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dc.rights.licenseKūrybinių bendrijų licencija / Creative Commons licenceen_US
dc.contributor.authorMaknickienė, Nijolė
dc.contributor.authorMasėnaitė, Jovita
dc.contributor.authorStasytytė, Viktorija
dc.contributor.authorMartinkutė-Kaulienė, Raimonda
dc.date.accessioned2024-11-26T08:49:33Z
dc.date.available2024-11-26T08:49:33Z
dc.date.issued2021
dc.date.submitted2021-02-25
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/155878
dc.description.abstractPurpose – The paper analyses two different paradigms of investor behaviour that exist in the financial market – the herding and contrarian behaviour. The main objective of the paper is to determine which pattern of investor behaviour better reflects the real changes in the prices of financial instruments in the financial markets. Research methodology – Algorithms of technical analysis, deep learning and classification of sentiments were used for the research; data of positions held by investors were analysed. Data mining was performed using “Tweet Sentiment Visualization” tool. Findings – The performed analysis of investor behaviour has revealed that it is more useful to ground financial decisions on the opinion of the investors contradicting the majority. The analysis of the data on the positions held by investors helped to make sure that the herding behaviour could have a negative impact on investment results, as the opinion of the majority of investors is less in line with changes in the prices of financial instruments in the market. Research limitations – The study was conducted using a limited number of investment instruments. In the future, more investment instruments can be analysed and additional forecasting methods, as well as more records in social networks can be used. Practical implications – Identifying which paradigm of investor behaviour is more beneficial to rely on can offer appropriate practical guidance for investors in order to invest more effectively in financial markets. Investors could use investor sentiment data to make practical investment decisions. All the methods used complement each other and can be combined into one investment decision strategy. Originality/Value – The study compared the ratio of open positions not only with real price changes but also with data obtained from the known technical analysis, deep learning and sentiment classification algorithms, which has not been done in previous studies. The applied methods allowed to achieve reliable and original results.en_US
dc.format.extent10 p.en_US
dc.format.mediumTekstas / Texten_US
dc.language.isoenen_US
dc.relation.urihttps://etalpykla.vilniustech.lt/handle/123456789/155629en_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.source.urihttp://cibmee.vgtu.lt/index.php/verslas/2021/paper/view/596en_US
dc.subjectinvestors behaviouren_US
dc.subjectherding behaviouren_US
dc.subjectinvestors’ sentimentsen_US
dc.subjectalgorithm of deep learningen_US
dc.subjectclassification algorithmen_US
dc.subjectforecastingen_US
dc.titleInvestigation of the herding and contrarian behaviour of investorsen_US
dc.typeKonferencijos publikacija / Conference paperen_US
dcterms.accessRightsLaisvai prieinamas / Openly availableen_US
dcterms.accrualMethodRankinis pateikimas / Manual submissionen_US
dcterms.alternativeContemporary financial managementen_US
dcterms.dateAccepted2021-04-01
dcterms.issued2021-05-14
dcterms.licenseCC BYen_US
dcterms.references44en_US
dc.description.versionTaip / Yesen_US
dc.contributor.institutionVilniaus Gedimino technikos universitetasen_US
dc.contributor.institutionVilnius Gediminas Technical Universityen_US
dc.contributor.facultyVerslo vadybos fakultetas / Faculty of Business Managementen_US
dc.contributor.departmentFinansų inžinerijos katedra / Department of Financial Engineeringen_US
dc.contributor.departmentDinamiškosios vadybos institutas / Institute of Dynamic Managementen_US
dcterms.sourcetitleInternational Scientific Conference „Contemporary Issues in Business, Management and Economics Engineering ‘2021“en_US
dc.identifier.eisbn9786094762604en_US
dc.identifier.eissn2538-8711en_US
dc.publisher.nameVilnius Gediminas Technical Universityen_US
dc.publisher.nameVilniaus Gedimino technikos universitetasen_US
dc.publisher.countryLithuaniaen_US
dc.publisher.countryLietuvaen_US
dc.publisher.cityVilniusen_US
dc.identifier.doihttps://doi.org/10.3846/cibmee.2021.596en_US


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