Araştırma Makalesi
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Factors Affecting Portfolios Created With Islamic And Traditional Perspective In Borsa İstanbul: Artifıcial Intelligence Supported Hybrid Model Proposal

Yıl 2025, Cilt: 18 Sayı: 2, 351 - 380, 11.08.2025
https://doi.org/10.29067/muvu.1639926

Öz

This research employs a hybrid artificial intelligence model to attempt to identify the elements influencing the portfolios constructed from the conventional and Islamic viewpoints. By applying the Fama French Five-Factor regression model combined with variables based on accounting and market, it is possible to identify potential differences in the factors influencing the portfolios developed from both a conventional and Islamic perspective. Furthermore, it is found that the effective gene parameters in the portfolios built from various viewpoints differed dependent on the evaluation performed using the hybrid model based on Artificial Neural Networks and developed through the use of Genetic Algorithm optimization. Additionally, it is found that, when combined with the other two models, the hybrid model, which is based on artificial neural networks and produced by genetic algorithm optimization, produces results that are more accurate. As a consequence, it becomes apparent to observe behavioral differences between the portfolios made using the traditional and Islamic perspectives.

Kaynakça

  • Abdelwahed, I. B., & Trabelsi, F. (2021). Fuzzy Expectation-Spread-Skewness Model For Shariah-Compliant Portfolio Optimisation. International Journal of Operational Research, 41(4), 447-476.
  • Aras, G., Çam, İ., Zavalsız, B., & Keskin, S. (2018). Fama-French Çok Faktör Varlık Fiyatlama Modellerinin Performanslarının Karşılaştırılması: Borsa İstanbul Üzerine Bir Uygulama. İstanbul Business Research, 47(2), 183-207. doi:https://doi.org/10.26650/ibr.2018.47.2.0026
  • Banz, R. (1981). The relationship between return and market value of common stocks. Journal of financial economics, 9(1), 3-18.
  • Bartels, R., & Goodhew, J. (1981). The robustness of the Durbin-Watson test. The Review of Economics and Statistics, 136-139.
  • Basu, S. (1983). The relationship between earnings' yield, market value and return for NYSE common stocks: Further evidence. Journal of financial economics, 12(1), 129-156.
  • Bottomley, C., Ooko, M., Gasparrini, A., & Keogh, R. (2023). In praise of Prais‐Winsten: An evaluation of methods used to account for autocorrelation in interrupted time series. Statistics in medicine, 42, 1277–1288. doi:10.1002/sim.9669
  • Bülbül, M. (2022). Akıllı Sulama Sistemi Modellemesi ve Tasarımı. Yayımlanmış Doktora Tezi. Erciyes Üniversitesi.
  • Bülbül, M., & Öztürk, C. (2022). Optimization, Modeling and Implementation of Plant Water Consumption Control Using Genetic Algorithm and Artificial Neural Network in a Hybrid Structure. Arabian Journal for Science and Engineering, 47(2), 2329–2343.
  • Bülbül, M., Harirchian, E., Işık, M., Hosseini, S., & Işık, E. (2022). A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings. Applied Sciences, 12(10), 5138. doi:https://doi.org/10.3390/app12105138
  • Cao, Q., Leggio, K., & Schniederjans, M. (2005). A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32(10), 2499-2512.
