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BORSA İSTANBUL’DA İŞLEM GÖREN ŞİRKETLERİN FİNANSAL PERFORMANSININ MABAC YÖNTEMİYLE ANALİZİ / Analysis of The Financial Performances of Companies Trading in Borsa Istanbul by MABAC Method

Year 2021, Volume: 5 Issue: 2, 211 - 234, 31.10.2021
https://doi.org/10.29216/ueip.929743

Abstract

Çok kriterleri karar verme yöntemleri finansal performans değerlendirilmesinde kullanılabilinecek yeni yaklaşımlardan biridir. Bu yöntemler karar verme sürecine dayanır. MABAC bu yöntemlerden biridir. Çalışmanın iki önemli amacı vardır. Birincisi, çok sayıda alternatif ve kriterin olduğu bir durumda başarılı finansal performans gösteren şirketlerin seçimidir. İkincisi, şirketlerin en başarılı ve göreceli olarak daha az başarılı olarak finansal performans sıralamasının yapılmasıdır. Bu çevrede, 2020-9 bilanço döneminde kâr açıklayan iki yüz seksen altı şirketin, Piyasa Değeri / Defter Değeri, Fiyat / Kazanç, Piyasa Değeri ve Net Kâr kriterleri dikkate alınarak MABAC yöntemiyle finansal performans sıralamaları elde edilmiştir. En başarılı şirketler, ISBTR, QNBFB, KCHOL, GARAN, KENT, ISCTR, ASELS, AKBNK, FROTO ve ENKAI; en az başarılı şirketler ise RODRG, GRNYO, BALAT, EMKEL, IZFAS, EKIZ, MMCAS, COSMO, SNKRN ve ATSYH olarak tespit edilmiştir.

