Modelling and Forecasting Cash Withdrawals in the Bank

Jarosław Bielak; Andrzej Burda; Mieczysław Kowerski; Krzysztof Pancerz

doi:10.56583/br.719

Published : 2015-12-17

Vol. 13 No. 4 (2015)

Modelling and Forecasting Cash Withdrawals in the Bank

Jarosław Bielak

Andrzej Burda

Mieczysław Kowerski

Krzysztof Pancerz

DOI: https://doi.org/10.56583/br.719

Abstract

The goal of the paper is searching for the optimal forecasting model estimating the amount of cash withdrawn daily by the customers of one of the Polish banks by means of statistical and machine learning methods. The methodology of model creation and assessment criteria are significantly different for the models considered in the paper — i.e., for ARMAX and MLP. However, the comparison of forecasts generated by both models seems to be useful. Variables and attributes reflecting the calendar effects, used in both models, in case of obtained errors of forecasts less than 20%, showed a significant, non-linear influence of this type of predictors on the amount of the daily cash withdrawals at the bank, and hence on the amount of the daily declared cash limit.

Keywords:

bank cash withdrawals forecasts, ARMAX and MLP models

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Bielak, J., Burda, A., Kowerski, M., & Pancerz, K. (2015). Modelling and Forecasting Cash Withdrawals in the Bank. Regional Barometer. Analyses & Prognoses, 13(4), 165–177. https://doi.org/10.56583/br.719

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References

Bielak, J. 2010. “Prognozowanie rynku pracy woj. lubelskiego z wykorzystaniem modeli ARIMA i ARIMAX.” Barometr Regionalny. Analizy i prognozy no. 1 (19):27–44.

Brockwell, P.J., and R.A. Davis. 2002. Introduction to Time Series and Forecasting. 2nd ed., Springer texts in statistics. New York: Springer. DOI: https://doi.org/10.1007/b97391

Cieślak, M. 2001. Prognozowanie gospodarcze. Metody i zastosowania. Warszawa: Wydawnictwo Naukowe PWN.

Cleveland, W.S., and S.J. Devlin. 1980. “Calendar Effects in Monthly Time-Series — Detection by Spectrum Analysis and Graphical Methods.” Journal of the American Statistical Association no. 75 (371):487–496. doi: 10.2307/2287636. DOI: https://doi.org/10.1080/01621459.1980.10477500

Cottrell, A., and R. Lucchetti “Jack”. 2015. “Gretl User’s Guide. Gnu Regression, Econometrics and Time-Series Library.”

Darwish, S.M. 2013. “A Methodology to Improve Cash Demand Forecasting for ATM Network.” International Journal of Computer and Electrical Engineering no. 5 (4):405–409. doi: 10.7763/IJCEE.2013.V5.741. DOI: https://doi.org/10.7763/IJCEE.2013.V5.741

Esteves, P.S., and P.M.M. Rodrigues. 2010. “Calendar Effects in Daily ATM Withdrawals.” Banco de Portugal. Working Papers (12):1–16, i-iv.

Fausett, L.V. 1994. Fundamentals of Neural Networks. Architectures, Algorithms, and Applications. Englewood Cliffs, NJ: Prentice-Hall.

Gurgul, H., and M. Suder. 2013a. “The Properties of ATMs Development Stages — an Empirical Analysis.” Statistics in Transition no. 14 (3):443–466

Gurgul, H., and M. Suder. 2013b. “Rozkład prawdopodobieństwa dziennych wypłat z bankomatów.” Wiadomości Statystyczne no. 58 (4):1–22.

Gurgul, H., and M. Suder. 2015. “Prognozowanie wypłat z bankomatów.” Wiadomości Statystyczne no. 60 (8):25–48. DOI: https://doi.org/10.5604/01.3001.0014.8305

Kufel, T. 2011. Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu GRETL. 3rd ed. Warszawa: Wydawnictwo Naukowe PWN.

Lee, M.H., Suhartono, and N.A. Hamzah. 2010. “Calendar Variation Model Based on ARIMAX for Forecasting Sales Data with Ramadhan Effect.” In Proceedings of the Regional Conference on Statistical Sciences 2010, edited by I. Ab Ghani, A.G. Hussin, I. Mohamed, Y.B. Wah and S.M. Deni, 349–361. Malaysia Institute of Statistics, Universiti Teknologi MARA.

Liu, L.M. 1980. “Analysis of Time-Series with Calendar Effects.” Management Science no. 26 (1):106–112. doi: 10.1287/mnsc.26.1.106. DOI: https://doi.org/10.1287/mnsc.26.1.106

Rumelhart, D.E., and J.L. McClelland. 1986. Parallel Distributed Processing. Explorations in the Microstructure of Cognition. 2 vols, Computational Models of Cognition and Perception. Cambridge, Mass.: MIT Press. DOI: https://doi.org/10.7551/mitpress/5236.001.0001

Simutis, R., D. Dilijonas, and L. Bastina. 2008. “Cash Demand Forecasting for ATM Using Neural Networks and Support Vector Regression Algorithms.” 20th International Conference, Euro Mini Conference Continuous Optimization and Knowledge-Based Technologies, Europt’2008:416–421.

StatSoft Inc. 2013. “Electronic Statistics Textbook.” Tulsa, OK: StatSoft. http://www.statsoft.com/textbook/.

Theil, H. 1961. Economic Forecasting and Policy. Amsterdam: North-Holland Pub. Co.

Theil, H. 1966. Applied Economic Forecasting, Studies in Mathematical and Managerial Economics. Amsterdam-Chicago: North-Holland Pub. Co.; Rand McNally.

Venkatesh, K., V. Ravi, A. Prinzie, and D. Van den Poel. 2014. “Cash Demand Forecasting in ATMs by Clustering and Neural Networks.” European Journal of Operational Research no. 232 (2):383–392. doi: 10.1016/j.ejor.2013.07.027. DOI: https://doi.org/10.1016/j.ejor.2013.07.027

Jarosław Bielak

br@akademiazamojska.edu.pl

Affiliation:

University of Management and Administration in Zamość Poland

Andrzej Burda

br@akademiazamojska.edu.pl

Affiliation:

University of Management and Administration in Zamość Poland

Mieczysław Kowerski

br@akademiazamojska.edu.pl

Affiliation:

University of Information Technology and Management in Rzeszów Poland

Krzysztof Pancerz

br@akademiazamojska.edu.pl

Affiliation:

University of Rzeszów Poland