4000
AsymmetricSurprise Negative Mean 0 -169 -213totaln=108 n=53 Median 0 0 80 Mode 0 -400 300 StandardDeviation 0 965 1235 Min 0 1600 2900 Max 0 -2800 -3900 Positive Mean 1 99 179 n=55 Median 1 200 230 Mode 1 200 1100 StandardDeviation 0 894 1289 Min 1 1900 5710 Max 1 -4000 -4000
71
APPENDIXB:Measureofeffectdirection
MeasureofeffectdirectionCalculatedprobabilityofthepricechangebeinginthesamedirectionastheeffectoutcomeforboththe10-minuteandthe20-minutemark
Indicator/Effect Outcome Probabilityofeffectoutcome
10min 20min
Non-farmPayroll
Surprise Negative 49% 47% 49%
Positive 51% 62% 71%
Growth Negative 54% 52% 47% Positive 46% 68% 68%
Revision Negative 46% 47% 51% Positive 54% 60% 69%
AsymmetricSurprise Negative 49% 47% 49% Positive 51% 62% 69%
CoreConsumerPriceIndex
Surprise Negative 28% 39% 52%
Positive 34% 49% 49% Neutral 35% 53% 50%
Growth Negative 31% 39% 45% Positive 25% 52% 48% Neutral 44% 50% 46%
Revision Negative 23% 48% 60% Positive 30% 47% 41% Neutral 47% 58% 58%
AsymmetricSurprise Negative 31% 39% 52% Positive 69% 51% 49%
CoreDurableGoods
Surprise Negative 49% 49% 43%
72
Positive 51% 60% 60%
Growth Negative 58% 46% 40% Positive 42% 58% 56%
Revision Negative 45% 42% 42% Positive 56% 53% 57%
AsymmetricSurprise Negative 49% 49% 43% Positive 51% 60% 60%
73
APPENDIXC:AbsoluteAveragePriceChangeperminute
74
APPENDIXD:ExplanatoryAnalysis–SPSSoutput
Non-farmPayroll
10-minuteforecastmodel:
1stMulti-regressionAnalysis(Surprise,Growth,Revision,AsymmetricSurprise)
Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
Change Statistics
R Square Change F Change
1 ,331a ,110 ,075 2297,88605 ,110 3,138
Model Summary
Model Change Statistics
df1 df2 Sig. F Change 1 4 102 ,018
a. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
Descriptive Statistics Mean Std. Deviation N Ten_Min_Delta 342,1495 2388,76815 107 Surprise -5,074766355000000 100,01902659999999
0 107
Growth -7,850467290000000 63,081977770000000 107
Revision 8,261682243000001 79,249229080000000 107 AsymmetricSurprise ,50 ,502 107
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig. B Std. Error Beta 1 (Constant) 524,727 440,788 1,190 ,237
Surprise 7,193 7,117 ,301 1,011 ,315 Growth 6,068 6,591 ,160 ,921 ,359 Revision -4,851 6,748 -,161 -,719 ,474 AsymmetricSurprise -115,654 687,497 -,024 -,168 ,867
75
ANOVAa
Model Sum of
Squares df Mean Square F Sig. 1 Regression 66270013,570 4 16567503,390 3,138 ,018b
Residual 538588592,000
102 5280280,314 Total 604858605,60
0 106
2ndMulti-regressionAnalysis(Surprise,Growth,Revision)
Coefficientsa
Model Unstandardized Coefficients
Standardized Coefficients
t Sig. B Std. Error Beta 1 (Constant) 461,326 227,515
2,028 ,045
Surprise 6,686 6,419 ,280 1,042 ,300
Growth 5,968 6,533 ,158 ,913 ,363
Revision -4,647 6,607 -,154 -,703 ,483
a. Dependent Variable: Ten_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
a. Dependent Variable: Ten_Min_Delta Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
Change Statistics
R Square Change F Change
1 ,331a ,109 ,083 2287,02126 ,109 4,214
Model Summary
Model Change Statistics
df1 df2 Sig. F Change 1 3 103 ,007
a. Predictors: (Constant), Revision, Growth, Surprise b. Predictors: (Constant), Revision, Growth, Surprise
76
a. Dependent Variable: Ten_Min_Delta
ANOVAa
Model Sum of
Squares df Mean Square F Sig. 1 Regression 66120583,160 3 22040194,390 4,214 ,007b
Residual 538738022,400
103 5230466,237 Total 604858605,60
0 106
3rdMulti-regressionAnalysis(Surprise,Growth)
Coefficientsa
Model Unstandardized Coefficients
Standardized Coefficients
t Sig. B Std. Error Beta 1 (Constant) 429,235 222,351 1,930 ,056
Surprise 2,599 2,721 ,109 ,955 ,342
Growth 9,413 4,314 ,249 2,182 ,031
a. Dependent Variable: Ten_Min_Delta
Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
