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EXAMINING THE FINANCIAL RATIOS AND PROFITABILITY OF COMPANIES BY USING ANFIS: A STUDY ON LEATHER AND CLOTHING INDUSTRIES TRAIDED IN BIST

Year 2022, Issue: 61, 369 - 384, 29.04.2022

Öznur Arslan , Mesut Polatgil , Ebuzer Arslan

https://doi.org/10.18070/erciyesiibd.1013035
Cited By: 1

Abstract

This study aims to predict Return on Equity (ROE), Return on Assets (ROA), and Return on Sales (ROS) of the textile industry companies traded in Borsa İstanbul (BİST). For this purpose, a dataset was prepared using 7-year (2013-2019) liquidity, financial structure, operation, and rate of return data of 20 companies operating in the textile industry. The financial ratios of these companies were subjected to principal components analysis and it was determined that the first 4 components explained approx. 84% of all the data. These first 4 components with eigenvalues higher than 1 were modeled using ANFIS and, as a result of the experimental study, it was determined that the model was successful at predetermining the companies’ return on equity at the level of 81%, return on assets at the level of 79%, and net profit margin at the level of 71%.

Keywords

Financial Management Return on Equity (ROE) Return on Assets (ROA) Return on Sales (ROS) Fuzzy Set Methods

References

  • Atsalakis, G. and Valavanis, K. 2009. Forecasting Stock Market Short-Term Trends Using A Neuro Fuzzy Based Methodology. Expert Systemswith Applications, 36: 10696-10707.
  • Birgili, E. and Esen, S. 2013. Teknik Analiz Yönteminin Bulanık Mantık Yaklaşımı İle Uygulanması: IMKB 30 Banka Hisseleri Örneği. Finans Politik ve Ekonomik Yorumlar, 50 (575): 95-113.
  • Boyacıoğlu, M. A. and Avcı, D. 2010. “Borsa Getirisinin Tahmini İçin Uyarlanabilir Ağ Tabanlı Bulanık Çıkarsama Sistemi (ANFIS): İstanbul Menkul Kıymetler Borsası Örneği”, Expert Systems with Applications, 37( 12): 7908-7912.
  • Bro, R. and Smilde A. K. 2014. Principal Component Analysis. Anal Methods, 6(9): 2812–2831.
  • Cheng, P., Quek, C. and Mah, M.L. 2007. “Predıctıng The Impact Of Antıcıpatory Actıon On U.S. Stock Market An Event Study Usıng ANFIS (A Neural Fuzzy Model)”, Computatıonal Intellıgencev An İnternational Journal, 23 (2): 117-141.
  • Divya, P. and Kumar, P. R. 2012. The Investment Portfolio Selection Using Fuzzy Logic Genetic Algorithm, International Journal of Engineering Research and Applications, 2 (5):2100-2105.
  • Doesken, B., Abraham, A. T. J., & Paprzycki, M. 2005. Real Stock Trading Using Soft Computing Models, İnformation technolog: Coding and computing. Conference Publications, 2: 162-167.
  • Dong, M. and Zhou, X. S. 2002. Exploring The Fuzzy Nature Of Technical Patterns Of U.S Stock Market, ICONIP'02-SEAL'02-FSKD'02, Singapore, 18-22.
  • Dourra H. and Sıy, P. 2002. Investment Using Technical Analysis And Fuzzy Logic. Fuzzy Sets And Systems, 127: 221-240.
  • Dourra H. and Sıy, P. 2001. Stock Evaluation Using Fuzzy Logic. International Journal Of Theoretical And Applied Finance, 4(4): 585-602.
