dc.contributor.author | Maknickienė, Nijolė | |
dc.contributor.author | Maknickas, Algirdas | |
dc.date.accessioned | 2023-09-18T20:50:36Z | |
dc.date.available | 2023-09-18T20:50:36Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 0883-9514 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/152872 | |
dc.description.abstract | Sharp falls or explosive growths in exchange markets, whether expected or not, generates new challenges for investors who want to protect their investments or achieve an optimum benefit during and after the turmoil. An anomaly of the exchange market, instigated by the Swiss National Bank, occurred when the Swiss Franc decoupled from the euro unexpectedly. The United Kingdom (UK) vote to withdraw from the European Union (Brexit), in contrast, was feared but expected. A comparison of the consequences of the anomalies gives us an unprecedented opportunity to investigate prediction capabilities of the EVOLINO Recurrent Neural Network Ensemble (ERNN) model following an anomaly. By introducing this new information to the ERNN model and analyzing its response, we increase investor resources during large exchange rate fluctuations; this will provide them with additional information that will help them construct different portfolios. Reaction to the anomaly was visible only after the anomaly occurred, this is when the model began to acquire data influenced by the extreme change. Comparing different strategies which are related or unrelated to the anomaly and orthogonal or not orthogonal for conservative, moderate, or aggressive trading shows that in order to profit from the anomaly, speculation depends on prediction-accuracy and on the sets of exchange-rate associated with the anomaly. | eng |
dc.format | PDF | |
dc.format.extent | p. 957-980 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | PsycInfo | |
dc.relation.isreferencedby | Current Contents / Engineering, Computing & Technology | |
dc.relation.isreferencedby | CompuMath Citation Index | |
dc.relation.isreferencedby | CSA- Electronics & Communications Abstracts | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.rights | Prieinamas tik institucijos(-ų) intranete | |
dc.source.uri | https://www.tandfonline.com/doi/pdf/10.1080/08839514.2020.1790249?needAccess=true | |
dc.source.uri | https://www.tandfonline.com/doi/full/10.1080/08839514.2020.1790249 | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:66495758/datastreams/MAIN/content | |
dc.title | Evolino recurrent neural network ensemble for speculation in exchange market in time of anomalies | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.references | 32 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Verslo vadybos fakultetas / Faculty of Business Management | |
dc.contributor.faculty | Mechanikos fakultetas / Faculty of Mechanics | |
dc.contributor.department | Mechanikos mokslo institutas / Institute of Mechanical Science | |
dc.subject.researchfield | S 004 - Ekonomika / Economics | |
dc.subject.researchfield | S 003 - Vadyba / Management | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.vgtuprioritizedfields | IK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems | |
dc.subject.ltspecializations | L103 - Įtrauki ir kūrybinga visuomenė / Inclusive and creative society | |
dc.subject.en | anomalies | |
dc.subject.en | financial markets | |
dc.subject.en | prediction | |
dc.subject.en | forecasting | |
dc.subject.en | artificial intelligence | |
dc.subject.en | neural networks | |
dc.subject.en | intervention | |
dcterms.sourcetitle | Applied artificial intelligence | |
dc.description.issue | iss. 13 | |
dc.description.volume | vol. 34 | |
dc.identifier.eissn | 1087-6545 | |
dc.publisher.name | Taylor & Francis | |
dc.publisher.city | Philadelphia | |
dc.identifier.doi | 10.1080/08839514.2020.1790249 | |
dc.identifier.elaba | 66495758 | |
dc.identifier.wos | 000551316500001 | |