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MAKİNE ÖĞRENMESİ YÖNTEMLERİ İLE SERMAYE YAPISININ BELİRLENMESİ; BİST’TE UYGULAMA

Yıl 2025, Cilt: 27 Sayı: 48, 94 - 110, 30.04.2025

Şafak Sönmez Soydaş

https://doi.org/10.18493/kmusekad.1483084

Öz

Bu çalışma, firmaların fonlama kararlarını önemli ölçüde etkileyen firmaya özgü faktörleri belirlemek ve geliştirmek için makine öğrenmesi yöntemlerine dayalı bir model oluşturmak amacıyla gerçekleştirilmiştir. Çalışma, 2014-2023 yılları arasında Borsa İstanbul’da orman, kâğıt ve basım endeksinde sıralanan firmalardan oluşmakta olup makine öğrenmesi yöntemleri ile sermaye yapısı kararları analiz edilmiştir. Makine öğrenmesi yöntemleri kullanılarak yapılan analizler sonucunda oluşturulan modeller içerisinde özellikle XGBoost yöntemi ile oluşturulan model ve Random Forest modelinin performans değerlerinin AdaBoost ve SVM modeline göre daha iyi performans gösterdiği tespit edilmiştir. Özellikle XGBoost ve Random Forest yöntemi ile oluşturulan modellerin kurumsal sermaye yapılarının belirlenmesi için daha iyi bir seçenek olduğu görülmüştür. En kötü performansın ise SVM modeli olduğu anlaşılmıştır.

