Using Stacked Generalization Model in Stock Price Forecasting: A Comparative Analysis on BIST100 Index

Year 2025, Volume: 9 Issue: 1, 305 - 322, 25.02.2025

Ahmed İhsan Şimşek

https://doi.org/10.25295/fsecon.1444407

Abstract

Investing in financial markets requires an adequately planned approach and decision-making process for both individual and institutional investors. The volatility of financial markets is influenced by intricate and constantly evolving factors, prompting investors, analysts, and financial experts to employ progressively sophisticated and data-centric methodologies to precisely forecast future price swings. Deep learning models for stock price prediction demonstrate the ability to comprehend intricate connections by amalgamating extensive datasets. The objective of this essay is to employ various machine learning models using daily data from the BIST100 index, a prominent financial indicator in Turkey. The models under question encompass Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Random Forest (RF), XGBoost and Stacked Generalization. The models' prediction skills were evaluated using RMSE, MSE, MAE, and R2 performance indicators. Based on the observed results, the Stacked Generalization model demonstrated greater performance in making predictions for the analyzed dataset. These findings offer valuable insights that should be considered when selecting models for similar analyses in the future.

Keywords

BIST100, Stacked Generalization, Stock Prediction, Decision Support, Time Series

Hisse Senedi Fiyat Tahmininde Stacked Generalization Modelinin Kullanımı: BIST100 Endeksi Üzerine Karşılaştırmalı Bir Analiz

Abstract

Finansal piyasalara yatırım yapmak hem bireysel hem de kurumsal yatırımcılar için önemli bir strateji ve karar alma sürecini gerektirmektedir. Finansal piyasaların oynaklığı, karmaşık ve sürekli gelişen faktörlerden etkilenerek yatırımcıları, analistleri ve finansal uzmanları gelecekteki fiyat dalgalanmalarını kesin olarak tahmin etmek için giderek daha karmaşık ve veri merkezli metodolojiler kullanmaya teşvik etmektedir. Hisse senedi fiyat tahmini için derin öğrenme modelleri, kapsamlı veri kümelerini birleştirerek karmaşık bağlantıları kavrama yeteneği sayesinde giderek daha çok tercih edilmektedir. Bu makalenin amacı, Türkiye'nin önemli finansal göstergelerinden biri olan BIST100 endeksine ait 04.01.2010-29.11.2023 tarihleri arasındaki 3494 günlük veri setini kullanarak en iyi tahmin yeteneğine sahip modeli belirlemektir. Bu kapsamda SVR, KNN, RF, XGBoost ve Stacked Generalization modeli kullanılmıştır. Modellerin tahmin becerileri RMSE, MSE, MAE ve R2 performans göstergeleri kullanılarak değerlendirilmiştir. Modellerin tahmin performanslarından elde edilen sonuçlara göre, Stacked generalization modeli analiz edilen veri kümesi için tahminler yapmada daha yüksek performans göstermiştir. Bu bulgular gelecekte benzer analizler için model seçerken dikkate alınması gereken değerli bilgiler sunmaktadır.

Keywords

BIST100, Stacked Generalization, Hisse Senedi Tahmini, Karar Destek, Zaman Serisi

