SavedAdd your journal

Journals / Fiscaoeconomia / 2026 / Volume: 9 Issue: 3

Analysis of the Financial Performance of Brokerage Houses in the Istanbul Stock Exchange Using the Statistical Variance and Mean Weight-Based MABAC Method

Borsa İstanbul’da Aracı Kurumların Finansal Performansının İstatistiksel Varyans ve Ortalama Ağırlığa Dayalı MABAC Yöntemiyle Analizi

Ümit Hasan Gözkonan(MANISA CELAL BAYAR UNIVERSITY)0000-0002-7187-6304
Gökhan Berk Özbek(BURSA ULUDAG UNIVERSITY)0000-0003-0288-069X
Pages
—
Publication Date
2025-08-25
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal that although there is variability in the rankings of brokerage houses in terms of financial performance, the Borda scores obtained from the rankings of the years helped to reveal high-performing brokerage houses.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study analyzes the financial performance of brokerage houses listed on the Istanbul Stock Exchange using the Multi-Attribute Border Approximation Area Comparison (MABAC) method, employing statistical variance and mean weight methods to determine criteria weights.
📊
Key Findings & Data:Nine brokerage houses were selected, and their financial performance was evaluated through seven key financial ratios.
💡
Conclusion & Impact:The findings reveal that although there is variability in the rankings of brokerage houses in terms of financial performance, the Borda scores obtained from the rankings of the years helped to reveal high-performing brokerage houses.
🔬Design:Research Article
📍Scope:2017 Verileri
⚙️Variables:Brokerage House · Financial Performance · Statistical Variance Method

Abstract

This study analyzes the financial performance of brokerage houses listed on the Istanbul Stock Exchange using the Multi-Attribute Border Approximation Area Comparison (MABAC) method, employing statistical variance and mean weight methods to determine criteria weights. Nine brokerage houses were selected, and their financial performance was evaluated through seven key financial ratios. These include liquidity ratios (current and cash ratios), financial structure ratios (leverage and financial debt ratios), and profitability ratios (return on assets, return on equity, and return on invested capital). The weighting of these criteria was determined through the statistical variance and mean weight methods, providing two distinct rankings that were consolidated using the Borda Count Method for a robust performance assessment. Each brokerage house’s financial performance was analyzed on an annual basis from 2017 to 2023. The findings reveal that although there is variability in the rankings of brokerage houses in terms of financial performance, the Borda scores obtained from the rankings of the years helped to reveal high-performing brokerage houses. As a result of the study, OSMEN was found to be a prominent brokerage house in terms of financial performance in the ranking based on total Borda scores. OYYAT ranked second and A1CAP ranked third in terms of financial performance. It is thought that the study can be used as a reliable reference for future performance analyses and can support more effective decision-making in terms of investment decisions in the sector.

Özet

Bu çalışmada, Borsa İstanbul'da işlem gören aracı kurumların finansal performansları, istatistiksel varyans ve ortalama ağırlık olarak bilinen kriter ağırlıklandırma yöntemleri kullanılarak, Çok Özellikli Sınır Yaklaşım Alanı Kıyaslaması (MABAC) yöntemi ile analiz edilmiştir. Dokuz aracı kurum seçilmiş ve finansal performansları yedi temel finansal oran üzerinden değerlendirilmiştir. Bunlar likidite oranları (cari ve nakit oranları), finansal yapı oranları (kaldıraç ve finansal borç oranları) ve karlılık oranlarıdır (aktif karlılığı, özsermaye karlılığı ve yatırılan sermaye karlılığı). Bu kriterlerin ağırlıklandırılması istatistiksel varyans ve ortalama ağırlık yöntemleriyle belirlenmiş ve sağlam bir performans değerlendirmesi için Borda Sayım Yöntemi kullanılarak konsolide edilen iki farklı sıralama sağlanmıştır. Her bir aracı kurumun finansal performansı 2017'den 2023'e kadar yıllık bazda analiz edilmiştir. Bulgular, finansal performans açısından aracı kurumların sıralamalarında değişkenlik olmasına rağmen, yıllara göre yapılan sıralamalardan elde edilen Borda skorlarının yüksek performans gösteren aracı kurumların ortaya çıkarılmasına yardımcı olduğunu ortaya koymuştur. Çalışma sonucunda, toplam Borda puanları baz alınarak yapılan sıralamada OSMEN'in finansal performans açısından en başarılı aracı kurum olduğu tespit edilmiştir. Finansal performans açısından ikinci sırada OYYAT, üçüncü sırada ise A1CAP yer almıştır. Çalışmanın gelecekteki performans analizleri için güvenilir bir referans olarak kullanılabileceği ve sektördeki yatırım kararları açısından daha etkin karar alınmasına destek olabileceği düşünülmektedir.

