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Amerikan 10 Yıllık Tahvil Faiz Oranlarına Dayanılarak BİST 100 Endeks Tahmininde Ağaç Tabanlı Regresyon Modelleri Uygulaması

Yıl 2021, Cilt: 25 Sayı: 2, 225 - 238, 30.12.2021
https://doi.org/10.51945/cuiibfd.1000827

Öz

Bu çalışmada Borsa İstanbul’da işlem gören BİST 100 endeksinin Amerikan hazine 10 yıllık gösterge tahvil faiz oranları aracılığıyla tahmin edilmesi amaçlanmıştır. Elde edilen 258 adet veri literatürde son yıllarda kullanılan iki adet matematiksel yöntem ile analiz edilmiştir. Zaman serisi alanında kullanılan Rastgele Orman (RF) Modeli ve Çok Değişkenli Uyarlanabilir Regresyon Eğrileri (MARS) Modeli bu çalışmada kullanılan ağaç tabanlı regresyon modelleridir. Kullanılan modellerde BİST 100 endeksi kapanış fiyatları bağımlı değişken; Amerikan hazine 10 yıllık gösterge tahvil faiz oranları bağımsız değişken olarak belirlenmiştir. Analiz aşamasında 206 adet veri modellerin eğitilmesinde, 52 adet veri ise modellerin test edilmesinde kullanılmıştır. Modellerin istatistiksel olarak başarılı olup olmadıkları, hata kareleri ortalaması (HKO) ve Nash–Sutcliffe model verimlilik katsayısı (NSE) başarı kriterleri ile test edilmiştir. Sonuçlar incelendiğinde, MARS modelinin en yüksek NSE değerine sahip olduğu ve Amerikan hazine 10 yıllık gösterge tahvil faiz oranlarının BİST 100 endeksini tahmin edebildiği görülmüştür. Ülkemizde finans alanında yapılan tahminlerde yeni olarak kullanılan bu yöntemler sayesinde daha başarılı yatırım kararlarının alınabileceği düşünülmektedir. Ayrıca çalışma ile oluşturulan modellerin daha sonra geliştirilerek diğer araştırmacılara ışık tutacağı düşünülmektedir.

