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dc.contributorArratia Quesada, Argimiro Alejandro
dc.contributor.authorAlvarez Vecino, Pol
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2019-05-16T11:06:39Z
dc.date.available2019-05-16T11:06:39Z
dc.date.issued2019-04-15
dc.identifier.urihttp://hdl.handle.net/2117/133070
dc.description.abstractWe present HPC.FASSR, a High-Performance Computation Fundamental Analysis Stock Screener and Ranker to compare many ML models and the criteria of famous Benjamin Graham for stock investing using fundamental data. FASSR is distributed so it can explore massive amounts of models in short time.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshMachine learning
dc.subject.lcshHigh performance computing
dc.subject.lcshArtificial intelligence
dc.subject.otheranàlisi financer
dc.subject.otheranàlisi fonamental
dc.subject.othercomputacio d'altes prestacions
dc.subject.othercomputació distribuida
dc.subject.otherAI
dc.subject.othersupervised learning
dc.subject.otherfinancial analysis
dc.subject.otherfundamental analysis
dc.subject.otherstock raking
dc.subject.otherstock screening
dc.subject.otherdistributed computing
dc.subject.otherhpc
dc.subject.otheraprenentatge supervisat
dc.titleA machine learning approach to stock screening with fundamental analysis
dc.typeMaster thesis
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacCàlcul intensiu (Informàtica)
dc.subject.lemacIntel·ligència artificial
dc.identifier.slug138115
dc.rights.accessOpen Access
dc.date.updated2019-04-26T04:01:21Z
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona


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