Prediksi Harga Penutupan Saham Gojek-Tokopedia Menggunakan Model Hybrid GARCH-LSTM
- Farhan Wegig Pramudito Universitas Sebelas Maret, Surakarta, Indonesia
- Kezia Jazzlyn Arianto Universitas Sebelas Maret, Surakarta, Indonesia
- Najma Humairoh Thoyib Universitas Sebelas Maret, Surakarta, Indonesia
-
Risquina Angelica Arvintyani
*
Universitas Sebelas Maret, Surakarta,
Indonesia
- Yudhistira Jalu Herlambang Universitas Sebelas Maret, Surakarta, Indonesia
- Shaifudin Zuhdi Universitas Sebelas Maret, Surakarta, Indonesia (*) Corresponding Author
Abstract
This study proposes the application of a hybrid GARCH–LSTM model to predict GoTo stock prices in the context of Indonesia's rapidly growing digital economy. GoTo stock prices are characterized by high volatility and a non-linear time series pattern, making them difficult to model using conventional approaches. Daily closing price data from 2022 to November 2025 are transformed into logarithmic returns to meet the stationarity assumption. The GARCH(1,1) model is used to estimate conditional volatility, which represents short-term risk dynamics and the volatility clustering phenomenon. Furthermore, historical returns and conditional volatility are used as additional features in the LSTM model to predict the next period's stock returns, which are then converted back into closing price predictions. The estimation results show that all GARCH parameters are statistically significant, indicating the persistence of volatility in GoTo stock data. Evaluation of the performance of the hybrid model on the test data produces an RMSE value of 3.126, an MAE of 2.245, and a coefficient of determination (R²) of 0.899, indicating that the model is able to represent stock price movement patterns well. These findings indicate that the hybrid GARCH–LSTM approach is effective in modeling stock price dynamics under highly volatile market conditions.
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References
M. Zahid, F. Iqbal, and D. Koutmos, “Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning,” Risks, vol. 10, no. 12, 2022, doi: 10.3390/risks10120237.
K. Kakade, A. K. Mishra, K. Ghate, and S. Gupta, “Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH-LSTM based Approach,” Intell. Syst. Accounting, Financ. Manag., vol. 29, no. 2, pp. 103–117, 2022, doi: 10.1002/isaf.1515.
J. Liu, “A Hybrid Model Integrating LSTM with Multiple GARCH-Type Models for Volatility and Var Forecast,” no. 1, 2023, doi: 10.4108/eai.6-1-2023.2330313.
K. Kazungu and J. R. Mboya, “Volatility of Stock Prices in Tanzania: Application of Garch Models To Dar Es Salaam Stock Exchange,” Asian J. Econ. Model., vol. 9, no. 1, pp. 15–28, 2021, doi: 10.18488/journal.8.2021.91.15.28.
D. B. Nugroho, D. Kurniawati, L. P. Panjaitan, Z. Kholil, B. Susanto, and L. R. Sasongko, “Empirical performance of GARCH, GARCH-M, GJR-GARCH and log-GARCH models for returns volatility,” J. Phys. Conf. Ser., vol. 1307, no. 1, 2019, doi: 10.1088/1742-6596/1307/1/012003.
H. Pan, Y. Tang, and G. Wang, “A Stock Index Futures Price Prediction Approach Based on the MULTI-GARCH-LSTM Mixed Model,” Mathematics, vol. 12, no. 11, 2024, doi: 10.3390/math12111677.
M. Diqi, I. W. Ordiyasa, and H. Hamzah, “Enhancing Stock Price Prediction Using Stacked Long Short-Term Memory,” IT J. Res. Dev., vol. 8, no. 2, pp. 164–174, 2024, doi: 10.25299/itjrd.2023.13486.
L. N. A. Mualifah, A. M. Soleh, and K. A. Notodiputro, “Comparison of GARCH, LSTM, and Hybrid GARCH-LSTM Models for Analyzing Data Volatility,” Int. J. Adv. Soft Comput. its Appl., vol. 16, no. 2, pp. 150–165, 2024, doi: 10.15849/IJASCA.240730.10.
K. Xu, Y. Wu, M. Jiang, W. Sun, and Z. Yang, “Hybrid LSTM-GARCH Framework for Financial Market Volatility Risk Prediction,” J. Comput. Sci. Softw. Appl., vol. 4, no. 5, pp. 22–29, 2024, [Online]. Available: https://www.mfacademia.org/index.php/jcssa/article/view/158
Y. Hu, J. Ni, and L. Wen, “A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction,” Phys. A Stat. Mech. its Appl., vol. 557, p. 124907, 2020, doi: 10.1016/j.physa.2020.124907.
I. M. Nur, R. Nugrahanto, and F. Fauzi, “Cryptocurrency Price Prediction: a Hybrid Long Short-Term Memory Model With Generalized Autoregressive Conditional Heteroscedasticity,” Barekeng, vol. 17, no. 3, pp. 1575–1584, 2023, doi: 10.30598/barekengvol17iss3pp1575-1584.
