IDEAS home Printed from https://ideas.repec.org/a/vrs/stintr/v17y2016i4p691-722n13.html
   My bibliography  Save this article

Calendar and Seasonal Effects on the Size of Withdrawals from Atms Managed By Euronet

Author

Listed:
  • Gurgul Henryk

    (AGH University of Science and Technology, Faculty of Management, Poznan, ; POLAND)

  • Suder Marcin

    (AGH University of Science and Technology, Faculty of Management, Poznan, ; POLAND)

Abstract

This study analyses the calendar effects on withdrawals from Automated Teller Machines (ATMs) (daily data) managed by the Euronet network for the period from January 2008 to March 2012. Our study focuses on the identification of specific calendar and seasonal effects in the ATM cash withdrawal series of the company in the Polish provinces of Małopolska and Podkarpackie. The results of the analysis show that withdrawals depend strongly on the day of the week. On Fridays more cash is withdrawn than on other days, and Saturdays and Sundays are the days of the week with the lowest level of withdrawals. In a month, it can be seen that cash withdrawals take place more often in the second and in the last weeks of the month. This observation suggests that withdrawals from ATMs can be related to the profile of wage withdrawals. In Poland, in the public sector wages are paid at the beginning of the month, and in the private sector at the end of the month. The time series of withdrawals also reflect seasonality. The largest amounts are withdrawn in July, August and December. Reason for the increased demand for cash are the summer holidays and the Christmas season. The results reflect consumer habits which show pronounced calendar and seasonal effects.

Suggested Citation

  • Gurgul Henryk & Suder Marcin, 2016. "Calendar and Seasonal Effects on the Size of Withdrawals from Atms Managed By Euronet," Statistics in Transition New Series, Polish Statistical Association, vol. 17(4), pages 691-722, December.
    • Handle: RePEc:vrs:stintr:v:17:y:2016:i:4:p:691-722:n:13
    DOI: 10.21307/stattrans-2016-047
    as

