The Problem of Outliers in Research on the Financial Standing of Construction Enterprises in Poland
DOI:
https://doi.org/10.15678/ZNUEK.2016.0949.0102Keywords:
outliers, financial standing, financial indicator, logit model, classificationAbstract
The results of an analysis of financial standing can be used to study the threat of going bankrupt. Financial indicators are used to evaluate enterprises’ financial standing. Thus, the data from financial statements is the basis for the examination of the financial position. The evaluation of data quality includes the identification of outliers, among other factors. This article presents the results of an empirical study done on how the method of detecting and eliminating outliers chosen influences the effectiveness of a logit model constructed on the basis of samples that either included the outliers or left them out. The research for the paper employed one- and multi-dimensional methods of detecting outliers and their combinations with an analysis of the discriminatory power of the financial indicators. Classification effectiveness of the logit model was assessed by sensitivity and specificity measures. The research covered the years 2005, 2007 and 2009.
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References
Barnett V., Lewis T. [1994], Outliers in Statistical Data, John Wiley, New York.
Ben-Gal I. [2005], Outlier Detection [w:] Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, red. O. Maimon i L. Rockach, Kluwer Academic Publishers, Dordrecht.
De Andrés J., Sánchez-Lasheras F., Lorca P., De Cos Juez F.J. [2011], A Hybrid Device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the Forecasting of Firms’ Bankruptcy, „Accounting and Management Information Systems”, vol. 10, nr 3.
Grubbs F.E. [1969], Procedures for Detecting Outlying Observations in Samples, „Technometrics”, vol. 11, nr 1, http://dx.doi.org/10.1080/00401706.1969.10490657. DOI: https://doi.org/10.2307/1266761
Hampel F.R. [1971], A General Qualitative Definition of Robustness, „The Annals of Mathematical Statistics”, vol. 42, nr 6, http://dx.doi.org/10.1214/aoms/1177693054. DOI: https://doi.org/10.1214/aoms/1177693054
Hampel F.R. [1974], The Influence Curve and Its Role in Robust Estimation, „Journal of the American Statistical Association”, vol. 69, nr 346, http://dx.doi.org/10.1080/01621459.1974.10482962. DOI: https://doi.org/10.2307/2285666
Hawkins D. [1980], Identification of Outliers, Chapman and Hall, London–New York.
Hodge V.J., Austin J. [2004], A Survey of Outlier Detection Methodologies, „Artificial Intelligence Review”, vol. 22, nr 2, http://dx.doi.org/10.1023/b:aire.0000045502.10941.a9. DOI: https://doi.org/10.1023/B:AIRE.0000045502.10941.a9
John G.H. [1995], Robust Decision Trees: Removing Outliers from Databases [w:] Proceedings of the First International Conference on Knowledge Discovery and Data Mining, red. U.M. Fayyad i R. Uthurusamy, AAAI Press, Menlo Park, CA.
Johnson R., Wichern D.W. [1992], Applied Multivariate Statistical Analysis, Prentice Hall, Upper Saddle River, NJ.
Kaufman L., Rousseeuw P.J. [1990], Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York.
Kosiorowski D. [2012], Statystyczne funkcje głębi w odpornej analizie ekonomicznej, Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie, Seria specjalna: Monografie, nr 208, Kraków.
Pawełek B., Kostrzewska J., Lipieta A. [2015], The Problem of Outliers in the Research on the Financial Standing of Construction Enterprises in Poland [w:] Proceedings of the 9th Professor Aleksander Zeliaś International Conference on Modelling and Forecasting of Socio-economic Phenomena, red. M. Papież i S. Śmiech, Foundation of the Cracow University of Economics, Cracow.
Pociecha J., Pawełek B., Baryła M., Augustyn S. [2014], Statystyczne metody prognozowania bankructwa w zmieniającej się koniunkturze gospodarczej, Fundacja Uniwersytetu Ekonomicznego w Krakowie, Kraków.
Ramaswamy S., Rastogi R., Shim K. [2000], Efficient Algorithms for Mining Outliers from Large Data Sets [w:] Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, May 16–18, 2000, Dallas, Texas, red. W. Chen, J. Naughton i P.A. Bernstein, Association for Computing Machinery, New York. DOI: https://doi.org/10.1145/342009.335437
Shumway T. [2001], Forecasting Bankruptcy More Accurately: A Simple Hazard Model, „The Journal of Business”, vol. 74(1). DOI: https://doi.org/10.1086/209665
Spicka J. [2013], The Financial Condition of the Construction Companies before Bankruptcy, „European Journal of Business and Management”, vol. 5, nr 23.
Tsai Ch.F., Cheng K.Ch. [2012], Simple Instance Selection for Bankruptcy Prediction, „Knowledge-Based Systems”, vol. 27, http://dx.doi.org/10.1016/j.knosys.2011.09.017. DOI: https://doi.org/10.1016/j.knosys.2011.09.017
Tukey J.W. [1977], Exploratory Data Analysis, Addison-Wesley, Reading, PA.
Williams G.J., Baxter R.A., He H.X., Hawkins S., Gu L. [2002], A Comparative Study of RNN for Outlier Detection in Data Mining, IEEE International Conference on Data-mining (ICDM’02), Maebashi City, Japan, CSIRO Technical Report CMIS-02/102.
Wu Y., Gaunt C., Gray S. [2010], A Comparison of Alternative Bankruptcy Prediction Models, „Journal of Contemporary Accounting and Economics”, vol. 6, nr 1, http://dx.doi.org/10.1016/j.jcae.2010.04.002. DOI: https://doi.org/10.1016/j.jcae.2010.04.002
Yu Q., Miche Y., Séverin E., Lendasse A. [2014], Bankruptcy Prediction Using Extreme Learning Machine and Financial Expertise, „Neurocomputing”, vol. 128, http://dx.doi.org/10.1016/j.neucom.2013.01.063. DOI: https://doi.org/10.1016/j.neucom.2013.01.063
Zuo Y. [2003], Projection-Based Depth Functions and Associated Medians, „The Annals of Statistics”, vol. 31, nr 5, http://dx.doi.org/10.1214/aos/1065705115. DOI: https://doi.org/10.1214/aos/1065705115
Zuo Y., Serfling R. [2000], General Notions of Statistical Depth Functions, „The Annals of Staistics”, vol. 28, nr 2, http://dx.doi.org/10.1214/aos/1016218226. DOI: https://doi.org/10.1214/aos/1016218226
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