Company Performance Measurement with Use of Genetic Algorithm

Company Performance Measurement with Use of Genetic Algorithm

Paper in proceedings (conference paper)

This paper deals with measurement of company performance. Different methods may be used for measuring of performance of companies. In this article, the authors pay attention to the use of value-based methods in the measuring of performance, like the economic value added concept (EVA); and the traditional measuring of performance and management of companies with the use of financial analysis indicators. This traditional approach is preferred by managers, while the use of EVA is often restrained due to a lack of input information or difficulties in calculation. In the course of research, the authors inquire into a question whether a relationship between the value of EVA and the selected indicators of traditional financial analysis may be found. In order to prove that, the authors employ genetic algorithms for clustering into different groups of performance and they embark on testing of linear dependence. They show that, to a certain degree of probability, this relationship may be proved with selected parameters. Outcomes of this research may be used for performance evaluation in the business practice. Conclusions of this research also may be exploited in the construction of creditworthiness and bankruptcy prediction models.

This paper deals with measurement of company performance. Different methods may be used for measuring of performance of companies. In this article, the authors pay attention to the use of value-based methods in the measuring of performance, like the economic value added concept (EVA); and the traditional measuring of performance and management of companies with the use of financial analysis indicators. This traditional approach is preferred by managers, while the use of EVA is often restrained due to a lack of input information or difficulties in calculation. In the course of research, the authors inquire into a question whether a relationship between the value of EVA and the selected indicators of traditional financial analysis may be found. In order to prove that, the authors employ genetic algorithms for clustering into different groups of performance and they embark on testing of linear dependence. They show that, to a certain degree of probability, this relationship may be proved with selected parameters. Outcomes of this research may be used for performance evaluation in the business practice. Conclusions of this research also may be exploited in the construction of creditworthiness and bankruptcy prediction models.

Performance, measurement, financial indicators, economic value added, clustering, generic algorithm

Performance, measurement, financial indicators, economic value added, clustering, generic algorithm

DOSTÁL, P.; PAVELKOVÁ, D.

2013

20.09.2012

WSEAS Press

978-1-61804-124-1

Advanced Finance and Auditing

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