Rodyti trumpą aprašą

dc.contributor.authorMaknickienė, Nijolė
dc.contributor.authorVaškevičiūtė, Agnetė
dc.date.accessioned2023-09-18T16:47:05Z
dc.date.available2023-09-18T16:47:05Z
dc.date.issued2017
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/116697
dc.description.abstractAbstract. Constantly evolving rational and irrational behaviour of individual investors in financial markets explains the limitations of efficient market hypothesis. Forecasting financial markets with increased volatility allows investors to retain investments when the market is ‘going crazy’. There are various ways to measure individual investor sentiments, including surveys, calculating the sentiment index and sentiment analysis of text. This study aims to compare different individual investor sentiment forecasting methods: econometric, text analysis and artificial intelligence. Trends, moving averages and Bollinger bands are standard econometric forecasting methods. They are required to have historical data strings, which allows for the assessment of prediction accuracy. Sentiment analysis from macroeconomic analyses and forex news predicts instant states of the finance market, not assessing the past. The ensemble of Evolino recurrent neural networks (EERNN) was successfully tested to predict the exchange rates. The prediction of sentiment survey data is a new investigation for the authors, and the prediction of EERNN is a distribution of expected values reflecting the probabilities of different states of market sentiments. The selected prediction method can be used to develop the trading strategy by combining it with other trading indicators. These studies can be applied to the activities of investment funds. An innovative model based on artificial intelligence and tested early in the exchange market was adopted to forecast individual investors' sentiments and create new opportunities for investors.eng
dc.formatPDF
dc.format.extentp. 335-337
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.ispartofseriesEconomies 2227-7099
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:23065674/datastreams/COVER/content
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:23065674/datastreams/ATTACHMENT_23104186/content
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:23065674/datastreams/ATTACHMENT_23104187/content
dc.subjectVE01 - Aukštos pridėtinės vertės ekonomika / High value-added economy
dc.titleComparison of predictions of behaviour of individual investors
dc.typeStraipsnis recenzuotame konferencijos darbų leidinyje / Paper published in peer-reviewed conference publication
dcterms.references36
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.contributor.departmentFinansų inžinerijos katedra / Department of Financial Engineering
dc.subject.researchfieldS 004 - Ekonomika / Economics
dc.subject.ltspecializationsL103 - Įtrauki ir kūrybinga visuomenė / Inclusive and creative society
dc.subject.enSentiment
dc.subject.enEvolino
dc.subject.enForecasting
dc.subject.enText classification
dc.subject.enArtificial intelligence
dcterms.sourcetitle7th Global Innovation and Knowledge Academy (GIKA) "Innovation, knowledge, judgment and decision-making as virtuous cycles", Lisbon, June 28-30, 2017 : conference proceedings
dc.publisher.nameTomsons Reuters
dc.publisher.cityLisbon
dc.identifier.elaba23065674


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