Stock Price Prediction and Risk Estimation Using Hybrid CNN-LSTM and VaR-ECF
DOI:
https://doi.org/10.52436/1.jutif.2025.6.3.4648Keywords:
CNN-LSTM, Deep Learning, Expected Capital Flow (ECF), Stock Price Prediction, Value at Risk (VaR)Abstract
Stock price prediction is a major challenge in the financial domain due to high volatility and complex movement patterns. Traditional methods such as fundamental and technical analysis often fail to capture the non-linear characteristics and fast-changing market dynamics, highlighting the need for more adaptive approaches. This study proposes a hybrid deep learning model, CNN-LSTM, which combines CNN's local feature extraction capabilities with LSTM’s ability to model long-term temporal dependencies. To incorporate risk management, the model is also integrated with the Value at Risk (VaR) approach using the Cornish-Fisher Expansion (ECF) to estimate potential losses under extreme market conditions. The study utilizes daily historical stock price data of PT Unilever Indonesia Tbk retrieved from Yahoo Finance. Model performance is evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), where the model achieves an MAE of 78.13 and a MAPE of 2.72%, indicating relatively low absolute and relative prediction errors. These results confirm that the CNN-LSTM approach effectively models stock price movements in dynamic market environments, and the integration with VaR-ECF provides a more comprehensive risk estimate. Thus, this approach not only enhances predictive accuracy but also offers valuable decision-support tools for investors in planning investment strategies.
Downloads
References
F. Seru, “ANALISIS RISIKO VAR DAN CVAR PADA HASIL PREDIKSI HARGA SAHAM PT. ASTRA INTERNATIONAL TBK,” Bulan Juni, 2023. [Online]. Available: http://journal.umpo.ac.id/index.php/silogisme
D. I. Asih Maruddani and D. Ispriyanti, “PEMODELAN HARGA SAHAM DENGAN GEOMETRIC BROWNIAN MOTION DAN VALUE AT RISK PT CIPUTRA DEVELOPMENT Tbk,” JURNAL GAUSSIAN, vol. 6, no. 2, pp. 261–270, 2020, [Online]. Available: http://ejournal-s1.undip.ac.id/index.php/gaussian
K. S. Moon and H. Kim, “Performance of deep learning in prediction of stock market volatility,” Econ Comput Econ Cybern Stud Res, vol. 53, no. 2, pp. 77–92, 2019, doi: 10.24818/18423264/53.2.19.05.
T. Trimono, I. Gede Susrama, K. Maulida H, and M. Idhom, “Model ARIMA-ARCH/GARCH dan Ensemble ARIMA-ARCH/GARCH untuk Prediksi Kerugian pada Harga Komoditas Pertanian,” Seminar Nasional Sains Data, vol. 2021.
M. Nasrudin, E. Setyowati, and S. S. May Wara, “Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector,” Jurnal Varian, vol. 8, no. 1, pp. 89–96, Nov. 2024, doi: 10.30812/varian.v8i1.4239.
M. Idhom, A. Fauzi, T. Trimono, and P. Riyantoko, “Time Series Regression: Prediction of Electricity Consumption Based on Number of Consumers at National Electricity Supply Company,” TEM Journal, vol. 12, no. 3, pp. 1575–1581, Aug. 2023, doi: 10.18421/TEM123-39.
W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, “A CNN-LSTM-based model to forecast stock prices,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/6622927.
N. Gupta and A. S. Jalal, “Integration of textual cues for fine-grained image captioning using deep CNN and LSTM,” Neural Comput Appl, vol. 32, no. 24, pp. 17899–17908, Dec. 2020, doi: 10.1007/s00521-019-04515-z.
M. Maliki, Cholissodin. Imam, and N. Yudistira, “Prediksi Pergerakan Harga Cryptocurrency Bitcoin terhadap Mata Uang Ru[iah menggunakan Algoritme LSTM,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. Vol. 6, No.7, p. 32593268, Jul. 2022.
H. Y. Kim and C. H. Won, “Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models,” Expert Syst Appl, vol. 103, pp. 25–37, Aug. 2020, doi: 10.1016/j.eswa.2018.03.002.
A. Arfan and L. ETP, “Perbandingan Algoritma Long Short-Term Memory dengan SVR Pada Prediksi Harga Saham di Indonesia,” PETIR, vol. 13, no. 1, pp. 33–43, Mar. 2020, doi: 10.33322/petir.v13i1.858.
D. A. Prasetya, A. P. Sari, P. A. Riyantoko, and T. M. Fahrudin, “The Effect of Information Quality and Service Quality on User Satisfaction of the Government of Kabupaten Malang,” TIERS Information Technology Journal, vol. 4, no. 1, pp. 32–42, Jun. 2023, doi: 10.38043/tiers.v4i1.4328.
