Determination of Individual Investors' Financial Risk Tolerance by Machine Learning Methods

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dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.contributor.authorAltuntas, Yahya
dc.contributor.authorKocamaz, Adnan Fatih
dc.contributor.authorUlkgun, Abdullah Mert
dc.date.accessioned2024-08-04T20:49:16Z
dc.date.available2024-08-04T20:49:16Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORKen_US
dc.description.abstractFinancial risk tolerance refers to the amount of risk that an investor is willing to take in order to obtain returns. In this study, it was aimed to heuristically determine the individual investor financial risk tolerance by using demographic and socioeconomic variables. For this purpose, a questionnaire consisting of two parts was applied to blond University Computer Engineering Department students and administrative and academic staff. In the first part of the questionnaire, demographic and socioeconomic information of the participants were taken, and in the second part, 13 questions aiming to measure the financial risk tolerance were asked. The participants were labeled as risk-averse, risk-neutral and risk-loving according to their answers. The obtained data were classified by decision tree, k-nearest neighbor and support vector machine methods. 10-fold cross-validation method was used to determine model performances. According to the results of the experiment, the best classification performance was obtained with a overall accuracy value of 66.67% using the decision tree classifier.en_US
dc.description.sponsorshipIstanbul Medipol Univen_US
dc.identifier.isbn978-1-7281-7206-4
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85100298961en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/99748
dc.identifier.wosWOS:000653136100268en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2020 28th Signal Processing and Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectfinancial risk toleranceen_US
dc.subjectrobo-advisoren_US
dc.subjectartificial learningen_US
dc.subjectclassificationen_US
dc.titleDetermination of Individual Investors' Financial Risk Tolerance by Machine Learning Methodsen_US
dc.typeConference Objecten_US

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