Comparative Study of Linear Regression, SVR, and XGBoost for Stock Price Prediction After a Stock Split
DOI:
https://doi.org/10.30871/jaic.v9i4.10220Keywords:
BBCA, Linear Regression, Stock Price Prediction, SVR, XGBoostAbstract
This study aims to identify the most effective regression method for predicting the closing stock price of Bank Central Asia (BBCA) following the stock split event on October 12, 2021. Accurate post-split price predictions are crucial for helping investors comprehend new market dynamics, yet there is limited research evaluating the performance of regression models on BBCA’s stock after such corporate actions. Using data obtained through web scraping from the Indonesia Stock Exchange, this study tested three regression algorithms Linear Regression, Support Vector Regression, and XGBoost Regressor on post-split data. The selected input features were open_price, first_trade, high, low, and volume, while the target was close_price. The dataset was divided using an 80:20 train-test split and evaluated with RMSE, MAPE, and R-squared metrics. Results showed that Linear Regression achieved the best performance RMSE: 50.41, MAPE: 0.0048, R²: 0.9971, followed by XGBoost RMSE: 69.12, MAPE: 0.0058, R²: 0.9946, and SVR RMSE: 80.98, MAPE: 0.0069, R²: 0.9925. These findings indicate that BBCA’s post-split stock data exhibits a linear pattern, making Linear Regression the most suitable and efficient method. This suggests that simpler models can outperform more complex algorithms when applied to stable and structured financial datasets.
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[1] E. Mardiyaningsih and R. Andhitiyara, “Analisis Perbandingan Sebelum Dan Sesudah Stock Split Dengan Tingkat Likuiditas Saham, Harga Saham, Dan Return Saham Pada Indeks Saham Kompas 100 Tahun 2014 - 2018,” J. Inf. Syst. Applied, Manag. Account. Res., vol. 4, no. 1, pp. 1–13, 2020, [Online]. Available: http://journal.stmikjayakarta.ac.id/index.php/jisamar/article/view/166
[2] A. Rohim, “Analisis Perbedaan Nilai Harga Saham Sebelum Dan Setelah Stock Split,” JMD J. Ris. Manaj. Bisnis Dewantara, vol. 4, no. 1, pp. 55–66, 2021, doi: 10.26533/jmd.v4i1.771.
[3] A. Wiejaya and I. Fenriana, “Prediksi Harga Saham Top 10 NASDAQ dengan Time Series Prophet,” vol. 7, no. 2, 2024, doi: 10.32877/bt.v7i2.1736.
[4] I. B. P. S. Ni Kadek Wiwik Yuniartini, “Dampak stock split terhadap harga saham dan aktivitas volume perdagangan saham di Bursa Efek Indonesia (Doctoral dissertation, Udayana University).
[5] P. Sabdowati and Z. Zulmaita, “Analisis Pengaruh Stock Split Pada Pergerakan Saham PT. Bank Central Asia Tbk,” Semin. Nas. Akunt. dan …, 2022, [Online]. Available: http://prosiding-old.pnj.ac.id/index.php/snampnj/article/download/5890/2993
[6] Galih Adhi Putratama, Satya Maulana Fahreza, and Yudhistira Rakha Ramandhani, “Evaluasi Komparatif Metode Machine Learning Untuk Memprediksi Perubahan Harga Saham,” Antivirus J. Ilm. Tek. Inform., vol. 17, no. 2, pp. 278–285, 2024, doi: 10.35457/antivirus.v17i2.2871.
[7] B. Jange, “Prediksi Harga Saham Bank BCA Menggunakan XGBoost,” Arbitr. J. Econ. Account., vol. 3, no. 2, pp. 231–237, 2022, doi: 10.47065/arbitrase.v3i2.495.
[8] Ricky, “Implementasi Model Machine Learning Dalam Memprediksi Return Saham,” UIN Syarif Hidayatullah Jakarta, 2022. [Online]. Available: http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf
[9] M. Magdalena, A. P. Safira, and I. Maulida, “Penerapan Algoritma Linear Regression Dalam Memprediksi Harga Saham Bank BRI,” vol. 2, no. 3, 2024.
[10] R. Julian and M. R. Pribadi, “Peramalan Harga Saham Pertambangan Pada Bursa Efek Indonesia (BEI) Menggunakan Long Short Term Memory (LSTM),” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 3, pp. 1570–1580, 2021, doi: 10.35957/jatisi.v8i3.1159.
[11] B. Jange, “Prediksi Harga Saham Bank BCA Menggunakan Prophet,” Arbitr. J. Econ. Account., vol. 3, no. 2, pp. 231–237, 2022, doi: 10.47065/arbitrase.v3i2.495.
[12] A. P. W. Ihda Innar Ridho, Cerah Fitri Ramadhani, “Penerapan Artificial Neural Network dengan Algoritma Backpropagation untuk Memprediksi Harga Saham,” J. Ris. Stat., vol. 8, pp. 107–118, 2023, doi: 10.29313/jrs.v3i2.2953.
[13] S. Soewignjo, Sediono, M. F. F. Mardianto, and E. Pusporani, “Prediksi Harga Saham Bank BCA (BBCA) Pasca Stock Split dengan Artificial Neural Network dengan Algoritma Backpropagation,” G-Tech J. Teknol. Terap., vol. 7, no. 4, pp. 1683–1693, 2023, doi: 10.33379/gtech.v7i4.3363.
[14] 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.
[15] A. A. Shehadeh, S. M. Alwadi, and M. I. Almaharmeh, “Detecting and Analysing Possible Outliers in Global Stock Market Returns,” Cogent Econ. Financ., vol. 10, no. 1, 2022, doi: 10.1080/23322039.2022.2066762.
[16] K. Alkhatib, H. Khazaleh, H. A. Alkhazaleh, A. R. Alsoud, and L. Abualigah, “A New Stock Price Forecasting Method Using Active Deep Learning Approach,” J. Open Innov. Technol. Mark. Complex., vol. 8, no. 2, 2022, doi: 10.3390/joitmc8020096.
[17] S. Kang and J.-K. Kim, “Stock Price Prediction Using Triple Barrier Labeling and Raw OHLCV Data: Evidence from Korean Markets,” 2025, [Online]. Available: https://arxiv.org/pdf/2504.02249v1
[18] J. M. H. Pinheiro et al., “The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks,” vol. XX, no. X, 2025, [Online]. Available: http://arxiv.org/abs/2506.08274
[19] R. Plachý, “Impact of trading volume on prediction of stock market development,” Acta Univ. Agric. Silvic. Mendelianae Brun., vol. 62, no. 6, pp. 1373–1380, 2014, doi: 10.11118/actaun201462061373.
[20] K. Qu, “Research on linear regression algorithm,” MATEC Web Conf., vol. 395, p. 01046, 2024, doi: 10.1051/matecconf/202439501046.
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