Hybrid Support Vector Regression-Genetic Algorithm Model for Forecasting Stock Prices

Muhammad Ulil Albab; Taghfirul Azhima Yoga Siswa; Rofilde Hasudungan

doi:10.24014/ijaidm.v9i1.39057

Hybrid Support Vector Regression-Genetic Algorithm Model for Forecasting Stock Prices

Muhammad Ulil Albab, Taghfirul Azhima Yoga Siswa, Rofilde Hasudungan

Abstract

The stock market exhibits a high level of volatility, which often leads to significant price fluctuations and increases the risk of financial losses for investors. Therefore, stock price prediction is an important tool to support investment decision-making, particularly for PT Aneka Tambang Tbk (ANTM.JK). This study aims to predict ANTM stock prices by applying the Support Vector Regression (SVR) method optimized using a Genetic Algorithm (GA). The data used in this study consist of 1202 historical stock price data of ANTM from September 11, 2020 to September 11, 2025, obtained from Investing.com, and the data are normalized using the Min-Max normalization method. The dataset is divided into training data and testing data using an 80:20 ratio, where 80% of the data are used for training and 20% for testing. The SVR model is constructed using the Radial Basis Function (RBF) kernel, while the GA is employed to optimize the SVR parameters in order to obtain the optimal parameter combination, with main GA parameters including population size of 50, 30 generations, crossover rate of 0.8, and mutation rate of 0.1. Model performance is evaluated by comparing the prediction results of SVR without optimization and GA-optimized SVR using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The experimental results indicate that the application of the GA improves the predictive performance of the model. The SVR model without optimization produces RMSE, MAE, and MAPE values of 85.48, 59.02, and 2.62%, respectively. After parameter optimization using GA, the model performance improves as indicated by reduced error values, with RMSE of 75.97, MAE of 52.42, and MAPE of 2.42%

Keywords

Genetic Algorithm; Machine Learning; Prediction; Stock; Support Vector Regression

References

S. Kumar S, D. Chitradevi, and S. Rajan, “Stock price prediction using deep learning LSTM (long short-term memory),” 2022 2nd Int. Conf. Adv. Comput. Innov. Technol. Eng. ICACITE 2022, pp. 1787–1791, 2022, doi: 10.1109/ICACITE53722.2022.9823639.

Y. P. Tsang, Y. Fan, Z. P. Feng, and Y. Li, “Examining supply chain vulnerability via an analysis of ESG-Prioritized firms amid the Russian-Ukrainian conflict,” J. Clean. Prod., vol. 434, no. May 2023, p. 139754, 2024, doi: 10.1016/j.jclepro.2023.139754.

J. Mahardhika, “Saham Antam (ANTM) Terus Diserok, Target Harga Masih Jauh,” Investor.id. Accessed: Sep. 16, 2025. [Online]. Available: https://investor.id/market/408852/saham-antam-antm-terus-diserok-target-harga-masih-jauh

Kustodian Sentral Efek Indonesia, “Statistik Pasar Modal Indonesia – Januari 2025.” Accessed: Sep. 18, 2025. [Online]. Available: https://web.ksei.co.id/files/Statistik_Publik_Januari_2025_final.pdf

CNN Indonesia, “Perbandingan Anjlok Parah IHSG: Krisis 1998, Covid-19, dan Hari Ini,” CNN Indonesia. Accessed: Sep. 18, 2025. [Online]. Available: https://www.cnnindonesia.com/ekonomi/20250318132738-92-1210179/perbandingan-anjlok-parah-ihsg-krisis-1998-covid-19-dan-hari-ini

Bank Indonesia, “Data Inflasi Indonesia,” Bank Indonesia. Accessed: Sep. 17, 2025. [Online]. Available: https://www.bi.go.id/id/statistik/indikator/data-inflasi.aspx?

Bank Indonesia, “BI-Rate Turun 25 BPS Menjadi 5,75%: Mempertahankan Stabilitas, Mendorong Pertumbuhan Ekonomi,” Bank Indonesia. Accessed: Sep. 17, 2025. [Online]. Available: https://www.bi.go.id/id/publikasi/ruang-media/news-release/Pages/sp_270825.aspx

Melina, Sukono, H. Napitupulu, and N. Mohamed, “A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review,” Risks, vol. 11, no. 3, 2023, doi: 10.3390/risks11030060.

L. N. Mintarya, J. N. M. Halim, C. Angie, S. Achmad, and A. Kurniawan, “Machine learning approaches in stock market prediction: A systematic literature review,” Procedia Comput. Sci., vol. 216, pp. 96–102, 2022, doi: 10.1016/j.procs.2022.12.115.

P. Soni, Y. Tewari, and D. Krishnan, “Machine Learning Approaches in Stock Price Prediction: A Systematic Review,” J. Phys. Conf. Ser., vol. 2161, no. 1, 2022, doi: 10.1088/1742-6596/2161/1/012065.

