Effectiveness of ATM withdrawal forecasting methods under different market conditions

Marcin Suder, Henryk Gurgul, Belem Barbosa, Artur Machno and Łukasz Lach

Technological Forecasting and Social Change, 2024, vol. 200, issue C

Abstract: This study aims to test the forecasting accuracy of recently implemented econometric tools as compared to the forecasting accuracy of widely used traditional models when predicting cash demand at ATMs. It also aims to verify whether the pandemic-driven change in market conditions impacted the predictive power of the tested models. Our conclusions were derived based on a data set that consisted of daily withdrawals from 61 ATMs of one of the largest European ATM networks operating in Krakow, Poland, and covered the period between January 2017 and April 2021.

Keywords: Forecasting ATM withdrawals; COVID-19; Machine learning; Bayesian VAR; SARIMA; Consumer behavior (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523007746

DOI: 10.1016/j.techfore.2023.123089

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