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

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  • 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
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    References listed on IDEAS

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    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. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    3. 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.
    4. 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.
    5. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    6. 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.
    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.
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