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Bankruptcy and Insolvency: An Exploration of Relevant Theories

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  • Adegbemi Babatunde Onakoya

    (Department of Economics, Banking and Finance, Babcock University, Ilishan - Remo, Nigeria,)

  • Ayooluwa Eunice Olotu

    (Department of Accounting, Babcock University, Ilishan - Remo, Nigeria)

Abstract

The essence of the law on bankruptcy is to collect the debt of an entity and distribute such asset among the contending claimholders. It is, also meant to resolve the broad issues of business failure in the context of the imminent or indeed the actual collapse of the indebted entity. The objective of the study is to explore relevant theories guiding the procedure of distribution or entitlement in bankruptcy among a group of agents. The study employed exploratory research method via an extended literature review, to investigate the underlying principles guiding the allocation of a given amount of a perfectly divisible good among a group of agents. The results of this extended literature review indicate that the procedure of distribution or entitlement in bankruptcy is supported by five of the theories reviewed while only value based theory posits the absence of any cogent solution to the financial distress of the debtor. The knowledge of theories is not enough for business survival, the ability to predict the possible occurrence of business failures is necessary. Market based models including the stock market option valuation approach perform better than the earlier models which rely heavily on historical accounting figures.

Suggested Citation

  • Adegbemi Babatunde Onakoya & Ayooluwa Eunice Olotu, 2017. "Bankruptcy and Insolvency: An Exploration of Relevant Theories," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 706-712.
    • Handle: RePEc:eco:journ1:2017-03-90
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    References listed on IDEAS

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    1. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    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. Lensberg, Terje & Eilifsen, Aasmund & McKee, Thomas E., 2006. "Bankruptcy theory development and classification via genetic programming," European Journal of Operational Research, Elsevier, vol. 169(2), pages 677-697, March.
    4. Lorenzo-Freire, S. & Alonso-Meijide, J.M. & Casas-Mendez, B. & Hendrickx, R.L.P., 2005. "Balanced Contributions for Multi-Issue Allocation Situations," Discussion Paper 2005-93, Tilburg University, Center for Economic Research.
    5. Alexander Karpov & Semyon Yurkov, 2012. "Generalized bankruptcy problem," HSE Working papers WP BRP 08/FE/2012, National Research University Higher School of Economics.
    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. Sayantan Ghosal & Marcus Miller, 2003. "Co-ordination Failure, Moral Hazard and Sovereign Bankruptcy Procedures," Economic Journal, Royal Economic Society, vol. 113(487), pages 276-304, April.
    8. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    9. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    10. Robert Aumann, 2010. "Some non-superadditive games, and their Shapley values, in the Talmud," International Journal of Game Theory, Springer;Game Theory Society, vol. 39(1), pages 3-10, March.
    11. Calleja, Pedro & Borm, Peter & Hendrickx, Ruud, 2005. "Multi-issue allocation situations," European Journal of Operational Research, Elsevier, vol. 164(3), pages 730-747, August.
    12. Aumann, Robert J. & Maschler, Michael, 1985. "Game theoretic analysis of a bankruptcy problem from the Talmud," Journal of Economic Theory, Elsevier, vol. 36(2), pages 195-213, August.
    13. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    14. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
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    Cited by:

    1. Alexey Litvinenko, 2023. "A Comparative Analysis of Altman's Z-Score and T. Jury's Cash-Based Credit Risk Models with The Application to The Production Company and The Data for The Years 2016-2022," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 22(3), pages 518-553, September.

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    More about this item

    Keywords

    Bankruptcy; Bankruptcy Theories; Exploratory Research; Genetic Programming Model;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • K11 - Law and Economics - - Basic Areas of Law - - - Property Law
    • K12 - Law and Economics - - Basic Areas of Law - - - Contract Law

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