Kaynakça

  • Başakın, E. E., Özger, M., & Ünal, N. E. (2019). Gri tahmin yöntemi ile İstanbul su tüketiminin modellenmesi. Politeknik Dergisi, 22(3), 755-761.
  • Bauder, R. A., & Khoshgoftaar, T. M. (2017, December). Medicare fraud detection using machine learning methods. In 2017 16th IEEE international conference on machine learning and applications (ICMLA) (pp. 858-865). IEEE.
  • Breiman, L. (2001). Random Forests. Machine learning, 45(1), 5-32.
  • Campbell, J. Y. (1987). Stock Returns and the Term Structure. Journal of Financial Economics, 18(2), 373-399.
  • Cao, N., Galvani, V., & Gubellini, S. (2017). Firm-Specific Stock and Bond Predictability: New Evidence from Canada. International Review of Economics & Finance, 51, 174-192.
  • Cihangir, Ç. K., & Tanrıöven, C. (2016). Abd Devlet Tahvili Faiz Oranlarındaki Değişimin Kurlara Etkisi; Kırılgan Paralar, Kırılgan Ekonomiler. İşletme Araştırmaları Dergisi, 8(4), 1-14.
  • Demirkol, D., Kartal, E., Şeneler, Ç., & Gülseçen, S. (2019). Bir Öğrenci Bilgi Sisteminin Kullanılabilirliğinin Makine Öğrenmesi Teknikleriyle Tahmin Edilmesi. Veri Bilimi, 2(1), 10-18.
  • Desai, V. S., & Bharati, R. (1998). The efficacy of Neural Networks in Predicting Returns on Stock and Bond Indices. Decision Sciences, 29(2), 405-423.
  • Devpura, N., Narayan, P. K., & Sharma, S. S. (2021). Bond Return Predictability: Evidence from 25 OECD Countries. Journal of International Financial Markets, Institutions and Money, 101301.
  • Duran, M., Özlü, P., & Ünalmis, D. (2010). TCMB Faiz Kararlarinin Hisse Senedi Piyasalari Üzerine Etkisi. Central Bank Review, 10(2), 23.
  • Elton, E. J., Gruber, M. J., Agrawal, D., & Mann, C. (2001). Explaining the Rate Spread on Corporate Bonds. The Journal of Finance, 56(1), 247-277.
  • Eyüboğlu, S., Eyüboğlu, K. (2018). Amerikan 10 Yıllık Tahvil Faizleri İle Gelişmekte Olan Ülke Borsaları Arasındaki İlişkinin Test Edilmesi. Yönetim Bilimleri Dergisi, 16(31), 443-459.
  • Fama, E. and K. French, 1993, Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics, 33, 3–56.
  • Fang, L., Yu, H., & Huang, Y. (2018). The role of Investor Sentiment in the Long-Term Correlation Between US Stock and Bond Markets. International Review of Economics & Finance, 58, 127-139.
  • Friedman, J. H., & Roosen, C. B. (1995). An Introduction to Multivariate Adaptive Regression Splines.
  • Genuer, R., Poggi, J. M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017). Random Forests for Big Data. Big Data Research, 9, 28-46.
  • Goh, A. T., Zhang, Y., Zhang, R., Zhang, W., & Xiao, Y. (2017). Evaluating Stability of Underground Entry-Type Excavations Using Multivariate Adaptive Regression Splines and Logistic Regression. Tunnelling and Underground Space Technology, 70, 148-154.
  • Green, C. J. (1991). ‘Quick’Methods of Estimating The Price Of Government Bonds. Oxford Bulletin of Economics and Statistics, 53(3), 295-311.
  • He, J., Harris, J. R., Sawada, M., & Behnia, P. (2015). A comparison of Classification Algorithms Using Landsat-7 and Landsat-8 Data for Mapping Lithology in Canada’s Arctic. International Journal of Remote Sensing, 36(8), 2252-2276.
  • Hotchkiss, E. S., & Ronen, T. (2002). The Informational Efficiency of the Corporate Bond Market: An Intraday Analysis. The Review of Financial Studies, 15(5), 1325-1354.
  • Investing, https://m.tr.investing.com (Erişim Tarihi: 20.08.2021).
  • Keim, D. B., & Stambaugh, R. F. (1986). Predicting Returns in The Stock and Bond Markets. Journal of Financial Economics, 17(2), 357-390.
  • Kirby, C. (1997). Measuring the Predictable Variation in Stock and Bond Returns. The Review of Financial Studies, 10(3), 579-630.
  • Lee, H. (2021). Time-Varying Comovement of Stock and Treasury Bond Markets in Europe: A Quantile Regression Approach. International Review of Economics & Finance, 75, 1-20.
  • Liaw, A., & Wiener, M. (2002). Classification and Regression by Random Forest. R news, 2(3), 18-22.
  • Nash, J.E., Sutcliffe, J. V. (1970), River Flow Forecasting through Conceptual Models. Part 1: A Discussion of Principles, J. Hydrol., 10 (3), 282–290.
  • Nazemi, A., Baumann, F., & Fabozzi, F. J. (2021). Intertemporal Defaulted Bond Recoveries Prediction via Machine Learning. European Journal of Operational Research.
  • Ohmi, H., & Okimoto, T. (2016). Trends in Stock-Bond Correlations. Applied Economics, 48(6), 536-552.
  • Scholz, M., Sperlich, S., & Nielsen, J. P. (2016). Nonparametric long term Prediction of Stock Returns with Generated Bond Yields. Insurance: Mathematics and Economics, 69, 82-96.
  • Scornet, E., Biau, G., & Vert, J. P. (2015). Consistency of Random Forests. The Annals of Statistics, 43(4), 1716-1741.
  • Sephton, P. (2001). Forecasting Recessions: Can We do Better on Mars. Federal Reserve Bank of St. Louis Review, 83(March/April 2001).

