IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2162-d844035.html
   My bibliography  Save this article

Logit Model for Estimating Non-Profit Organizations’ Financial Status as a Part of Non-Profit Financial Management

Author

Listed:
  • Jaroslav Mazanec

    (Department of Quantitative Methods and Economic Informatics, The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 01 Zilina, Slovakia)

  • Viera Bartosova

    (Department of Economics, The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 01 Zilina, Slovakia)

  • Patrik Bohm

    (Department of Quantitative Methods and Economic Informatics, The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 01 Zilina, Slovakia)

Abstract

The non-profit sector plays an important role in the American and European continents, as non-profit organizations support the development of civil society and help people in need. However, most non-profit organizations (NPO) are financially dependent on various donors from the private sector. Nowadays, non-profit organizations focus on improving their non-profit financial management. This research aims to assess the financial status of Slovak non-profit organizations, using binary logistic regression. The initial sample includes 351 Slovak NPOs, which are divided into a training and test sub-sample. The data were obtained from Amadeus, FinStat, the Ministry of Finance of the Slovak Republic, and the Ministry of Interior of the Slovak Republic. The logit model shows that the significant variables are equity ratio, debt ratio, operating margin, and type of NPO using the statistical–analytical program IBM SPSS 25. The model also implies that non-profit organizations should focus on the revenue structure and revenues from the sale of products. The prediction model correctly classifies 97.03% of NPOs in the training sub-sample and 96.61% of NPOs in the test sub-sample. Moreover, more than 70% of vulnerable NPOs are correctly classified.

Suggested Citation

  • Jaroslav Mazanec & Viera Bartosova & Patrik Bohm, 2022. "Logit Model for Estimating Non-Profit Organizations’ Financial Status as a Part of Non-Profit Financial Management," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2162-:d:844035
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2162/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/13/2162/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gila Burde, 2018. "Improved Methods for Predicting the Financial Vulnerability of Nonprofit Organizations," Administrative Sciences, MDPI, vol. 8(1), pages 1-8, February.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    3. Katarina Valaskova & Tomas Kliestik & Maria Kovacova, 2018. "Management of financial risks in Slovak enterprises using regression analysis," Oeconomia Copernicana, Institute of Economic Research, vol. 9(1), pages 105-121, March.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    5. Cordery, Carolyn J. & Sim, Dalice & Baskerville, Rachel F., 2013. "Three models, one goal: Assessing financial vulnerability in New Zealand amateur sports clubs," Sport Management Review, Elsevier, vol. 16(2), pages 186-199.
    6. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    7. Keating, Elizabeth K. & Fischer, Mary & Gordon, Teresa P. & Greenlee, Janet, 2005. "Assessing Financial Vulnerability in the Nonprofit Sector," Working Paper Series rwp05-002, Harvard University, John F. Kennedy School of Government.
    8. Ahmad Ahmadpour Kasgari & Seyyed Hasan Salehnezhad & Fatemeh Ebadi, 2013. "The Bankruptcy Prediction by Neural Networks and Logistic Regression," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 3(4), pages 146-152, October.
    9. Maria Kovacova & Tomas Kliestik, 2017. "Logit and Probit application for the prediction of bankruptcy in Slovak companies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 12(4), pages 775-791, December.
    10. Carolyn J. Cordery & Dalice Sim & Rachel F. Baskerville, 2013. "Three models, one goal: Assessing financial vulnerability in New Zealand amateur sports clubs," Sport Management Review, Taylor & Francis Journals, vol. 16(2), pages 186-199, April.
    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. Juan Alejandro Gallegos Mardones & Jorge Andrés Moraga Palacios, 2023. "Chilean Universities and Universal Gratuity: Suggestions for a Model to Evaluate the Effects on Financial Vulnerability," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    2. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    3. Lenka Papíková & Mário Papík, 2022. "Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 254-281, October.
    4. Elizabeth A. M. Searing, 2021. "Resilience in Vulnerable Small and New Social Enterprises," Sustainability, MDPI, vol. 13(24), pages 1-21, December.
    5. Michal Pavlicko & Jaroslav Mazanec, 2022. "Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group," Mathematics, MDPI, vol. 10(8), pages 1-22, April.
    6. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.
    7. Javed Iqbal & Furrukh Bashir & Rashid Ahmad & Hina Arshad, 2022. "Predicting Bankruptcy through Neural Network:Case of PSX Listed Companies," iRASD Journal of Management, International Research Alliance for Sustainable Development (iRASD), vol. 4(2), pages 299-315, june.
    8. Jaroslav Mazanec & Viera Bartosova, 2021. "Prediction Model as Sustainability Tool for Assessing Financial Status of Non-Profit Organizations in the Slovak Republic," Sustainability, MDPI, vol. 13(17), pages 1-22, August.
    9. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    10. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    11. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    12. Berta SILVA & Ronelle BURGER, 2015. "Financial vulnerability: an empirical study of Ugandan NGOs," CIRIEC Working Papers 1515, CIRIEC - Université de Liège.
    13. Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    14. Lucia Svabova & Lucia Michalkova & Marek Durica & Elvira Nica, 2020. "Business Failure Prediction for Slovak Small and Medium-Sized Companies," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    15. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    16. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    17. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    18. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    19. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    20. Lauren Stagnol, 2015. "Designing a corporate bond index on solvency criteria," EconomiX Working Papers 2015-39, University of Paris Nanterre, EconomiX.

    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:gam:jmathe:v:10:y:2022:i:13:p:2162-:d:844035. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.