IDEAS home Printed from https://ideas.repec.org/p/sek/iacpro/0802215.html
   My bibliography  Save this paper

Performance Measurment of State-Owned Banks in Turkish Banking Sector with Grey Relational Analysis Method

Author

Listed:
  • S. Öznur Sakinc

    (Hitit University)

Abstract

Banking sector has a considerable impact on the development and growth of the national economy. Increase in the performance of this sector having an important place in the financial system of country, means to positive effects on the general economy. Today, importance of globalization and private capitalis increasing. But State-owned banks in Turkish Banking Sector have an important share of 30%. The main goal of private banks is profitability, so they don?t support the activities with low return, even though the people need. For these reasons state-owned banks were selected, in this study for measuring their performance. Performance measurement involves the process of assessing and reporting of the business activities in terms of success, effectiveness and timing. Sustainability in Performance Measurement provides a significant instrument for feedback of business for the planning in the next period. Business might develop their own service and product quality and they progress strategies to increase the performances of employees, revise their goals and make some revisions in the budget if it needs.Thus, an increase will be supplied in the efficiency and effectiveness of business. In this study, The Performances of State-owned banks in Turkish Banking Sector are analyzed with grey relational analysis method.In the analysis, four years of financial data is used related with banks between 2010-213 years. These data were analyzed by 15 ration which determine; capital adequacy, liquidity, asset quality and profitability criteria.

Suggested Citation

  • S. Öznur Sakinc, 2014. "Performance Measurment of State-Owned Banks in Turkish Banking Sector with Grey Relational Analysis Method," Proceedings of International Academic Conferences 0802215, International Institute of Social and Economic Sciences.
  • Handle: RePEc:sek:iacpro:0802215
    as

    Download full text from publisher

    File URL: https://iises.net/proceedings/13th-international-academic-conference-antibes/table-of-content/detail?cid=8&iid=065&rid=2215
    File Function: First version, 2014
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feng, Cheng-Min & Wang, Rong-Tsu, 2000. "Performance evaluation for airlines including the consideration of financial ratios," Journal of Air Transport Management, Elsevier, vol. 6(3), pages 133-142.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mahmut BAKIR & Şahap AKAN & Kasım KIRACI & Darjan KARABASEVIC & Dragisa STANUJKIC & Gabrijela POPOVIC, 2020. "Multiple-Criteria Approach of the Operational Performance Evaluation in the Airline Industry: Evidence from the Emerging Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 149-172, July.
    2. Francis, Graham & Humphreys, Ian & Fry, Jackie, 2005. "The nature and prevalence of the use of performance measurement techniques by airlines," Journal of Air Transport Management, Elsevier, vol. 11(4), pages 207-217.
    3. Hyungjin Shin & Gyumin Lee & Jaenam Lee & Sehoon Kim & Inhong Song, 2023. "Assessment of Agricultural Drought Vulnerability with Focus on Upland Fields and Identification of Primary Management Areas," Sustainability, MDPI, vol. 15(3), pages 1-16, February.
    4. Chen Jo-Hui & Diaz John Francis T., 2021. "Application of grey relational analysis and artificial neural networks on currency exchange-traded notes (ETNs)," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-17, April.
    5. Thanh Ngo & Kan Wai Hong Tsui, 2022. "Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines," Operational Research, Springer, vol. 22(4), pages 3411-3434, September.
    6. Namık Kemal Erdoğan & Serpil Altınırmak & Çağlar Karamaşa, 2016. "Comparison of multi criteria decision making (MCDM) methods with respect to performance of food firms listed in BIST," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 5(1), pages 67-90.
    7. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
    8. Roman Vavrek, 2019. "Evaluation of the Impact of Selected Weighting Methods on the Results of the TOPSIS Technique," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(06), pages 1821-1843, November.
    9. Berna Bulgurcu, 2013. "Financial Performance Ranking of the Automotive Industry Firms in Turkey: Evidence from an Entropy-Weighted Technique," International Journal of Economics and Financial Issues, Econjournals, vol. 3(4), pages 844-851.
    10. Wang, Rong-Tsu & Ho, Chien-Ta & Feng, Cheng-Min & Yang, Yung-Kai, 2004. "A comparative analysis of the operational performance of Taiwan's major airports," Journal of Air Transport Management, Elsevier, vol. 10(5), pages 353-360.
    11. Mahmut Baydaş & Dragan Pamučar, 2022. "Determining Objective Characteristics of MCDM Methods under Uncertainty: An Exploration Study with Financial Data," Mathematics, MDPI, vol. 10(7), pages 1-25, March.
    12. Andreas-Daniel Cocis & Larissa Batrancea & Horia Tulai, 2021. "The Link between Corporate Reputation and Financial Performance and Equilibrium within the Airline Industry," Mathematics, MDPI, vol. 9(17), pages 1-12, September.
    13. Ioannis E. Tsolas, 2020. "Financial Performance Assessment of Construction Firms by Means of RAM-Based Composite Indicators," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
    14. Pyoungsoo Lee & Yong Won Seo, 2017. "Directions for Social Enterprise from an Efficiency Perspective," Sustainability, MDPI, vol. 9(10), pages 1-16, October.
    15. Simonelli, Felice & Caroli, Matteo Giuliano, 2013. "Harmonization of market entry regulation for the operation of air services in the European Union: A comparative survey of the implementation of Regulation (EC) no. 1008/2008 by Member States' authorit," Journal of Air Transport Management, Elsevier, vol. 27(C), pages 39-45.
    16. María Carmen Carnero, 2020. "Fuzzy TOPSIS Model for Assessment of Environmental Sustainability: A Case Study with Patient Judgements," Mathematics, MDPI, vol. 8(11), pages 1-43, November.
    17. Hasan Dinçer & Ozlem Olgu Akdeniz & Umit Hacioglu, 2018. "Competitive strategy selection in the European banking sector using a hybrid decision-making approach," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 36(1), pages 213-242.
    18. Kasım KİRACI & Mehmet YAŞAR, 2020. "The Determinants of Airline Operational Performance: An Empirical Study on Major World Airlines," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(43).
    19. Roman Vavrek & Jiří Bečica, 2020. "Capital City as a Factor of Multi-Criteria Decision Analysis—Application on Transport Companies in the Czech Republic," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
    20. Serhat KARAOGLAN & Serap SAHIN, 2018. "BIST XKMYA Isletmelerinin Finansal Performanslarinin Cok Kriterli Karar Verme Yontemleri Ile Olcumu ve Yontemlerin Karsilastirilmasi," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 18(1), pages 63-80.

    More about this item

    Keywords

    Banking Sector; Performance In Banks; Performance Measurement; Grey Relational Analyze Method; State-Owned Banks;
    All these keywords.

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sek:iacpro:0802215. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klara Cermakova (email available below). General contact details of provider: https://iises.net/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.