References

  • Ayçin, E. (2020). Çok Kriterli Karar Verme Bilgisayar Uygulamalı Çözümler (2.Basım). Ankara: Nobel Yayıncılık. Biswas, T. and Saha, P. (2019). Selection of Commercially Available Scooters by New MCDM Method. International Journal of Data and Network Science, 3(2), 137-144.
  • Božanić, D. I., Pamučar, D. S. and Karović, S. M. (2016). Use of the fuzzy AHP-MABAC Hybrid Model in Ranking Potential Locations for Preparing Laying-Up Positions. Vojnotehnički Glasnik, 64(3), 705-729.
  • Bozanic, D., Tešić, D. and Kočić, J. (2019). Multi-Criteria FUCOM–Fuzzy MABAC Model for the Selection of Location for Construction of Single-Span Bailey Bridge. Decision Making: Applications in Management and Engineering, 2(1), 132-146.
  • Bozanic, D., Tešić, D. and Milić, A. (2020). Multicriteria Decision Making Model with Z-Numbers Based on FUCOM and MABAC Model. Decision Making: Applications in Management and Engineering, 3(2), 19-36.
  • Bozanic, D., Tešić, D. Milićević, J. (2018). A Hybrid Fuzzy AHP-MABAC Model: Application in the Serbian Army–The Selection of the Location for Deep Wading as A Technique of Crossing the River by Tanks. Decision Making: Applications in Management and Engineering, 1(1), 143-164.
  • Büyüközkan, G., Mukul, E. and Kongar, E. (2021). Health Tourism Strategy Selection Via SWOT Analysis and Integrated Hesitant Fuzzy Linguistic AHP-MABAC approach. Socio-Economic Planning Sciences, 74, 1-14.
  • Chatterjee, P., Mondal, S., Boral, S., Banerjee, A. and Chakraborty, S. (2017). A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method. Facta Universitatis. Series: Mechanical Engineering, 15(3), 439-456.
  • Delice, E. K., Adar T., Emeç, Ş. and Akkaya, G. (2019). A Comprehensive Analysis of Location Selection Problem for Underground Waste Containers Using Integrated MC-HFLTS&MAIRCA and MABAC Methods. Avrupa Bilim ve Teknoloji Dergisi, Özel Sayı, 15-33.
  • Dorfeshan, Y., and Mousavi, S.M. (2019). A Novel Interval Type-2 Fuzzy Decision Model Based On Two New Versions of Relative Preference Relation-Based MABAC and WASPAS Methods (With an Application in Aircraft Maintenance Planning). Neural Computing and Applications, 32(3), 1-19.
  • Fan, J., Guan, R. and Wu, M. (2020). Z-MABAC Method for The Selection of Third-Party Logistics Suppliers in Fuzzy Environment. IEEE Access, 8, 199111-199119.
  • Gigović, L., Pamučar, D., Božanić, D. and Ljubojević, S. (2017). Application of The GIS-DANP-MABAC Multi-Criteria Model for Selecting the Location of Wind Farms: A Case Study of Vojvodina, Serbia. Renewable Energy, 103, 501-521.
  • Gong, J. W., Li, Q., Yin, L. and Liu, H.C. (2020). Undergraduate Teaching Audit and Evaluation Using an Extended MABAC Method Under Q‐Rung Orthopair Fuzzy Environment. International Journal of Intelligent Systems, 35(12), 1912-1933.
  • Ji, P., Zhang, H. Y. and Wang, J.Q. (2018). Selecting an Outsourcing Provider Based On the Combined MABAC–ELECTRE Method Using Single-Valued Neutrosophic Linguistic Sets. Computers & Industrial Engineering, 120, 429-441.
  • Jia, F., Liu, Y. and Wang, X. (2019). An Extended MABAC Method for Multi-Criteria Group Decision Making Based On Intuitionistic Fuzzy Rough Numbers. Expert Systems with Applications, 127, 241-255.
  • Liang, R. X., He, S.S., Wang, J.Q., Chen, K. and Li, L. (2019). An Extended MABAC Method for Multi-Criteria Group Decision-Making Problems Based On Correlative Inputs of Intuitionistic Fuzzy Information. Computational and Applied Mathematics, 38(3), 112-140.
  • Liang, W., Zhao, G., Wu, H. and Dai, B. (2019). Risk Assessment of Rockburst Via an Extended MABAC Method Under Fuzzy Environment. Tunnelling and Underground Space Technology, 83, 533-544.
  • Liu, P. and Zhang, P. (2020). A Normal Wiggly Hesitant Fuzzy MABAC Method Based On CCSD and Prospect Theory for Multiple Attribute Decision Making. International Journal of Intelligent Systems, 36(1), 447-477.
  • Liu, P., Xu, H. and Pedrycz, W. (2020). A Normal Wiggly Hesitant Fuzzy Linguistic Projection‐Based Multiattributive Border Approximation Area Comparison Method. International Journal of Intelligent Systems, 35(3), 432-469.
  • Liu, P., Zhu, B., Wang, P. and Shen, M. (2020). An Approach Based On Linguistic Spherical Fuzzy Sets for Public Evaluation of Shared Bicycles in China. Engineering Applications of Artificial Intelligence, 87, 1-15.
  • Liu, R., Hou, L, X., Liu, H.C. and Lin, W. (2020). Occupational Health and Safety Risk Assessment Using an Integrated SWARA-MABAC Model Under Bipolar Fuzzy Environment. Computational and Applied Mathematics, 39(4), 1-17.
