Academic journal article Asian Social Science

Z-Score for Bankruptcy Forecasting of the Companies Producing Building Materials

Academic journal article Asian Social Science

Z-Score for Bankruptcy Forecasting of the Companies Producing Building Materials

Article excerpt


The article is dedicated to developing of methodological basis for the probability of enterprises' bankruptcy forecasting. The authors have identified the reasons of the existing bankruptcy forecasting models failure, the necessity of models developing for companies in various industries on the basis of the existing instruments. As a result of the multivariate discriminate analysis we offer Z-scores based on the average annual rates of change and taking into account the peculiarities of the industry companies. The model is developed for companies specializing in the production of building materials, it has a high level of productivity - 90.4%.

Keywords: region's economic area, regional competition, regional competitive advantages, competitive positioning, economic activities, industrial enterprise's resource potential, economic area "growth points"

1. Introduction

The high level of competition and the unstable economic environment necessitate the use of crisis management instruments. One of the information sources for the development and implementation of preventive tactical measures providing the reduction of the enterprises' insolvency risk are the results of the bankruptcy forecasting models (Ravi Kumar & Ravi, 2007; Shuang, Yuan, Zhang, & Yu, 2011; Yakupova & Absalyamova, 2014). Systematization of research on the application of bankruptcy forecasting methods is presented in the work by Ravi Kumar, P., Ravi, V. (Nasir, John, Bennett, & Russell, 2000) dealing with the following classification: (i) statistical techniques, (ii) neural networks, (iii) case-based reasoning, (iv) decision trees, (iv)operational research, (v) evolutionary approaches, (vi) rough set based techniques, (vii) other techniques subsuming fuzzy logic, support vector machine and isotonic separation and (viii) softcomputing subsuming seamless hybridization of all the above-mentioned techniques.

Feng Yu Lin, McClean, S. offered a data mining approach to the forecasting of corporate failure and proved, that a hybrid method produces higher forecasting accuracy than individual classifiers (Burganova, Novak, & Salahieva, 2014).

Problems of application, advantages and disadvantages of AdaBoost and neural networks are presented in the works (Alfaro, García, Gámez, & Elizondo, 2008; Azmitov, Ivanovskiy, & Korabelnikova, 2014). Dimitras, A. I., Slowinski, R., Susmaga, R., Zopounidis, C. (1999) offer to use rough sets. Du Jardin, P., Séverin, E. suggested to use a Kohonen map to increase the forecasting horizon of a financial failure model (2011).

Despite the great diversity of approaches and methods for the forecasting of bankruptcy the greatest preference is often given to the quantitative, based on statistical methods.

Balcaen, S., Ooghe, H. investigated the classic statistical business failure forecasting models and their related problems (2006), Shuang, Q., Yuan, Y., Zhang, M., Yu, D. described based on Fisher's Linear Discriminant Analysis (2012).

As supporters of quantitative methods, we agree with the researchers, who suppose that the Z-scores and R-accounts stipulating the use of the absolute values of the coefficients cannot bear universal character. Simultaneous application of different "universal" models often leads to the opposite results, which casts doubt on their practical significance.

The results obtained by the existing models have turned out to be multidirectional due to a number of circumstances.

Firstly, due to the quality of the sampling in the formation of models. Large volume of samples regardless of industrial and regional characteristics cannot be sufficient grounds for the formation of the model. Large disparities in the development of industries within the same region, different structure of assets, marketable products and its consumption require sampling of businesses from the sample industry, and, if possible, within the limits of one region. …

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