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Factors Affecting Business Failure of Small and Very Small Greek Family Enterprises

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  • Nikolaos Arnis
  • Kostas Karamanis
  • Georgios Kolias

Abstract

This article investigates the factors that lead small and very small Greek businesses to financial failure using financial and accounting ratios as well as corporate governance characteristics. Our data set consists of 136 small and very small firms that went bankrupt, which were matched with a sample of 472 non bankrupt enterprises formed by random selection based on year, sector and sub-sector determinants, from 2003 to 2014. The total firm-year observations for bankrupt and non bankrupt companies were 940 and 5,041 respectively. Applying a Logit model for panel data, the results showed a significant impact on the likelihood of small and very small firms failing due to factors such as the type and the amount of bank lending, the level of profitability, cash flows, and liquidity.The data also support a statistically significant correlation of the probability of failure with non-financial factors such as Duality on the Board and CEO gender.The results of this paper will be useful for both banks and managers of small and micro businesses.

Suggested Citation

  • Nikolaos Arnis & Kostas Karamanis & Georgios Kolias, 2020. "Factors Affecting Business Failure of Small and Very Small Greek Family Enterprises," Accounting and Finance Research, Sciedu Press, vol. 9(2), pages 1-35, May.
    • Handle: RePEc:jfr:afr111:v:9:y:2020:i:2:p:35
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    References listed on IDEAS

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    1. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    2. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    3. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(2), pages 1477-1493, 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. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    6. 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.
    7. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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