Skip to main content

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 432))

Abstract

The article presents the research results of normality distribution of financial ratios. Distributions are presented in the form of histograms and probability distribution density function of the ratios. The study normality of the ratios cover the period of five years. For businesses, the fallen was the period from one to five years before the bankruptcy. But for companies operating it was analogous period of five years in relation to undertakings fallen.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    See [5, 11, 27]; for more models.

  2. 2.

    Using the useful indicators can be investigated, among others, the causes of business failure, see [5658].

  3. 3.

    The usefulness of these indicators was verified in many studies, including in [7, 23, 41].

  4. 4.

    Detailed information on the selection of financial indicators can be seen in [58].

References

  1. Agarwal, A.: Neural networks and their extensions for business decision making. Ph.D. dissertation, Ohio State University (1993)

    Google Scholar 

  2. Altman, E.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Finance 23(4), 589–609 (1968)

    Article  Google Scholar 

  3. Altman, E.I., Haldeman, R.G., Narayanan, P.: ZETA analysis: a new model to identify bankruptcy risk of corporations. J. Bank. Finance 1(1), 29–54 (1977)

    Article  Google Scholar 

  4. Altman, E.I., Loris, B.: A financial early warning system for over-the-counter brokerdealers. J. Finance 31(4), 1201–1217 (1976)

    Article  Google Scholar 

  5. Altman, E.I., Saunders, A.: Credit risk measurement: developments over the last 20 years. J. Bank. Finance 21, 1721–1742 (1998)

    Google Scholar 

  6. Anandarajan, M., Lee, P., Anandarajan, A.: Bankruptcy predication using neural networks. In: Anandarajan, M., Anandarajan, A., Srinivasan, C. (eds.) Business Intelligence Techniques: A Perspective from Accounting and Finance. Springer, Germany (2004)

    Chapter  Google Scholar 

  7. Appenzeller, D. (Hadasik): Upadłość przedsiębiorstw w Polsce i metody jej prognozowania. Zeszyty Naukowe, Seria II, nr 153, AE, Poznan (1998)

    Google Scholar 

  8. Aziz, A., Emanuel, D.C., Lawson, G.H.: Bankruptcy prediction—an investigation of cash flow based models. J. Manage. Stud. 25(5), 419–437 (1988)

    Article  Google Scholar 

  9. Beaver, W.H.: Financial ratios as predictors of failure. J. Acc. Res 5, 71–111 (1966)

    Google Scholar 

  10. Bell, T., Ribar, G., Verchio, J.: Neural nets versus logistic regression: A comparison of each model’s ability to predict commercial bank failures. In: Proceedings of the 1990 D&T, University of Kansas Symposium on Auditing Problems (1990)

    Google Scholar 

  11. Bellovary, J.L., Giacomino, D.E., Akers, M.D.: A Review of Bankruptcy Prediction Studies: 1930 to Present. J. Financ. Educ. 33, 1–42 (2007)

    Google Scholar 

  12. Blum, M.: Failing company discriminant analysis. J. Account. Res. 12(1), 1–25 (1974)

    Article  Google Scholar 

  13. Cadden D.: Neural networks and the mathematics of chaos—an investigation of these methodologies as accurate predictions of corporate bankruptcy. In: The First International Conference on Artificial Intelligence Applications of Wall Street. IEEE Computer Society Press, New York (1991)

    Google Scholar 

  14. Chang, P.C., Afifi, A.A.: Classification based on dichotomous and continuous variables. J. Am. Stat. Assoc. 69(346), 336–339 (1974)

    Article  MATH  Google Scholar 

  15. Coats, P., Fant, L.: A neural network approach to forecasting financial distress. J. Bus. Forecast. Methods Syst. 10(4), 9–12 (1992)

    Google Scholar 

  16. Deakin, E.: A discriminant analysis of predictors of business failure. J. Account. Res. 10(1), 167–179 (1972)

    Article  Google Scholar 

  17. Deakin, E.: Business failure prediction: an empirical analysis. In: Altman, E.I. (ed.) Financial Crisis: Institutions and Markets in a Fragile Environment, pp. 72–98. Wiley, New York (1977)

    Google Scholar 

  18. Edmister, R.: An empirical test of financial ratio analysis for small business failure rediction. J. Financ. Quant. Anal. 7(2), 1477–1493 (1972)