S. Rifky, W. S. Lestari, I. M. D. M. Sanjaya, and F. M. Sinaga, Teori dan Penerapan AI di Berbagai Bidang, no. June. 2024.

A. H. Khan et al., “A performance comparison of machine learning models for stock market prediction with novel investment strategy,” PLoS One, vol. 18, no. 9 September, pp. 1–19, 2023, doi: 10.1371/journal.pone.0286362.

A. Virigineni, M. Tanuj, A. Mani, and R. Subramani, “Stock Forecasting using HMM and SVR,” Proc. IC3IoT 2022, 2022, doi: 10.1109/IC3IOT53935.2022.9767969.

H. Oukhouya and K. El Himdi, “Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market,” p. 39, 2023, doi: 10.3390/iocma2023-14409.

S. A. Amellia Kharis, A. H. A. Zili, M. Malik, W. Nuryaningrum, and A. Putri, “Comparing machine learning models for Indonesia stock market prediction,” Indones. J. Electr. Eng. Comput. Sci., vol. 38, no. 1, pp. 508–516, 2025, doi: 10.11591/ijeecs.v38.i1.pp508-516.

O. A. Montesinos-López, A. M. López, and J. Crossa, Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer, 2022. doi: 10.1007/978-3-030-89010-0.

Y. Zhao, G. Zheng, J. Wang, J. Liu, S. Dong, and J. Xin, “Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm,” Sensors, vol. 25, no. 9, pp. 1–19, 2025, doi: 10.3390/s25092915.

P. Nayak, V. S. Kushwah, J. Parashar, and A. Shukla, “Stock Price Prediction Using LSTM and SVM,” Lect. Notes Networks Syst., vol. 971, pp. 291–302, 2024, doi: 10.1007/978-981-97-2089-7_26.

S. R. Thumu and G. Nellore, “Improving Cryptocurrency Price Prediction Accuracy with Multi-Kernel Support Vector Regression Approach,” Int. Res. J. Multidiscip. Technovation, vol. 6, no. 4, pp. 20–31, 2024, doi: 10.54392/irjmt2443.

S. Ding, Z. Zhang, L. Guo, and Y. Sun, “An optimized twin support vector regression algorithm enhanced by ensemble empirical mode decomposition and gated recurrent unit,” Inf. Sci. (Ny)., vol. 598, pp. 101–125, 2022, doi: 10.1016/j.ins.2022.03.060.

N. M. Farhan and B. Setiaji, “Optimization by Nature: A Review of Genetic Algorithm Techniques,” Indones. J. Comput. Sci., vol. 12, no. 2, pp. 284–301, 2023, [Online]. Available: http://ijcs.stmikindonesia.ac.id/ijcs/index.php/ijcs/article/view/3135

U. Singh, K. Saurabh, N. Trehan, R. Vyas, and O. P. Vyas, “GA-LSTM: Performance Optimization of LSTM driven Time Series Forecasting: GA-LSTM: Performance Optimization..: U. Singh et al.,” Comput. Econ., vol. 66, no. 0123456789, pp. 1–36, 2024, doi: 10.1007/s10614-024-10769-0.

M. S. Hasan, M. Tarequzzaman, M. Moznuzzaman, and M. A. Ahad Juel, “Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm,” Heliyon, vol. 11, no. 2, p. e41765, 2025, doi: 10.1016/j.heliyon.2025.e41765.

K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Glob. Transitions Proc., vol. 3, no. 1, pp. 91–99, 2022, doi: 10.1016/j.gltp.2022.04.020.

M. Munawaroh, T. Azhima, Y. Siswa, and W. J. Pranoto, “Optimasi Metode BMR dan Simulated Annealing untuk Algoritma SVM dalam Mengatasi Imbalanced dan High Dimensional Data Stunting,” JPIP, vol. 5, no. 3, pp. 1–19, 2024, [Online]. Available: https:// ijurnal.com/1/index.php/jpip/article/view/43

N. Huyghues-beaufond, S. Tindemans, P. Falugi, M. Sun, and G. Strbac, “Robust and automatic data cleansing method for short-term load forecasting of distribution feeders,” Appl. Energy, vol. 261, no. November 2019, p. 114405, 2020, doi: 10.1016/j.apenergy.2019.114405.

A. Kurniadi Hermawan, A. Nugroho, and Edora, “Analisa Data Mining Untuk Prediksi Penyakit Ginjal Kronik Dengan Algoritma Regresi Linier,” Bull. Inf. Technol., vol. 4, no. 1, pp. 37–48, 2023, doi: 10.47065/bit.v4i1.475.

M. Buyukkececi and M. C. Okur, “A Comprehensive Review of Feature Selection and Feature Selection Stability in Machine Learning,” Gazi Univ. J. Sci., vol. 36, no. 4, pp. 1506–1520, 2023, doi: 10.35378/gujs.993763.