  • Chen, C.-Y., & Ye, f. (2004). Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis. In I. 2. Distribution (Ed.)., (pp. 789-794). IEEE.
  • Cox, S., & Britten, J. (2019). The Fama-French five-factor model: evidence from the Johannesburg Stock Exchange. Investment Analysts Journal, 48(3), 240-261.
  • Çömlekçi, İ., & Sondemir, S. (2021). İslami Üç Faktör Varlık Fiyatlama Modeli; Katılım Endeksi Üzerine Bir Uygulama. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 8(1), 203-211. doi:http://dx.doi.org/10.18506/anemon.521179
  • Doğan, M., Kevser, M., & Leyli Demirel, B. (2022). Testing the Augmented Fama–French Six-Factor Asset Pricing Model with Momentum Factor for Borsa İstanbul. Hindawi Discrete Dynamics in Nature and Sociecy. doi:https://doi.org/10.1155/2022/3392984
  • Durmuşkaya, S., & Garayev, K. (2017). Genetik Algortima ile Portföy Seçiminde Kriz Dönemi Etkisi, BİST-30’da Bir Uygulama. İşletme Bilimi Dergisi, 5(3), 173-187.
  • Elahi, Y., & Abd Aziz, M. (2011). New Model For Shariah-Compliant Portfolio Optimization Under Fuzzy Environment. In International Conference on Informatics Engineering and Information Science, 210-217.
  • Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
  • Fama, E. F. (1991). Efficient capital markets: II. The journal of finance, 46(5), 1575-1617.
  • Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics(116), 1-22.
  • Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
  • Fama, E., & French, K. (1996). The CAPM is wanted, dead or alive. The Journal of Finance, 51(5), 1947-1958.
  • Fernandez, A., & Gomez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, 34, 1177–1191.
  • Gujarati, D. N., & Porter, D. C. (2018). Temel Ekonometri. (Ü. Şenesen, & G. Şenesen Günlük, Trans.) İstanbul: Literatür.
  • Hanif, M. (2011). Risk and Return under Shari’a Framework An Attempt to Develop Shari’a Compliant Asset Pricing Model-SCAPM. Pakistan Journal of Commerce and Social Sciences (PJCSS), 5(2), 283-292.
  • Hao, T., Song, G., & Du, H. (2023). PSO-TA-LSTM: a long and short term memory model combining time attention and adaptive particle swarm optimization for stock forecasting. International Journal of General Systems, 52(7), 876–893. doi:https://doi.org/10.1080/03081079.2023.2222888
  • Jagannathan, R., & McGrattan, E. (1995). The CAPM debate. Federal Reserve Bank of Minneapolis Quarterly Review, 19(4), 2-17.
  • Jaiswal, V., Sharma, V., & Varma, S. (2019). An implementation of novel genetic based clustering algorithm for color image segmentation. TELKOMNIKA, 17(3), 1461-1467. doi:10.12928/TELKOMNIKA.v17i3.10072
  • Kaczmarek, K., Dymova, L., & Sevastjanov, P. (2020). A Simple View on the Interval and Fuzzy Portfolio Selection Problems. Entropy, 22(932).
  • Kakıllı Acaravcı, S., & Karaömer, Y. (2018). The Comparative Performance Evaluation of the Fama-French Five Factor Model in Turkey. İşletme ve İktisat Çalışmaları Dergisi, 6(3), 1-12.
  • Kaya, E. (2021). Relative performances of asset pricing models for BIST 100 index. Spanish Journal of Finance and Accounting/Revista Española de Financiación y Contabilidad, 50(3), 280-301.
  • Kendall, G., & Su, Y. (2005). article swarm optimisation approach in the construction of optimal risky portfolios. In I. P. Applications (Ed.).
  • Kutlu, M., & Kalaycı, Ş. (2020). Alternatif Varlık Fiyatlandırma Modelleri ve Borsa İstanbul'da Uygulama. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 18(Özel Sayı), 193-2016. doi:10.18026/cbayarsos.551301