References

• Aha, D., Kibler, D.W. & Albert, M.K. (1991). Instance-based learning algorithms. Mach Learn, 6, 37–66
• Ahmed, R., Bibi, M. & Syed, S. (2023). Improving heart disease prediction accuracy using a hybrid machine learning approach: A comparative study of SVM and KNN algorithms. International Journal of Computations, Information and Manufacturing (IJCIM), 3(1), 49-54.
• Amra, I. A. A. & Maghari, A. Y. (2017, May). Students performance prediction using KNN and Naïve Bayesian. 2017 8th International Conference on Information Technology (ICIT) (p. 909-913). IEEE.
• Armağan, İ. Ü. (2023). Price prediction of The Borsa Istanbul Banks Index with traditional methods and artificial neural networks. Borsa Istanbul Review.
• Ashtiani, M. N. & Raahmei, B. (2023). News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review. Expert Systems with Applications, 119509.
• Ayyildiz, N. & Iskenderoglu, O. (2023). Prediction of stock index movement directions using machine learning methods: An application on developing countries. Journal of Financial Economics and Banking, 4(2), 68-78.
• Ayyildiz, N. & Iskenderoglu, O. (2024). How effective is machine learning in stock market predictions?. Heliyon, 10(2). https://doi.org/10.1016/j.heliyon.2024.e24123
• Bhanja, S. & Das, A. (2019). Deep learning-based integrated stacked model for the stock market prediction. Int. J. Eng. Adv. Technol, 9(1), 5167-5174.
• Box, G. E., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
• Burgess-Hull, A. J., Brooks, C. S., Epstein, D. H., Gandhi, D. & Oviedo, E. (2022). Using machine learning to predict treatment adherence in patients on medication for opioid use disorder. Journal of Addiction Medicine, 17(1), 28-34. https://doi.org/10.1097/adm.0000000000001019
• Chen, M. Y. & Chen, B. T. (2015). A hybrid fuzzy time series model based on granular computing for stock price forecasting. Information Sciences, 294, 227-241.
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297.
• Diqi, M., Ordiyasa, I. W. & Hamzah, H. (2024). Enhancing stock price prediction using stacked long short-term memory. IT Journal Research and Development, 8(2), 164-174.
• Du, Y., Li, J. Q., Chen, X. L., Duan, P. Y. & Pan, Q. K. (2022). Knowledge-based reinforcement learning and estimation of distribution algorithm for flexible job shop scheduling problem. IEEE Transactions on Emerging Topics in Computational Intelligence.
• Elmuna, E. A. F., Chamidy, T. & Nugroho, F. (2023). Optimization of the random forest method using principal component analysis to predict house prices. International Journal of Advances in Data and Information Systems, 4(2), 155-166. https://doi.org/10.25008/ijadis.v4i2.1290
• Fereydooni, A. & Mahootchi, M. (2023). An algorithmic trading system based on a stacked generalization model and hidden Markov model in the foreign exchange market. Global Finance Journal, 56, 100825.
• Gabriel, A. M. & Ugochukwu, W. M. (2012). Volatility estimation and stock price prediction in the Nigerian stock market. International Journal of Financial Research, 3(1), 2.
• Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M. & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.
• Gaytan, J. C. T., Ateeq, K., Rafiuddin, A., Alzoubi, H. M., Ghazal, T. M., Ahanger, T. A., Chaudhary, S. & Viju, G. K. (2022). AI-based prediction of capital structure: Performance comparison of ANN SVM and LR models. Computational Intelligence and Neurosciene, 2022.
• Gurjar, M., Naik, P., Mujumdar, G. & Vaidya, T. (2018). Stock market prediction using ANN. International Research Journal of Engineering and Technology, 5(3), 2758-61.
• Gyamerah, S. A., Ngare, P. & Ikpe, D. (2019, May). On stock market movement prediction via stacking ensemble learning method. 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (p. 1-8). IEEE.
• Hadaya, J., Verma, A., Sanaiha, Y., Ramezani, R., Qadir, N. & Benharash, P. (2022). Machine learning-based modeling of acute respiratory failure following emergency general surgery operations. Plos One, 17(4), e0267733. https://doi.org/10.1371/journal.pone.0267733
• Harishkumar, K. S., Yogesh, K. M. & Gad, I. (2020). Forecasting air pollution particulate matter (PM2. 5) using machine learning regression models. Procedia Computer Science, 171, 2057-2066.
• Henrique, B. M., Sobreiro, V. A. & 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.
• Htun, H. H., Biehl, M. & Petkov, N. (2023). Survey of feature selection and extraction techniques for stock market prediction. Financial Innovation, 9(1), 26.
• Hu, Z., Zhao, Y. & Khushi, M. (2021). A survey of forex and stock price prediction using deep learning. Applied System Innovation, 4(1), 9.
• Huang, Y., Deng, C., Zhang, X. & Bao, Y. (2020). Forecasting of stock price index using support vector regression with multivariate empirical mode decomposition. Journal of Systems and Information Technology, 24(2), 75-95. https://doi.org/10.1108/jsit-12-2019-0262
• Hutagalung, S. V., Yennimar, Y., Rumapea, E. R., Hia, M. J. G., Sembiring, T. & Manday, D. R. (2023). Comparison of support vector regression and random forest regression algorithms on gold price predictions. Jurnal Sistem Informasi Dan Ilmu Komputer Prima(JUSIKOM PRIMA), 7(1), 255-262. https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4125
• Jiang, M., Liu, J., Zhang, L. & Liu, C. (2020). An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 541, 122272.
• Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, 115537.
• Jorgenson, D. W., Weitzman, M. L., ZXhang, Y. X., Haxo, Y. M. & Mat, Y. X. (2023). Can neural networks predict stock market?. AC Investment Research Journal, 220(44).
• Kelany, O., Aly, S. & Ismail, M. A. (2020). Deep learning model for financial time series prediction. 2020 14th International Conference on Innovations in Information Technology (IIT) (p. 120-125). IEEE.
• Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H. & Alfakeeh, A. S. (2020). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 1-24.
• Kilimci, Z. H. & Duvar, R. (2020). An efficient word embedding and deep learning based model to forecast the direction of stock exchange market using Twitter and financial news sites: A case of Istanbul stock exchange (BIST 100). IEEE Access, 8, 188186-188198.
• Kim, K. J. & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert systems with Applications, 19(2), 125-132.
• Kulkarni, M., Jadha, A. & Dhingra, D. (2020). Time series data analysis for stock market prediction. Proceedings of the International Conference on Innovative Computing & Communications (ICICC).
• 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.
• Kurani, A., Doshi, P., Vakharia, A. & Shah, M. (2023). A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science, 10(1), 183-208.
• Li, Y. & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics, 1-11.
• Liu, C. (2024). Implied volatility forecasting for American options based on random forest regressor, linear regression model. Advances in Economics Management and Political Sciences, 85(1), 154-160. https://doi.org/10.54254/2754-1169/85/20240867
• Lv, D., Yuan, S., Li, M. & Xiang, Y. (2019). An empirical study of machine learning algorithms for stock daily trading strategy. Mathematical Problems in Engineering.
• Mahboob, K., Shahbaz, M. H., Ali, F. & Qamar, R. (2023). Predicting the Karachi Stock Price index with an Enhanced multi-layered Sequential Stacked Long-Short-Term Memory Model. VFAST Transactions on Software Engineering, 11(2), 249-255.
• Mandal, U., Chakraborty, A., Mahato, P. & Das, G. (2023). LinVec: A stacked ensemble machine learning architecture for analysis and forecasting of time-series data. Indian Journal of Science and Technology, 16(8), 570-582.
• Mandic, D. P. & Chambers, J. (2001). Recurrent neural networks for prediction: Learning algorithms architectures and stability. John Wiley & Sons, Inc.
• Massaoudi, M., Refaat, S. S., Chihi, I., Trabelsi, M., Oueslati, F. S. & Abu-Rub, H. (2021). A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting. Energy, 214, 118874.
• Mehtab, S. & Sen, J. (2020). Stock price prediction using convolutional neural networks on a multivariate timeseries. arXiv preprint arXiv:2001.09769.
• Mehtab, S., Sen, J. & Dasgupta, S. (2020). Robust analysis of stock price time series using CNN and LSTM-based deep learning models. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (p. 1481-1486).
• Mehtab, S., Sen, J. & Dutta, A. (2021). Stock price prediction using machine learning and LSTM-based deep learning models. Machine Learning and Metaheuristics Algorithms, and Applications: Second Symposium, SoMMA 2020 (p. 88-106). Chennai, India, October 14–17, 2020, Revised Selected Papers 2. Springer Singapore.
• Muhammad, T., Aftab, A. B., Ibrahim, M., Ahsan, M. M., Muhu, M. M., Khan, S. I., & Alam, M. S. (2023). Transformer-based deep learning model for stock price prediction: A case study on Bangladesh stock market. International Journal of Computational Intelligence and Applications, 2350013.
• Mukherjee, S., Sadhukhan, B., Sarkar, N., Roy, D. & De, S. (2023). Stock market prediction using deep learning algorithms. CAAI Transactions on Intelligence Technology, 8(1), 82-94.
• Nikolopoulos, C. & Fellrath, P. (1994). A hybrid expert system for investment advising. Expert Systems, 11(4), 245-250.
• Oukhouya, H. & El Himdi, K. (2023, April). Comparing machine learning methods—SVR, XGBOOST, LSTM, and MLP—for forecasting the Moroccan stock market. Computer Sciences & Mathematics Forum (Vol. 7, No. 1, p. 39). MDPI.
• OuYang, Z. (2024). Research on the diamond price prediction based on linear regression, decision tree and random forest. Highlights in Business, Economics and Management, 24, 248-257. https://doi.org/10.54097/13ccwv59
• 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.
• Phyu, T. N. (2009). Survey of classification techniques in data mining. Proceedings of the International Multiconference of Engineers and Computer Scientists (Vol. 1, No. 5, pp. 727-731). Citeseer.
