Can exponential smoothing do better than seasonal random walk for earnings per share forecasting in Poland?

Wojciech Kuryłek ()
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Wojciech Kuryłek: University of Warsaw, Faculty of Management

Bank i Kredyt, 2023, vol. 54, issue 6, 673-696

Abstract: The accurate prediction of listed companies’ earnings plays a critical role in successful investing. This piece of research contrasts estimation errors of the seasonal random walk model and exponential smoothing models employed in the earnings per share (EPS) data for Polish listed businesses from the timespan between 2008–2009. The models are compared using the mean arctangent absolute percentage error (MAAPE) metric. The best model across all quarters and years is the seasonal random walk (SRW) model, when contrasted with the other models studied regardless of the analysed time spans and error metrics. Contrary to the results obtained from the US market, the more intricate exponential smoothing model, comprising a seasonal and a trend component, does not suitably explain the behaviour of Polish companies. This could be attributable to the simpler demeanour of the Polish market and the absence of a trend in the EPS data.

Keywords: earnings per share; random walk; exponential smoothing; financial forecasting; Warsaw Stock Exchange (search for similar items in EconPapers)
JEL-codes: C01 C02 C12 C14 C58 G17 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:nbp:nbpbik:v:54:y:2023:i:6:p:673-696

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