Effectiveness of ATM withdrawal forecasting methods under different market conditions

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Suder, M (Author) Other; Gurgul, H (Author) Other; Barbosa, B (Author) FEP; Machno, A (Author) Other; Lach, L (Author) Other

Journal

Title: Technological Forecasting and Social Change

Vol. 200

ISSN: 0040-1625

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-00Z-RDS

DOI: 10.1016/j.techfore.2023.123089

Abstract (EN): 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. The results proved that the recently implemented methods of forecasting ATM withdrawals were more accurate as compared to the traditional ones, with XGBoost providing the best forecasts in the majority of the tested cases. Moreover, it was found that the pandemic-driven change in market conditions affected the predictive power of the models. Both of these results seem particularly useful for improving the efficiency of ATM networks.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16