  • Lin, C.-M., & Gen, M. (2007). An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Applied mathematical sciences, 1(5), 201-210.
  • Liu, S.-T. (2011). A fuzzy modeling for fuzzy portfolio optimization. Expert Systems with Applications, 38(11), 13803-13809.
  • Liu, Y.-J., & Zhang, G. (2013). Fuzzy Portfolio Optimization Model Under Real Constraints. Insurance: Mathematics and Economics. doi:http://dx.doi.org/10.1016/j.insmatheco.2013.09.005
  • Ma, X. (2023). Enterprise financial early warning based on improved particle swarm optimization algorithm and data mining. Soft Computing, 1-9.
  • Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
  • Öztemel, E. (2012). Yapay Sinir Ağları (3. ed.). İstanbul: Papatya Yayıncılık.
  • Öztürk, E. (2006). Çoklu doğrusal regresyon modeli. In Ş. Kalaycı, SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri (pp. 259-269). Ankara: Asil Yayınevi.
  • Park, R., & Mitchell, B. (1980). Estimating the autocorrelated error model with trended data. Journal of Econometrics, 13(2), 185-201. doi:https://doi.org/10.1016/0304-4076(80)90014-7
  • Pelitli, D. (2007). Portföy Analizinde Bulanık Mantık Yaklaşımı ve Uygulama Örneği. Yayınlanmış Yükseklisans Tezi. Denizli.
  • Perret-Gentil, C., & Victoria-Feser, M.-P. (2005). Robust mean-variance portfolio selection. Perret-Gentil, Cédric and Victoria-Feser, Maria-Pia, Robust Mean-Variance Portfolio Selection (April 2005). Available at SSRN: https://ssrn.com/abstract=721509. doi:http://dx.doi.org/10.2139/ssrn.721509
  • Prasad, N., & Rao, J. (1990). The estimation of the mean squared error of small-area estimators. Journal of the American statistical association, 85(409), 163-171.
  • Sayılgan, G. (2019). Soru ve Yanıtlarıyla İşletme Finansmanı (8. ed.). Ankara: Siyasal Kitabevi.
  • Sembiring, F. (2018). Three-Factor and Five-Factor Models: Implementation of Fama and French Model on Market Overreaction Conditions. J. Fin. Bank. Review, 3(4), 77-83.
  • Sharpe, W. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance , 19(3), 425-442.
  • Şen, Z. (2004). Yapay Sinir Ağları İlkeleri. İstanbul: Su Vakfı Yayınları.
  • Vaz de Melo Mendes, B., & Pereira Câmara Leal, R. (2005). Robust multivariate modeling in finance. International Journal of Managerial Finance, 1(2), 95-106.
  • Vercher, E., Bermúdez, J., & Segura, J. V. (2007). Fuzzy Portfolio Optimization Under Downside Risk Measures. Fuzzy Sets and Systems(158), 769-782.
  • Yan, W. (2023). Dynamic Financial Asset Allocation Strategy Based on Particle Swarm Optimization Algorithm. Proceedings of the 3rd International Conference on Big Data Economy and Information Management, BDEIM. Zhengzhou.
  • Yang, X. (2006). Improving portfolio efficiency: A genetic algorithm approach. Computational Economics, 28(1), 1-14.
  • Yerdelen Tatoğlu, F. (2018a). İleri Panel Analizi Stata Uygulamalı (3. ed.). İstanbul: Beta.
  • Yardelen Tatoğlu, F. (2018b). Panel Veri Ekonometrisi (4. b.). İstanbul: Beta.
  • Yılmaz, A. (2022). Yapay Zeka (10. ed.). İstanbul: Kodlab.
  • Zeren, F., Yılmaz, T., & Belke, M. (10-13 Ekim 2018). Fama French Beş Faktörlü Modelin Geçerliliğinin Test Edilmesi: Türkiye Örneği. Uluslararası Katılımlı 22. Finans Sempozyumu. Mersin.