🔖

Cite This Article

Gözkonan, Ü. H., Özbek, G. B. (2025). Analysis of the Financial Performance of Brokerage Houses in the Istanbul Stock Exchange Using the Statistical Variance and Mean Weight-Based MABAC Method. Fiscaoeconomia, Volume: 9 Issue: 3. https://doi.org/10.25295/fsecon.1581399

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (62)

  1. Adar, T., & Delice, E. K. (2019). New integrated approaches based on MC-HFLTS for healthcare waste treatment technology selection. Journal of Enterprise Information Management, 32(4), 688-711. https://doi.org/10.1108/JEIM-10-2018-0235
  2. Akbulut, O. Y. (2020). Finansal performans ile pay senedi getirisi arasındaki ilişkinin bütünleşik CRITIC ve MABAC ÇKKV teknikleriyle ölçülmesi: Borsa İstanbul çimento sektörü firmaları üzerine ampirik bir uygulama. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (40), 471-488. https://doi.org/10.30794/pausbed.683330
  3. Aktaş, H., & Kargin, M. (2007). Türk sermaye piyasasındaki aracı kurumların etkinlik ve verimliliği. Iktisat Isletme ve Finans, 22(258), 97-117.
  4. Allen, F., & Santomero, A. M. (1997). The theory of financial intermediation. Journal of Banking & Finance, 21(11-12), 1461-1485. https://doi.org/10.1016/S0378-4266(97)00032-0
  5. Altıntaş, F. F. (2022). OECD grubundaki Avrupa ülkelerinin vergi rekabetçiliği performanslarının analizi: İstatistiksel varyans prosedürü tabanlı OCRA yöntemi ile bir uygulama. JOEEP: Journal of Emerging Economies and Policy, 7(2), 104–119.
  6. Aras, G., Tezcan, N., & Kutlu Furtuna, O. (2018). Comprehensive evaluation of the financial performance for intermediary institutions based on multi-criteria decision making method. Journal of Capital Markets Studies, 2(1), 37-49.
  7. Ardil, C. (2021). Freighter aircraft selection using entropic programming for multiple criteria decision making analysis. International Journal of Mathematical and Computational Sciences, 15(12), 119-126.
  8. Aytekin, A. (2023). Çok Kriterli Karar Analizi (2nd ed.). Nobel Akademik Yayıncılık, Ankara.
  9. Azeem, M., Ali, J., & Ali, J. (2024). Complex Fermatean fuzzy partitioned Maclaurin symmetric mean operators and their application to hostel site selection. OPSEARCH. https://doi.org/10.1007/s12597-024-00813-w
  10. Barber, B. M., Lehavy, R., & Trueman, B. (2007). Comparing the stock recommendation performance of investment banks and independent research firms. Journal of Financial Economics, 85(2), 490-517. https://doi.org/10.1016/j.jfineco.2005.09.004
  11. Bayram, N. (2016). Veri zarflama analizi ve toplam faktör verimliliği: Aracı kurumlar üzerine bir uygulama. Verimlilik Dergisi, (2), 7-44.
  12. Bose, S., Mandal, N., & Nandi, T. (2020). Comparative and experimental study on hybrid metal matrix composites using additive ratio assessment and multi-attributive border approximation area comparison methods varying the different weight percentage of the reinforcements. Materials Today: Proceedings, 22, 1745-1754. https://doi.org/10.1016/j.matpr.2020.03.007
  13. Büyüközkan, G., Mukul, E., & Kongar, E. (2021). Health tourism strategy selection via SWOT analysis and integrated hesitant fuzzy linguistic AHP-MABAC approach. Socio-Economic Planning Sciences, 74, 100929. https://doi.org/10.1016/j.seps.2020.100929
  14. Capital Market Law. (2012, December 6). Turkey–Legal Gazette (No: 28513). https://www.mevzuat.gov.tr/mevzuat?MevzuatNo=6362&MevzuatTur=1&MevzuatTertip=5