  • Luo, S.Z. and Liang, W. Z. (2019). Optimization of Roadway Support Schemes with Likelihood-Based MABAC Method. Applied Soft Computing, 80, 80-92.
  • Luo, S.Z. and Xing, L.N. (2019). A Hybrid Decision Making Framework for Personnel Selection Using BWM. MABAC and PROMETHEE. International Journal of Fuzzy Systems, 21(8), 2421-2434.
  • Mishra, A.R., Chandel, A. and Motwani, D. (2020). Extended MABAC Method Based On Divergence Measures for Multi-Criteria Assessment of Programming Language with Interval-Valued Intuitionistic Fuzzy Sets. Granular Computing, 5(1), 97-117.
  • Nunić, Z. (2018). Evaluation and Selection of Manufacturer PVC Carpentry Using FUCOM-MABAC Model. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 13-28.
  • Özdağoğlu, A., Keleş, M.K. ve Işıldak, B. (2021). Havalimanlarının Bulanık DEMATEL ve MABAC Yöntemleri İle Sıralanması. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1), 46-67.
  • Pamučar, D. and Ćirović, G. (2015). The Selection of Transport and Handling Resources in Logistics Centers Using Multi-Attributive Border Approximation Area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016-3028.
  • Pamučar, D., Petrović, I. and Ćirović, G. (2018b). Modification of the Best–Worst and MABAC Methods: A Novel Approach Based On Interval-Valued Fuzzy-Rough Numbers. Expert Systems with Applications, 91, 89-106.
  • Pamučar, D., Stević, Ž. and Zavadskas, E. K. (2018a). Integration of Interval Rough AHP and Interval Rough MABAC Methods for Evaluating University Web Pages. Applied Soft Computing, 67, 141-163.
  • Peng, X. and Dai, J. (2018). Approaches to Single-Valued Neutrosophic MADM Based on MABAC. TOPSIS and New Similarity Measure with Score Function. Neural Computing and Applications, 29(10), 939-954.
  • Peng, X. and Yang, Y. (2016). Pythagorean Fuzzy Choquet Integral Based MABAC Method For Multiple Attribute Group Decision Making. International Journal of Intelligent Systems, 31(10), 989-1020.
  • Peng, X., Dai, J. and Yuan, H. (2017). Interval-Valued Fuzzy Soft Decision Making Methods Based on MABAC Similarity Measure and EDAS. Fundamenta Informaticae, 152(4), 373-396.
  • Sharma, H. K., Roy, J., Kar, S. and Prentkovskis, O. (2018). Multi Criteria Evaluation Framework for Prioritizing Indian Railway Stations Using Modified Rough AHP-MABAC method. Transport and Telecommunication Journal, 19(2), 113-127.
  • Shen, K.W., Wang, X.K., Qiao, D. and Wang, J.Q. (2019). Extended Z-MABAC Method Based On Regret Theory And Directed Distance For Regional Circular Economy Development Program Selection With Z-Information. IEEE Transactions on Fuzzy Systems, 28(8), 1851-1862.
  • Sonar, H.C. and Kulkarni, S.D. (2021). An Integrated AHP-MABAC Approach for Electric Vehicle Selection. Research in Transportation Business & Management, 100665, 1-8.
  • Sun, R., Hu, J., Zhou, J. and Chen, X. (2018). A Hesitant Fuzzy Linguistic Projection-Based MABAC Method for Patients’ Prioritization. International Journal of Fuzzy Systems, 20(7), 2144-2160.
  • Ulutaş, A. (2019). Entropi ve MABAC Yöntemleri İle Personel Seçimi. OPUS Uluslararası Toplum Araştırmaları Dergisi. 13(19). 1552-1573.
  • Vesković, S., Stević, Ž., Stojić, G., Vasiljević, M. and Milinković, S. (2018). Evaluation of The Railway Management Model by Using a New İntegrated Model DELPHI-SWARA-MABAC. Decision Making: Applications in Management and Engineering, 1(2), 34-50.
  • Wang, J., Wei, G., Wei, C. and Wei, Y. (2020). MABAC Method for Multiple Attribute Group Decision Making Under Q-Rung Orthopair Fuzzy Environment. Defence Technology, 16(1), 208-216.
  • Wei, G., Wei, C., Wu, J. and Wang, H. (2019). Supplier Selection of Medical Consumption Products with A Probabilistic Linguistic MABAC Method. International Journal of Environmental Research and Public Health, 16(24), 1-15.
  • Xu, X.G., Shi, H., Zhang, L.J. and Liu, H.C. (2019). Green Supplier Evaluation and Selection with an Extended MABAC Method Under the Heterogeneous Information Environment. Sustainability, 11(23), 1-16.
  • Xue, Y.X., You, J.X., Lai, X.D. and Liu, H.C. (2016). An Interval-Valued Intuitionistic Fuzzy MABAC Approach for Material Selection with Incomplete Weight Information. Applied Soft Computing, 38, 703-713.
  • Yu, S.M., Wang, J. and Wang, J.Q. (2017). An Interval Type-2 Fuzzy Likelihood-Based MABAC Approach and Its Application in Selecting Hotels On a Tourism Website. International Journal of Fuzzy Systems, 19(1), 47-61.
  • Zhang, S., Wei, G., Alsaadi, F. E., Hayat, T., Wei, C. and Zhang, Z. (2020). MABAC Method for Multiple Attribute Group Decision Making Under Picture 2-Tuple Linguistic Environment. Soft Computing, 24(8), 5819-5829.