    Article  Google Scholar 

  19. Fitzpatrick, P.J.: A comparison of ratios of successful industrial enterprises with those of failed firms. Certified Public Accountant 12, 598–605 (1932)

    Google Scholar 

  20. Fulmer, J., Moon, J., Gavin, T., Erwin, J.: A bankruptcy classification model for small firms. J. Commercial Bank Lending 66(11), 25–37 (1984)

    Google Scholar 

  21. Gao, L.: Study of business failure in the hospitality industry from both micro economic and macroeconomic perspectives. Ph.D. dissertation, University of Nevada, Las Vegas (1999)

    Google Scholar 

  22. George, C.: The effect of the going-concern audit decision on survival. Ph.D. dissertation, Memphis State University (1991)

    Google Scholar 

  23. Gombola, M.J., Haskins, M.E., Ketz, J.E., Williams, D.D.: Cash flow in bankruptcy prediction. In: Financial Management, pp. 55–65 (1987)

    Google Scholar 

  24. Grover, J.: Validation of a cash flow model: a non-bankruptcy approach. Ph.D. dissertation, Nova Southeastern University (2003)

    Google Scholar 

  25. Guan, Q.: Development of optimal network structures for back-propagationtrained neural networks. Ph.D. dissertation, University of Nebraska (1993)

    Google Scholar 

  26. Hopwood, W., McKeown, J., Mutchler, J.: A reexamination of auditor versus model accuracy within the context of the going-concern opinion decision. Contemp. Account. Res. 10(2), 409–431 (1994)

    Article  Google Scholar 

  27. Jones, F.: Current techniques in bankruptcy prediction. J. Account. Lit. 131–164 (1987)

    Google Scholar 

  28. Karels, G.V., Prakash, A.J.: Multivariate normality and forcasting of business bankruptcy. J. Bus. Finance Account. 14(4), 573–593 (1987)

    Article  Google Scholar 

  29. Ketz, J.K.: The effect of general price-level adjustments on the predictive ability of financial ratios. J. Account. Res. 273–284 (1978)

    Google Scholar 

  30. Koh, H.C.: Model predictions and auditor assessments of going concern status. Account. Bus. Res. 21(84), 331–338 (1991)

    Article  Google Scholar 

  31. Koh, H., Killough, L.: The use of multiple discriminant analysis in the assessment of the going-concern status of an audit client. J. Bus. Finance Account. 17(2), 179–192 (1990)

    Article  Google Scholar 

  32. Koster, A., Sondak, N., Bourbia, W.: A business application of artificial neural network systems. J. Comput. Inf. Syst. 31(2), 3–9 (1990)

    Google Scholar 

  33. Laitinen, E.: Financial ratios and different failure processes. J. Bus. Finance Account. 18(5), 649–673 (1991)

    Article  Google Scholar 

  34. Li, M.Y.L., Miu, P.: A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: a binary quantile regression approach. J. Empirical Finance 17, 818–833 (2010)

    Google Scholar 

  35. Libby, R.: Accounting ratios and the prediction of failure: some behavioral evidence. J. Account. Res. 13(1), 150–161 (1975)

    Article  Google Scholar 

  36. Lindsay, D.H., Campbell, A.: A chaos approach to bankruptcy prediction. J. Appl. Bus. Res. 12(4), 1–9 (1996)

    Article  Google Scholar 

  37. Lyandres, E., Zhdanov, A.: Investment opportunities and bankruptcy prediction. J. Finan. Markets 16, 439–476 (2013)

    Google Scholar 

  38. Merwin, C.L.: Financing small corporations in five manufacturing industries, 1926-86. National Bureau of Economic Research, New York (1942)

    Google Scholar 

  39. Meyer, P., Pifer, H.: Prediction of bank failures. J. Finance 25(4), 853–868 (1970)

    Article  Google Scholar 

  40. Moses, D., Liao, S.S.: On developing models for failure prediction. J. Commercial Bank Lending 69, 27–38 (1987)

    Google Scholar 

  41. Mossman, C.E., Bell, G.G., Swartz, L.M., Turtle, H.: An empirical comparison of bankruptcy models. Financ. Rev. 33, 35–54 (1998)

    Google Scholar 

  42. Nour, M.: Improved clustering and classification algorithms for the Kohonen selforganizing neural network. Ph.D. dissertation, Kent State University (1994)

