DC FieldValueLanguage
dc.contributor.authorLoukeris, Nikolaos-
dc.contributor.authorEleftheriadis, Iordanis-
dc.date.accessioned2024-04-23T11:02:52Z-
dc.date.available2024-04-23T11:02:52Z-
dc.date.issued2012-08-
dc.identifiergoogle_scholar-XlKYe28AAAAJ:roLk4NBRz8UC-
dc.identifier.issn547-4836-
dc.identifier.otherXlKYe28AAAAJ:roLk4NBRz8UC-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2226-
dc.description.abstractInvestment portfolios are into a dynamic process of optimisation to maximize expected profits. Under the diminishing expectations that the crisis produces, investors seek reliable information to evaluate their assets. Accounting statements include significant valuable data on the economic health of companies. The vast amount of accounting data and financial indices, can only be analyzed with sharp techniques of econometrics, or artificial intelligence. Hybrid neural-genetic Time Lag Recurrent Network is quite capable to process temporal information such as financial-accounting data. Further comparisons take place to the Multi Layer Perceptrons and Logistic Regressions. Additionally hybrid neural-genetic Time Lag Recurrent Network with Cross Validation and no hidden layers performed significantly higher than networks of the same architecture but with less hidden layers.en_US
dc.language.isoenen_US
dc.relation.ispartofProceedings of the International Summer Conference of the International Academy of Business and Public Administration Disciplines (IABPAD)en_US
dc.sourceProceedings of the International Summer Conference of the International …, 2012-
dc.titleBankruptcy prediction into hybrids of time lag recurrent networks with genetic optimisation, multi layer perceptrons neural nets, and Bayesian logistic regressionen_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Summer Conference of the International Academy of Business and Public Administration Disciplines (IABPAD), 1-5 August 2012, Honolulu, Hawaii, USAen_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.linkhttps://www.researchgate.net/publication/260018775_Bankruptcy_Prediction_into_Hybrids_of_Time_Lag_Recurrent_Networks_with_Genetic_optimisation_Multi_Layer_Perceptrons_Neural_Nets_and_Bayesian_Logistic_Regressionen_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsSubscriptionen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusnot verifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeConference Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-1891-8245-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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