Amerikan 10 Yıllık Tahvil Faiz Oranlarına Dayanılarak BİST 100 Endeks Tahmininde Ağaç Tabanlı Regresyon Modelleri Uygulaması

Yıl 2021, Cilt: 25 Sayı: 2, 225 - 238, 30.12.2021
https://doi.org/10.51945/cuiibfd.1000827

Öz

In this study, it is aimed to estimate the BIST 100 index traded in Borsa Istanbul using the US Treasury 10-year benchmark bond interest rates. The 258 data obtained were analyzed with two mathematical methods used in the literature in recent years. Random Forest (RF) Model and Multivariate Adaptive Regression Spline (MARS) Model used in the time series field are tree-based regression models used in this study. In the models used, the BIST 100 index closing prices are the dependent variable; The US Treasury 10-year benchmark bond interest rates were determined as the independent variable. During the analysis phase, 206 data were used in training the models and 52 data were used in testing the models. Whether the models were statistically successful or not was tested with the success criteria of mean squares of error (MSE) and Nash–Sutcliffe model efficiency coefficient (NSE). When the results are analyzed, it is seen that the MARS model has the highest NSE value and the US Treasury 10-year benchmark bond interest rates can predict the BIST 100 index. It is thought that more successful investment decisions will be made thanks to these new methods used in the estimations made in the field of finance in our country. In addition, it is believed that the models created by the study will be developed later and shed light on other researchers.

