Forecasting correctness of incurring credit with the aid of E.I. Altman’s, J. Gajdka’s and D. Stos’s discriminant analysis models on the example of 200 studied companies from Opole and Silesian provinces

Purpose: The main purpose of this article is to examine the usefulness of multiple discriminant analysis models in assessing the financial condition of individual enterprises, the state of the economy, and its sectors. The study assessed the financial situation of Polish listed companies at the end of the global economic crisis that started in 2007. Methodology/approach: Seven discriminant functions were used to assess the actual changes in the finan-cial situation of listed companies during the period of 2009–2014. In order to diagnose the end of the crisis, the period in which countries emerged from the global economic crisis was taken into account. The study covered 175 Polish companies listed on the regulated market operated by the Warsaw Stock Exchange, whose standalone financial statements were used. These companies belong to 22 sectors of the economy. It was assumed that the number of companies at risk of bankruptcy should have decreased during this period. Findings: The study showed that it is difficult to determine when the crisis ended and stopped affecting Polish listed companies. Their financial condition gradually improved during the period 2013–2014. Multiple discriminant analysis models are useful in assessing the risk of bankruptcy, but the study results show that it is safer to use several models simultaneously and to eliminate outliers. Research limitations/implications: The discriminant models used in the study are suitable for conducting research on large populations within enterprises and can be used by state and financial institutions (including banks) and authorities in Poland to facilitate the conduct of economic statistics, forecasting economic situation, etc. Originality/value: In Poland, many studies have been carried out on the usefulness of multiple discrimi-nant analysis models for the purposes of forecasting the bankruptcy of individual enterprises. However, there are few studies devoted to assessing the usefulness of the models in conducting research on large populations within enterprises (i.e., assessing the state of the economy and its sectors). This research helps to explore and fill this research gap.

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Distance Representations of Consumer Perceptions: Evaluating Appropriateness by Using Diagnostics

Journal of Marketing Research ◽

10.1177/002224379803500201 ◽

1998 ◽

Vol 35 (2) ◽

pp. 137-153 ◽

Cited By ~ 4

Author(s):

Sanjoy Ghose

Keyword(s):

Discriminant Analysis ◽

Empirical Analysis ◽

Input Data ◽

Consumer Perceptions ◽

Market Structures ◽

Diagnostic Measures ◽

Tree Models ◽

Tree Representations ◽

Analysis Models ◽

Related Measure

To understand consumer perceptions of product/market structures, marketers must choose from a wide variety of spatial and tree models. Because spatial and tree representations in general possess different distance patterns, diagnostic measures calculated from the input data of dissimilarities or similarities should be able to indicate how appropriate a certain type of representation might be for a given set of input data. In this article, the author draws from previous literature on the characteristics of diagnostic measures and representation models to develop some partial hypotheses about how well the measures might indicate the appropriateness (in terms of fit) of different models. Empirical analysis indicates that the skewness diagnostic is clearly the best predictor of the appropriateness of representation models; this finding is consistent across a variety of comparable spatial and tree models. Centrality and the reciprocity-related measure, in conjunction with skewness, are useful for specific types of space–tree pairs. The author uses the U-Method (closely related to jackknifing) of prediction, in conjunction with discriminant analysis models, to show that the diagnostics can predict the relative appropriateness of spaces versus trees with accuracy levels substantially greater than what would be expected by chance.

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