Anahtar Kelimeler

Sermaye Yapısı Finansal Oran Makine Öğrenme

Kaynakça

  • Ahmed, S., Alshater, M. M., El Ammari, A. ve Hammami, H. (2022). Artificial Intelligence and Machine Learning in Finance: A Bibliometric Review. Research in International Business and Finance, 61, 101646.
  • Akkaynak, B. (2022). Sermaye Yapısı Teorileri ve Türk bankacılık Sisteminin Sermaye Yapısı Belirleyicileri. Akdeniz İİBF Dergisi, 22(1), 57-68.
  • Aksoy, B. (2020). Sigorta Şirketlerinin Derecelendirilmesinde Makine Öğrenmesi Yöntemleri Tahmin Performansının Karşılaştırılması: Türkiye Örneği. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD), 12(23), 579-597.
  • Allen, D. E. (1991). The Determinants of Capital Structure of Listed Australian Companies: The Financial Manager’s Perspective. Australian Journal of Management, 16(2), 103-128.
  • Anwar, W. (2012). Cross-İndustry Determinants of Capital Structure: Evidence from Pakistani Data, International Journal of Management and Innovation, 4(1), 79-86.
  • Arsov, S. ve Naumoski, A. (2016). Determinants of Capital Structure: An Empirical Study of Companies from Selected Post-Transition Economies. Journal of Economics and Business, 34(1), 119-146.
  • Asche, F., Sikveland, M. ve Zhang D. (2018). Profitability in Norwegian Salmon Farming: The impact of Firm Size and Price variability, Aquacult. Econ. Manag., 22 (3), 306-317.
  • Ay, Ş. (2020, Nisan). Model Performansini Değerlendirmek–Metrikler. https://medium.com/deep-learning-turkiye/model-performans%C4%B1n%C4%B1-de%C4%9Ferlendirmek-metrikler-cb6568705b1.
  • Basak, S., Kar, S., Saha, S., Khaidem, L. ve Dey, S. R. (2019). Predicting the Direction of Stock Market Prices Using Tree-Based Classifiers. The North American Journal of Economics and Finance, 47, 552-567.
  • Basnet, R., Mukkamala, S. ve Sung, A. H. (2008). Detection of Phishing Attacks: A Machine Learning Approach. In Soft Computing Applications in Industry (ss. 373-383). Springer, Berlin, Heidelberg.
  • Bauer, P. (2004). Determinants of Capital Structure: Empirical Evidence from Czech Republic. Czech Journal of Economics and Finance, 54(1-2), 2–21.
  • Bokpin, G. A. (2009). Macroeconomic Development and Capital Structure Decisions of Firms: Evidence from Emerging Market Economies. Studies in Economics and Finance, 26(2), 129–142. http://dx.doi.org/10.1108/10867370910963055.
  • Bontempı, M. E. (2002). The Dynamic Specification of the Modified Pecking Order Theory: It’s Relevance to Italy. Empirical Economics, 27, 1- 22.
  • Booth, L., Aivazian, V., Demirguc-Kunt, A., ve Maksimovic, V. (2001). Capital Structures in Developing Countries. The Journal of Finance, 56(1), 87-130.
  • Breiman, L. (2001). Randem Frorest. Machine Learning, 45(1), 5- 32.
  • Burges, C. J. C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. https://doi.org/10.1023/A:1009715923555.
  • Chaboud, A. P., Chiquoine, B., Hjalmarsson, E. ve Vega, C. (2014). Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market. The Journal of Finance, 69(5), 2045-2084.
  • Chen, T. ve Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785.
  • Cortes, C. ve Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018.
  • Dang, V. A., Kim, M. ve Shin, Y. (2012). Asymmetric Capital Structure Adjustments: New Evidence From Dynamic Panel Threshold Models. Journal of Empirical Finance, 19(4), 465-482.
  • De Jong, A., Kabir, R. ve Nguyen, T., T. (2008). Capital Structure Around the World: The Roles of Firm- and Country-Specific Determinants. Journal of Banking & Finance, 32 (2008), 1954-1969.
  • Doğan, S. ve Türe, H. (2022). Makine Öğrenmesi Teknikleri ile Ülke Riski Tahmini. Fiscaoeconomia, 6(3), 1126-1151. Dondurmacı, G. A. ve Çınar, A. (2014). Finans Sektöründe Veri Madenciliği Uygulaması. Akademik Sosyal Araştırmalar Dergisi, 2(1), 258-271.
