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Towards a general non-parametric model of corporate performance

Author

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  • Fernandez-Castro, A
  • Smith, P

Abstract

Many problems arise in applying conventional statistical methods to accounting ratios, often relating to assumptions about the statistical distribution of ratios. This paper presents a non-parametric model of corporate performance which obviates the need for specification of statistical distributions or functional form. Instead it seeks to identify outstanding performance in a number of dimensions, and to measure the performance of less efficient firms in relation to their efficient peers. The method is illustrated with data relating to 27 failed UK companies and their peers. The strengths and weaknesses of the model are discussed, and it is concluded that, although the model is not a panacea for the problems of interpreting corporate performance, it does offer a useful addition to the armoury of the financial statement analyst. In particular, it offers a technology which is intermediate between the crudity of simple ratio analysis and the complexity of regression analysis.

Suggested Citation

  • Fernandez-Castro, A & Smith, P, 1994. "Towards a general non-parametric model of corporate performance," Omega, Elsevier, vol. 22(3), pages 237-249, May.
    • Handle: RePEc:eee:jomega:v:22:y:1994:i:3:p:237-249
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    Cited by:

    1. Karagiannis, Giannis & Ravanos, Panagiotis, 2023. "A composite indicator of social inclusion for EU based on the inverted BoD model," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    2. Shahari, Mohd Ridzwan & See, Kok Fong & Mohammed, Noor Syahireen & Yu, Ming-Miin, 2023. "Constructing the performance index of Malaysia’s district health centers using effectiveness-based hierarchical data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    3. Demirbag, Mehmet & McGuinness, Martina & Akin, Ahmet & Bayyurt, Nizamettin & Basti, Eyup, 2016. "The professional service firm (PSF) in a globalised economy: A study of the efficiency of securities firms in an emerging market," International Business Review, Elsevier, vol. 25(5), pages 1089-1102.
    4. Dariush Akbarian, 2021. "Network DEA based on DEA-ratio," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    5. Liu, W.B. & Zhang, D.Q. & Meng, W. & Li, X.X. & Xu, F., 2011. "A study of DEA models without explicit inputs," Omega, Elsevier, vol. 39(5), pages 472-480, October.
    6. Barniv, Ran & Mehrez, Abraham & Kline, Douglas M., 2000. "Confidence intervals for controlling the probability of bankruptcy," Omega, Elsevier, vol. 28(5), pages 555-565, October.
    7. Fusco, Elisa & Vidoli, Francesco & Sahoo, Biresh K., 2018. "Spatial heterogeneity in composite indicator: A methodological proposal," Omega, Elsevier, vol. 77(C), pages 1-14.
    8. Paradi, Joseph C. & Zhu, Haiyan, 2013. "A survey on bank branch efficiency and performance research with data envelopment analysis," Omega, Elsevier, vol. 41(1), pages 61-79.
    9. Moon, Tae Hee & Sohn, So Young, 2008. "Technology scoring model for reflecting evaluator's perception within confidence limits," European Journal of Operational Research, Elsevier, vol. 184(3), pages 981-989, February.
    10. Sahoo, Biresh K. & Singh, Ramadhar & Mishra, Bineet & Sankaran, Krithiga, 2017. "Research productivity in management schools of India during 1968-2015: A directional benefit-of-doubt model analysis," Omega, Elsevier, vol. 66(PA), pages 118-139.

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