Borsa İstanbul'da İslami ve Geleneksel Perspektifle Oluşturulan Portföyleri Etkileyen Faktörler: Yapay Zeka Destekli Hibrit Model Önerisi

Yıl 2025, Cilt: 18 Sayı: 2, 351 - 380, 11.08.2025
https://doi.org/10.29067/muvu.1639926

Öz

Bu araştırma geleneksel ve İslami bakış açılarından oluşturulan portföyleri etkileyen unsurları belirlemeye çalışmak için hibrit bir yapay zekâ modeli önermektedir. Muhasebe ve piyasa bazlı değişkenlerle birleştirilmiş Fama French Beş Faktörlü regresyon modeli uygulanarak hem geleneksel hem de İslami bakış açısıyla geliştirilen portföyleri etkileyen faktörlerdeki potansiyel farklılıkların belirlenmesi mümkün olmaktadır. Ayrıca, Yapay Sinir Ağları’na dayalı ve Genetik Algoritma optimizasyonu kullanılarak geliştirilen hibrit model ile yapılan değerlendirmeye bağlı olarak, farklı bakış açılarından oluşturulan portföylerdeki etkili gen parametrelerinin değişiklik gösterdiği, buna ek olarak yapay sinir ağlarına dayalı ve genetik algoritma optimizasyonu ile üretilen hibrit modelin, diğer iki modelle birleştirildiğinde daha doğru sonuçlar verdiği tespit edilmiştir. Sonuç olarak, geleneksel ve İslami bakış açılarıyla oluşturulan portföyler arasındaki davranışsal farklılıkları gözlemlemek mümkün hale gelmektedir.