  • Fernandes, M., Medeiros, M. C. ve Scharth, M. (2014). Modeling and Predicting the CBOE Market Volatility Index. Journal of Banking & Finance, 40, 1-10.
  • Frank, M. Z. ve Goyal, V. K. (2009). Capital Structure Decisions: Which Factors are Reliably İmportant? Financial Management, 38(1), 1–37. http://dx.doi.org/10.1111/j.1755-053X.2009.01026.x.
  • Gaud, P., Jani, E., Hoesli, M. ve Bender, A. (2005). The Capital Structure of Swiss Companies: An Empirical Analysis Using Dynamic Panel Data. European Financial Management, 11(1), 51-69.
  • Gourio F. (2013). Credit Risk and Disaster Risk. Am. Econ. J. Macroecon., 5(3), 1-34.
  • Graham, J. R. ve Leary, M. T. (2011). A Review of Empirical Capital Structure Research and Directions for the Future. Annu. Rev. Financ. Econ., 3(1), 309-345.
  • Graham, J. R., Leary, M. T. ve Roberts, M., R. (2015). A Century of Capital Structure: The Leveraging of Corporate America. Journal Of Financial Economics, 118(3), 658-683.
  • Harris, M. and Raviv, A. (1991). The Theory of Capital Structure. The Journal of Finance, 46(1), 297-355. Henrique, B. M., Sobreiro, V. A. ve Kimura, H. (2018). Stock Price Prediction Using Support Vector Regression on
  • Daily and up to the Minute Prices. The Journal of Finance and Data Science, 4(3), 183-201.
  • Hutchinson, R. ve Hunter, R. (1995). Determinants of Capital Structure in the Retailing Sector in the UK. The International Review of Retail, Distribution and Consumer Research, 5(1), 63-78.
  • İskenderoğlu, Ö., Karadeniz, E. ve Atioğlu, E. (2012). Türk Bankacılık Sektöründe Büyüme, Büyüklük ve Sermaye Yapısı Kararlarının Kârlılığa Etkisinin Analizi. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 7(1), 291-311.
  • Jensen, M. C. (1986). Agency Costs of Free Cash Flow, Corporate Finance and Takeovers. The American Economic Review, 76(2), 323-329.
  • Jensen, M. C. ve Meckling, W. H., (1976). Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. J. Financ. Econ., 3(4), 305-360.
  • Katagiri, M., (2014). A Macroeconomic Approach to Corporate Capital Structure. Journal of Monetary Economics, 66, 79-94.
  • Kesgin, H. T., Shakeri, S., Bulut, N., Yüzük, S., ve Aktaş, M. S. (2019). Bankrupcy Risk Forecast Based on Company Balance Sheet Data Using Machine Learning. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (ss. 195-200). IEEE.
  • Khandani, A. E., Kim, A. J., ve Lo, A. W. (2010). Consumer Credit-Risk Models via Machine-Learning Algorithms. Journal of Banking & Finance, 34(11), 2767-2787.
  • Kouki, M. ve Said, H. B. (2012). Capital Structure Determinants: New Evidence from French panel Data. International Journal Of Business and Management, 7(1), 214–229.
  • Kraus, A. ve Litzenberger, R. H. (1973). A State-Preference Model of Optimal Financial Leverage. The Journal of Finance, 28(4), 911-922.
  • Külter, B. ve Demirgüneş, K. (2007). Perakendeci Firmalarda Kârlılığı Etkileyen Değişkenler: Hisse Senetleri İMKB’de İşlem Gören Perakendeci Firmalar Üzerinde Ampirik Bir Çalışma. Ç.Ü. Sosyal Bilimler Enstitüsü Dergisi, 16(1), 445-460.
  • Leland, H. E. ve Pyle, D. H. (1977). Informational Asymmetries, Financial Structure and Financial Intermediation. The Journal of Finance, 32(2), 371-387.
  • Lemmon, M. L., Roberts, M. R. ve Zender, J. F. (2008). Back to the Beginning: Persistence and the Cross-Section of Corporate Capital Structure. The Journal of Finance, 63(4), 1575-1608.
  • Li, L. ve Islam, S., Z., (2019). Firm and Industry Specific Determinants of Capital Structure: Evidence from the Australian Market. International Review of Economics & Finance, 59, 425-437.
  • Lööf, H. (2003). Dynamic Optimal Capital Structure and Technological Chang. Center for European Economic Research, Discussion Paper, 3 – 6.
  • Modigliani, F. ve Miller, M., H. (1958). The Cost of Capital, Corporation Finance And The Theory Of İnvestment. American Economic Review, 48(3), 261-297.
