市场调研财务顾问分支与总部机构业务拓展和人员选拔文档格式.docx

市场调研财务顾问分支与总部机构业务拓展和人员选拔文档格式.docx

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2.1.Creditratings

3.Researchdesignandmethodology

3.1.Datacollection

3.2.Themodel

4.Results

5.Conclusion

Acknowledgements

AppendixA.Independentvariables

AppendixB.OutputK-meansclustering

References

Anintelligent-agent-basedfuzzygroupdecisionmakingmodelforfinancialmulticriteriadecisionsupport:

Thecaseofcreditscoring 

EuropeanJournalofOperationalResearch

Creditriskanalysisisanactiveresearchareainfinancialriskmanagementandcreditscoringisoneofthekeyanalyticaltechniquesincreditriskevaluation.Inthisstudy,anovelintelligent-agent-basedfuzzygroupdecisionmaking（GDM）modelisproposedasaneffectivemulticriteriadecisionanalysis（MCDA）toolforcreditriskevaluation.Inthisproposedmodel,someartificialintelligenttechniques,whichareusedasintelligentagents,arefirstusedtoanalyzeandevaluatetherisklevelsofcreditapplicantsoverasetofpre-definedcriteria.Thentheseevaluationresults,generatedbydifferentintelligentagents,arefuzzifiedintosomefuzzyopinionsoncreditrisklevelofapplicants.Finally,thesefuzzificationopinionsareaggregatedintoagroupconsensusandmeantimethefuzzyaggregatedconsensusisdefuzzifiedintoacrispaggregatedvaluetosupportfinaldecisionfordecision-makersofcredit-grantinginstitutions.Forillustrationandverificationpurposes,asimplenumericalexampleandthreereal-worldcreditapplicationapprovaldatasetsarepresented.

2.Methodologyformulation

3.Experimentalstudy

3.1.Anillustrativenumericalexample

3.2.Empiricalcomparisonswithdifferentcreditdatasets

3.2.1.DatasetI:

Englandcreditapplicationexample

3.2.2.DatasetII:

Japanesecreditcardapplicationexample

3.2.3.DatasetIII:

Germancreditcardapplicationexample

3.2.4.Furtherdiscussions

4.Conclusions

Sovereigncreditratings,capitalflowsandfinancialsectordevelopmentinemergingmarkets 

EmergingMarketsReview,

Howdoesthesovereigncreditratingshistoryprovidedbyindependentratingsagenciesaffectdomesticfinancialsectordevelopmentandinternationalcapitalinflowstoemergingcountries?

WeaddressthisquestionutilizingacomprehensivedatasetofsovereigncreditratingsfromStandardandPoor'

sfrom1995–2003foracross-sectionof51emergingmarkets.Withinapaneldataestimationframework,weexaminefinancialsectordevelopmentandtheinfluenceofsovereigncreditratingsprovision,controllingforvariouseconomicandcorporategovernancefactorsidentifiedinthefinancialdevelopmentliterature.Wefindstrongevidencethatoursovereigncreditratingmeasuresdoaffectfinancialintermediarysectordevelopmentsandcapitalflows.Wefindthati）long-termforeigncurrencysovereigncreditratingsareimportantforencouragingfinancialintermediarydevelopmentandforattractingcapitalflows.ii）Long-termlocalcurrencyratingsstimulatedomesticmarketgrowthbutdiscourageinternationalcapitalflows.iii）Short-termratings（bothforeignandlocalcurrencydenominated）retardallformsoffinancialdevelopmentsandcapitalflows.Thereareimportantimplicationsinthisresearchforpolicymakerstoencouragetheprovisionoflonger-termcreditratingstopromotefinancialdevelopmentinemergingeconomies.

2.Theoreticalmotivations

3.Datadescriptionsandmodellingissues

3.1.Sovereignratings

3.2.Financialmarketvariables

3.3.Controlvariables

3.4.Internationalfinancialflows

4.Empiricalmodel

5.Empiricalresults

5.1.Financialsectordevelopmentwithsovereigncreditratings

5.2.Robustnesschecksoffinancialsectordevelopmentestimations

5.3.Internationalcapitalflowswithsovereigncreditratings

5.4.Robustnesschecksofcapitalflowsestimations

6.Conclusions

AppendixA.LineartransformationofS&

P'

