Stacked Gated Recurrent Units and Indonesian Stock Predictions: A New Approach to Financial Forecasting
DOI:
https://doi.org/10.35960/ikomti.v5i1.1106Keywords:
Stock Price Prediction, Stacked Gated Recurrent Unit (GRU), Deep Learning, Time Series Forecasting, Financial ForecastingAbstract
This research paper introduces a novel approach to predicting stock prices using a Stacked Gated Recurrent Unit (GRU) model. The model was trained on historical data from the top 10 companies listed on the Indonesia Stock Exchange, covering the period from July 6, 2015, to October 14, 2021. The performance of the model was evaluated using key metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The results demonstrated promising performance, with average RMSE, MAE, and MAPE values of 0.00592, 0.00529, and 0.01654, respectively, indicating a high level of accuracy in the model's predictions. The average R2 value of 0.97808 further suggests a high degree of predictive power, with the model able to explain a significant proportion of the variance in the stock prices. These findings highlight the effectiveness of the Stacked GRU model in capturing stock price patterns and making accurate predictions. The practical implications of this research are significant, as the model provides a powerful tool for forecasting future stock price trends, which can be utilized in investment decision-making, financial analysis, and risk management. Future research could explore other deep learning architectures, incorporate additional features, or consider different evaluation metrics to enhance the model's performance further.
References
R. Li, “New Energy Vehicles Industry Stock Price Prediction Based on ARIMA: Tesla, NIO and BAIC BluePark,” BCPBM, vol. 38, pp. 3375–3382, Mar. 2023, doi: 10.54691/bcpbm.v38i.4310.
K. R. Dahal et al., “A comparative study on effect of news sentiment on stock price prediction with deep learning architecture,” PLOS ONE, vol. 18, no. 4, p. e0284695, Apr. 2023, doi: 10.1371/journal.pone.0284695.
T. Hu, “Stock Price Prediction of the TMT Industry Based on Machine-Learning,” HSET, vol. 49, pp. 250–255, May 2023, doi: 10.54097/hset.v49i.8514.
Y. Fu and H. Xiao, “Stock Price Prediction Model based on Dual Attention and TCN,” in Signal, Image Processing and Embedded Systems Trends, Academy and Industry Research Collaboration Center (AIRCC), Nov. 2022, pp. 99–114. doi: 10.5121/csit.2022.122007.
M. Kabir Ahmed, G. Maksha Wajiga, N. Vachaku Blamah, and B. Modi, “Stock Market Forecasting Using ant Colony Optimization Based Algorithm,” AJMCM, vol. 4, no. 3, p. 52, 2019, doi: 10.11648/j.ajmcm.20190403.11.
C. Zhao, P. Hu, X. Liu, X. Lan, and H. Zhang, “Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction,” Mathematics, vol. 11, no. 5, 2023, doi: 10.3390/math11051130.
Wenqing Yao, Yuting Gu, Sanfei Chang, Jing Li, Qingbo Zhao, and Fangli Ge, “Stock price analysis and forecasting based on machine learning,” presented at the Proc.SPIE, Dec. 2022, p. 1250660. doi: 10.1117/12.2662176.
M. Diqi, A. Sahal, and F. N. Aini, “Multi-Step Vector Output Prediction of Time Series Using EMA LSTM,” Jurnal Online Informatika, vol. 8, no. 1.
W. Chen, H. Zhang, M. K. Mehlawat, and L. Jia, “Mean–variance portfolio optimization using machine learning-based stock price prediction,” Applied Soft Computing, vol. 100, 2021, doi: 10.1016/j.asoc.2020.106943.
X. Chen, “Stock Price Prediction Using Machine Learning Strategies,” BCPBM, vol. 36, pp. 488–497, Jan. 2023, doi: 10.54691/bcpbm.v36i.3507.
H. Song and H. Choi, “Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models,” Applied Sciences, vol. 13, no. 7, 2023, doi: 10.3390/app13074644.
M. Diqi, S. H. Mulyani, and R. Pradila, “DeepCov: Effective Prediction Model of COVID-19 Using CNN Algorithm,” SN Computer Science, vol. 4, no. 4, p. 396, May 2023, doi: 10.1007/s42979-023-01834-w.
M. Ghadimpour and S. babak Ebrahimi, “Forecasting Financial Time Series Using Deep Learning Networks: Evidence from Long-Short Term Memory and Gated Recurrent Unit,” Iranian Journal of Finance, vol. 6, no. 4, pp. 81–94, 2022, doi: 10.30699/ijf.2022.313164.1286.
C. Y. Kang, C. P. Lee, and K. M. Lim, “Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit,” Data, vol. 7, no. 11, 2022, doi: 10.3390/data7110149.
S. Wang, C. Shao, J. Zhang, Y. Zheng, and M. Meng, “Traffic flow prediction using bi-directional gated recurrent unit method,” Urban Informatics, vol. 1, no. 1, p. 16, Dec. 2022, doi: 10.1007/s44212-022-00015-z.
D. Endalie, G. Haile, and W. Taye, “Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction,” PLOS ONE, vol. 17, no. 8, p. e0273156, Aug. 2022, doi: 10.1371/journal.pone.0273156.
C. Li and G. Qian, “Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network,” Applied Sciences, vol. 13, no. 1, 2023, doi: 10.3390/app13010222.
Y. Wang, J. Tang, V. P. Vimal, J. R. Lackner, P. DiZio, and P. Hong, “Crash Prediction Using Deep Learning in a Disorienting Spaceflight Analog Balancing Task,” Front. Physiol., vol. 13, p. 806357, Jan. 2022, doi: 10.3389/fphys.2022.806357.
C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Front. Energy Res., vol. 9, p. 652801, Mar. 2021, doi: 10.3389/fenrg.2021.652801.
R. Ballester-Ripoll, D. Steiner, and R. Pajarola, “Multiresolution Volume Filtering in the Tensor Compressed Domain,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 10, pp. 2714–2727, Oct. 2018, doi: 10.1109/TVCG.2017.2771282.
D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Applied Soft Computing, vol. 97, p. 105524, Dec. 2020, doi: 10.1016/j.asoc.2019.105524.
S. Yuan, “Review of Root-Mean-Square Error Calculation Methods for Large Deployable Mesh Reflectors,” International Journal of Aerospace Engineering, vol. 2022, p. 5352146, Aug. 2022, doi: 10.1155/2022/5352146.
F. Yang, X. Yao, W. Zhang, and Y. Su, “Research and analysis of performance evaluation metrics for estimation algorithms,” in 2016 35th Chinese Control Conference (CCC), Jul. 2016, pp. 5195–5199. doi: 10.1109/ChiCC.2016.7554162.
A. de Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean Absolute Percentage Error for regression models,” Neurocomputing, vol. 192, pp. 38–48, Jun. 2016, doi: 10.1016/j.neucom.2015.12.114.
D. Zhang, “A Coefficient of Determination for Generalized Linear Models,” The American Statistician, vol. 71, no. 4, pp. 310–316, Oct. 2017, doi: 10.1080/00031305.2016.1256839.
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