  • Esen, S. 2013. Bulanık Mantık Yaklaşımıyla Teknik Analiz Yönteminin Uygulanması: İMKB 30 Örneği, Yayımlanmamış Doktora Tezi, Sakarya Üniversitesi, Sosyal Bilimler Enstitüsü, Sakarya, TR.
  • Granato, D., Santos, J. S., Escher, G. B., Ferreira, B. L. and Maggio, R. M. 2018. Use Of Principal Component Analysis (PCA) And Hierarchical Cluster Analysis (HCA) For Multivariate Association Between Bioactive Compounds And Functional Properties İn Foods: A Critical Perspective. Trends İn Food Science & Technology, 72: 83-90.
  • Gülgör, G. 2010. İMKB 30 Endeksinde Klasik ve Bulanık Analitik Hiyerarşi Süreci İle Portföy Seçimi Ve Performanslarının Karşılaştırılması, Yayımlanmamış Yüksek Lisans Tezi, Osmangazi Üniversitesi, Sosyal Bilimler Enstitüsü, Eskişehir, TR.
  • Jang J. 1993. ANFIS: Adaptive-Network-Based Fuzzy İnference System. Ieee Trans Syst Man Cybern Syst, 23(3): 665-685.
  • Karapınar, A., Bayırlı, R., Bal, H., Altay, A., and Bal, E. Ç. 2007. İleri Düzey SPK Lisanslama Sınavlarına Hazırlık, Gazi Kitabevi, Ankara.
  • Khcherem, F. and Bouri, A. 2009. “Fuzzy Logic And Investment Strategy”, Global Economy & Finance Journal, 2 (2): 22-37.
  • Koç, S. and Ulucan, S. 2016. “Finansal Başarısızlıkların Tespitinde Kullanılan Altman Z Yönteminin Bulanık Mantık (ANFIS) Yöntemi İle Test Edilmesi: Teknoloji Ve Tekstil Sektöründe Bir Uygulama”, Maliye Finans Yazıları, (106):147-168.
  • Lam, S.S. 2001. “A Genetic Fuzzy Expert System For Stock Market Timing”, Proceedings of the IEEE Conference on Evolutionary Computation, (1): 410–417.
  • Li, H. 2019. Multivariate Time Series Clustering Based On Common Principal Component Analysis. Neurocomputing, 349: 239-247.
  • Mangale, C., Meena S. and Birchha, V. 2015. “Prediction Of Stock Values Based On Fuzzy Logic Using Fundamental Analysis”, International Journal on Recent and Innovation Trends in Computing and Communication, 3 (10):5806-5810.
  • Pelitli, D. 2007. Portföy Analizinde Bulanık Mantık Yaklaşımı Ve Uygulama Örneği, Yayımlanmamış Yüksek Lisans Tezi, Pamukkale Üniversitesi, Sosyal Bilimler Enstitüsü, Denizli, TR.
  • Selimoğlu, S. and Orhan, A. 2015. “Finansal Başarısızlığın Oran Analizi Ve Diskriminant Analizi Kullanılarak Ölçümlenmesi: BİST’te İşlem Gören Dokuma, Giyim Eşyası Ve Deri İşletmeleri Üzerine Bir Araştırma”. Muhasebe ve Finansman Dergisi, (66):21-40.
  • Thiagarajah, K. Appadoo S. and . Thavaneswaran, A. 2007. “Option Valuation Model With Adaptive Fuzzy Numbers”. Computers And Mathematics With Applications, (53):831-841.
  • Yıldız, B. and Akkoç, S. 2016. “Çalışma Sermayesi Ve Karlılık İlişkisinin Keşifsel Bir Araçla (ANFIS) İncelenmesi”, Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 11(1): 285-308.
  • Yörük, N., Karaca, S. S., Hekim, M. ve Tuna, İ. 2013. “Sermaye Yapısını Etkileyen Faktörler ve Finansal Oranlar İle Hisse Getirisi Arasındaki İlişkinin ANFIS Yöntemi İle İncelenmesi: İMKB 100’de Bir Uygulama”, Anadolu Üniversitesi Sosyal Bilimler Dergisi, 13(2): 101-114.

FİNANSAL ORANLAR İLE FİRMA KARLILIKLARININ ANFIS YÖNTEMİ İLE İNCELENMESİ: BİST’DE İŞLEM GÖREN TEKSTİL GİYİM EŞYASI VE DERİ SEKTÖRÜ ÜZERİNE BİR ARAŞTIRMA