ssovereigncreditratings

AppendixB.Listofemergingmarketcountriesstudied

AppendixC.Variabledefinitionsanddatadefinitions

Modellingcreditratingbyfuzzyadaptivenetwork 

MathematicalandComputerModelling

Humanjudgmentplaysanimportantroleintheratingofenterprisefinancialconditions.Therecentlydevelopedfuzzyadaptivenetwork（FAN）,whichcanhandlesystemswhosebehaviourisinfluencedbyhumanjudgment,appearstobeideallysuitedforthemodellingofthiscreditratingproblem.Inthispaper,FANisusedtomodelthecreditratingofsmallfinancialenterprises.Toillustratetheapproach,thedataofthecreditratingproblemisfirstrepresentedbytheuseoffuzzynumbers.Then,theFANnetworkbasedoninferencerulesisconstructed.Andfinally,thenetworkistrainedorlearnedbyusingthefuzzynumbertrainingdata.Themainadvantagesoftheproposednetworkaretheabilityforlinguisticrepresentation,linguisticaggregationandthelearningabilityoftheneuralnetwork.

2.Aggregationofcreditrating

3.Fuzzyadaptivenetwork

4.Fuzzyadaptivenetworkforcreditrating

4.1.Creditratingmodellingbasedoncreditscore

4.2.CreditratingmodellingbasedontheTaiwandata

5.Discussions

金融数量分析

信用管理

金融建模

分析工具

开发

数学

MATLAB

JAVA编程

金融工程

理学、金融学复合专业

证券从业资格、期货从业资格

信用评级、金融证券咨询和信息服务

金融机构

QuantitativeAnalysisofFinance

CreditManagement

Financialmodeling

AnalysisTools

Development

Mathematics

JAVAProgramming

FinancialEngineering

Science,financecomplexprofessional

Qualificationsecurities,futuresqualification

Creditrating,financialandsecuritiesadvisoryandinformationservices

Financialinstitutions

CreditratingdynamicsandMarkovmixturemodels 

JournalofBanking&

Finance

Despitemountingevidencetothecontrary,creditmigrationmatrices,usedinmanycreditriskandpricingapplications,aretypicallyassumedtobegeneratedbyasimpleMarkovprocess.Basedonempiricalevidence,weproposeaparsimoniousmodelthatisamixtureof（two）Markovchains,wherethemixingisonthespeedofmovementamongcreditratings.WeestimatethismodelusingcreditratinghistoriesandshowthatthemixturemodelstatisticallydominatesthesimpleMarkovmodelandthatthedifferencesbetweentwomodelscanbeeconomicallymeaningful.Thenon-Markovpropertyofourmodelimpliesthatthefuturedistributionofafirm’sratingsdependsnotonlyonitscurrentratingbutalsoonitspastratinghistory.Indeedwefindthattwofirmswithidenticalcurrentcreditratingscanhavesubstantiallydifferenttransitionprobabilityvectors.Wealsofindthatconditioningonthestateofthebusinesscycleorindustrygroupdoesnotremovetheheterogeneitywithrespecttotherateofmovement.WegoontocomparetheperformanceofmixtureandMarkovchainusingout-of-samplepredictions.

2.Markovmixturemodeling

2.1.Themixtureprocess

2.2.Prediction

2.2.1.Fullinformation

2.2.2.Limitedinformation:

Currentrating

2.2.3.Limitedinformation:

Initialandcurrentrating

3.Data,estimation,andresults

3.1.Estimatingandcomparingthemodels

3.2.Firm-specificmigrationvectors

3.3.Industryandbusinesscycleeffects

3.4.Out-of-sampleforecasting

3.5.Financialimpactofmixturemodels

4.Concludingremarks

Appendix.Appendix

QualificationandcertificationforthecompetitiveedgeinIntegratedDesign 

CIRPJournalofManufacturingScienceandTechnology

CompetitiveProductDesignismoreandmorelinkedtomasteringthechallengeofthecomplexityandthemultidisciplinarynatureofmodernproductsinanintegratedfashionfromtheveryearliestphasesofproductdevelopment.DesignEngineersareincreasinglyconfrontedwiththeneedtomasterseveraldifferentengineeringdisciplinesinordertogetasufficientunderstandingofaproductoraservice.Industrialistsdemandforthecertificationoftherequiredskills,aswellasfortheirinternationalrecognitionandexchangeability.ThispaperdescribesaninnovativeapproachtoestablishatrainingcurriculumandacertificationinthedomainofIntegratedEngineeringonaEuropeanlevel.ItshowsthekeycompetencesthathavebeenidentifiedforthenewjobroleofIntegratedDesignEngineers,aswellastheirrelevancetosystemcompetence,whichisconsideredoneofthemostvitalsuccessfactorsofcompetitiveproductdesign.It