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dc.contributor.authorFalas, Tasosen
dc.contributor.authorCharitou, Andreasen
dc.contributor.authorCharalambous, Chrisen
dc.coverage.spatialPiscataway, NJ, United Statesen
dc.creatorFalas, Tasosen
dc.creatorCharitou, Andreasen
dc.creatorCharalambous, Chrisen
dc.date.accessioned2019-04-24T06:29:33Z
dc.date.available2019-04-24T06:29:33Z
dc.date.issued1994
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/46812en
dc.description.abstractThe feasibility of using artificial neural networks (ANNs) for predicting future earnings by stock market and capital market investors was evaluated. A multilayer perceptron feedforward neural network architecture with an efficient and robust training algorithm was used in the study because of its suitability as a classifier in accounting and business processes. The logistic regression method was also examined in parallel with the ANN approach to compare the performance of the two methods.en
dc.language.isoengen
dc.publisherIEEEen
dc.sourceIEEE International Conference on Neural Networks - Conference Proceedingsen
dc.subjectMathematical modelsen
dc.subjectRegression analysisen
dc.subjectFinancial data processingen
dc.subjectDecision support systemsen
dc.subjectAlgorithmsen
dc.subjectIndustrial economicsen
dc.subjectArtificial intelligenceen
dc.subjectArtificial neural networksen
dc.subjectComputer architectureen
dc.subjectEarnings changesen
dc.subjectForecastingen
dc.subjectFunction minimizationen
dc.subjectIterative methodsen
dc.subjectManagement scienceen
dc.subjectNeural networksen
dc.titleApplication of artificial neural networks in the prediction of earningsen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.description.volume6
dc.description.startingpage3629
dc.description.endingpage3633
dc.author.facultyΣχολή Οικονομικών Επιστημών και Διοίκησης / Faculty of Economics and Management
dc.author.departmentΤμήμα Λογιστικής και Χρηματοοικονομικής / Department of Accounting and Finance
dc.type.uhtypeConference Objecten
dc.contributor.orcidCharitou, Andreas [0000-0003-1080-9121]
dc.description.totalnumpages3629-3633
dc.gnosis.orcid0000-0003-1080-9121


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