Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/313467 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Banking and Financial Economics (JBFE) [ISSN:] 2353-6845 [Issue:] 19 [Year:] 2023 [Pages:] 26-43
Publisher: 
University of Warsaw, Faculty of Management, Warsaw
Abstract: 
The proper forecasting of listed companies' earnings is crucial for their appropriate pricing. This paper compares forecast errors of different univariate time-series models applied for the earnings per share (EPS) data for Polish companies from the period between the last financial crisis of 2008-2009 and the pandemic shock of 2020. The best model is the seasonal random walk (SRW) model across all quarters, which describes quite well the behavior of the Polish market compared to other analyzed models. Contrary to the findings regarding the US market, this time-series behavior is well described by the naive seasonal random walk model, whereas in the US the most adequate models are of a more sophisticated ARIMA type. Therefore, the paper demonstrates that conclusions drawn for the US might not hold for emerging economies because of the much simpler behavior of these markets that results in the absence of autoregressive and moving average parts.
Subjects: 
earnings per share
time series
random walk
ARIMA
financial forecasting
Warsaw Stock Exchange
JEL: 
C01
C02
C12
C14
C58
G17
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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