  15. Çelik, İ. E. (2019). 2008 global krizi sonrası Türkiye’de aracı kurumlarda etkinlik ve etkinliği belirleyen faktörler: 2008-2017 dönemi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(3), 479-494.
  16. Çelik, S. (2020). Türk katılım bankacılığı sektöründe performans analizi: Bütünleşik CRITIC ve MABAC uygulaması. İslam Ekonomisi ve Finansı Dergisi (İEFD), 6(2), 312-335.
  17. Çetin, Ö. O., & Karataş, M. (2024). BİST'te işlem gören otomotiv şirketlerinin kârlılık performansının LOPCOW ve MABAC yöntemleriyle analizi. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 14(3), 1470-1496. https://doi.org/10.30783/nevsosbilen.1513524
  18. Çetin, T., & Oğuz, F. (2012). Efficiency and productivity of the brokerage houses in Turkey. Regulation and Competition in the Turkish Banking and Financial Markets. Nova Science Publishers, New York.
  19. Demir, G., Özyalçın, A. T., & Bircan, H. (2021). Çok kriterli karar verme yöntemleri ve ÇKKV yazılımı ile problem çözümü (1. Baskı). Nobel Akademik Yayıncılık, Ankara.
  20. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers Ops Res, 22(7), 763–770. https://doi.org/https://doi.org/10.1016/0305-0548(94)00059-H
  21. Ecer, F. (2020). Çok kriterli karar verme—Geçmişten günümüze kapsamlı bir yaklaşım (1. Baskı). Seçkin Yayıncılık.
  22. Emovon, I., & Samuel, O. D. (2017). An integrated Statistical Variance and VIKOR methods for prioritising power generation problems in Nigeria. Journal of Engineering and Technology, 8(1), 92-104.
  23. Fattouh, M., & Eisa, A. (2023). The significance of weighting in multicriteria decision-making methods: A case study on robot selection. Engineering Research Journal (ERJ), 46(3), 339–352. https://doi.org/10.21608/ERJM.2023.211769.1263
  24. Feng, J. (2024). Multi-attribute perceptual fuzzy information decision-making technology in investment risk assessment of green finance Projects. Journal of Intelligent Systems, 33(1), 20230189. https://doi.org/10.1515/jisys-2023-0189
  25. Fertekligil, A. (1993). Türkiye’de borsa’nın tarihçesi. İMKB Yayınları, İstanbul.
  26. Gülençer, I., & Türkoglu, S. P. (2020). Gelişmekte olan Asya ve Avrupa ülkelerinin finansal gelişmişlik performansının istatistiksel varyans prosedürü temelli OCRA yöntemiyle analizi. Third Sector Social Economic Review, 55(2), 1330-1344.
  27. Günay, B., & Kaya, İ. (2017). Borsa İstanbul’da yer alan aracı kurumların performansının çok kriterli karar verme yöntemleri ile değerlendirilmesi. Manisa Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 15(2), 141-164. https://doi.org/10.18026/cbayarsos.323997
  28. Irvanizam, I., Zi, N. N., Zuhra, R., Amrusi, A., & Sofyan, H. (2020). An extended MABAC method based on triangular fuzzy neutrosophic numbers for multiple-criteria group decision making problems. Axioms, 9(3), 104. https://doi.org/10.3390/axioms9030104
  29. Işık, Ö. (2020). SD tabanlı MABAC Ve WASPAS yöntemleriyle kamu sermayeli kalkınma ve yatırım bankalarının performans analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (29), 61-78. https://doi.org/10.18092/ulikidince.705148
  30. Ivković, Z., & Jegadeesh, N. (2004). The timing and value of forecast and recommendation revisions. Journal of Financial Economics, 73(3), 433-463. https://doi.org/10.1016/j.jfineco.2004.03.002
  31. Jee, D.-H., & Kang, K.-J. (2000). A method for optimal material selection aided with decision making theory. Materials & Design, 21(3), 199–206. https://doi.org/10.1016/S0261-3069(99)00066-7
