IDEAS home Printed from https://ideas.repec.org/a/rsg/littra/2016-034.html

Cash Management of a Company Using Neural Networks

Author

Listed:
  • Petr Å uleÅ™

Abstract

Cash management is one of the most important indicators of a company´s activities and plays an important part in decision-making. Cash management or cash flow management are incomes and expenses for a certain period. The aim of cash management is to mobilize, check and plan a company´s financial resources, which is not easy. The aim of financial managers is to find an effective and flexible tool for improving the processes for the optimization of cash management. One such tool appears to be a system of artificial neural networks. These networks are very flexible and outperform other models, including linear regression models, in many ways. However, networks also have certain pitfalls, which include the sensitivity of the input data, the longer time associated with training the networks, as well as the lack of possibilities to define the architecture and other parameters of the network. This article attempts to predict the future development of various kinds of cash flows. In so doing, it hopes to identify a suitable neural network that is able to predict these cash flows. This process involved the generation of 1000 accidental artificial neural structures, of which the 5 most suitable were preserved. A sensitivity analysis was subsequently carried out. The results of the research show that neural networks can be effectively used to predict the cash flow developments within a company.

Suggested Citation

  • Petr Å uleÅ™, 2016. "Cash Management of a Company Using Neural Networks," Littera Scripta, VSTE, vol. 9(3), pages 125-140, May.
  • Handle: RePEc:rsg:littra:2016-034
    DOI: 10.36708/LS.2016.I03.010
    as

    Download full text from publisher

    File URL: https://littera-scripta.com/articles/cash-management-of-a-company-using-neural-networks-e9057dd4
    Download Restriction: no

    File URL: https://littera-scripta.com/api/v1/public/files/1751b86d-72e6-444b-8922-c078901b3688
    Download Restriction: no

    File URL: https://libkey.io/10.36708/LS.2016.I03.010?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
    ---><---

    References listed on IDEAS

    as
    1. Ágoston, Kolos Cs. & Benedek, Gábor & Gilányi, Zsolt, 2016. "Pareto improvement and joint cash management optimisation for banks and cash-in-transit firms," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1074-1082.
    2. Kroes, James R. & Manikas, Andrew S., 2014. "Cash flow management and manufacturing firm financial performance: A longitudinal perspective," International Journal of Production Economics, Elsevier, vol. 148(C), pages 37-50.
    3. Namazi, Mohammad & Shokrolahi, Ahmad & Sadeghzadeh Maharluie, Mohammad, 2016. "Detecting and ranking cash flow risk factors via artificial neural networks technique," Journal of Business Research, Elsevier, vol. 69(5), pages 1801-1806.
    4. Venkatesh, Kamini & Ravi, Vadlamani & Prinzie, Anita & Poel, Dirk Van den, 2014. "Cash demand forecasting in ATMs by clustering and neural networks," European Journal of Operational Research, Elsevier, vol. 232(2), pages 383-392.
    5. Moraes, Marcelo Botelho da Costa & Nagano, Marcelo Seido, 2014. "Evolutionary models in cash management policies with multiple assets," Economic Modelling, Elsevier, vol. 39(C), pages 1-7.
    Full references (including those not matched with items on IDEAS)

    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. Ishfaq Gulzar & S. M. Imamul Haque, 2023. "The Implications of Corporate Governance on the Working Capital Efficiency of Manufacturing Companies: Evidence from Emerging Market," Jindal Journal of Business Research, , vol. 12(2), pages 160-173, December.
    2. Wei‐Shao Wu & Sandy Suardi, 2025. "Heterogeneity in the effects of bank lines of credit on capital investment efficiency," The Financial Review, Eastern Finance Association, vol. 60(2), pages 601-621, May.
    3. Meng, Zhiyi & Li, Eldon Y. & Qiu, Rui, 2020. "Environmental sustainability with free-floating carsharing services: An on-demand refueling recommendation system for Car2go in Seattle," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    4. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    5. Lee, Chien-Chiang & Wang, Chih-Wei & Liu, Fengyun, 2024. "Does green credit promote the performance of new energy companies and how? The role of R&D investment and financial development," Renewable Energy, Elsevier, vol. 235(C).
    6. Gerasimos G. Rompotis, 2024. "Financial performance and cash flow: Evidence from the US banking industry," Research Papers in Economics and Finance, Poznań University of Economics and Business, vol. 8(1), pages 61-90, June.
    7. Vinay Singh & Bhasker Choubey & Stephan Sauer, 2024. "Liquidity forecasting at corporate and subsidiary levels using machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(3), September.
    8. Yeliz Ekinci & Nicoleta Serban & Ekrem Duman, 2021. "Optimal ATM replenishment policies under demand uncertainty," Operational Research, Springer, vol. 21(2), pages 999-1029, June.
    9. Francisco Salas-Molina & David Pla-Santamaria & Juan A. Rodriguez-Aguilar, 2018. "A multi-objective approach to the cash management problem," Annals of Operations Research, Springer, vol. 267(1), pages 515-529, August.
    10. Salas-Molina, Francisco & Martin, Francisco J. & Rodríguez-Aguilar, Juan A. & Serrà, Joan & Arcos, Josep Ll., 2017. "Empowering cash managers to achieve cost savings by improving predictive accuracy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 403-415.
    11. Barron, Yonit, 2023. "A stochastic card balance management problem with continuous and batch-type bilateral transactions," Operations Research Perspectives, Elsevier, vol. 10(C).
    12. Guo, Jiguang & Jia, Fu & Chen, Lujie, 2026. "How generative AI adoption affects supply chain resilience: An operations and supply chain management perspective," Technological Forecasting and Social Change, Elsevier, vol. 224(C).
    13. Francisco Salas-Molina & Juan A. Rodríguez-Aguilar & Montserrat Guillen, 2023. "A multidimensional review of the cash management problem," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-35, December.
    14. Muhammad Ramzan & Wee‐Yeap Lau, 2023. "Impact of asset preferences on firm performance over its life cycle: Is agency theory or neo‐classical theory more relevant?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(1), pages 595-607, January.
    15. Eugenia I. Toki & Giorgos Tatsis & Vasileios A. Tatsis & Konstantinos Plachouras & Jenny Pange & Ioannis G. Tsoulos, 2023. "Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication," Mathematics, MDPI, vol. 11(7), pages 1-15, March.
    16. Ágoston, Kolos Cs. & Benedek, Gábor & Gilányi, Zsolt, 2016. "Pareto improvement and joint cash management optimisation for banks and cash-in-transit firms," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1074-1082.
    17. Galil, Koresh & Lindner, Thomas & Shapir, Offer Moshe & Zeidan, Rodrigo, 2026. "Cash conversion cycle and bargaining power in the product market:A global perspective," Finance Research Letters, Elsevier, vol. 96(C).
    18. Miriyala, Srinivas Soumitri & Subramanian, Venkat & Mitra, Kishalay, 2018. "TRANSFORM-ANN for online optimization of complex industrial processes: Casting process as case study," European Journal of Operational Research, Elsevier, vol. 264(1), pages 294-309.
    19. María Luisa Saavedra García & Jaime Lo� Uribe, 2018. "Flujo de efectivo para las pymes: una propuesta para los sectores automotor y de tecnologías de la información en México," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 10(2), pages 287-308.
    20. Hakim Lyngstadaas, 2020. "Packages or systems? Working capital management and financial performance among listed U.S. manufacturing firms," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(4), pages 403-450, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:rsg:littra:2016-034. 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: Robin Kunju Mol Raj (email available below). General contact details of provider: https://littera-scripta.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.