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Neural Networks and their application in the fields of corporate finance

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  • Eric Severin

    () (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, SAMOS - Statistique Appliquée et MOdélisation Stochastique - UP1 - Université Panthéon-Sorbonne, CIS - Laboratory of Computer and Information Science - TKK - TKK Helsinki University of Technology)

Abstract

This article deals with the usefulness of neuronal networks in the area of corporate finance. Firstly, we highlight the initial applications of neural networks. One can distinguish two main types: layer networks and self organizing maps. As Altman al. (1994) underlined, the use of layer networks has improved the reclassifying rate in models of bankruptcy forecasting. These first applications improved bankruptcy forecasting by showing a relationship between capital structure and corporate performance. The results highlighted in our second part, show the pertinence of the use of the algorithm of Kohonen applied to qualitative variables (KACM). More particularly, in line with Altman (1968, 1984), one can suggest the coexistence of negative and positive effects of financial structure on performance. This result allows us to question scoring models and to conclude as to a non-linear relationship. In a larger framework, the methodology of Kohonen has allowed a better perception of the factors able to explain the leasing financing (Cottrell et al., 1996). The objective is here to explain the factors of the choice between leasing and banking loans. By using different variables, we highlight the characteristics of firms which most often use leasing. The corporate financing policy could be explained by: the cost of the financing, advantages of leasing or by the minimization of agency costs in leasing, we highlight a relationship between resorting to leasing and credit rationing.

Suggested Citation

  • Eric Severin, 2008. "Neural Networks and their application in the fields of corporate finance," Working Papers hal-00325117, HAL.
  • Handle: RePEc:hal:wpaper:hal-00325117
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00325117
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    References listed on IDEAS

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    1. Wruck, Karen Hopper, 1990. "Financial distress, reorganization, and organizational efficiency," Journal of Financial Economics, Elsevier, vol. 27(2), pages 419-444, October.
    2. Altman, Edward I, 1984. "A Further Empirical Investigation of the Bankruptcy Cost Question," Journal of Finance, American Finance Association, vol. 39(4), pages 1067-1089, September.
    3. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    4. Sharpe, Steven A. & Nguyen, Hien H., 1995. "Capital market imperfections and the incentive to lease," Journal of Financial Economics, Elsevier, vol. 39(2-3), pages 271-294.
    5. Jensen, Michael C, 1986. "Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers," American Economic Review, American Economic Association, vol. 76(2), pages 323-329, May.
    6. Opler, Tim C & Titman, Sheridan, 1994. "Financial Distress and Corporate Performance," Journal of Finance, American Finance Association, vol. 49(3), pages 1015-1040, July.
    7. 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.
    8. Stulz, ReneM. & Johnson, Herb, 1985. "An analysis of secured debt," Journal of Financial Economics, Elsevier, vol. 14(4), pages 501-521, December.
    9. V. Sivarama Krishnan & R. Charles Moyer, 1994. "Bankruptcy Costs and the Financial Leasing Decision," Financial Management, Financial Management Association, vol. 23(2), Summer.
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    Keywords

    neural netwoks; SOM; corporate finance;

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