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IntradaytradingimpactofU.S.economicnewsontheEUR/USD

BachelorThesisforObtainingtheDegree

BachelorofScience

InternationalManagement

SubmittedtoSilviaBressan

ThomasJandejsek

1221004

Vienna,15.06.2016

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Affidavit

IherebyaffirmthatthisBachelor’sThesisrepresentsmyownwrittenworkandthatI

haveusednosourcesandaidsotherthanthoseindicated.Allpassagesquotedfrom

publicationsorparaphrasedfromthesesourcesareproperlycitedandattributed.

The thesiswasnot submitted in the sameor ina substantially similar version,not

evenpartially,toanotherexaminationboardandwasnotpublishedelsewhere.

15.06.2016

Date Signature

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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,


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


Revision).............................................................................................................52

Table13…CoreConsumerPriceIndex10-minuteModelSummary.........................52

Table14…CoreConsumerPriceIndex10-minuteANOVAtest................................53



Table 16 … Core Consumer Price Index 20-minute Coefficients (Surprise, Revision,


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Table17…CoreConsumerPriceIndex20-minuteModelSummary.........................54

Table18…CoreConsumerPriceIndex20-minuteANOVAtest................................55

Table 19 … Core Curable Goods Orders 10-minute Coefficients (Surprise, Growth,




Table21…CoreCurableGoodsOrders10-minuteModelSummary........................56

Table22…CoreCurableGoodsOrders10-minuteANOVAtest................................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

19

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.


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


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



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



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%



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%


CoreDurableGoods


72

Positive 51% 60% 60%

Growth Negative 58% 46% 40% Positive 42% 58% 56%

Revision Negative 45% 42% 42% Positive 56% 53% 57%


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


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



Change Statistics

R Square Change F Change

1 ,331a ,109 ,083 2287,02126 ,109 4,214

Model Summary



a. Predictors: (Constant), Revision, Growth, Surprise b. Predictors: (Constant), Revision, Growth, Surprise

76


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


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


Model Summary



Change Statistics R Square Change F Change

1 ,324a ,105 ,088 2281,45888 ,105 6,103

Model Summary



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


b. Predictors: (Constant), Growth, Surprise

77

20-minuteforecastingmodel:


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


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


1 Regression 75636921,600 4 18909230,400 2,118 ,084b

Residual 910632020,50 102 8927764,906

Total 986268942,10 106




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


1 3 103 ,042

79


b. Predictors: (Constant), AsymmetricSurprise, Revision, Surprise

3rdMulti-regressionAnalysis(Surprise,Revision)

Coefficientsa

Model


Standardized

Coefficients


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


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


1 2 104 ,023 a. Predictors: (Constant), Revision, Surprise

ANOVAa

Model

Sum of


1 Regression 69365451,010 2 34682725,500 3,934 ,023b

Residual 916903491,100 104 8816379,722

Total 986268942,100 106


b. Predictors: (Constant), Revision, Surprise

ANOVAa

Model

Sum of


1 Regression 75312769,520 3 25104256,510 2,838 ,042b

Residual 910956172,50 103 8844234,685

Total 986268942,10 106

80






Ten_Min_Delta 110,0935 889,11287 107

Surprise ,001869158880000 ,101852009000000 107

Growth -,009345794390000 ,086365513600000 107

Revision ,009345794390000 ,081879698100000 107


Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients


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


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


1 4 102 ,047 a. Predictors: (Constant), AsymmetricSurprise, Revision, Growth, Surprise

81

ANOVAa

Model

Sum of


1 Regression 7495960,938 4 1873990,234 2,505 ,047b

Residual 76299338,130 102 748032,727

Total 83795299,070 106



2ndMulti-regressionAnalysis(Surprise,Growth,Revision)

Coefficientsa

Model


Standardized

Coefficients


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


1 3 103 ,025 a. Predictors: (Constant), Revision, Growth, Surprise

ANOVAa

Model

Sum of


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





Twenty_Min_Delta 187,1963 1347,72610 107

Surprise ,001869158880000 ,101852009000000 107

Growth -,009345794390000 ,086365513600000 107

Revision ,009345794390000 ,081879698100000 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


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


1 4 102 ,424


83

2ndMulti-regressionAnalysis(Surprise,Revision,AsymmetricSurprise)

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients


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


1 3 103 ,342 a. Predictors: (Constant), AsymmetricSurprise, Revision, Surprise

ANOVAa

Model

Sum of


1 Regression 7099366,567 4 1774841,642 ,976 ,424b

Residual 185435392,300 102 1817994,042

Total 192534758,900 106



84

ANOVAa

Model

Sum of


1 Regression 6112271,307 3 2037423,769 1,126 ,342b

Residual 186422487,60 103 1809927,064

Total 192534758,90 106


b. Predictors: (Constant), AsymmetricSurprise, Revision, Surprise

Core Durable Goods Orders





Ten_Min_Delta -31,7757 939,25182 107

Surprise -,111214953000000 2,714403479000000 107

Growth -,242990654000000 1,596524063000000 107

Revision ,220560748000000 1,866119893000000 107


Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients


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


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



ANOVAa

Model

Sum of


1 Regression 5331888,103 4 1332972,026 1,542 ,196b

Residual 88180674,510 102 864516,417

Total 93512562,620 106



2ndMulti-regressionAnalysis(Surprise,Growth,AsymmetricSurprise)

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients


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


1 3 103 ,110 a. Predictors: (Constant), AsymmetricSurprise, Growth, Surprise

ANOVAa

Model

Sum of


1 Regression 5287958,825 3 1762652,942 2,058 ,110b

Residual 88224603,790 103 856549,551

Total 93512562,620 106


b. Predictors: (Constant), AsymmetricSurprise, Growth, Surprise





Twenty_Min_Delta -18,9720 1277,10753 107

Surprise -,111214953000000 2,714403479000000 107

Growth -,242990654000000 1,596524063000000 107

Revision ,220560748000000 1,866119893000000 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


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



ANOVAa

Model

Sum of


1 Regression 6411640,114 4 1602910,028 ,982 ,421b

Residual 166474746,800 102 1632105,361

Total 172886386,900 106



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


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


1 3 103 ,318 a. Predictors: (Constant), AsymmetricSurprise, Growth, Surprise

ANOVAa

Model

Sum of


1 Regression 5778613,305 3 1926204,435 1,187 ,318b

Residual 167107773,600 103 1622405,569

Total 172886386,900 106


b. Predictors: (Constant), AsymmetricSurprise, Growth, Surprise

89

3rdMulti-regressionAnalysis(Growth,AsymmetricSurprise)


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


1 2 104 ,250 a. Predictors: (Constant), AsymmetricSurprise, Growth

ANOVAa

Model

Sum of


1 Regression 4549083,629 2 2274541,814 1,405 ,250b

Residual 168337303,300 104 1618627,916

Total 172886386,900 106


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

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