Year 2022, Issue: 61, 369 - 384, 29.04.2022

Öznur Arslan , Mesut Polatgil , Ebuzer Arslan

https://doi.org/10.18070/erciyesiibd.1013035
Cited By: 1

Abstract

Bu çalışmanın amacı, Borsa İstanbul’da (BİST) işlem gören tekstil sanayi işletmelerinin öz sermaye karlılığı (ROE), Aktif karlılığı (ROA) ve Net kar marjı (ROS) oranlarının önceden tahmin edilmesidir. Bu amaca yönelik tekstil sektöründe faaliyet gösteren 20 firmanın (2013-2019) yedi yıllık likidite, finansal yapı, faaliyet ve karlılık oranları hesaplanarak veri seti oluşturulmuştur. Firmaların finans oranları temel bileşenler analizine tabi tutulmuş ve ilk dört bileşenin toplam verinin yaklaşık %84’ünü açıklayabildiği tespit edilmiştir. Özdeğeri 1’den büyük olan ilk 4 bileşen ANFİS’le modellenerek yapılan deneysel çalıma sonucunda işletmelerinin öz sermaye karlılığını %81, aktif karlılığını %79 ve net kar marjını ise %71 oranında önceden tahmin etmede başarılı olduğu tespit edilmiştir.

Keywords

Finansal Yönetim Öz Sermaye Karlılığı Aktif Karlılık Net Kar Marjı Bulanık Mantık

References

  • Atsalakis, G. and Valavanis, K. 2009. Forecasting Stock Market Short-Term Trends Using A Neuro Fuzzy Based Methodology. Expert Systemswith Applications, 36: 10696-10707.
  • Birgili, E. and Esen, S. 2013. Teknik Analiz Yönteminin Bulanık Mantık Yaklaşımı İle Uygulanması: IMKB 30 Banka Hisseleri Örneği. Finans Politik ve Ekonomik Yorumlar, 50 (575): 95-113.
  • Boyacıoğlu, M. A. and Avcı, D. 2010. “Borsa Getirisinin Tahmini İçin Uyarlanabilir Ağ Tabanlı Bulanık Çıkarsama Sistemi (ANFIS): İstanbul Menkul Kıymetler Borsası Örneği”, Expert Systems with Applications, 37( 12): 7908-7912.
  • Bro, R. and Smilde A. K. 2014. Principal Component Analysis. Anal Methods, 6(9): 2812–2831.
  • Cheng, P., Quek, C. and Mah, M.L. 2007. “Predıctıng The Impact Of Antıcıpatory Actıon On U.S. Stock Market An Event Study Usıng ANFIS (A Neural Fuzzy Model)”, Computatıonal Intellıgencev An İnternational Journal, 23 (2): 117-141.
  • Divya, P. and Kumar, P. R. 2012. The Investment Portfolio Selection Using Fuzzy Logic Genetic Algorithm, International Journal of Engineering Research and Applications, 2 (5):2100-2105.
  • Doesken, B., Abraham, A. T. J., & Paprzycki, M. 2005. Real Stock Trading Using Soft Computing Models, İnformation technolog: Coding and computing. Conference Publications, 2: 162-167.
  • Dong, M. and Zhou, X. S. 2002. Exploring The Fuzzy Nature Of Technical Patterns Of U.S Stock Market, ICONIP'02-SEAL'02-FSKD'02, Singapore, 18-22.
  • Dourra H. and Sıy, P. 2002. Investment Using Technical Analysis And Fuzzy Logic. Fuzzy Sets And Systems, 127: 221-240.
  • Dourra H. and Sıy, P. 2001. Stock Evaluation Using Fuzzy Logic. International Journal Of Theoretical And Applied Finance, 4(4): 585-602.
  • Esen, S. 2013. Bulanık Mantık Yaklaşımıyla Teknik Analiz Yönteminin Uygulanması: İMKB 30 Örneği, Yayımlanmamış Doktora Tezi, Sakarya Üniversitesi, Sosyal Bilimler Enstitüsü, Sakarya, TR.
  • Granato, D., Santos, J. S., Escher, G. B., Ferreira, B. L. and Maggio, R. M. 2018. Use Of Principal Component Analysis (PCA) And Hierarchical Cluster Analysis (HCA) For Multivariate Association Between Bioactive Compounds And Functional Properties İn Foods: A Critical Perspective. Trends İn Food Science & Technology, 72: 83-90.