Kaynakça

  • Başakın, E. E., Özger, M., & Ünal, N. E. (2019). Gri tahmin yöntemi ile İstanbul su tüketiminin modellenmesi. Politeknik Dergisi, 22(3), 755-761.
  • Bauder, R. A., & Khoshgoftaar, T. M. (2017, December). Medicare fraud detection using machine learning methods. In 2017 16th IEEE international conference on machine learning and applications (ICMLA) (pp. 858-865). IEEE.
  • Breiman, L. (2001). Random Forests. Machine learning, 45(1), 5-32.
  • Campbell, J. Y. (1987). Stock Returns and the Term Structure. Journal of Financial Economics, 18(2), 373-399.
  • Cao, N., Galvani, V., & Gubellini, S. (2017). Firm-Specific Stock and Bond Predictability: New Evidence from Canada. International Review of Economics & Finance, 51, 174-192.
  • Cihangir, Ç. K., & Tanrıöven, C. (2016). Abd Devlet Tahvili Faiz Oranlarındaki Değişimin Kurlara Etkisi; Kırılgan Paralar, Kırılgan Ekonomiler. İşletme Araştırmaları Dergisi, 8(4), 1-14.
  • Demirkol, D., Kartal, E., Şeneler, Ç., & Gülseçen, S. (2019). Bir Öğrenci Bilgi Sisteminin Kullanılabilirliğinin Makine Öğrenmesi Teknikleriyle Tahmin Edilmesi. Veri Bilimi, 2(1), 10-18.
  • Desai, V. S., & Bharati, R. (1998). The efficacy of Neural Networks in Predicting Returns on Stock and Bond Indices. Decision Sciences, 29(2), 405-423.
  • Devpura, N., Narayan, P. K., & Sharma, S. S. (2021). Bond Return Predictability: Evidence from 25 OECD Countries. Journal of International Financial Markets, Institutions and Money, 101301.
  • Duran, M., Özlü, P., & Ünalmis, D. (2010). TCMB Faiz Kararlarinin Hisse Senedi Piyasalari Üzerine Etkisi. Central Bank Review, 10(2), 23.
  • Elton, E. J., Gruber, M. J., Agrawal, D., & Mann, C. (2001). Explaining the Rate Spread on Corporate Bonds. The Journal of Finance, 56(1), 247-277.
  • Eyüboğlu, S., Eyüboğlu, K. (2018). Amerikan 10 Yıllık Tahvil Faizleri İle Gelişmekte Olan Ülke Borsaları Arasındaki İlişkinin Test Edilmesi. Yönetim Bilimleri Dergisi, 16(31), 443-459.
  • Fama, E. and K. French, 1993, Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics, 33, 3–56.
  • Fang, L., Yu, H., & Huang, Y. (2018). The role of Investor Sentiment in the Long-Term Correlation Between US Stock and Bond Markets. International Review of Economics & Finance, 58, 127-139.
  • Friedman, J. H., & Roosen, C. B. (1995). An Introduction to Multivariate Adaptive Regression Splines.
  • Genuer, R., Poggi, J. M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017). Random Forests for Big Data. Big Data Research, 9, 28-46.
  • Goh, A. T., Zhang, Y., Zhang, R., Zhang, W., & Xiao, Y. (2017). Evaluating Stability of Underground Entry-Type Excavations Using Multivariate Adaptive Regression Splines and Logistic Regression. Tunnelling and Underground Space Technology, 70, 148-154.
  • Green, C. J. (1991). ‘Quick’Methods of Estimating The Price Of Government Bonds. Oxford Bulletin of Economics and Statistics, 53(3), 295-311.
  • He, J., Harris, J. R., Sawada, M., & Behnia, P. (2015). A comparison of Classification Algorithms Using Landsat-7 and Landsat-8 Data for Mapping Lithology in Canada’s Arctic. International Journal of Remote Sensing, 36(8), 2252-2276.
  • Hotchkiss, E. S., & Ronen, T. (2002). The Informational Efficiency of the Corporate Bond Market: An Intraday Analysis. The Review of Financial Studies, 15(5), 1325-1354.
  • Investing, https://m.tr.investing.com (Erişim Tarihi: 20.08.2021).
  • Keim, D. B., & Stambaugh, R. F. (1986). Predicting Returns in The Stock and Bond Markets. Journal of Financial Economics, 17(2), 357-390.
  • Kirby, C. (1997). Measuring the Predictable Variation in Stock and Bond Returns. The Review of Financial Studies, 10(3), 579-630.
  • Lee, H. (2021). Time-Varying Comovement of Stock and Treasury Bond Markets in Europe: A Quantile Regression Approach. International Review of Economics & Finance, 75, 1-20.
  • Liaw, A., & Wiener, M. (2002). Classification and Regression by Random Forest. R news, 2(3), 18-22.
  • Nash, J.E., Sutcliffe, J. V. (1970), River Flow Forecasting through Conceptual Models. Part 1: A Discussion of Principles, J. Hydrol., 10 (3), 282–290.
  • Nazemi, A., Baumann, F., & Fabozzi, F. J. (2021). Intertemporal Defaulted Bond Recoveries Prediction via Machine Learning. European Journal of Operational Research.
  • Ohmi, H., & Okimoto, T. (2016). Trends in Stock-Bond Correlations. Applied Economics, 48(6), 536-552.
  • Scholz, M., Sperlich, S., & Nielsen, J. P. (2016). Nonparametric long term Prediction of Stock Returns with Generated Bond Yields. Insurance: Mathematics and Economics, 69, 82-96.
  • Scornet, E., Biau, G., & Vert, J. P. (2015). Consistency of Random Forests. The Annals of Statistics, 43(4), 1716-1741.
  • Sephton, P. (2001). Forecasting Recessions: Can We do Better on Mars. Federal Reserve Bank of St. Louis Review, 83(March/April 2001).
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makaleleri
Yazarlar

Salim Sercan Sarı 0000-0003-2607-5249

Yayımlanma Tarihi 30 Aralık 2021
Gönderilme Tarihi 25 Eylül 2021
Kabul Tarihi 28 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 25 Sayı: 2

Kaynak Göster

APA Sarı, S. S. (2021). Amerikan 10 Yıllık Tahvil Faiz Oranlarına Dayanılarak BİST 100 Endeks Tahmininde Ağaç Tabanlı Regresyon Modelleri Uygulaması. Çukurova Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 25(2), 225-238. https://doi.org/10.51945/cuiibfd.1000827