  • Modigliani, F. ve Miller, M., H., (1963). Corporate Income Taxes and the Cost of Capital: A correction. The American Economic Review, 53(3), 433-443.
  • Moghaddam, A. H., Moghaddam, M. H. ve Esfandyari, M. (2016). Stock Market Index Prediction Using Artificial Neural Network. Journal of Economics, Finance and Administrative Science, 21(41), 89-93.
  • Myers, S. ve Majluf N. (1984). Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have. Journal of Financial Economics, 13, 187-221.
  • Myers, S., C. (1984). The Capital Structure Puzzle. J. Finance, 39(3), 574-592.
  • Nguyen, T. ve Wu, J. (2011). Capital Structure Determinants and Convergence. Bankers, Markets and İnvestors. 111, 43–53.
  • Nimalathasan, B. (2010). Capital Structure and Its İmpact on Profit Ability: A Study of Listed Manufacturing Companies in SRI Lanka. Ekonomika, Journal for Economic Theory and Practice and Social Issues, 56(4), 83-92.
  • Noyan, M. (2019, Ekim). Yeni Başlayanlar için Makine Öğrenmesi Algoritmaları. https://merveenoyan.medium.com/yeni-ba%C5%9Flayanlar-i%C3%A7in-makine-%C3%B6%C4%9Frenmesi-algoritmalar%C4%B1-6b89b3a67750.
  • O’Brien, T. ve Vanderheiden, P. (1987). Empirical Measurement of Operating Leverage for Growing Firms. Financial Management, 16(2), 45-53.
  • Ogiriki, T. ve Werigbelegha, A. P. (2015). Determinants of Capital Structure and Firm’s Performance in Nigeria (1989-2014): An Empirical İnvestigation Approach. International Journal of Management and Economics Invention, 1(10), 471-479.
  • Pabuçcu, H. (2019). Borsa Endeksi Hareketlerinin Tahmini: Trend Belirleyici Veri. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 22(1), 246-256.
  • Pratheepkanth, P. (2011). Capital Structure and Financial Performance: Evidence from Selected Business Companies in Colombo Stock Exchange SRI Lanka. Researchers World, 2(2), 171- 183.
  • Qian, Y., Tian, Y. ve Wirjanto, T. N. (2007). An Empirical İnvestigation into the Capital-Structure Determinants of Publicly Listed Chinese Companies. SSRN eLibrary.
  • Ross, S. A. (1977). The Determination of Financial Structure: The Incentive Signalling Approach. The Bell Fournal of Economics, 8(1), 23- 40.
  • Sakal, M. (2020, Haziran). Makine Öğrenmesi Algoritmaları Kısa Açıklamaları. http://muratsakal.com/?p=230. Saona, P., Martín, P. S. ve Jara, M. (2018). Group Affiliation and Ownership Concentration as Determinants of Capital Structure Decisions: Contextualizing the Facts for an Emerging Economy. Emerging Markets Finance & Trade, 54, 3312-3329.
  • Sheikh, N., A. ve Wang, Z. (2011). Determinants Capital Structure, an Emprical Study of Firms in Manifacturing Industry of Pakistan. Managerial Finance, 37(2), 117-133.
  • Shubita, M. F. ve Alsawalhah, J. M. (2012). The Relationship Between Capital Structure and Profitability. International Journal of Business and Social Science. 3(16), 104-112.
  • Sufi, A. (2009). The Real Effects of Debt Certification: Evidence from the Introduction of Bank Loan Ratings. Review of Financial Studies, 22(4), 1659-1691.
  • Şahin, O. (2011). KOBİ’lerde Finansal Performansı Belirleyen Faktörler. ZKÜ Sosyal Bilimler Dergisi. 7(14), 183-200. Şeyranlıoğlu, O. ve Karavardar, A. (2022). Karar Ağaçları Algoritması ile Modıglıanı-Mıller Teorilerinin Testi: Holding Şirketleri Üzerine Bir Uygulama. Asya Studies, 6(21), 303-316.
  • Terim, B. ve Kayalı, C. (2009). Sermaye Yapısını Belirleyici Etmenler: Türkiye’de İmalat Sanayi Örneği. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 7(1),125-154.
  • Ulusoy, T. (2008). Systematic Risk and Firm Financial Structure: Evidence on Istanbul Stock Exchange. The Business Review, Cambridge, 11(2), 226-231.
  • Yang, C. J., Huang, W. K. ve Lin, K. P. (2023). Three-Dimensional Printing Quality Inspection Based on Transfer Learning with Convolutional Neural Networks. Sensors, 23(1), 491.

