Selection of orthogonal investment portfolio using Evolino RNN trading model
| dc.rights.license | Kūrybinių bendrijų licencija / Creative Commons licence | en_US |
| dc.contributor.author | Maknickienė, Nijolė | |
| dc.date.accessioned | 2024-10-21T10:02:17Z | |
| dc.date.available | 2024-10-21T10:02:17Z | |
| dc.date.issued | 2014 | |
| dc.identifier.issn | 1877-0428 | en_US |
| dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/155346 | |
| dc.description.abstract | Investing in financial market require the reliable predicting of expecting returns, assessment of risk and reliability. Principle of portfolio orthogonality was using to reduce the risk of the investment. An artificial intelligence system may reveal new opportunities for using this principle. Prediction of recurrent neural networks ensemble is stochastically informative distribution, which is helpful for portfolio selection. Shape and parameters of distribution influence decision making in currency market. Assessment of portfolio riskiness, finding most orthogonal elements of portfolio, influence better results for trading in real market. | en_US |
| dc.format.extent | 8 p. | en_US |
| dc.format.medium | Tekstas / Text | en_US |
| dc.language.iso | en | en_US |
| dc.relation.uri | https://etalpykla.vilniustech.lt/handle/123456789/155081 | en_US |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.source.uri | https://www.sciencedirect.com/science/article/pii/S1877042813056024 | en_US |
| dc.subject | artificial intelligence | en_US |
| dc.subject | forecasting | en_US |
| dc.subject | ensembles | en_US |
| dc.subject | prediction | en_US |
| dc.subject | portfolio management | en_US |
| dc.title | Selection of orthogonal investment portfolio using Evolino RNN trading model | en_US |
| dc.type | Konferencijos publikacija / Conference paper | en_US |
| dcterms.accessRights | Laisvai prieinamas / Openly available | en_US |
| dcterms.accrualMethod | Rankinis pateikimas / Manual submission | en_US |
| dcterms.issued | 2014-01-24 | |
| dcterms.license | CC BY NC ND | en_US |
| dcterms.references | 39 | en_US |
| dc.description.version | Taip / Yes | en_US |
| dc.contributor.institution | Vilniaus Gedimino technikos universitetas | en_US |
| dc.contributor.institution | Vilnius Gediminas Technical University | en_US |
| dc.contributor.faculty | Verslo vadybos fakultetas / Faculty of Business Management | en_US |
| dcterms.sourcetitle | Procedia - Social and Behavioral Sciences | en_US |
| dc.description.volume | vol. 110 | en_US |
| dc.publisher.name | Elsevier | en_US |
| dc.identifier.doi | https://doi.org/10.1016/j.sbspro.2013.12.962 | en_US |
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Except where otherwise noted, this item's license is described as Kūrybinių bendrijų licencija / Creative Commons licence