B. S. Kim and T. G. Kim, “Cooperation of simulation and data model for performance analysis of complex systems,” International Journal of Simulation Modelling, vol. 18, no. 4, pp. 608–619, Dec. 2019, doi: 10.2507/IJSIMM18(4)491.
M. L. Brocardo, I. Traore, I. Woungang, and M. S. Obaidat, “Authorship verification using deep belief network systems,” International Journal of Communication Systems, vol. 30, no. 12, Aug. 2019, doi: 10.1002/dac.3259.
P. Aji Riyantoko, T. Maulana Fahruddin, K. Maulida Hindrayani, and E. Maya Safitri, “ANALISIS PREDIKSI HARGA SAHAM SEKTOR PERBANKAN MENGGUNAKAN ALGORITMA LONG-SHORT TERMS MEMORY (LSTM),” Seminar Nasional Informatika, vol. 2020.
Z. Jin, Y. Yang, and Y. Liu, “Stock closing price prediction based on sentiment analysis and LSTM,” Neural Comput Appl, vol. 32, no. 13, pp. 9713–9729, Jul. 2020, doi: 10.1007/s00521-019-04504-2.
I. Athallah Taufik and A. Muhaimin, “Prediction of the Islamic Stock Price Index and Risk of Loss Using the LSTM (Long Short-Term Memory) and VaR (Value at Risk) Methods,” vol. 4, no. 1, pp. 11–22, doi: 10.3390/xxxxx.
A. Yadav, C. K. Jha, and A. Sharan, “Optimizing LSTM for time series prediction in Indian stock market,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 2091–2100. doi: 10.1016/j.procs.2020.03.257.
S. Borovkova and I. Tsiamas, “An ensemble of LSTM neural networks for high-frequency stock market classification,” J Forecast, vol. 38, no. 6, pp. 600–619, Sep. 2019, doi: 10.1002/for.2585.
R. Faza Inaku and J. C. Chandra, “Implementasi Data Mining dalam Prediksi Harga Saham Menggunakan Metode Long Short Term Memory (LSTM),” Jurnal TICOM: Technology of Information and Communication, vol. 12, no. 1, 2023.
N. C. Petersen, F. Rodrigues, and F. Camara Pereira, “Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network.”
V. D. Ta, C. M. Liu, and D. A. Tadesse, “Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading,” Applied Sciences (Switzerland), vol. 10, no. 2, Jan. 2020, doi: 10.3390/app10020437.
C. I. Garcia, F. Grasso, A. Luchetta, M. C. Piccirilli, L. Paolucci, and G. Talluri, “A comparison of power quality disturbance detection and classification methods using CNN, LSTM and CNN-LSTM,” Applied Sciences (Switzerland), vol. 10, no. 19, pp. 1–22, Oct. 2020, doi: 10.3390/app10196755.
P. Aji Riyantoko and F. Agista, “Model ARMA-GARCH dan Ensemble ARMA-GARCH untuk Prediksi Value-at-Risk pada Portofolio Saham,” Seminar Nasional Sains Data, vol. 2022.
A. Arimond SDGLabsai Sweden et al., “NEURAL NETWORKS AND VALUE AT RISK IN ASSET MANAGEMENT A PREPRINT Acknowledgments: We are grateful for comments from Neural Networks and Value at Risk in Asset Management A PREPRINT Neural Networks and Value at Risk in Asset Management A PREPRINT.”
Trimono and F. Agista, “Model ARMA-GARCH Prediksi Value-at-Risk pada Saham”.
R. Andespa and D. I. Asih Maruddani, “EXPECTED SHORTFALL DENGAN EKSPANSI CORNISH-FISHER UNTUK ANALISIS RISIKO INVESTASI SEBELUM DAN SESUDAH PANDEMI COVID-19 DILENGKAPI GUI R,” vol. 11, no. 2, pp. 173–182, 2022, [Online]. Available: https://ejournal3.undip.ac.id/index.php/gaussian/
M. R. Firmansyah, R. Ilyas, and F. Kasyidi, “Klasifikasi Klimat Ilmiah Menggunakan Reccurent Neural Network,” 2020.
D. A. Prasetya, A. Sanusi, G. Chandrarin, E. Roikhah, I. Mujahidin, and R. Arifuddin, “Small and Medium Enterprises Problem and Potential Solutions for Waste Management,” Journal of Southwest Jiaotong University, vol. 54, no. 6, 2019, doi: 10.35741/issn.0258-2724.54.6.21.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Alvi Yuana Febriyanti, Dwi Arman Prasetya, Trimono
This work is licensed under a Creative Commons Attribution 4.0 International License.