MACHINE LEARNING METHODS IN CAPITAL STRUCTURE DECISIONS; APPLICATION IN BIST

Yıl 2025, Cilt: 27 Sayı: 48, 94 - 110, 30.04.2025

Şafak Sönmez Soydaş

https://doi.org/10.18493/kmusekad.1483084

Öz

This study was conducted to create a model based on machine learning methods to determine and develop firm-specific factors that significantly affect the funding decisions of firms. The study consists of firms listed in the forest, paper and printing index of Borsa Istanbul between 2014-2023, and their capital structure decisions were analyzed with machine learning methods. As a result of the analyzes made using machine learning methods, it was determined that the performance values of the model created with the XGBoost method and the Random Forest model, in particular, performed better than the AdaBoost and SVM models. It was seen that the models created with the XGBoost and Random Forest methods were a better option for determining corporate capital structures. It was understood that the SVM model had the worst performance value.

Anahtar Kelimeler

Capital Structure Financial Ratio Machine Learning

Kaynakça

  • Ahmed, S., Alshater, M. M., El Ammari, A. ve Hammami, H. (2022). Artificial Intelligence and Machine Learning in Finance: A Bibliometric Review. Research in International Business and Finance, 61, 101646.
  • Akkaynak, B. (2022). Sermaye Yapısı Teorileri ve Türk bankacılık Sisteminin Sermaye Yapısı Belirleyicileri. Akdeniz İİBF Dergisi, 22(1), 57-68.
  • Aksoy, B. (2020). Sigorta Şirketlerinin Derecelendirilmesinde Makine Öğrenmesi Yöntemleri Tahmin Performansının Karşılaştırılması: Türkiye Örneği. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD), 12(23), 579-597.
  • Allen, D. E. (1991). The Determinants of Capital Structure of Listed Australian Companies: The Financial Manager’s Perspective. Australian Journal of Management, 16(2), 103-128.
  • Anwar, W. (2012). Cross-İndustry Determinants of Capital Structure: Evidence from Pakistani Data, International Journal of Management and Innovation, 4(1), 79-86.
  • Arsov, S. ve Naumoski, A. (2016). Determinants of Capital Structure: An Empirical Study of Companies from Selected Post-Transition Economies. Journal of Economics and Business, 34(1), 119-146.
  • Asche, F., Sikveland, M. ve Zhang D. (2018). Profitability in Norwegian Salmon Farming: The impact of Firm Size and Price variability, Aquacult. Econ. Manag., 22 (3), 306-317.
  • Ay, Ş. (2020, Nisan). Model Performansini Değerlendirmek–Metrikler. https://medium.com/deep-learning-turkiye/model-performans%C4%B1n%C4%B1-de%C4%9Ferlendirmek-metrikler-cb6568705b1.
  • Basak, S., Kar, S., Saha, S., Khaidem, L. ve Dey, S. R. (2019). Predicting the Direction of Stock Market Prices Using Tree-Based Classifiers. The North American Journal of Economics and Finance, 47, 552-567.
  • Basnet, R., Mukkamala, S. ve Sung, A. H. (2008). Detection of Phishing Attacks: A Machine Learning Approach. In Soft Computing Applications in Industry (ss. 373-383). Springer, Berlin, Heidelberg.
  • Bauer, P. (2004). Determinants of Capital Structure: Empirical Evidence from Czech Republic. Czech Journal of Economics and Finance, 54(1-2), 2–21.
  • Bokpin, G. A. (2009). Macroeconomic Development and Capital Structure Decisions of Firms: Evidence from Emerging Market Economies. Studies in Economics and Finance, 26(2), 129–142. http://dx.doi.org/10.1108/10867370910963055.
  • Bontempı, M. E. (2002). The Dynamic Specification of the Modified Pecking Order Theory: It’s Relevance to Italy. Empirical Economics, 27, 1- 22.
  • Booth, L., Aivazian, V., Demirguc-Kunt, A., ve Maksimovic, V. (2001). Capital Structures in Developing Countries. The Journal of Finance, 56(1), 87-130.
  • Breiman, L. (2001). Randem Frorest. Machine Learning, 45(1), 5- 32.
  • Burges, C. J. C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. https://doi.org/10.1023/A:1009715923555.
  • Chaboud, A. P., Chiquoine, B., Hjalmarsson, E. ve Vega, C. (2014). Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market. The Journal of Finance, 69(5), 2045-2084.
  • Chen, T. ve Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785.
  • Cortes, C. ve Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018.
  • Dang, V. A., Kim, M. ve Shin, Y. (2012). Asymmetric Capital Structure Adjustments: New Evidence From Dynamic Panel Threshold Models. Journal of Empirical Finance, 19(4), 465-482.