  32. Kaygın, C. Y., Bağcı, H., & Tanır, D. (2019). BIST Kobi sanayi şirketlerinin finansal performanslarının MW ve Gri ilişkisel analiz yöntemleri ile ölçülmesi. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(20), 944-965.
  33. Kılıçarslan, A., & Sucu, M. Ç. (2022). Marka değerinin Hirose yöntemiyle tespiti ve finansal performans analizi: Borsa İstanbul’da işlem gören aracı kurumlar üzerine bir uygulama. İşletme Araştırmaları Dergisi, 14(3), 1937-1952. https://doi.org/10.20491/isarder.2022.1481
  34. Kilic, O., & Çerçioğlu, H. (2016). Application of compromise multiple criteria decision making methods for evaluation of TCDD's railway lines projects. Journal of the Faculty of Engineering and Architecture of Gazi University, 31(1), 211-220.
  35. Köse, A., & Akıllı, K. (2021). Financial performance of brokerage firms evaluation with Vikor method. Cumhuriyet University Journal of Economics & Administrative Sciences, 22(2), 168-192. https://doi.org/10.18505/cumiibf.6987969
  36. Krishankumar, R., Ravichandran, K. S., Liu, P., Kar, S., & Gandomi, A. H. (2021). A decision framework under probabilistic hesitant fuzzy environment with probability estimation for multi-criteria decision making. Neural Computing and Applications, 33(14), 8417–8433. https://doi.org/10.1007/s00521-020-05595-y
  37. Kurnaz, E., Karcıoglu, R., & Yıldıztekin İ. (2014). the comparison of financial performance of intermediary firms traded on Istanbul stock exchange by using Topsis method. International Conference on Economic and Social Studies, Sarajevo - Bosnia and Herzegovina. (24-25 April, 2014).
  38. Liu, P., & Zhang, P. (2021). 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. https://doi.org/10.1002/int.22306
  39. Luo, S. Z., & Xing, L. N. (2019). A hybrid decision making framework for personnel selection using BWM, MABAC and PROMETHEE. International Journal of Fuzzy Systems, 21, 2421-2434. https://doi.org/10.1007/s40815-019-00745-4
  40. Mishra, A. R., Garg, A. K., Purwar, H., Rana, P., Liao, H., & Mardani, A. (2021). An extended intuitionistic fuzzy multi-attributive border approximation area comparison approach for smartphone selection using discrimination measures. Informatica, 32(1), 119-143. https://doi.org/10.15388/20-INFOR430
  41. Okay, G., & Köse, A. (2015). Financial performance analysis of brokerage firms quoted on the Istanbul Stock Exchange using the TOPSIS method of analysis. International Journal of Business and Social Science, 6(8), 68-77.
  42. Özcan Buckley, A., & Türkoğlu, S. P. (2022). Çocuk dostu kent yaklaşımına yönelik politika eğilimlerinin istatistiksel varyans prosedürü temelli OCRA yöntemiyle ölçümü. Giresun Üniversitesi İktisadi ve İdari Bilimler Dergisi, 8(2), 311–330. https://doi.org/10.46849/guiibd.1214042
  43. Pala, O. (2022). Imv ve Waspas Tabanlı Aracı Kurum Performans Değerlendirilmesi. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23(3), 266-281. https://doi.org/10.53443/anadoluibfd.1103297
  44. Pamučar, D., & 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. https://doi.org/10.1016/j.eswa.2014.11.057
  45. Pamučar, D., Stević, Ž., & Zavadskas, E. K. (2018). Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Applied Soft Computing, 67, 141-163. https://doi.org/10.1016/j.asoc.2018.02.057
  46. Rao, R. V., & Patel, B. K. (2010). A subjective and objective integrated multiple attribute decision making method for material selection. Materials & Design, 31(10), 4738–4747. https://doi.org/10.1016/j.matdes.2010.05.014
