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An Application of Autoregressive Distributed Lag-Models for Earnings per Share Forecasting in Poland

dc.contributor.authorKuryłek, Wojciech
dc.date.accessioned2025-06-30T17:17:12Z
dc.date.available2025-06-30T17:17:12Z
dc.date.issued2025-04-10
dc.identifier.issn0208-6018
dc.identifier.urihttp://hdl.handle.net/11089/55806
dc.description.abstractThis investigation delves into the significance of precise earnings forecasts for publicly traded companies in achieving investment success. It emphasises the importance of this aspect, particularly in markets with limited analyst coverage, such as emerging markets including Poland. The study assesses the accuracy of predictions generated using the Autoregressive Distributed Lag (ARDL) framework with the XGBoost type of modelling and various methods of combining forecasts compared to the seasonal random walk model. Positioned as an intermediate step between time series and multivariate forecasting, these models are applied to earnings per share (EPS) data of companies listed on the Warsaw Stock Exchange from 2009 to 2019, i.e. the last financial crisis and the pandemic shock. The seasonal random walk model attained the lowest error rates based on the Mean Arctangent Absolute Percentage Error (MAAPE) metric, a conclusion substantiated by rigorous statistical tests and robustness checks employing different periods and error metrics. The enhanced performance of the simpler seasonal random walk model may be ascribed to the relatively uncomplicated nature of the Polish stock market.en
dc.description.abstractNiniejszy 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.pl
dc.language.isoen
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl
dc.relation.ispartofseriesActa Universitatis Lodziensis. Folia Oeconomica;370en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectearnings per shareen
dc.subjectrandom walken
dc.subjectAutoregressive Distributed Lagsen
dc.subjectXGBoosten
dc.subjectfinancial forecastingen
dc.subjectWarsaw Stock Exchangeen
dc.subjectzysk na akcjępl
dc.subjectbłądzenie losowepl
dc.subjectmodel autoregresyjny z rozkładem opóźnieńpl
dc.subjectXGBoostpl
dc.subjectprognozowanie finansowepl
dc.subjectGiełda Papierów Wartościowych w Warszawiepl
dc.titleAn Application of Autoregressive Distributed Lag-Models for Earnings per Share Forecasting in Polanden
dc.title.alternativeCzy modele autoregresyjne z rozkładem opóźnień mogą poprawić prognozowanie zysku na akcję w Polsce?pl
dc.typeArticle
dc.page.number1-19
dc.contributor.authorAffiliationUniversity of Warsaw, Faculty of Management, Warsaw, Polanden
dc.identifier.eissn2353-7663
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dc.contributor.authorEmailwkurylek@wz.uw.edu.pl
dc.identifier.doi10.18778/0208-6018.370.01
dc.relation.volume1


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