  • De Jong, A., Kabir, R. ve Nguyen, T., T. (2008). Capital Structure Around the World: The Roles of Firm- and Country-Specific Determinants. Journal of Banking & Finance, 32 (2008), 1954-1969.
  • Doğan, S. ve Türe, H. (2022). Makine Öğrenmesi Teknikleri ile Ülke Riski Tahmini. Fiscaoeconomia, 6(3), 1126-1151. Dondurmacı, G. A. ve Çınar, A. (2014). Finans Sektöründe Veri Madenciliği Uygulaması. Akademik Sosyal Araştırmalar Dergisi, 2(1), 258-271.
  • Fernandes, M., Medeiros, M. C. ve Scharth, M. (2014). Modeling and Predicting the CBOE Market Volatility Index. Journal of Banking & Finance, 40, 1-10.
  • Frank, M. Z. ve Goyal, V. K. (2009). Capital Structure Decisions: Which Factors are Reliably İmportant? Financial Management, 38(1), 1–37. http://dx.doi.org/10.1111/j.1755-053X.2009.01026.x.
  • Gaud, P., Jani, E., Hoesli, M. ve Bender, A. (2005). The Capital Structure of Swiss Companies: An Empirical Analysis Using Dynamic Panel Data. European Financial Management, 11(1), 51-69.
  • Gourio F. (2013). Credit Risk and Disaster Risk. Am. Econ. J. Macroecon., 5(3), 1-34.
  • Graham, J. R. ve Leary, M. T. (2011). A Review of Empirical Capital Structure Research and Directions for the Future. Annu. Rev. Financ. Econ., 3(1), 309-345.
  • Graham, J. R., Leary, M. T. ve Roberts, M., R. (2015). A Century of Capital Structure: The Leveraging of Corporate America. Journal Of Financial Economics, 118(3), 658-683.
  • Harris, M. and Raviv, A. (1991). The Theory of Capital Structure. The Journal of Finance, 46(1), 297-355. Henrique, B. M., Sobreiro, V. A. ve Kimura, H. (2018). Stock Price Prediction Using Support Vector Regression on
  • Daily and up to the Minute Prices. The Journal of Finance and Data Science, 4(3), 183-201.
  • Hutchinson, R. ve Hunter, R. (1995). Determinants of Capital Structure in the Retailing Sector in the UK. The International Review of Retail, Distribution and Consumer Research, 5(1), 63-78.
  • İskenderoğlu, Ö., Karadeniz, E. ve Atioğlu, E. (2012). Türk Bankacılık Sektöründe Büyüme, Büyüklük ve Sermaye Yapısı Kararlarının Kârlılığa Etkisinin Analizi. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 7(1), 291-311.
  • Jensen, M. C. (1986). Agency Costs of Free Cash Flow, Corporate Finance and Takeovers. The American Economic Review, 76(2), 323-329.
  • Jensen, M. C. ve Meckling, W. H., (1976). Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. J. Financ. Econ., 3(4), 305-360.
  • Katagiri, M., (2014). A Macroeconomic Approach to Corporate Capital Structure. Journal of Monetary Economics, 66, 79-94.
  • Kesgin, H. T., Shakeri, S., Bulut, N., Yüzük, S., ve Aktaş, M. S. (2019). Bankrupcy Risk Forecast Based on Company Balance Sheet Data Using Machine Learning. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (ss. 195-200). IEEE.
  • Khandani, A. E., Kim, A. J., ve Lo, A. W. (2010). Consumer Credit-Risk Models via Machine-Learning Algorithms. Journal of Banking & Finance, 34(11), 2767-2787.
  • Kouki, M. ve Said, H. B. (2012). Capital Structure Determinants: New Evidence from French panel Data. International Journal Of Business and Management, 7(1), 214–229.
  • Kraus, A. ve Litzenberger, R. H. (1973). A State-Preference Model of Optimal Financial Leverage. The Journal of Finance, 28(4), 911-922.
  • Külter, B. ve Demirgüneş, K. (2007). Perakendeci Firmalarda Kârlılığı Etkileyen Değişkenler: Hisse Senetleri İMKB’de İşlem Gören Perakendeci Firmalar Üzerinde Ampirik Bir Çalışma. Ç.Ü. Sosyal Bilimler Enstitüsü Dergisi, 16(1), 445-460.
  • Leland, H. E. ve Pyle, D. H. (1977). Informational Asymmetries, Financial Structure and Financial Intermediation. The Journal of Finance, 32(2), 371-387.
  • Lemmon, M. L., Roberts, M. R. ve Zender, J. F. (2008). Back to the Beginning: Persistence and the Cross-Section of Corporate Capital Structure. The Journal of Finance, 63(4), 1575-1608.
  • Li, L. ve Islam, S., Z., (2019). Firm and Industry Specific Determinants of Capital Structure: Evidence from the Australian Market. International Review of Economics & Finance, 59, 425-437.
  • Lööf, H. (2003). Dynamic Optimal Capital Structure and Technological Chang. Center for European Economic Research, Discussion Paper, 3 – 6.
  • Modigliani, F. ve Miller, M., H. (1958). The Cost of Capital, Corporation Finance And The Theory Of İnvestment. American Economic Review, 48(3), 261-297.
  • Modigliani, F. ve Miller, M., H., (1963). Corporate Income Taxes and the Cost of Capital: A correction. The American Economic Review, 53(3), 433-443.