R. L. Manogna, V. Dharmaji, and S. Sarang, “A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India,” J. Big Data, vol. 12, no. 1, 2025, doi: 10.1186/s40537-025-01131-8.
N. Tripathy et al., “Bitcoin volatility forecasting: a comparative analysis of conventional econometric models with deep learning models,” Int. J. Electr. Comput. Eng., vol. 15, no. 1, pp. 614–623, 2025, doi: 10.11591/ijece.v15i1.pp614-623.
N.-B.-V. Le, Y.-S. Seo, and J.-H. Huh, “AgTech: Volatility Prediction for Agricultural Commodity Exchange Trading Applied Deep Learning,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3479868.
Z. Huang, I. Sangiorgi, and A. Urquhart, “Journal of International Financial Markets , Forecasting Bitcoin volatility using machine learning techniques,” J. Int. Financ. Mark. Institutions Money, vol. 97, p. 102064, 2024, doi: 10.1016/j.intfin.2024.102064.
I. M. A. Dharmaningrat, H. Margaretha, and K. V. I. Saputra, “Predicting the Volatility of Jakarta Composite Index Using GARCH and LSTM with Volume-Up Strategy Approach,” Journal of Information Systems Engineering and Business Intelligence, vol. 11, no. 3, pp. 311–322, Oct. 2025, doi: 10.20473/jisebi.11.3.311-322.
D. Léber and B. Egyed, “The Sentiment Augmented GARCH-LSTM Hybrid Model for Value-at-Risk Forecasting,” Comput Econ, 2025, doi: 10.1007/s10614-025-11042-8.
M. Ez-zaiym, Y. Senhaji, M. Rachid, K. el Moutaouakil, and V. Palade, “Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting,” Mathematics, vol. 13, no. 13, July 2025, doi: 10.3390/math13132068.
E. Nsengiyumva, J. K. Mung’atu, and C. Ruranga, “Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk,” FinTech, vol. 4, no. 2, June 2025, doi: 10.3390/fintech4020022.
N. V. Atoi, “Testing Volatility in Nigeria Stock Market using GARCH Models,” CBN Journal of Applied Statistics, vol. 5, no. 2, Dec. 2014. [Online]. Available: https://dc.cbn.gov.ng/jas/vol5/iss2/4
K. Kakade, I. Jain, and A. K. Mishra, “Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach,” Resour. Policy, vol. 78, p. 102903, Sep. 2022, doi: 10.1016/J.RESOURPOL.2022.102903.
H. Yıldırım and F. V. Bekun, “Predicting volatility of bitcoin returns with ARCH , GARCH and EGARCH models,” Futur. Bus. J., vol. 9, no. 1, pp. 1–8, 2023, doi: 10.1186/s43093-023-00255-8.
A. G. Medina and E. A. Moreno, LSTM – GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios, vol. 63, no. 4. Springer US, 2024. doi: 10.1007/s10614-023-10373-8.
J. K. Mutinda and A. K. Langat, “Stock price prediction using combined GARCH-AI models,” Sci. African, vol. 26, no. July, p. e02374, 2024, doi: 10.1016/j.sciaf.2024.e02374.
H. Agarwal, G. Mahajan, A. Shrotriya, and D. Shekhawat, “ScienceDirect Predictive Data Analysis : Leveraging RNN and LSTM Techniques for Time Series Dataset,” Procedia Comput. Sci., vol. 235, no. 2023, pp. 979–989, 2024, doi: 10.1016/j.procs.2024.04.093.
J. Crooks, “Long Short-Term Memory Networks : Overcoming Vanishing Gradient Problem in Recurrent Neural Networks,” vol. 12, pp. 1–2, 2023, doi: 10.37421/2090-4886.2023.12.212.
H. Yadav and A. Thakkar, “NOA-LSTM : An efficient LSTM cell architecture for time series forecasting,” Expert Syst. Appl., vol. 238, no. PF, p. 122333, 2024, doi: 10.1016/j.eswa.2023.122333.
J. Kim, H. Kim, H. G. Kim, D. Lee, and S. Yoon, “A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges,” Artif Intell Rev, vol. 58, no. 7, July 2025, doi: 10.1007/s10462-025-11223-9.
R. C. Staudemeyer and E. R. Morris, “a tutorial into Long Short-Term Memory Recurrent Neural Networks,” Sept. 2019, [Online]. Available: http://arxiv.org/abs/1909.09586
P. Zhao, H. Zhu, W. Siu, H. Ng, and D. L. Lee, “From GARCH to Neural Network for Volatility Forecast,” 2024. [Online]. Available: www.aaai.org
A. Petrozziello et al., “Deep learning for volatility forecasting in asset management,” Soft Comput., vol. 26, no. 17, pp. 8553–8574, 2022, doi: 10.1007/s00500-022-07161-1.
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Copyright (c) 2026 Farhan Wegig Pramudito, Kezia Jazzlyn Arianto, Najma Humairoh Thoyib, Risquina Angelica Arvintyani, Yudhistira Jalu Herlambang, Syaifudin Zuhdi
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