ANALYSIS OF THE FINANCIAL PERFORMANCES OF COMPANIES TRADING IN BORSA ISTANBUL BY MABAC METHOD / Borsa İstanbul’da İşlem Gören Şirketlerin Finansal Performansının MABAC Yöntemiyle Analizi

Year 2021, Volume: 5 Issue: 2, 211 - 234, 31.10.2021
https://doi.org/10.29216/ueip.929743

Abstract

Multi-criteria decision making methods are one of the new approaches that can be used in financial performance evaluation. These methods are based on the decision-making process. MABAC is one of these methods. The study has two important aims. The first is the selection of companies with successful financial performance in a situation where there are many alternatives and criteria. Second, companies are ranked by financial performance as the most successful and relatively less successful. In this environment, financial performance rankings of two hundred and eighty-six companies that declared profits in the 2020-9 balance sheet period were obtained using the MABAC method, taking into account the Market Value / Book Value, Price / Earnings, Market Value and Net Profit criteria. The most successful companies are ISBTR, QNBFB, KCHOL, GARAN, KENT, ISCTR, ASELS, AKBNK, FROTO and ENKAI; The least successful companies were determined as RODRG, GRNYO, BALAT, EMKEL, IZFAS, EKIZ, MMCAS, COSMO, SNKRN and ATSYH.

