ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)

An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms

Apostolos G. Christopoulos, Ioannis G. Dokas, Iraklis Kollias and John Leventides

Correspondence: Ioannis G. Dokas,

Department of Economics, Democritus University of Thrace, Greece

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The foremost aim of this paper is to propose a reliable methodology regarding the selection process of financial ratios as input variables in the construction of corporate failure prediction models. In this paper soft set theory is introduced. In the first stage, emphasis is given on the state of liquidity as a measure for the classification of a group of NASDAQ listed firms in two a priori groups (failed and non-failed) using four liquidity criteria as follows: current ratio 1, current liabilities to total liabilities 70%, Equity to Liabilities 0 and Total Debt to Total Assets 70%. In the second stage, a parameter reduction algorithm is applied in order to determine, from a group of ratios, those which provide significant predictive power and optimize the classification accuracy of the model. A tabular representation of a soft set is constructed in order to select the input variables in the model based on the importance degree of each financial ratio. The findings show that the primary assumptions relevant to the definition of failure based on the soft set theory approach are confirmed, though the majority of the significant ratios in the applied sample of listed firms are related to the analysis of profitability.


  Failure prediction, definition of failure, liquidity and profitability, financial ratios, soft set theory, Logistic regression.


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