Stock return predictability based on world  news sentiment

dc.contributor.author	Kharysh, Ostap
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