References

  • Ayçin, E. (2020). Çok Kriterli Karar Verme Bilgisayar Uygulamalı Çözümler (2.Basım). Ankara: Nobel Yayıncılık. Biswas, T. and Saha, P. (2019). Selection of Commercially Available Scooters by New MCDM Method. International Journal of Data and Network Science, 3(2), 137-144.
  • Božanić, D. I., Pamučar, D. S. and Karović, S. M. (2016). Use of the fuzzy AHP-MABAC Hybrid Model in Ranking Potential Locations for Preparing Laying-Up Positions. Vojnotehnički Glasnik, 64(3), 705-729.
  • Bozanic, D., Tešić, D. and Kočić, J. (2019). Multi-Criteria FUCOM–Fuzzy MABAC Model for the Selection of Location for Construction of Single-Span Bailey Bridge. Decision Making: Applications in Management and Engineering, 2(1), 132-146.
  • Bozanic, D., Tešić, D. and Milić, A. (2020). Multicriteria Decision Making Model with Z-Numbers Based on FUCOM and MABAC Model. Decision Making: Applications in Management and Engineering, 3(2), 19-36.
  • Bozanic, D., Tešić, D. Milićević, J. (2018). A Hybrid Fuzzy AHP-MABAC Model: Application in the Serbian Army–The Selection of the Location for Deep Wading as A Technique of Crossing the River by Tanks. Decision Making: Applications in Management and Engineering, 1(1), 143-164.
  • Büyüközkan, G., Mukul, E. and Kongar, E. (2021). Health Tourism Strategy Selection Via SWOT Analysis and Integrated Hesitant Fuzzy Linguistic AHP-MABAC approach. Socio-Economic Planning Sciences, 74, 1-14.
  • Chatterjee, P., Mondal, S., Boral, S., Banerjee, A. and Chakraborty, S. (2017). A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method. Facta Universitatis. Series: Mechanical Engineering, 15(3), 439-456.
  • Delice, E. K., Adar T., Emeç, Ş. and Akkaya, G. (2019). A Comprehensive Analysis of Location Selection Problem for Underground Waste Containers Using Integrated MC-HFLTS&MAIRCA and MABAC Methods. Avrupa Bilim ve Teknoloji Dergisi, Özel Sayı, 15-33.
  • Dorfeshan, Y., and Mousavi, S.M. (2019). A Novel Interval Type-2 Fuzzy Decision Model Based On Two New Versions of Relative Preference Relation-Based MABAC and WASPAS Methods (With an Application in Aircraft Maintenance Planning). Neural Computing and Applications, 32(3), 1-19.
  • Fan, J., Guan, R. and Wu, M. (2020). Z-MABAC Method for The Selection of Third-Party Logistics Suppliers in Fuzzy Environment. IEEE Access, 8, 199111-199119.
  • Gigović, L., Pamučar, D., Božanić, D. and Ljubojević, S. (2017). Application of The GIS-DANP-MABAC Multi-Criteria Model for Selecting the Location of Wind Farms: A Case Study of Vojvodina, Serbia. Renewable Energy, 103, 501-521.
  • Gong, J. W., Li, Q., Yin, L. and Liu, H.C. (2020). Undergraduate Teaching Audit and Evaluation Using an Extended MABAC Method Under Q‐Rung Orthopair Fuzzy Environment. International Journal of Intelligent Systems, 35(12), 1912-1933.
  • Ji, P., Zhang, H. Y. and Wang, J.Q. (2018). Selecting an Outsourcing Provider Based On the Combined MABAC–ELECTRE Method Using Single-Valued Neutrosophic Linguistic Sets. Computers & Industrial Engineering, 120, 429-441.
  • Jia, F., Liu, Y. and Wang, X. (2019). An Extended MABAC Method for Multi-Criteria Group Decision Making Based On Intuitionistic Fuzzy Rough Numbers. Expert Systems with Applications, 127, 241-255.
  • Liang, R. X., He, S.S., Wang, J.Q., Chen, K. and Li, L. (2019). An Extended MABAC Method for Multi-Criteria Group Decision-Making Problems Based On Correlative Inputs of Intuitionistic Fuzzy Information. Computational and Applied Mathematics, 38(3), 112-140.
  • Liang, W., Zhao, G., Wu, H. and Dai, B. (2019). Risk Assessment of Rockburst Via an Extended MABAC Method Under Fuzzy Environment. Tunnelling and Underground Space Technology, 83, 533-544.
  • Liu, P. and Zhang, P. (2020). A Normal Wiggly Hesitant Fuzzy MABAC Method Based On CCSD and Prospect Theory for Multiple Attribute Decision Making. International Journal of Intelligent Systems, 36(1), 447-477.
  • Liu, P., Xu, H. and Pedrycz, W. (2020). A Normal Wiggly Hesitant Fuzzy Linguistic Projection‐Based Multiattributive Border Approximation Area Comparison Method. International Journal of Intelligent Systems, 35(3), 432-469.
  • Liu, P., Zhu, B., Wang, P. and Shen, M. (2020). An Approach Based On Linguistic Spherical Fuzzy Sets for Public Evaluation of Shared Bicycles in China. Engineering Applications of Artificial Intelligence, 87, 1-15.
  • Liu, R., Hou, L, X., Liu, H.C. and Lin, W. (2020). Occupational Health and Safety Risk Assessment Using an Integrated SWARA-MABAC Model Under Bipolar Fuzzy Environment. Computational and Applied Mathematics, 39(4), 1-17.
  • Luo, S.Z. and Liang, W. Z. (2019). Optimization of Roadway Support Schemes with Likelihood-Based MABAC Method. Applied Soft Computing, 80, 80-92.
  • Luo, S.Z. and Xing, L.N. (2019). A Hybrid Decision Making Framework for Personnel Selection Using BWM. MABAC and PROMETHEE. International Journal of Fuzzy Systems, 21(8), 2421-2434.