A. Gupta, Akansha, K. Joshi, M. Patel, and V. Pratap, “Visualization and forecasting of stock’s closing price using machine learning,” Multimed. Tools Appl., vol. 83, no. 29, pp. 72471–72489, 2024, doi: 10.1007/s11042-024-18376-9.

X. Wang, S. Gao, S. Zhou, Y. Guo, Y. Duan, and D. Wu, “Prediction of House Price Index Based on Bagging Integrated WOA-SVR Model,” Math. Probl. Eng., vol. 2021, pp. 1–15, 2021, doi: 10.1155/2021/3744320.

E. Katya, “Exploring Feature Engineering Strategies for Improving Predictive Models in Data Science,” Res. J. Comput. Syst. Eng., vol. 4, no. 2, pp. 201–215, 2023, doi: 10.52710/rjcse.88.

A. Pradasyah and A. Baita, “Comparative Study of Support Vector Regression and Long Short-Term Memory for Stock Price Prediction,” J. Appl. Informatics Comput., vol. 9, no. 4, pp. 1301–1311, 2025.

P. J. M. Ali, “Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements,” ARO-The Sci. J. Koya Univ., vol. X, no. 1, pp. 85–91, 2022, doi: 10.14500/aro.10955.

V. R. Joseph, “Optimal ratio for data splitting,” Stat. Anal. Data Min., vol. 15, no. 4, pp. 531–538, 2022, doi: 10.1002/sam.11583.

B. Supri, Rudianto, Abdurohim, Badriatul Mawadah, and Helmi Ali, “Asian Stock Index Price Prediction Analysis Using Comparison of Split Data Training and Data Testing,” JEMSI (Jurnal Ekon. Manajemen, dan Akuntansi), vol. 9, no. 4, pp. 1403–1408, 2023, doi: 10.35870/jemsi.v9i4.1339.

T. B. Sianturi, I. Cholissodin, and N. Yudistira, “Penerapan Algoritma Long Short-Term Memory (LSTM) berbasis Multi Fungsi Aktivasi Terbobot dalam Prediksi Harga Ethereum,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 3, pp. 1101–1107, 2023, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/12384

Y. Syukriyah and A. Purnama, “Modelling Time Series Data for Stock Prices Prediction Using Bidirectional Long Short-Term Memory,” KNOWBASE Int. J. Knowl. Database, vol. 04, no. 02, pp. 115–129, 2024, doi: 10.30983/knowbase.v4i2.8759.

W. Shao, X. Lin, Y. Hunag, L. Qu, W. Zhuo, and H. Liu, “Effect of Kernel Functions on the Performance of Support Vector Regression Algorithm in Predicting Patient-Specific Organ Doses from CT Scans,” Radiat. Sci. Technol., vol. 11, no. 1, pp. 1–11, 2025, doi: 10.11648/j.rst.20251101.11.

R. Haitham, A. Hussein, and A. Hussein, “Improving the prediction accuracy of a model using SVR with genetic algorithm,” ournal Econ. Adm. Sci., vol. 31, no. 145, 2025, doi: 10.33095/tzpewm39.

K. L. Du, B. Jiang, J. Lu, J. Hua, and M. N. S. Swamy, “Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions,” Mathematics, vol. 12, no. 24, 2024, doi: 10.3390/math12243935.

R. E. Caraka, R. C. Chen, T. Toharudin, M. Tahmid, B. Pardamean, and R. M. Putra, “EVALUATION PERFORMANCE OF SVR GENETIC ALGORITHM AND HYBRID PSO IN RAINFALL FORECASTING,” ICIC Int., vol. 11, no. 7, pp. 631–639, 2020, doi: 10.24507/icicelb.11.07.631.

R. R. Abo-Alsabeh and A. Salhi, “The Genetic Algorithm: A study survey,” Iraqi J. Sci., vol. 63, no. 3, pp. 1215–1231, 2022, doi: 10.24996/ijs.2022.63.3.27.

W. Zhou, Z. Yan, and L. Zhang, “A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction,” Sci. Rep., vol. 14, no. 1, pp. 1–16, 2024, doi: 10.1038/s41598-024-55243-x.

H. Zhang, Z. Nie, and Y. Lian, “A Semi-Trailer Path Planning Method Considering the Surrounding Traffic Conditions and Vehicle Roll Stability,” Appl. Sci., vol. 15, no. 5, 2025, doi: 10.3390/app15052353.

A. Jierula, S. Wang, T. M. Oh, and P. Wang, “Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data,” Appl. Sci., vol. 11, no. 5, pp. 1–21, 2021, doi: 10.3390/app11052314.

DOI: http://dx.doi.org/10.24014/ijaidm.v9i1.39057

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