Kaynakça

  • Abdelwahed, I. B., & Trabelsi, F. (2021). Fuzzy Expectation-Spread-Skewness Model For Shariah-Compliant Portfolio Optimisation. International Journal of Operational Research, 41(4), 447-476.
  • Aras, G., Çam, İ., Zavalsız, B., & Keskin, S. (2018). Fama-French Çok Faktör Varlık Fiyatlama Modellerinin Performanslarının Karşılaştırılması: Borsa İstanbul Üzerine Bir Uygulama. İstanbul Business Research, 47(2), 183-207. doi:https://doi.org/10.26650/ibr.2018.47.2.0026
  • Banz, R. (1981). The relationship between return and market value of common stocks. Journal of financial economics, 9(1), 3-18.
  • Bartels, R., & Goodhew, J. (1981). The robustness of the Durbin-Watson test. The Review of Economics and Statistics, 136-139.
  • Basu, S. (1983). The relationship between earnings' yield, market value and return for NYSE common stocks: Further evidence. Journal of financial economics, 12(1), 129-156.
  • Bottomley, C., Ooko, M., Gasparrini, A., & Keogh, R. (2023). In praise of Prais‐Winsten: An evaluation of methods used to account for autocorrelation in interrupted time series. Statistics in medicine, 42, 1277–1288. doi:10.1002/sim.9669
  • Bülbül, M. (2022). Akıllı Sulama Sistemi Modellemesi ve Tasarımı. Yayımlanmış Doktora Tezi. Erciyes Üniversitesi.
  • Bülbül, M., & Öztürk, C. (2022). Optimization, Modeling and Implementation of Plant Water Consumption Control Using Genetic Algorithm and Artificial Neural Network in a Hybrid Structure. Arabian Journal for Science and Engineering, 47(2), 2329–2343.
  • Bülbül, M., Harirchian, E., Işık, M., Hosseini, S., & Işık, E. (2022). A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings. Applied Sciences, 12(10), 5138. doi:https://doi.org/10.3390/app12105138
  • Cao, Q., Leggio, K., & Schniederjans, M. (2005). A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32(10), 2499-2512.
  • Chen, C.-Y., & Ye, f. (2004). Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis. In I. 2. Distribution (Ed.)., (pp. 789-794). IEEE.
  • Cox, S., & Britten, J. (2019). The Fama-French five-factor model: evidence from the Johannesburg Stock Exchange. Investment Analysts Journal, 48(3), 240-261.
  • Çömlekçi, İ., & Sondemir, S. (2021). İslami Üç Faktör Varlık Fiyatlama Modeli; Katılım Endeksi Üzerine Bir Uygulama. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 8(1), 203-211. doi:http://dx.doi.org/10.18506/anemon.521179
  • Doğan, M., Kevser, M., & Leyli Demirel, B. (2022). Testing the Augmented Fama–French Six-Factor Asset Pricing Model with Momentum Factor for Borsa İstanbul. Hindawi Discrete Dynamics in Nature and Sociecy. doi:https://doi.org/10.1155/2022/3392984
  • Durmuşkaya, S., & Garayev, K. (2017). Genetik Algortima ile Portföy Seçiminde Kriz Dönemi Etkisi, BİST-30’da Bir Uygulama. İşletme Bilimi Dergisi, 5(3), 173-187.
  • Elahi, Y., & Abd Aziz, M. (2011). New Model For Shariah-Compliant Portfolio Optimization Under Fuzzy Environment. In International Conference on Informatics Engineering and Information Science, 210-217.
  • Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
  • Fama, E. F. (1991). Efficient capital markets: II. The journal of finance, 46(5), 1575-1617.
  • Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics(116), 1-22.
  • Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
  • Fama, E., & French, K. (1996). The CAPM is wanted, dead or alive. The Journal of Finance, 51(5), 1947-1958.
  • Fernandez, A., & Gomez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, 34, 1177–1191.
  • Gujarati, D. N., & Porter, D. C. (2018). Temel Ekonometri. (Ü. Şenesen, & G. Şenesen Günlük, Trans.) İstanbul: Literatür.
  • Hanif, M. (2011). Risk and Return under Shari’a Framework An Attempt to Develop Shari’a Compliant Asset Pricing Model-SCAPM. Pakistan Journal of Commerce and Social Sciences (PJCSS), 5(2), 283-292.
  • Hao, T., Song, G., & Du, H. (2023). PSO-TA-LSTM: a long and short term memory model combining time attention and adaptive particle swarm optimization for stock forecasting. International Journal of General Systems, 52(7), 876–893. doi:https://doi.org/10.1080/03081079.2023.2222888
  • Jagannathan, R., & McGrattan, E. (1995). The CAPM debate. Federal Reserve Bank of Minneapolis Quarterly Review, 19(4), 2-17.
  • Jaiswal, V., Sharma, V., & Varma, S. (2019). An implementation of novel genetic based clustering algorithm for color image segmentation. TELKOMNIKA, 17(3), 1461-1467. doi:10.12928/TELKOMNIKA.v17i3.10072
  • Kaczmarek, K., Dymova, L., & Sevastjanov, P. (2020). A Simple View on the Interval and Fuzzy Portfolio Selection Problems. Entropy, 22(932).