Change Statistics R Square Change F Change
1 ,324a ,105 ,088 2281,45888 ,105 6,103
Model Summary
Model Change Statistics
df1 df2 Sig. F Change 1 2 104 ,003
a. Predictors: (Constant), Growth, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 63532925,560 2 31766462,780 6,103 ,003b
Residual 541325680,100 104 5205054,616
Total 604858605,600 106
a. Dependent Variable: Ten_Min_Delta
b. Predictors: (Constant), Growth, Surprise
77
20-minuteforecastingmodel:
1stMulti-regressionAnalysis(Surprise,Growth,Revision,AsymmetricSurprise)
Descriptive Statistics
Mean Std. Deviation N
Twenty_Min_Delta 601,2150 3050,31539 107
Surprise -5,074766355000000 100,0190265999999
90
107
Growth -7,850467290000000 63,08197777000000
0
107
Revision 8,261682243000001 79,24922908000000
0
107
AsymmetricSurprise ,50 ,502 107
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 4 102 ,084
a. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
B Std. Error Beta
1 (Constant) 336,406 573,155 ,587 ,559
Surprise 7,035 9,254 ,231 ,760 ,449
Growth 1,633 8,570 ,034 ,191 ,849
Revision -5,531 8,774 -,144 -,630 ,530
AsymmetricSurpris
e
711,403 893,951 ,117 ,796 ,428
a. Dependent Variable: Twenty_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,277a ,077 ,040 2987,93656 ,077 2,118
78
2ndMulti-regressionAnalysis(Surprise,Revision,AsymmetricSurprise)
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 332,254 570,055 ,583 ,561
Surprise 8,308 6,374 ,272 1,303 ,195
Revision -6,732 6,075 -,175 -1,108 ,270
AsymmetricSurprise 726,687 886,170 ,120 ,820 ,414
a. Predictors: (Constant), AsymmetricSurprise, Revision, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 75636921,600 4 18909230,400 2,118 ,084b
Residual 910632020,50 102 8927764,906
Total 986268942,10 106
a. Dependent Variable: Twenty_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
a. Dependent Variable: Twenty_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,276a ,076 ,049 2973,92580 ,076 2,838
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 3 103 ,042
79
a. Dependent Variable: Twenty_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Revision, Surprise
3rdMulti-regressionAnalysis(Surprise,Revision)
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 732,248 294,552 2,486 ,015
Surprise 12,005 4,498 ,394 2,669 ,009
Revision -8,486 5,677 -,220 -1,495 ,138
a. Dependent Variable: Twenty_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,265a ,070 ,052 2969,23891 ,070 3,934
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 2 104 ,023 a. Predictors: (Constant), Revision, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 69365451,010 2 34682725,500 3,934 ,023b
Residual 916903491,100 104 8816379,722
Total 986268942,100 106
a. Dependent Variable: Twenty_Min_Delta
b. Predictors: (Constant), Revision, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 75312769,520 3 25104256,510 2,838 ,042b
Residual 910956172,50 103 8844234,685
Total 986268942,10 106
80
CoreConsumerPriceIndex
10-minuteforecastingmodel:
1stMulti-regressionAnalysis(Surprise,Growth,Revision,AsymmetricSurprise)
Descriptive Statistics
Mean Std. Deviation N
Ten_Min_Delta 110,0935 889,11287 107
Surprise ,001869158880000 ,101852009000000 107
Growth -,009345794390000 ,086365513600000 107
Revision ,009345794390000 ,081879698100000 107
AsymmetricSurprise ,69 ,464 107
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 210,198 234,578 ,896 ,372
Surprise 19384,388 6243,222 2,221 3,105 ,002
Growth -18055,009 6302,909 -1,754 -2,865 ,005
Revision -18832,003 6463,768 -1,734 -2,913 ,004
AsymmetricSurpris
e
-186,636 315,341 -,097 -,592 ,555
a. Dependent Variable: Ten_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,299a ,089 ,054 864,88885 ,089 2,505