    Google Scholar 

  43. Ohlson, J.: Financial ratios and the probabilistic prediction of bankruptcy. J. Account. Res. 18(1), 109–131 (1980)

    Article  MathSciNet  Google Scholar 

  44. Patterson, D.: Bankruptcy prediction: a model for the casino industry. Ph.D. dissertation, University of Nevada, Las Vegas (2001)

    Google Scholar 

  45. Pettway, R., Sinkey Jr, J.: Establishing on-site banking examination priorities: an early warning system using accounting and market information. J. Finance 35(1), 137–150 (1980)

    Article  Google Scholar 

  46. Platt, H.D., Platt, M.B.: Development of a class of stable predictive variables: the case of bankruptcy prediction. J. Bus. Account. 17, 31–51 (1990)

    Google Scholar 

  47. Platt, H., Platt, M., Pedersen, J.: Bankruptcy discrimination with real variables. J. Bus. Finance Account. 21(4), 491–509 (1994)

    Article  Google Scholar 

  48. Reisz, A.S., Perlich, C.: A market-based framework for bankruptcy prediction. J. Financ. Stab. 3, 85–131 (2007)

    Google Scholar 

  49. Rujoub, M., Cook, D., Hay, L.: Using cash flow ratios to predict business failures. J. Manag. 1(7), 75–90 (1995)

    Google Scholar 

  50. Salchenberger, L., Cinar, E., Lash, N.: Neural networks: a new tool for predicting bank failures. Decis. Sci. 23, 899–916 (1992)

    Google Scholar 

  51. Serrano-Cinca, C.: Self organizing neural networks for financial diagnosis. Decis. Support Syst. 17, 227–238 (1996)

    Google Scholar 

  52. Shumway, T.: Forecasting bankruptcy more accurately: a simple hazard model. J. Bus. 74(1), 101–124 (2001)

    Article  MathSciNet  Google Scholar 

  53. Sinkey, J. Jr.: A multivariate statistical analysis of the characteristics of problem banks. J. Finance 30(1), 21–36 (1975)

    Google Scholar 

  54. Tam, K.: Neural network models and the prediction of bankruptcy. Omega 19(5), 429–445 (1991)

    Article  Google Scholar 

  55. Tam, K., Kiang, M.: Managerial applications of neural networks—the case of bank failure predictions. Manage. Sci. 38(7), 926–947 (1992)

    Article  MATH  Google Scholar 

  56. Tomczak, S.: Comparative analysis of liquidity ratios of bankrupt manufacturing companies. Bus. Econ. Horiz. 10(3), 151–164 (2014a)

    Google Scholar 

  57. Tomczak, S.: Comparative analysis of the bankrupt companies of the sector of animal slaughtering and processing. Equilibrium. Q. J. Econ. Econ. Policy 3, 59–86 (2014b)

    Google Scholar 

  58. Tomczak, S.: The early warning system. J. Manage. Financ. Sci. 7(16), 51–74 (2014)

    Google Scholar 

  59. Wilcox, J.W.: A prediction of business failure using accounting data. J. Account. Res. 11, 163–179 (1973)

    Google Scholar 

  60. Wilson, R., Sharda, R.: Bankruptcy prediction using neural networks. Decis. Support Syst. 11(5), 545–557 (1994)

    Article  Google Scholar 

  61. Winakor, A., Smith, R.F.: Changes in financial structure of unsuccessful industrial companies. In: Bureau of Business Research, Bulletin No. 51 (1935)

    Google Scholar 

  62. Zavgren, C.: The prediction of corporate failure: the state of the art. J. Account. Lit. 2, 1–37 (1983)

    Google Scholar 

  63. Zhang, G., Hu, M., Patuwo, B., Indro, d: Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. Eur. J. Oper. Res. 116(1), 16–32 (1999)

    Article  MATH  Google Scholar 

  64. Zmijewski, M.: Methodological issues related to the estimation of financial distress prediction models. J. Account. Res. 22(Suppl.), 59–86 (1984)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sebastian Klaudiusz Tomczak .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Tomczak, S.K., Wilimowska, Z. (2016). Testing the Probability Distribution of Financial Ratios. In: Wilimowska, Z., Borzemski, L., Grzech, A., Świątek, J. (eds) Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part IV. Advances in Intelligent Systems and Computing, vol 432. Springer, Cham. https://doi.org/10.1007/978-3-319-28567-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28567-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28565-8

  • Online ISBN: 978-3-319-28567-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics