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dc.contributor.author | Kharysh, Ostap![]() |
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dc.date.accessioned | 2024-02-14T15:36:03Z | |
dc.date.available | 2024-02-14T15:36:03Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Kharysh, Ostap. Stock return predictability based on world news sentiment / Kharysh, Ostap; Supervisor: Yarema Okhrin; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2019. – 51 p. | uk |
dc.identifier.uri | https://er.ucu.edu.ua/handle/1/4471 | |
dc.language.iso | en | uk |
dc.title | Stock return predictability based on world news sentiment | uk |
dc.type | Preprint | uk |
dc.status | Публікується вперше | uk |
dc.description.abstracten | Modern stock trading leans on various algorithms, attitudes and data resources in order to win on arbitrage. One of such is news. There is a compelling amount of investigations based on the news provided by specific agencies, but none of them cover the world news. In our research, we investigated how stock prediction could benefit from the world news. Based on the GDELT Project data we created the news preprocessing algorithms to capture the stock related news and made several inves- tigations to explore the benefits which trader could receive in case of including this source to his/her trading strategy. We proved the efficiency of world news for stock return volatility predictions. | uk |