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Improved Methods for Predicting the Financial Vulnerability of Nonprofit Organizations

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  • Gila Burde

    (Department of Management, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel)

Abstract

Using hazard analysis procedures, this study undertakes a longitudinal examination of Israeli Nonprofit Organizations’ (NPOs’) financial vulnerability arising from governmental funding instability. Funding instability is characterized by time-at-risk, which measures the level of financial instability faced by an NPO and reflects the different funding situations it encounters. The vulnerability is expressed by the hazard rate (HR), which measures the speed at which NPOs’ close at a given point in time. The probability of an NPO failure is then estimated. The improvements presented in the current work are concerned with the methods of estimation of time at risk, which is a key variable in the hazard analysis, and testing a robustness of the method. The generalized time-at-risk, which measures the “level of instability” more consistently reflecting different situations encountered by a NPO, is introduced. The definition of generalized time-at-risk contains arbitrary coefficients whose values the current study determines using some optimization procedure. The optimization incorporates the idea of testing a possibility of using the results for predicting financial vulnerability by dividing the set of 2660 NPOs into two approximately equivalent samples. The coefficients in the time-at-risk definition are optimized by minimizing the average distance between the HR–time-at-risk curves based on these two samples.

Suggested Citation

  • Gila Burde, 2018. "Improved Methods for Predicting the Financial Vulnerability of Nonprofit Organizations," Administrative Sciences, MDPI, vol. 8(1), pages 1-8, February.
  • Handle: RePEc:gam:jadmsc:v:8:y:2018:i:1:p:3-:d:132258
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    References listed on IDEAS

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    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    2. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    3. Bauer, Julian & Agarwal, Vineet, 2014. "Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 432-442.
    4. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    5. 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.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    7. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    8. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    9. 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.
    10. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    11. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    12. Mark A Hager & Joseph Galaskiewicz & Jeff A Larson, 2004. "Structural embeddedness and the liability of newness among nonprofit organizations," Public Management Review, Taylor & Francis Journals, vol. 6(2), pages 159-188, June.
    13. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    14. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.
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    Cited by:

    1. 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.

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