Rodyti trumpą aprašą

dc.contributor.authorMaknickienė, Nijolė
dc.contributor.authorMasėnaitė, Jovita
dc.contributor.authorStasytytė, Viktorija
dc.contributor.authorMartinkutė-Kaulienė, Raimonda
dc.date.accessioned2023-09-18T20:44:11Z
dc.date.available2023-09-18T20:44:11Z
dc.date.issued2021
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/152187
dc.description.abstractPurpose – The paper analyses two different paradigms of investor behaviour that exist in the financial mar-ket – 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 ap-propriate 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.eng
dc.formatPDF
dc.format.extentp. 1-10
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://doi.org/10.3846/cibmee.2021.596
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:95097444/datastreams/MAIN/content
dc.titleInvestigation of the herding and contrarian behaviour of investors
dc.typeStraipsnis recenzuotame konferencijos darbų leidinyje / Paper published in peer-reviewed conference publication
dcterms.accessRightsThis is an open-access article distributed under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references44
dc.type.pubtypeP1d - Straipsnis recenzuotame konferencijos darbų leidinyje / Article published in peer-reviewed conference proceedings
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyVerslo vadybos fakultetas / Faculty of Business Management
dc.subject.researchfieldS 004 - Ekonomika / Economics
dc.subject.vgtuprioritizedfieldsEV02 - Aukštos pridėtinės vertės ekonomika / High Value-Added Economy
dc.subject.ltspecializationsL103 - Įtrauki ir kūrybinga visuomenė / Inclusive and creative society
dc.subject.eninvestors behaviour
dc.subject.enherding behaviour
dc.subject.eninvestors’ sentiments
dc.subject.enalgorithm of deep learning
dc.subject.enclassification algorithm
dc.subject.enforecasting
dcterms.sourcetitleInternational scientific conference "Contemporary issues in business, management and economics engineering 2021, 13–14 May 2021, Vilnius, Lithuania
dc.publisher.nameVilnius Gediminas Technical University
dc.publisher.cityVilnius
dc.identifier.doi10.3846/cibmee.2021.596
dc.identifier.elaba95097444


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