Please use this identifier to cite or link to this item: https://rfos.fon.bg.ac.rs/handle/123456789/2811
Full metadata record
DC FieldValueLanguage
dc.creatorRakićević, Aleksandar-
dc.creatorMilošević, Pavle-
dc.creatorDragović, Ivana-
dc.creatorPoledica, Ana-
dc.creatorZukanović, Milica-
dc.creatorJanusz, Andrzej-
dc.creatorŚlęzak, Dominik-
dc.date.accessioned2024-11-08T14:14:01Z-
dc.date.available2024-11-08T14:14:01Z-
dc.date.issued2024-11-04-
dc.identifier.urihttps://rfos.fon.bg.ac.rs/handle/123456789/2811-
dc.description.abstractPredictive analytics aims to empower finance professionals to make data-driven decisions, anticipate customer behavior, and navigate the complexities of the financial landscape. One of the tasks in this domain is the prediction of stock trend movements. The goal of the FedCSIS 2024 Data Science Challenge was to build such predictive models based on the financial fundamental data. Such models could have a vital role in algorithmic or manual trading, providing trading signals for making decisions about the time and direction of stock trades. We describe the prepared dataset and challenge task. We also summarize the challenge outcomes and provide insights about the most successful machine learning techniques applied.sr
dc.language.isoensr
dc.relationSENSEI project co-financed by EU Smart Growth Operational Programme 2014–2020 under GameINN project POIR.01.02.00-00-0184/17-00.sr
dc.relationUniversity of Belgrade – Faculty of Organizational Sciences and Ministry of Science, Technological Development, and Innovation of the Republic of Serbia, institutional funding, Grant no. 200151.sr
dc.rightsopenAccesssr
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.source2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS)sr
dc.subjectdata science competitions; KnowledgePit.ai platform; stock market data; automatic tradingsr
dc.titlePredicting Stock Trends Using Common Financial Indicators: A Summary of FedCSIS 2024 Data Science Challenge Held on KnowledgePit.ai Platformsr
dc.typeconferenceObjectsr
dc.rights.licenseBYsr
dc.identifier.doi10.15439/2024F7912-
dc.type.versionpublishedVersionsr
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeconferenceObject-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Radovi istraživača / Researchers’ publications
Show simple item record

SCOPUSTM   
Citations

5
checked on Nov 17, 2025

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons