Abstract
Financial time series forecasting is a challenging task in machine learning. The noisy and non-stationary nature of financial time series requires efficient support vector machine solvers. In this study, we consider the problem of predicting the future ranks of stocks as a multi-class classification task. We design a pairwise to multi-class classification scheme based on an effective online SVM solver. We treat ranks of daily stock prices as class labels and use pairwise combinations of stocks rather than individual stocks. To assess the proposed approach, we consider an example based on the prices of three stocks from Borsa Istanbul. The experimental results reveal that the algorithm successfully predicts the ranks of stocks from different sectors. We also present a comparative analysis with commonly used classifiers. The results indicate that the proposed approach exhibits better classification performance than other classifiers for all stocks, especially in near future prediction.
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References
Alam, T. M., Shaukat, K., Mushtaq, M., Ali, Y., Khushi, M., Luo, S., & Wahab, A. (2021). Corporate bankruptcy prediction: An approach towards better corporate world. The Computer Journal, 64(11), 1731–1746.
Ben-Hur, A., & Noble, W. S. (2005). Kernel methods for predicting protein-protein interactions. Bioinformatics, 21(suppl.1), i38–i46.
Bordes, A., Ertekin, S., Weston, J., & Bottou, L. (2005). Fast kernel classifiers with online and active learning. Journal of Machine Learning Research, 6, 1579–1619.
Cao, L., & Tay, F. E. (2001). Financial forecasting using support vector machines. Neural Computing & Applications, 10(2), 184–192.
Cao, L.-J., & Tay, F. E. H. (2003). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks, 14(6), 1506–1518.
Chang, P. C., & Liu, C. H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34(1), 135–144.
Cortez, P. (2010). Data mining with neural networks and support vector machine using the R/rminer tool. Industrial conference on data mining (pp. 572–583).
Fenerich, A., Steiner, M. T. A., Neto, P. J. S. , Tochetto, E., Tsutsumi, D., Assef, F. M., & dos Santos, B. S. (2020). Use of machine learning techniques in bank credit risk analysis. Revista Internacional de Metodos Numericos Para Calculo y Diseno En Ingenieria, 36(3).
Feng, F., He, X., Wang, X., Luo, C., Liu, Y., & Chua, T.-S. (2019). Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS), 37(2), 27.
Fernandes, F. D. S., Stasinakis, C., & Zekaite, Z. (2019). Forecasting government bond spreads with heuristic models: Evidence from the eurozone periphery. Annals of Operations Research, 282(1–2), 87–118.
Guo, Y., Han, S., Shen, C., Li, Y., Yin, X., & Bai, Y. (2018). An adaptive SVR for high-frequency stock price forecasting. IEEE Access, 6, 11397–11404.
Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1–2), 307–319.
Lee, Y., Lin, Y., & Wahba, G. (2004). Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data. Journal of the American Statistical Association, 99(465), 67–81.
Li, Z., Li, Y., Yu, F., & Ge, D. (2014). Adaptively weighted support vector regression for financial time series prediction. International joint conference on neural networks (IJCNN) (pp. 3062–3065).
Liu, T.-Y. (2011). Learning to rank for information retrieval. Springer Science & Business Media.
Lu, C. J., Lee, T. S., & Chiu, C. C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125.
Misaghi, S., & Sheijani, O. S. (2017). A hybrid model based on support vector regression and modified harmony search algorithm in time series prediction. 5th Iranian joint congress on fuzzy and intelligent systems (CFIS), (pp. 54–60).
Nava, N., Di Matteo, T., & Aste, T. (2018). Financial time series forecasting using empirical mode decomposition and support vector regression. Risks, 6(1), 1–21.
Ouahilal, M., El Mohajir, M., Chahhou, M., & El Mohajir, B. E. (2017). A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction. Journal of Big Data, 4(31), 1–22.
Pant, M., & Kumar, S. (2022). Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method. Granular Computing, 7(4), 861–879.
Pardalos, P. M., & Kundakcioglu, O. E. (2009). Classification via mathematical programming. Applied and Computational Mathematics, 8(1), 23–35.
Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines. Technical Report MSR-TR-98-14, Microsoft Research.
Qian, B., Li, H., Wang, J., Wang, X., & Davidson, I. (2013). Active learning to rank using pairwise supervision. Proceedings of the 2013 SIAM international conference on data mining (pp. 297–305).
Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of Machine Learning Research, 5, 101–141.
Schölkopf, B., & Smola, A. J. (2001). Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press.
Simian, D., Stoica, F., & Bărbulescu, A. (2020). Automatic optimized support vector regression for financial data prediction. Neural Computing and Applications, 32(7), 2383–2396.
Song, Q., Liu, A., & Yang, S. Y. (2017). Stock portfolio selection using learning-to-rank algorithms with news sentiment. Neurocomputing, 264, 20–28.
Tas, E. (2017). A single pairwise model for classification using online learning with kernels. Hacettepe Journal of Mathematics and Statistics, 46(3), 547–557.
Tsai, M.-F., & Wang, C.-J. (2013). Risk ranking from financial reports. European conference on information retrieval (pp. 804–807).
Tsai, C. F. (2020). Two-stage hybrid learning techniques for bankruptcy prediction. Statistical Analysis and Data Mining, 13(6), 565–572.
Wang, L., & Zhu, J. (2010). Financial market forecasting using a two-step kernel learning method for the support vector regression. Annals of Operations Research, 174(1), 103–120.
Weston, J., & Watkins, C. (1998). Multi-class support vector machines Multi-class support vector machines. Technical Report CSD-TR-98-04, Department of Computer Science, Royal Holloway University of London.
Yu, L., Yao, X., Zhang, X., Yin, H., & Liu, J. (2020). A novel dual-weighted fuzzy proximal support vector machine with application to credit risk analysis. International Review of Financial Analysis, 71, 101577.
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Tas, E., Atli, A.H. Stock Price Ranking by Learning Pairwise Preferences. Comput Econ 63, 513–528 (2024). https://doi.org/10.1007/s10614-022-10350-7
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DOI: https://doi.org/10.1007/s10614-022-10350-7