BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

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Autor
Radomska Sylwia (Uniwersytet Warszawski)
Tytuł
Prognozowanie indeksu WIG20 za pomocą sieci neuronowych NARX i metody SVM
Źródło
Bank i Kredyt, 2021, nr 5, s. 457-472, wykr., tab., bibliogr. 44 poz.
Bank & Credit
Słowa kluczowe
Warszawski Indeks Giełdowy (WIG), Prognozowanie, Metody prognozowania, Sieci neuronowe, Metoda wektorów nośnych (SVM), Przegląd literatury, Modele regresji, Regresja liniowa
Warsaw Stock Exchange Index, Forecasting, Forecasting methods, Neural networks, Support Vector Machines (SVM), Literature review, Regression models, Linear regression
Uwagi
Klasyfikacja JEL: G17, G14, C45, C53
summ., streszcz.
Abstrakt
Celem artykułu jest porównanie błędu prognoz indeksu WIG20 uzyskanych za pomocą tradycyjnej metody statystycznej oraz metod uczenia maszynowego: metody wektorów nośnych i metamodelu opartego na sieciach neuronowych NARX. Przeprowadzona analiza wskazuje, że metoda SVM pozwoliła na uzyskanie prognoz o największej precyzji (najniższych wartościach błędów ex post ). Obie metody uczenia maszynowego cechowały się istotnie większą dokładnością prognoz w porównaniu z zastosowaną metodą statystyczną w okresie styczeń 2017 - marzec 2018 r. Tradycyjne modele statystyczne wymagają szeregu założeń dotyczących zależności pomiędzy zmiennymi, szeroko krytykowanych w literaturze za arbitralność. Charakterystyka modeli uczenia maszynowego z jednej strony podkreśla ich zdolność do wykrywania złożonych i nieliniowych zależności w danych historycznych, ale z drugiej strony wskazuje na ich inne ograniczenia metodologiczne. (abstrakt oryginalny)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Pełny tekst
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Bibliografia
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Cytowane przez
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ISSN
0137-5520
Język
pol
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