    Download full text from publisher

    File URL: https://doi.org/10.21307/stattrans-2016-047
    Download Restriction: no
    File URL: https://libkey.io/10.21307/stattrans-2016-047?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sullivan, Ryan & Timmermann, Allan & White, Halbert, 2001. "Dangers of data mining: The case of calendar effects in stock returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 249-286, November.
    2. Paulo Rodrigues & Paulo Esteves, 2010. "Calendar effects in daily ATM withdrawals," Economics Bulletin, AccessEcon, vol. 30(4), pages 2587-2597.
    3. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    4. Ken Holden & Magdi El-Bannany, 2004. "Investment in information technology systems and other determinants of bank profitability in the UK," Applied Financial Economics, Taylor & Francis Journals, vol. 14(5), pages 361-365.
    5. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    6. Geoffrey R. Gerdes & May X. Liu & Darrel W. Parke & Jack K. Walton, 2005. "Trends in the use of payment instruments in the United States," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), vol. 91(Spr), pages 180-201.
    7. Lon-Mu Liu, 1980. "Note---Analysis of Time Series with Calendar Effects," Management Science, INFORMS, vol. 26(1), pages 106-112, January.
    8. Paulo Esteves, 2009. "Are ATM/POS Data Relevant When Nowcasting Private Consumption?," Working Papers w200925, Banco de Portugal, Economics and Research Department.
    9. Heli Snellman & Matti Viren, 2009. "ATM networks and cash usage," Applied Financial Economics, Taylor & Francis Journals, vol. 19(10), pages 841-851.
    10. Cláudia Duarte, 2016. "A Mixed Frequency Approach to Forecast Private Consumption with ATM/POS Data," Working Papers w201601, Banco de Portugal, Economics and Research Department.
    11. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Suder, Marcin & Gurgul, Henryk & Barbosa, Belem & Machno, Artur & Lach, Łukasz, 2024. "Effectiveness of ATM withdrawal forecasting methods under different market conditions," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Henryk Gurgul & Marcin Suder, 2018. "Impact of ATM location on its profi tability in Malopolskie and Podkarpackie provinces," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 19(1), pages 49-73.
    3. Marcin Suder & Tomasz Wójtowicz & Rafał Kusa & Henryk Gurgul, 2023. "Challenges for ATM management in times of market variability caused by the COVID-19 pandemic crisi," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 445-465, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Henryk Gurgul & Marcin Suder, 2013. "Modeling of withdrawals from selected ATMs of the "Euronet" network," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 13, pages 65-82.
    2. Henryk Gurgul & Marcin Suder, 2013. "The Properties of ATMs Development Stages - an Empirical Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 443-466, September.
    3. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    4. Cláudia Duarte, 2016. "A Mixed Frequency Approach to Forecast Private Consumption with ATM/POS Data," Working Papers w201601, Banco de Portugal, Economics and Research Department.
    5. Lourenço, Nuno & Rua, António, 2021. "The Daily Economic Indicator: tracking economic activity daily during the lockdown," Economic Modelling, Elsevier, vol. 100(C).
    6. Giancarlo Bruno & Edoardo Otranto, 2006. "The choice of time interval in seasonal adjustment: A heuristic approach," Statistical Papers, Springer, vol. 47(3), pages 393-417, June.
    7. Mauricio Gallardo & Hernán Rubio, 2009. "Diagnóstico de estacionalidad con X-12-ARIMA," Economic Statistics Series 76, Central Bank of Chile.
    8. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 9473, Victoria University of Wellington, School of Economics and Finance.
    9. Kroes, James R. & Manikas, Andrew S. & Gattiker, Thomas F., 2018. "Operational leanness and retail firm performance since 1980," International Journal of Production Economics, Elsevier, vol. 197(C), pages 262-274.
    10. Quenneville, Benoit & Ladiray, Dominique & Lefrancois, Bernard, 2003. "A note on Musgrave asymmetrical trend-cycle filters," International Journal of Forecasting, Elsevier, vol. 19(4), pages 727-734.
    11. Massmann, Michael & Mitchell, James, 2003. "Reconsidering the evidence: Are Eurozone business cycles converging," ZEI Working Papers B 05-2003, University of Bonn, ZEI - Center for European Integration Studies.
    12. Roy Verbaan & Wilko Bolt & Carin van der Cruijsen, 2017. "Using debit card payments data for nowcasting Dutch household consumption," DNB Working Papers 571, Netherlands Central Bank, Research Department.
    13. Hai Yue Liu & Xiao Lan Chen, 2017. "The imported price, inflation and exchange rate pass-through in China," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1279814-127, January.
    14. Carlos A. Medel, 2018. "A Comparison Between Direct and Indirect Seasonal Adjustment of the Chilean GDP 1986–2009 with X-12-ARIMA," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 47-87, April.
    15. Stefania D'Amico & Athanasios Orphanides, 2008. "Uncertainty and disagreement in economic forecasting," Finance and Economics Discussion Series 2008-56, Board of Governors of the Federal Reserve System (U.S.).
    16. Kirchner, Robert, 1999. "Auswirkungen des neuen Saisonbereinigungsverfahrens Census X-12-ARIMA auf die aktuelle Wirtschaftsanalyse in Deutschland," Discussion Paper Series 1: Economic Studies 1999,07, Deutsche Bundesbank.
    17. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    18. Flávio de Freitas Val & Wagner Piazza Gaglianone & Marcelo Cabus Klotzle & Antonio Carlos Figueiredo Pinto, 2017. "Estimating the Credibility of Brazilian Monetary Policy using Forward Measures and a State-Space Model," Working Papers Series 463, Central Bank of Brazil, Research Department.
    19. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.
    20. Carlos A. Medel, 2018. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," International Economic Journal, Taylor & Francis Journals, vol. 32(3), pages 331-371, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:stintr:v:17:y:2016:i:4:p:691-722:n:13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.