Czy modele autoregresyjne z rozkładem opóźnień mogą poprawić prognozowanie zysku na akcję w Polsce?
DOI:
https://doi.org/10.18778/0208-6018.370.01Słowa kluczowe:
zysk na akcję, błądzenie losowe, model autoregresyjny z rozkładem opóźnień, XGBoost, prognozowanie finansowe, Giełda Papierów Wartościowych w WarszawieAbstrakt
Niniejszy artykuł analizuje znaczenie dokładnych prognoz zysków spółek notowanych na giełdzie dla osiągnięcia sukcesu inwestycyjnego. Podkreśla wagę tego aspektu, szczególnie na rynkach o ograniczonym pokryciu przez analityków, takich jak rynki wschodzące, do których zaliczana jest Polska. W badaniu dokonano oceny trafności prognoz generowanych przy użyciu metody autoregresyjnej z rozkładem opóźnień przy różnych metodach łączenia prognoz w porównaniu z sezonowym modelem błądzenia losowego. Modele te, stanowiące etap pośredni pomiędzy szeregami czasowymi a prognozowaniem uwzględniającym wiele zmiennych objaśniających, mają zastosowanie do danych dotyczących zysku na akcję spółek notowanych na Giełdzie Papierów Wartościowych w Warszawie w latach 2008–2019, tj. między ostatnim kryzysem finansowym a szokiem spowodowanym pandemią. Model sezonowego błądzenia losowego osiągnął najniższe poziomy błędów na podstawie metryki średniego argus tangensa bezwzględnego błędu procentowego. Wniosek ten jest poparty rygorystycznymi testami statystycznymi i kontrolami odporności z wykorzystaniem różnych okresów oraz wskaźników błędu. Lepszą wydajność prostszego modelu sezonowego błądzenia losowego można przypisać stosunkowo nieskomplikowanemu charakterowi polskiego rynku.
Pobrania
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