  47. Roy, J., Chatterjee, K., Bandyopadhyay, A., & Kar, S. (2018). Evaluation and selection of medical tourism sites: A rough analytic hierarchy process based multi‐attributive border approximation area comparison approach. Expert Systems, 35(1), e12232. https://doi.org/10.1111/exsy.12232
  48. Ryan, P., & Taffler, R. J. (2006). Do brokerage houses add value? The market impact of UK sell-side analyst recommendation changes. The British Accounting Review, 38(4), 371-386. https://doi.org/10.1016/j.bar.2006.07.015
  49. Shaik, K. (2014). The market players. Managing derivatives contracts. Apress, Berkeley. https://doi.org/10.1007/978-1-4302-6275-6_5
  50. Shanian, A., & Savadogo, O. (2006). TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell. Journal of Power Sources, 159(2), 1095–1104. https://doi.org/10.1016/j.jpowsour.2005.12.092
  51. Sharma, S. K., Mahapatra, S. S., & Parappagoudar, M. B. (2016). Benchmarking of product recovery alternatives in reverse logistics. Benchmarking: An International Journal, 23(2), 406–424. https://doi.org/10.1108/BIJ-01-2014-0002
  52. Su, C., Zhang, H., & Joseph, N. L. (2022). The performance of UK stock recommendation revisions: Does brokerage house reputation matter?. International Journal of Finance & Economics, 27(3), 3051-3070. https://doi.org/10.1002/ijfe.2312
  53. Sutrisno, A., Wuisang, C. E. V., & Yusupa, A. (2023). Disaster readiness assessment model using integrated statistical variance and proximity value index. International Journal of Emergency Services, 12(2), 197–212. https://doi.org/10.1108/IJES-09-2022-0050
  54. Şeyranlıoğlu, O., & Kara, M. A. (2024). Aracı kurumların borsa performanslarının entropi ve CODAS yöntemleri ile değerlendirilmesi. Yönetim ve Ekonomi Dergisi, 31(1), 183-202. https://doi.org/10.18657/yonveek.1271659
  55. Tezergil, S. A. (2018). Aracı kurumların finansal performanslarının Topsis yöntemi ile incelenmesi. Mali Çözüm Dergisi, 28, 43-58.
  56. Trung, D. D., Dudić, B., Van Duc, D., Son, N. H., & Ašonja, A. (2024). Comparison of MCDM methods effectiveness in the selection of plastic injection molding machines. Teknomekanik, 7(1), 1–19. https://doi.org/10.24036/TEKNOMEKANIK.V7I1.29272
  57. Üçler, Y. T. (2024). Comparison of G-7 countries' macroeconomic performance with SD and MABAC methods. Politik Ekonomik Kuram, 8(1), 243-255. https://doi.org/10.30586/pek.1411404
  58. Vavrek, R. (2019). Evaluation of the impact of selected weighting methods on the results of the TOPSIS technique. International Journal of Information Technology & Decision Making, 18(06), 1821-1843. https://doi.org/10.1142/S021962201950041X
  59. Womack, K. L. (1996). Do brokerage analysts' recommendations have investment value?. The Journal of Finance, 51(1), 137-167. https://doi.org/10.1111/j.1540-6261.1996.tb05205.x
  60. Yıldırım, H. (2024). Factoring sector in Turkey: General overview and evaluation of factoring companies' financial performance by multi-criteria decision-making techniques. Politik Ekonomik Kuram, 8(2), 277-302. https://doi.org/10.30586/pek.1419202
  61. Zardari, N. H., Ahmed, K., Shirazi, S. M., & Yusop, Z. B. (2015). Weighting methods and their effects on multi-criteria decision-making model outcomes in water resources management. Springer International Publishing. https://doi.org/10.1007/978-3-319-12586-2.
  62. Zhang, X., Wang, C., Li, E., & Xu, C. (2014). Assessment model of ecoenvironmental vulnerability based on improved entropy weight method. The Scientific World Journal, 2014(1), 797814. https://doi.org/10.1155/2014/797814.