  • Kakıllı Acaravcı, S., & Karaömer, Y. (2018). The Comparative Performance Evaluation of the Fama-French Five Factor Model in Turkey. İşletme ve İktisat Çalışmaları Dergisi, 6(3), 1-12.
  • Kaya, E. (2021). Relative performances of asset pricing models for BIST 100 index. Spanish Journal of Finance and Accounting/Revista Española de Financiación y Contabilidad, 50(3), 280-301.
  • Kendall, G., & Su, Y. (2005). article swarm optimisation approach in the construction of optimal risky portfolios. In I. P. Applications (Ed.).
  • Kutlu, M., & Kalaycı, Ş. (2020). Alternatif Varlık Fiyatlandırma Modelleri ve Borsa İstanbul'da Uygulama. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 18(Özel Sayı), 193-2016. doi:10.18026/cbayarsos.551301
  • Lin, C.-M., & Gen, M. (2007). An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Applied mathematical sciences, 1(5), 201-210.
  • Liu, S.-T. (2011). A fuzzy modeling for fuzzy portfolio optimization. Expert Systems with Applications, 38(11), 13803-13809.
  • Liu, Y.-J., & Zhang, G. (2013). Fuzzy Portfolio Optimization Model Under Real Constraints. Insurance: Mathematics and Economics. doi:http://dx.doi.org/10.1016/j.insmatheco.2013.09.005
  • Ma, X. (2023). Enterprise financial early warning based on improved particle swarm optimization algorithm and data mining. Soft Computing, 1-9.
  • Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
  • Öztemel, E. (2012). Yapay Sinir Ağları (3. ed.). İstanbul: Papatya Yayıncılık.
  • Öztürk, E. (2006). Çoklu doğrusal regresyon modeli. In Ş. Kalaycı, SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri (pp. 259-269). Ankara: Asil Yayınevi.
  • Park, R., & Mitchell, B. (1980). Estimating the autocorrelated error model with trended data. Journal of Econometrics, 13(2), 185-201. doi:https://doi.org/10.1016/0304-4076(80)90014-7
  • Pelitli, D. (2007). Portföy Analizinde Bulanık Mantık Yaklaşımı ve Uygulama Örneği. Yayınlanmış Yükseklisans Tezi. Denizli.
  • Perret-Gentil, C., & Victoria-Feser, M.-P. (2005). Robust mean-variance portfolio selection. Perret-Gentil, Cédric and Victoria-Feser, Maria-Pia, Robust Mean-Variance Portfolio Selection (April 2005). Available at SSRN: https://ssrn.com/abstract=721509. doi:http://dx.doi.org/10.2139/ssrn.721509
  • Prasad, N., & Rao, J. (1990). The estimation of the mean squared error of small-area estimators. Journal of the American statistical association, 85(409), 163-171.
  • Sayılgan, G. (2019). Soru ve Yanıtlarıyla İşletme Finansmanı (8. ed.). Ankara: Siyasal Kitabevi.
  • Sembiring, F. (2018). Three-Factor and Five-Factor Models: Implementation of Fama and French Model on Market Overreaction Conditions. J. Fin. Bank. Review, 3(4), 77-83.
  • Sharpe, W. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance , 19(3), 425-442.
  • Şen, Z. (2004). Yapay Sinir Ağları İlkeleri. İstanbul: Su Vakfı Yayınları.
  • Vaz de Melo Mendes, B., & Pereira Câmara Leal, R. (2005). Robust multivariate modeling in finance. International Journal of Managerial Finance, 1(2), 95-106.
  • Vercher, E., Bermúdez, J., & Segura, J. V. (2007). Fuzzy Portfolio Optimization Under Downside Risk Measures. Fuzzy Sets and Systems(158), 769-782.
  • Yan, W. (2023). Dynamic Financial Asset Allocation Strategy Based on Particle Swarm Optimization Algorithm. Proceedings of the 3rd International Conference on Big Data Economy and Information Management, BDEIM. Zhengzhou.
  • Yang, X. (2006). Improving portfolio efficiency: A genetic algorithm approach. Computational Economics, 28(1), 1-14.
  • Yerdelen Tatoğlu, F. (2018a). İleri Panel Analizi Stata Uygulamalı (3. ed.). İstanbul: Beta.
  • Yardelen Tatoğlu, F. (2018b). Panel Veri Ekonometrisi (4. b.). İstanbul: Beta.
  • Yılmaz, A. (2022). Yapay Zeka (10. ed.). İstanbul: Kodlab.
  • Zeren, F., Yılmaz, T., & Belke, M. (10-13 Ekim 2018). Fama French Beş Faktörlü Modelin Geçerliliğinin Test Edilmesi: Türkiye Örneği. Uluslararası Katılımlı 22. Finans Sempozyumu. Mersin.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Diler Türkoğlu 0000-0001-5247-1590

Fatih Konak 0000-0002-6917-5082

Erken Görünüm Tarihi 21 Temmuz 2025
Yayımlanma Tarihi 11 Ağustos 2025
Gönderilme Tarihi 14 Şubat 2025
Kabul Tarihi 17 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 18 Sayı: 2

Kaynak Göster

APA Türkoğlu, D., & Konak, F. (2025). Factors Affecting Portfolios Created With Islamic And Traditional Perspective In Borsa İstanbul: Artifıcial Intelligence Supported Hybrid Model Proposal. Journal of Accounting and Taxation Studies, 18(2), 351-380. https://doi.org/10.29067/muvu.1639926

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Makale göndermek için https://dergipark.org.tr/tr/journal/591/submission/step/manuscript/new