  • Mishra, A.R., Chandel, A. and Motwani, D. (2020). Extended MABAC Method Based On Divergence Measures for Multi-Criteria Assessment of Programming Language with Interval-Valued Intuitionistic Fuzzy Sets. Granular Computing, 5(1), 97-117.
  • Nunić, Z. (2018). Evaluation and Selection of Manufacturer PVC Carpentry Using FUCOM-MABAC Model. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 13-28.
  • Özdağoğlu, A., Keleş, M.K. ve Işıldak, B. (2021). Havalimanlarının Bulanık DEMATEL ve MABAC Yöntemleri İle Sıralanması. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1), 46-67.
  • Pamučar, D. and Ćirović, G. (2015). The Selection of Transport and Handling Resources in Logistics Centers Using Multi-Attributive Border Approximation Area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016-3028.
  • Pamučar, D., Petrović, I. and Ćirović, G. (2018b). Modification of the Best–Worst and MABAC Methods: A Novel Approach Based On Interval-Valued Fuzzy-Rough Numbers. Expert Systems with Applications, 91, 89-106.
  • Pamučar, D., Stević, Ž. and Zavadskas, E. K. (2018a). Integration of Interval Rough AHP and Interval Rough MABAC Methods for Evaluating University Web Pages. Applied Soft Computing, 67, 141-163.
  • Peng, X. and Dai, J. (2018). Approaches to Single-Valued Neutrosophic MADM Based on MABAC. TOPSIS and New Similarity Measure with Score Function. Neural Computing and Applications, 29(10), 939-954.
  • Peng, X. and Yang, Y. (2016). Pythagorean Fuzzy Choquet Integral Based MABAC Method For Multiple Attribute Group Decision Making. International Journal of Intelligent Systems, 31(10), 989-1020.
  • Peng, X., Dai, J. and Yuan, H. (2017). Interval-Valued Fuzzy Soft Decision Making Methods Based on MABAC Similarity Measure and EDAS. Fundamenta Informaticae, 152(4), 373-396.
  • Sharma, H. K., Roy, J., Kar, S. and Prentkovskis, O. (2018). Multi Criteria Evaluation Framework for Prioritizing Indian Railway Stations Using Modified Rough AHP-MABAC method. Transport and Telecommunication Journal, 19(2), 113-127.
  • Shen, K.W., Wang, X.K., Qiao, D. and Wang, J.Q. (2019). Extended Z-MABAC Method Based On Regret Theory And Directed Distance For Regional Circular Economy Development Program Selection With Z-Information. IEEE Transactions on Fuzzy Systems, 28(8), 1851-1862.
  • Sonar, H.C. and Kulkarni, S.D. (2021). An Integrated AHP-MABAC Approach for Electric Vehicle Selection. Research in Transportation Business & Management, 100665, 1-8.
  • Sun, R., Hu, J., Zhou, J. and Chen, X. (2018). A Hesitant Fuzzy Linguistic Projection-Based MABAC Method for Patients’ Prioritization. International Journal of Fuzzy Systems, 20(7), 2144-2160.
  • Ulutaş, A. (2019). Entropi ve MABAC Yöntemleri İle Personel Seçimi. OPUS Uluslararası Toplum Araştırmaları Dergisi. 13(19). 1552-1573.
  • Vesković, S., Stević, Ž., Stojić, G., Vasiljević, M. and Milinković, S. (2018). Evaluation of The Railway Management Model by Using a New İntegrated Model DELPHI-SWARA-MABAC. Decision Making: Applications in Management and Engineering, 1(2), 34-50.
  • Wang, J., Wei, G., Wei, C. and Wei, Y. (2020). MABAC Method for Multiple Attribute Group Decision Making Under Q-Rung Orthopair Fuzzy Environment. Defence Technology, 16(1), 208-216.
  • Wei, G., Wei, C., Wu, J. and Wang, H. (2019). Supplier Selection of Medical Consumption Products with A Probabilistic Linguistic MABAC Method. International Journal of Environmental Research and Public Health, 16(24), 1-15.
  • Xu, X.G., Shi, H., Zhang, L.J. and Liu, H.C. (2019). Green Supplier Evaluation and Selection with an Extended MABAC Method Under the Heterogeneous Information Environment. Sustainability, 11(23), 1-16.
  • Xue, Y.X., You, J.X., Lai, X.D. and Liu, H.C. (2016). An Interval-Valued Intuitionistic Fuzzy MABAC Approach for Material Selection with Incomplete Weight Information. Applied Soft Computing, 38, 703-713.
  • Yu, S.M., Wang, J. and Wang, J.Q. (2017). An Interval Type-2 Fuzzy Likelihood-Based MABAC Approach and Its Application in Selecting Hotels On a Tourism Website. International Journal of Fuzzy Systems, 19(1), 47-61.
  • Zhang, S., Wei, G., Alsaadi, F. E., Hayat, T., Wei, C. and Zhang, Z. (2020). MABAC Method for Multiple Attribute Group Decision Making Under Picture 2-Tuple Linguistic Environment. Soft Computing, 24(8), 5819-5829.
There are 43 citations in total.

Details

Primary Language Turkish
Subjects Finance
Journal Section RESEARCH ARTICLES
Authors

Hakan Altın 0000-0002-0012-0016

Publication Date October 31, 2021
Published in Issue Year 2021 Volume: 5 Issue: 2

Cite

APA Altın, H. (2021). BORSA İSTANBUL’DA İŞLEM GÖREN ŞİRKETLERİN FİNANSAL PERFORMANSININ MABAC YÖNTEMİYLE ANALİZİ / Analysis of The Financial Performances of Companies Trading in Borsa Istanbul by MABAC Method. Uluslararası Ekonomi İşletme Ve Politika Dergisi, 5(2), 211-234. https://doi.org/10.29216/ueip.929743

Recep Tayyip Erdogan University
Faculty of Economics and Administrative Sciences
Department of Economics
RIZE / TÜRKİYE