  • Gülgör, G. 2010. İMKB 30 Endeksinde Klasik ve Bulanık Analitik Hiyerarşi Süreci İle Portföy Seçimi Ve Performanslarının Karşılaştırılması, Yayımlanmamış Yüksek Lisans Tezi, Osmangazi Üniversitesi, Sosyal Bilimler Enstitüsü, Eskişehir, TR.
  • Jang J. 1993. ANFIS: Adaptive-Network-Based Fuzzy İnference System. Ieee Trans Syst Man Cybern Syst, 23(3): 665-685.
  • Karapınar, A., Bayırlı, R., Bal, H., Altay, A., and Bal, E. Ç. 2007. İleri Düzey SPK Lisanslama Sınavlarına Hazırlık, Gazi Kitabevi, Ankara.
  • Khcherem, F. and Bouri, A. 2009. “Fuzzy Logic And Investment Strategy”, Global Economy & Finance Journal, 2 (2): 22-37.
  • Koç, S. and Ulucan, S. 2016. “Finansal Başarısızlıkların Tespitinde Kullanılan Altman Z Yönteminin Bulanık Mantık (ANFIS) Yöntemi İle Test Edilmesi: Teknoloji Ve Tekstil Sektöründe Bir Uygulama”, Maliye Finans Yazıları, (106):147-168.
  • Lam, S.S. 2001. “A Genetic Fuzzy Expert System For Stock Market Timing”, Proceedings of the IEEE Conference on Evolutionary Computation, (1): 410–417.
  • Li, H. 2019. Multivariate Time Series Clustering Based On Common Principal Component Analysis. Neurocomputing, 349: 239-247.
  • Mangale, C., Meena S. and Birchha, V. 2015. “Prediction Of Stock Values Based On Fuzzy Logic Using Fundamental Analysis”, International Journal on Recent and Innovation Trends in Computing and Communication, 3 (10):5806-5810.
  • Pelitli, D. 2007. Portföy Analizinde Bulanık Mantık Yaklaşımı Ve Uygulama Örneği, Yayımlanmamış Yüksek Lisans Tezi, Pamukkale Üniversitesi, Sosyal Bilimler Enstitüsü, Denizli, TR.
  • Selimoğlu, S. and Orhan, A. 2015. “Finansal Başarısızlığın Oran Analizi Ve Diskriminant Analizi Kullanılarak Ölçümlenmesi: BİST’te İşlem Gören Dokuma, Giyim Eşyası Ve Deri İşletmeleri Üzerine Bir Araştırma”. Muhasebe ve Finansman Dergisi, (66):21-40.
  • Thiagarajah, K. Appadoo S. and . Thavaneswaran, A. 2007. “Option Valuation Model With Adaptive Fuzzy Numbers”. Computers And Mathematics With Applications, (53):831-841.
  • Yıldız, B. and Akkoç, S. 2016. “Çalışma Sermayesi Ve Karlılık İlişkisinin Keşifsel Bir Araçla (ANFIS) İncelenmesi”, Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 11(1): 285-308.
  • Yörük, N., Karaca, S. S., Hekim, M. ve Tuna, İ. 2013. “Sermaye Yapısını Etkileyen Faktörler ve Finansal Oranlar İle Hisse Getirisi Arasındaki İlişkinin ANFIS Yöntemi İle İncelenmesi: İMKB 100’de Bir Uygulama”, Anadolu Üniversitesi Sosyal Bilimler Dergisi, 13(2): 101-114.
There are 25 citations in total.

Details

Primary Language English
Journal Section Makaleler
Authors

Öznur Arslan 0000-0001-5973-9107

Mesut Polatgil 0000-0002-7503-2977

Ebuzer Arslan 0000-0001-6154-6216

Early Pub Date April 28, 2022
Publication Date April 29, 2022
Acceptance Date January 21, 2022
Published in Issue Year 2022 Issue: 61

Cite

APA Arslan, Ö., Polatgil, M., & Arslan, E. (2022). EXAMINING THE FINANCIAL RATIOS AND PROFITABILITY OF COMPANIES BY USING ANFIS: A STUDY ON LEATHER AND CLOTHING INDUSTRIES TRAIDED IN BIST. Erciyes Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi(61), 369-384. https://doi.org/10.18070/erciyesiibd.1013035

Cited By

Forecasting the Profitability of the Textile Sector in Emerging European Countries Using Artificial Neural Networks
Fibres & Textiles in Eastern Europe
https://doi.org/10.2478/ftee-2024-0035
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