Relevance Vector Machines for Index Direction Predictions: An Application on Borsa Istanbul

Year 2024, Volume: 19 Issue: 2, 594 - 610, 01.08.2024

Cemile Özgür

https://doi.org/10.17153/oguiibf.1400125

Abstract

This study investigates index prediction performance of Relevance Vector Machines (RVM) and frequently applied Ridge Regression and Support Vector Machines (SVM). Daily prices of BIST Banks and BIST Financials indices of Borsa Istanbul are used to obtain one-day-ahead predictions of the algorithms. According to estimated performance measures, RVM yielded mostly the best metrics in both periods of BIST Banks. While SVM obtained the best performance metrics on BIST Financials index, metrics of RVM were not far from the best. Overall, the results indicate the applicability of RVM in predicting index directions and has a potential to be a good rival of SVM.

Keywords

Index Prediction, Support Vector Machines, Ridge Regression, Relevance Vector Machines

Endeks Yönü Tahmininde İlgililik Vektör Makineleri: Borsa İstanbul Üzerine Bir Uygulama

Abstract

Bu çalışma, İlgililik Vektör Makineleri (İVM) ile sıklıkla uygulanan Destek Vektör Makineleri (DVM) ve Ridge Regresyonunun endeks tahmin performansını araştırmaktadır. Algoritmaların bir gün sonrası tahminlerinin elde edilmesi amacıyla Borsa İstanbul’un BIST Banka ve BIST Mali endekslerinin günlük fiyat serileri kullanılmıştır. Hesaplanan performans ölçütlerine göre İVM, BIST Banka’nın her iki periyodunda da çoğunlukla en iyi ölçütleri sağlamıştır. BIST Mali endeksinin en iyi performans ölçütlerini DVM elde etmişken, İVM’nin ölçütleri en iyiden çok uzakta değildir. Genel olarak sonuçlar, İVM’nin endeks yönü tahmininde uygulanabilirliğini ve DVM’nin iyi bir rakibi olma potansiyeline sahip olduğunu belirtmektedir.

Keywords

Endeks Tahmini, Destek Vektör Makineleri, Ridge Regresyonu, İlgililik Vektör Makineleri