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 4 102 ,047 a. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
81
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 7495960,938 4 1873990,234 2,505 ,047b
Residual 76299338,130 102 748032,727
Total 83795299,070 106
a. Dependent Variable: Ten_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
2ndMulti-regressionAnalysis(Surprise,Growth,Revision)
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 80,805 84,765 ,953 ,343
Surprise 19370,332 6223,455 2,219 3,112 ,002
Growth -18753,777 6171,787 -1,822 -3,039 ,003
Revision -19493,987 6346,150 -1,795 -3,072 ,003 a. Dependent Variable: Ten_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,294a ,086 ,060 862,15674 ,086 3,244
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 3 103 ,025 a. Predictors: (Constant), Revision, Growth, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 7233932,036 3 2411310,679 3,244 ,025b
Residual 76561367,030 103 743314,243
Total 83795299,070 106 a. Dependent Variable: Ten_Min_Delta
b. Predictors: (Constant), Revision, Growth, Surprise
82
20-minuteforecastingmodel:
1stMulti-regressionAnalysis(Surprise,Growth,Revision,AsymmetricSurprise)
Descriptive Statistics
Mean Std. Deviation N
Twenty_Min_Delta 187,1963 1347,72610 107
Surprise ,001869158880000 ,101852009000000 107
Growth -,009345794390000 ,086365513600000 107
Revision ,009345794390000 ,081879698100000 107
AsymmetricSurprise ,69 ,464 107
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
B Std. Error Beta
1 (Constant) 515,494 365,698 1,410 ,162
Surprise 10103,737 9732,955 ,764 1,038 ,302
Growth -7240,362 9826,005 -,464 -,737 ,463
Revision -10285,805 10076,778 -,625 -1,021 ,310
AsymmetricSurpris
e
-460,853 491,605 -,159 -,937 ,351
a. Dependent Variable: Twenty_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,192a ,037 -,001 1348,33009 ,037 ,976
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 4 102 ,424
a. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
83
2ndMulti-regressionAnalysis(Surprise,Revision,AsymmetricSurprise)
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 575,025 355,870 1,616 ,109
Surprise 3158,323 2420,739 ,239 1,305 ,195
Revision -3004,978 1972,691 -,183 -1,523 ,131
AsymmetricSurprise -528,708 481,831 -,182 -1,097 ,275 a. Dependent Variable: Twenty_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,178a ,032 ,004 1345,33530 ,032 1,126
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 3 103 ,342 a. Predictors: (Constant), AsymmetricSurprise, Revision, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 7099366,567 4 1774841,642 ,976 ,424b
Residual 185435392,300 102 1817994,042
Total 192534758,900 106
a. Dependent Variable: Twenty_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
84
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 6112271,307 3 2037423,769 1,126 ,342b
Residual 186422487,60 103 1809927,064
Total 192534758,90 106
a. Dependent Variable: Twenty_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Revision, Surprise
Core Durable Goods Orders
10-minuteforecastingmodel:
1stMulti-regressionAnalysis(Surprise,Growth,Revision,AsymmetricSurprise)
Descriptive Statistics
Mean Std. Deviation N
Ten_Min_Delta -31,7757 939,25182 107
Surprise -,111214953000000 2,714403479000000 107
Growth -,242990654000000 1,596524063000000 107
Revision ,220560748000000 1,866119893000000 107
AsymmetricSurprise ,50 ,502 107
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) -226,207 167,311 -1,352 ,179
Surprise -85,992 94,098 -,249 -,914 ,363
Growth 131,667 127,297 ,224 1,034 ,303
Revision -20,441 90,679 -,041 -,225 ,822
AsymmetricSurpris
e
438,640 272,678 ,235 1,609 ,111
a. Dependent Variable: Ten_Min_Delta
85
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,239a ,057 ,020 929,79375 ,057 1,542
