Szczegóły publikacji
Opis bibliograficzny
Impact of observation classification on the result of ANN analysis based on the example of WTI oil options / Radosław PUKA, Bartosz ŁAMASZ, Marek MICHALSKI // W: Proceedings of the 39th International Business Information Management Association Conference (IBIMA) [Dokument elektroniczny] : 30-31 May 2022, Granada, Spain. — Wersja do Windows. — Dane tekstowe. — [Norristown] : International Business Information Management Association (IBIMA), cop. 2022. — (Proceedings of the... International Business Information Management Association Conference ; ISSN 2767-9640). — e-ISBN: 978-0-9998551-8-8. — S. 868–876. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://u.pcloud.link/publink/show?code=kZXQSVVZAgQIAgJUYN78h... [2022-07-20]. — Bibliogr. s. 875–876, Abstr.
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 141220 |
|---|---|
| Data dodania do BaDAP | 2022-07-27 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | International Business Information Management 2022 |
| Czasopismo/seria | Proceedings of the... International Business Information Management Association Conference |
Abstract
The risk of changes in crude oil prices is particularly important from the perspective of enterprises dealing in selling and processing this commodity. Moreover, in the era of the war in Ukraine, Covid-19 and possible future oil supply disruptions caused by unforeseen geopolitical factors, it may be assumed that the volatility of prices in the oil market will be high. The subject of the study is to investigate the possibility of using artificial neural networks to generate signals to take long position in European WTI call options. This paper shows that an important factor in making decisions to use call options is the risk appetite directly related to the desired return from the purchase of options. Moreover, we showed that the number of observation classes significantly affects network results for the options market. The we also demonstrated that artificial neural networks can be a useful tool supporting the process of hedging against the risk of oil price increases.