• Polamuri, S. R., Srinivas, K. & Mohan, A. K. (2020). Multi model-based hybrid prediction algorithm (MM-HPA) for stock market prices prediction framework (SMPPF). Arabian Journal for Science and Engineering, 45, 10493-10509.
• Puspitasari, D. A. & Rustam, Z. (2018). Application of SVM-KNN using SVR as feature selection on stock analysis for Indonesia stock exchange. AIP Conference Proceedings (Vol. 2023, No. 1). AIP Publishing.
• Roh, T. H. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33(4), 916-922.
• Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S. & Kim, H. C. (2021). Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics, 10(21), 2717.
• Sabancı, D., Kılıçarslan, S. & Adem, K. (2023). An application on forecasting for stock market prices: hybrid of some metaheuristic algorithms with multivariate adaptive regression splines. International Journal of Intelligent Computing and Cybernetics, 16(4), 847-866.
• 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(3), 1956.
• Schimohr, K., Doebler, P. & Scheiner, J. (2022). Prediction of bike‐sharing trip counts: comparing parametric spatial regression models to a geographically weighted XGBOOST algorithm. Geographical Analysis, 55(4), 651-684. https://doi.org/10.1111/gean.12354
• Shah, J., Vaidya, D. & Shah, M. (2022). A comprehensive review on multiple hybrid deep learning approaches for stock prediction. Intelligent Systems with Applications, 200111.
• Sidogi, T., Mongwe, W. T., Mbuvha, R. & Marwala, T. (2022). Fusing sell-side analyst bidirectional forecasts using machine learning. IEEE Access, 10, 76966-76974.
• Soepriyanto, B. (2021). Comparative analysis of K-NN and naïve bayes methods to predict stock prices. International Journal of Computer and Information System (IJCIS), 2(2), 49-53. https://doi.org/10.29040/ijcis.v2i2.32
• Soni, P., Tewari, Y. & Krishnan, D. (2022). Machine learning approaches in stock price prediction: A systematic review. Journal of Physics: Conference Series (Vol. 2161, No. 1, p. 012065). IOP Publishing.
• Umer, M., Awais, M. & Muzammul, M. (2019). Stock market prediction using machine learning (ML) algorithms. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), 97-116.
• Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media.
• Vijaya, P., Raju, G. & Ray, S. K. (2016). Artificial neural network-based merging score for Meta search engine. Journal of Central South University, 23, 2604-2615.
• Vijh, M., Chandola, D., Tikkiwal, V. A. & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia Computer Science, 167, 599-606.
• Vinay, N. & Mahaveerakannan, R. (2023). Analyze the lack of accuracy in stock price prediction using novel k-nearest neighbors regression compared with logistic regression to improve accuracy. 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (p. 1-5). IEEE.
• Wang, J., Li, C., Li, J., Qin, S., Wang, J., Chen, Z., … & Wang, G. (2020). Development and internal validation of risk prediction model of metabolic syndrome in oil workers. BMC Public Health, 20(1). https://doi.org/10.1186/s12889-020-09921-w
• Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.
• Xing, F., Luo, R., Liu, M., Zhou, Z., Xiang, Z. & Duan, X. (2022). A new random forest algorithm-based prediction model of post-operative mortality in geriatric patients with hip fractures. Frontiers in Medicine, 9. https://doi.org/10.3389/fmed.2022.829977
• Xu, Z., Gao, Y. & Jin, Y. (2014). Application of an optimized SVR model of machine learning. International Journal of Multimedia and Ubiquitous Engineering, 9(6), 67-80.
• Yakut, E. & Gemici, E. (2017). Predicting stock return classification through LR, C5. 0, CART and SVM methods, and comparing the methods used: an application at BIST in Turkey. Ege Academic Review, 17(4), 461-479.
• Yang, K., Zhang, G., Bi, C., Guan, Q., Xu, H. & Xu, S. (2023). Improving CNN-base stock trading by considering data heterogeneity and burst. arXiv preprint arXiv:2303.09407.
• Yiğit, Ö. E., Alp, S. & Ersoy, Ö. Z. (2020). Prediction of BIST price indices: A comparative study between traditional and deep learning methods. Sigma Journal of Engineering and Natural Sciences, 38(4), 1693-1704.
• Yu, W., Lu, Y., Shou, H., Xu, H., Shi, L., Geng, X., … & Song, T. (2022). A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms. Cancer Medicine, 12(6), 6867-6876. https://doi.org/10.1002/cam4.5477
• Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T. & Veloso, M. (2023). Financial time series forecasting using CNN and Transformer. arXiv preprint arXiv:2304.04912.
• Zhang, X., Li, Z., Zhao, Y. & Wang, L. (2023). Carbon trading and COVID-19: A hybrid machine learning approach for international carbon price forecasting. Annals of Operations Research, 1-29.
• Zouzou, Y., & Çıtakoğlu, H. (2021). Reference evapotranspiration prediction from limited climatic variables using support vector machines and Gaussian processes. Avrupa Bilim ve Teknoloji Dergisi, (28), 346-351.