  • Moghaddam, A. H., Moghaddam, M. H. ve Esfandyari, M. (2016). Stock Market Index Prediction Using Artificial Neural Network. Journal of Economics, Finance and Administrative Science, 21(41), 89-93.
  • Myers, S. ve Majluf N. (1984). Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have. Journal of Financial Economics, 13, 187-221.
  • Myers, S., C. (1984). The Capital Structure Puzzle. J. Finance, 39(3), 574-592.
  • Nguyen, T. ve Wu, J. (2011). Capital Structure Determinants and Convergence. Bankers, Markets and İnvestors. 111, 43–53.
  • Nimalathasan, B. (2010). Capital Structure and Its İmpact on Profit Ability: A Study of Listed Manufacturing Companies in SRI Lanka. Ekonomika, Journal for Economic Theory and Practice and Social Issues, 56(4), 83-92.
  • Noyan, M. (2019, Ekim). Yeni Başlayanlar için Makine Öğrenmesi Algoritmaları. https://merveenoyan.medium.com/yeni-ba%C5%9Flayanlar-i%C3%A7in-makine-%C3%B6%C4%9Frenmesi-algoritmalar%C4%B1-6b89b3a67750.
  • O’Brien, T. ve Vanderheiden, P. (1987). Empirical Measurement of Operating Leverage for Growing Firms. Financial Management, 16(2), 45-53.
  • Ogiriki, T. ve Werigbelegha, A. P. (2015). Determinants of Capital Structure and Firm’s Performance in Nigeria (1989-2014): An Empirical İnvestigation Approach. International Journal of Management and Economics Invention, 1(10), 471-479.
  • Pabuçcu, H. (2019). Borsa Endeksi Hareketlerinin Tahmini: Trend Belirleyici Veri. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 22(1), 246-256.
  • Pratheepkanth, P. (2011). Capital Structure and Financial Performance: Evidence from Selected Business Companies in Colombo Stock Exchange SRI Lanka. Researchers World, 2(2), 171- 183.
  • Qian, Y., Tian, Y. ve Wirjanto, T. N. (2007). An Empirical İnvestigation into the Capital-Structure Determinants of Publicly Listed Chinese Companies. SSRN eLibrary.
  • Ross, S. A. (1977). The Determination of Financial Structure: The Incentive Signalling Approach. The Bell Fournal of Economics, 8(1), 23- 40.
  • Sakal, M. (2020, Haziran). Makine Öğrenmesi Algoritmaları Kısa Açıklamaları. http://muratsakal.com/?p=230. Saona, P., Martín, P. S. ve Jara, M. (2018). Group Affiliation and Ownership Concentration as Determinants of Capital Structure Decisions: Contextualizing the Facts for an Emerging Economy. Emerging Markets Finance & Trade, 54, 3312-3329.
  • Sheikh, N., A. ve Wang, Z. (2011). Determinants Capital Structure, an Emprical Study of Firms in Manifacturing Industry of Pakistan. Managerial Finance, 37(2), 117-133.
  • Shubita, M. F. ve Alsawalhah, J. M. (2012). The Relationship Between Capital Structure and Profitability. International Journal of Business and Social Science. 3(16), 104-112.
  • Sufi, A. (2009). The Real Effects of Debt Certification: Evidence from the Introduction of Bank Loan Ratings. Review of Financial Studies, 22(4), 1659-1691.
  • Şahin, O. (2011). KOBİ’lerde Finansal Performansı Belirleyen Faktörler. ZKÜ Sosyal Bilimler Dergisi. 7(14), 183-200. Şeyranlıoğlu, O. ve Karavardar, A. (2022). Karar Ağaçları Algoritması ile Modıglıanı-Mıller Teorilerinin Testi: Holding Şirketleri Üzerine Bir Uygulama. Asya Studies, 6(21), 303-316.
  • Terim, B. ve Kayalı, C. (2009). Sermaye Yapısını Belirleyici Etmenler: Türkiye’de İmalat Sanayi Örneği. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 7(1),125-154.
  • Ulusoy, T. (2008). Systematic Risk and Firm Financial Structure: Evidence on Istanbul Stock Exchange. The Business Review, Cambridge, 11(2), 226-231.
  • Yang, C. J., Huang, W. K. ve Lin, K. P. (2023). Three-Dimensional Printing Quality Inspection Based on Transfer Learning with Convolutional Neural Networks. Sensors, 23(1), 491.
Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonometrik ve İstatistiksel Yöntemler, Finansal Ekonomi
Bölüm Araştırma Makalesi
Yazarlar

Şafak Sönmez Soydaş 0000-0002-7174-8652

Erken Görünüm Tarihi 29 Nisan 2025
Yayımlanma Tarihi 30 Nisan 2025
Gönderilme Tarihi 13 Mayıs 2024
Kabul Tarihi 12 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 27 Sayı: 48

Kaynak Göster

APA Soydaş, Ş. S. (2025). MAKİNE ÖĞRENMESİ YÖNTEMLERİ İLE SERMAYE YAPISININ BELİRLENMESİ; BİST’TE UYGULAMA. Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi, 27(48), 94-110. https://doi.org/10.18493/kmusekad.1483084

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