Prediktív modellek teljesítményének vizsgálata Covid-19 és az orosz-ukrán háború időszakában
DOI:
https://doi.org/10.35551/PFQ_2023_2_7Kulcsszavak:
COVID-19, árfolyam-előrejelzés, mesterséges intelligencia, prediktív algoritmusok, G17, G11, C45, C53Absztrakt
Tanulmányunkban arra a kérdésre keressük a választ, hogy mennyire hatékonyan lehet a mesterséges intelligencia segítségével előrejelezni a részvénypiaci trendeket a világ vezető részvénypiacain a 2010. 01. 01. és a 2022. 09. 16. közötti időszakban. A Covid-19 és az orosz–ukrán háború erőteljesen éreztette hatását a tőkepiacokon is, ezért egy rendkívül volatilitásintenzív környezetben folyt a vizsgálat. Az elemzés során három időintervallumon két különböző komplexitású gépi tanulási algoritmust (döntési fa, LSTM) és egy parametrikus statisztikai modellt (lineáris regresszió) alkalmaztunk. A kapott eredmények kiértékelését az átlagos abszolút százalékos hiba alapján (MAPE) értékeltük. Tanulmányunkban igazoltuk, hogy a prediktív modellek a kiemelt volatilitású időszakban jobban tudnak teljesíteni, mint a lineáris regresszió. Emellett fontos eredményünk, hogy az orosz–ukrán háborút követő időszakban jobban teljesítettek az előrejelző modellek, mint a Covid-19 kitörése után. Az árfolyam-előrejelzés a fundamentális és technikai elemzések során is fontos szerepet kaphat, beépíthető az algoritmikus kereskedés döntési szempontjai közé, azonban akár önmagában is alkalmas lehet a kereskedés automatizálására.
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