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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2013

01.12.2013 | Original Article

Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: a case study of Tehran stock exchange

verfasst von: Kamran Rezaie, Vahid Majazi Dalfard, Loghman Hatami-Shirkouhi, Salman Nazari-Shirkouhi

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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Abstract

Efficiency appraisal of the companies in stock exchange and selecting the type of industries and companies is an important subject for financial managers and also investors because it can reduce the risk of investment, noticeably. In these situations, investors need information to facilitate the investing process. One of the ways that can be used in this regard is evaluation and ranking of the companies. The goal of this study is to do a comprehensive appraising and ranking of the companies in a stock exchange. To this end, the indices needed to appraise the efficiency of exchange companies in the literature are reviewed. Then, using field research, those indices are recognized as mainly affecting the efficiency of companies existing in Tehran stock exchange. Since the financial status of a company is not fixed throughout time, there is a source of uncertainty in data of such problem. To address this uncertainty, fuzzy data envelopment analysis (FDEA) is applied to rank companies. Finally, the results with the ranking given by the organization in charge are compared. Results show that FDEA is a suitable and efficient approach to model the fluctuations and uncertainties existing in financial data and factors affecting the efficiency of a corporation.
Nächster Artikel Adaptive fuzzy tuning of PID controllers

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Metadaten
Titel
Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: a case study of Tehran stock exchange
verfasst von
Kamran Rezaie
Vahid Majazi Dalfard
Loghman Hatami-Shirkouhi
Salman Nazari-Shirkouhi
Publikationsdatum
01.12.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1209-6

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