References

• Akcan, A., & Kartal, C. (2011), “İMKB Sigorta Endeksini Olusturan Sirketlerin Hisse Senedi Fiyatlarının Yapay Sinir Ağları İle Tahmini”, Muhasebe Ve Finansman Dergisi, 51: 27-40.
• Aksoy, B. (2021), “Pay senedi fiyat yönünün makine öğrenmesi yöntemleri ile tahmini: Borsa İstanbul örneği”, Business and Economics Research Journal, 12(1): 89-110. http://dx.doi.org/10.20409/berj.2021.312
• Avcı, E. (2007), “FORECASTING DAILY AND SESSIONAL RETURNS OF THE ISE-100 INDEX WITH NEURAL NETWORK MODELS”, Doğuş Üniversitesi Dergisi, 8(2): 128-142.
• Ballings, M., Poel, D.V., Hespeels, N., & Gryp, R. (2015), “Evaluating multiple classifiers for stock price direction prediction”, Expert Systems with Applications, 42(20): 7046–7056. https://doi.org/10.1016/j.eswa.2015.05.013
• Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, J. Econ., 31: 307-327.
• Borsa Istanbul (2023), https://www.borsaistanbul.com/ (Access date: 25.09.2023).
• Boyacıoğlu, M.A., & Avcı, D. (2010), “An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange”, Expert Systems with Applications, 37: 7908–7912. https://doi.org/10.1016/j.eswa.2010.04.045
• Cortes, C., & Vapnik, V. (1995), “Support-vector networks”, Machine Learning, 20(3): 273-297.
• Diler, A.I. (2003), “Predicting direction of ISE national-100 index with backpropagation trained neural network”, Journal of Istanbul Stock Exchange, 7(25–26): 65–81.
• Engle, R. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation”, Econometrica, 50(4): 987-1007.
• Engle, R. (1984), “Chapter 13 Wald, likelihood ratio, and Lagrange multiplier tests in econometrics”, Handbook of Econometrics, North Holland, 2: 775–826.
• Filiz, E., Akogul, S., & Karaboğa, H.A. (2021), “Büyük Dünya Endeksleri Kullanılarak BIST-100 Endeksi Değişim Yönünün Makine Öğrenmesi Algoritmaları ile Sınıflandırılması”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 10(2): 432–441. https://doi.org/10.17798/bitlisfen.889007
• Gündüz, H., Çataltepe, Z., & Yaslan, Y. (2017) "Stock daily return prediction using expanded features and feature selection", Turkish Journal of Electrical Engineering and Computer Sciences, 25(6): 4829-4840. https://doi.org/10.3906/elk-1704-256
• Henrique, B.M., Sobreiro, V.A., & Kimura, H. (2019), “Literature review: Machine learning techniques applied to financial market prediction”, Expert Systems with Applications, 124: 226-251. https://doi.org/10.1016/j.eswa.2019.01.012
• Hoerl, A., & Kennard, R. (1970), “Ridge Regression: Biased Estimation for Nonorthogonal Problems”, Technometrics, 12(1): 55-67. https://doi.org/10.2307/1267351
• Huang, S.-C., & Wu, T.-K. (2008), “Combining wavelet-based feature extractions with relevance vector machines for stock index forecasting”, Expert Systems, 25: 133-149. https://doi.org/10.1111/j.1468-0394.2008.00443.x
• Hyndman, R.J., & Athanasopoulos, G. (2018), Forecasting: principles and practice (2. ed.). Retrieved from: OTexts.com/fpp2. (Access date: 01.03.2021).
• Hyndman, R.J., & Koehler, A.B. (2006), “Another look at measures of forecast accuracy”, International Journal of Forecasting, 22(4): 679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001 https://tr.investing.com (Access date: August 25, 2023).
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013), An Introduction to Statistical Learning with Applications in R: Springer.
• Kara, Y., Boyacioglu, M.A., & Baykan, Ö.K. (2011), “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange”, Expert Systems with Applications, (38): 5311-5319.
• Kartal, C. (2020). “Destek Vektör Makineleri ile Borsa Endekslerinin Tahmini”, İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 9(2): 1394-1418.
• Kecman, V. (2005), “Support vector machines–an introduction”, Support vector machines: Theory and applications (pp. 1-47), Springer.
• Kılıç, A., Güloğlu, B., Yalçın, A., & Üstündağ, A. (2023), “Big data–enabled sign prediction for Borsa Istanbul intraday equity prices”, Borsa Istanbul Review, 23(2): 38-52. https://doi.org/10.1016/j.bir.2023.08.005
• Kuhn, M. (2008), “Building Predictive Models in R Using the caret Package”, Journal of Statistical Software, 28(5): 1–26. https://doi.org/10.18637/jss.v028.i05
• Kumbure, M.M., Lohrmann, C., Luukka, P., & Porras, J. (2022), “Machine learning techniques and data for stock market forecasting: A literature review”, Expert Systems with Applications, 197: 116659. https://doi.org/10.1016/j.eswa.2022.116659
• Ljung, G.M., & Box, G.E.P. (1978), “On a measure of lack of fit in time series models”, Biometrika, 65: 297–303.
• Nikou, M., Mansourfar, G., & Bagherzadeh, J. (2019), “Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms”, Intell Sys Acc Fin Mgmt, 26: 164–174. https://doi.org/10.1002/isaf.1459
• Özgür, C., & Sarıkovanlık, V. (2022), “FORECASTING BIST100 AND NASDAQ INDICES WITH SINGLE AND HYBRID MACHINE LEARNING ALGORITHMS”, Economic Computation and Economic Cybernetics Studies and Research, 3(56): 235-250. https://doi.org/10. 24818/18423264/56.3.22.15
• Oztekin, A., Kizilaslan, R., Freund, S., & Iseri, A. (2016), “A Data Analytic Approach to Forecasting Daily Stock Returns in an Emerging Market”, European Journal of Operational Research, 253(3): 697-710. https://doi.org/10.1016/j.ejor.2016.02.056
• Pabuçcu, H. (2019), “Borsa endeksi hareketlerinin makine öğrenme algoritmaları ile tahmini”, Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23): 179-190. https://doi.org/10.18092/ulikidince.484138
• R Core Team (2019), “R: A Language and Environment for Statistical Computing: R Foundation for Statistical Computing”. Retrieved from: https://www.r-project.org/
• Sahu, S.K., Mokhade, A., & Bokde, N.D. (2023), “An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges”, Applied Sciences, 13: 1956. https://doi.org/10.3390/app13031956
• Smola, A.J., & Schölkopf, B. (2004), “A tutorial on support vector regression”, Statistics and Computing, 14: 199–222.
• Tipping, M.E. (2001), “Sparse Bayesian learning and the relevance vector machine”, Journal of Machine Learning Research, 1: 211–244.
• Ünlü, K.D., Potas, N., & Yılmaz, M. (2021), “Forecasting Direction of BIST 100 Index: An Integrated Machine Learning Approach”, Chaos, Complexity and Leadership 2020, Springer Proceeding in Complexity, Springer. https://doi.org/10.1007/978-3-030-74057-3_5
• Vapnik, V. (1995), The Nature of Statistical Learning Theory, Berlin: Springer Science & Business Media. Vapnik, V. (1998), Statistical Learning Theory, Toronto: John Wiley & Sons.
• Vapnik, V., & Chervonenkis A. (1974), Theory of Pattern Recognition [in Russian]. Nauka, Moscow. (German Translation: Wapnik W. & Tscherwonenkis A., Theorie der Zeichenerkennung, Akademie-Verlag, Berlin, 1979). Vapnik V.N. (1982), Estimation of Dependences Based on Empirical Data, Berlin: Springer.
• Yümlü, S., Gürgen, F.S., & Okay, N. (2005), “A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction”, Pattern Recognition Letters, 26(13): 2093-2103. https://doi.org/10.1016/j.patrec.2005.03.026