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 4 102 ,196 a. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 5331888,103 4 1332972,026 1,542 ,196b
Residual 88180674,510 102 864516,417
Total 93512562,620 106
a. Dependent Variable: Ten_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
2ndMulti-regressionAnalysis(Surprise,Growth,AsymmetricSurprise)
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) -222,190 165,591 -1,342 ,183
Surprise -101,861 62,155 -,294 -1,639 ,104
Growth 153,429 82,591 ,261 1,858 ,066
AsymmetricSurprise 428,728 267,866 ,229 1,601 ,113 a. Dependent Variable: Ten_Min_Delta
86
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,238a ,057 ,029 925,49962 ,057 2,058
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 3 103 ,110 a. Predictors: (Constant), AsymmetricSurprise, Growth, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 5287958,825 3 1762652,942 2,058 ,110b
Residual 88224603,790 103 856549,551
Total 93512562,620 106
a. Dependent Variable: Ten_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Growth, Surprise
20-minuteforecastingmodel:
1stMulti-regressionAnalysis(Surprise,Growth,Revision,AsymmetricSurprise)
Descriptive Statistics
Mean Std. Deviation N
Twenty_Min_Delta -18,9720 1277,10753 107
Surprise -,111214953000000 2,714403479000000 107
Growth -,242990654000000 1,596524063000000 107
Revision ,220560748000000 1,866119893000000 107
AsymmetricSurprise ,50 ,502 107
87
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
B Std. Error Beta
1 (Constant) -234,081 229,885 -1,018 ,311
Surprise -134,704 129,291 -,286 -1,042 ,300
Growth 197,269 174,906 ,247 1,128 ,262
Revision 77,594 124,593 ,113 ,623 ,535
AsymmetricSurpris
e
457,619 374,660 ,180 1,221 ,225
a. Dependent Variable: Twenty_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,193a ,037 -,001 1277,53879 ,037 ,982
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 4 102 ,421 a. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 6411640,114 4 1602910,028 ,982 ,421b
Residual 166474746,800 102 1632105,361
Total 172886386,900 106
a. Dependent Variable: Twenty_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise
88
2ndMulti-regressionAnalysis(Surprise,Growth,AsymmetricSurprise)
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1 (Constant) -249,329 227,897 -1,094 ,276
Surprise -74,468 85,542 -,158 -,871 ,386
Growth 114,661 113,668 ,143 1,009 ,315
AsymmetricSurprise 495,245 368,656 ,195 1,343 ,182
a. Dependent Variable: Twenty_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,183a ,033 ,005 1273,73685 ,033 1,187
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 3 103 ,318 a. Predictors: (Constant), AsymmetricSurprise, Growth, Surprise
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 5778613,305 3 1926204,435 1,187 ,318b
Residual 167107773,600 103 1622405,569
Total 172886386,900 106
a. Dependent Variable: Twenty_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Growth, Surprise
89
3rdMulti-regressionAnalysis(Growth,AsymmetricSurprise)
a. Dependent Variable: Twenty_Min_Delta
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change
1 ,162a ,026 ,008 1272,25309 ,026 1,405
Model Summary
Model
Change Statistics
df1 df2 Sig. F Change
1 2 104 ,250 a. Predictors: (Constant), AsymmetricSurprise, Growth
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 4549083,629 2 2274541,814 1,405 ,250b
Residual 168337303,300 104 1618627,916
Total 172886386,900 106
a. Dependent Variable: Twenty_Min_Delta
b. Predictors: (Constant), AsymmetricSurprise, Growth
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1 (Constant) -154,294 199,816 -,772 ,442
Growth 54,796 90,401 ,069 ,606 ,546
AsymmetricSurprise 294,522 287,316 ,116 1,025 ,308