Optimization Of Recurrent Neural Network In Indonesia Stock Exchange Price Prediction Modeling
Abstract
One of the online trades is stock trading on the stock exchange. To increase the number of investors, the government invites the public to invest in the capital market by buying shares regularly and periodically in the form of shares. With the increase in potential investors in the stock market, deep knowledge about stock trading is needed so that the returns are as desired. This research can help to make it easier for the public to predict the desired stock price through machine learning technology even though they do not have more in-depth technical knowledge about stocks. Three types of algorithms RNN, LSTM, GRU are used to find the best method by optimizing parameters so as to get an r-square error of 0.96 through the number of epochs of 100 and a learning rate of 0.01.
Downloads
Download data is not yet available.
References
[1] “Yuk Nabung Saham,” Bursa Efek Indonesia, 2017. https://yuknabungsaham.idx.co.id/about-yns (accessed Sep. 05, 2021).
[2] P. Dey et al., “Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains,” Algorithms, vol. 14, no. 8, p. 251, Aug. 2021, doi: 10.3390/a14080251.
[3] Y. Lin, S. Liu, H. Yang, and H. Wu, “Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme,” IEEE Access, vol. 9, pp. 101433–101446, 2021, doi: 10.1109/ACCESS.2021.3096825.
[4] S. Ravikumar and P. Saraf, “Prediction of Stock Prices using Machine Learning (Regression, Classification) Algorithms,” in 2020 International Conference for Emerging Technology (INCET), Jun. 2020, pp. 1–5, doi: 10.1109/INCET49848.2020.9154061.
[5] M. A. Istiake Sunny, M. M. S. Maswood, and A. G. Alharbi, “Deep Learning-Based Stock Price Prediction Using LSTM and Bi-Directional LSTM Model,” in 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), Oct. 2020, pp. 87–92, doi: 10.1109/NILES50944.2020.9257950.
[6] K. Kumar and M. T. U. Haider, “Enhanced Prediction of Intra-day Stock Market Using Metaheuristic Optimization on RNN–LSTM Network,” New Gener. Comput., vol. 39, no. 1, pp. 231–272, Apr. 2021, doi: 10.1007/s00354-020-00104-0.
[7] A. S. Saud and S. Shakya, “Analysis of look back period for stock price prediction with RNN variants: A case study on banking sector of NEPSE,” Procedia Comput. Sci., vol. 167, pp. 788–798, 2020, doi: 10.1016/j.procs.2020.03.419.
[8] “Comparative-Analysis-of-RNNs-in-Stock-Price-Prediction.” https://github.com/ehfahad/Comparative-Analysis-of-RNNs-in-Stock-Price-Prediction/blob/main/Comparative-Analysis-of-RNNs-in-Stock-Price-Prediction.zip (accessed Sep. 05, 2021).
[9] PT. Bursa Efek Indonesia, “Kep-00061/BEI/07-2021,” Jakarta, 2021. [Online]. Available: https://www.idx.co.id/media/10022/peraturan_ii_a_perdagangan_efek_bersifat_ekuitas.pdf.
[10] Suyanto DR.ST.M.Sc, K. Nur Ramadhani S.T., M.T, and S. Mandala Ph.D, Modernisasi Machine Learning Untuk Big Data, 1st ed. Bandung: Informatika Bandung, 2019.
[11] Y. Wang, W. Liao, and Y. Chang, “Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting,” Energies, vol. 11, no. 8, p. 2163, Aug. 2018, doi: 10.3390/en11082163.
[12] B. Santosa and A. Umam, Data Mining dan Big Data Analytics, 2nd ed. Yogyakarta: Penebar Media Pustaka, 2018.
[13] D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, p. e623, Jul. 2021, doi: 10.7717/peerj-cs.623.
[14] “Google Colab.” https://colab.research.google.com/ (accessed Sep. 05, 2021).
[15] “Yahoo Finance.” https://finance.yahoo.com/.
[16] “Daftar Penerapan Saham BEI.” https://github.com/nausya/tesis/tree/main/indosaham (accessed Jan. 02, 2021)
[2] P. Dey et al., “Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains,” Algorithms, vol. 14, no. 8, p. 251, Aug. 2021, doi: 10.3390/a14080251.
[3] Y. Lin, S. Liu, H. Yang, and H. Wu, “Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme,” IEEE Access, vol. 9, pp. 101433–101446, 2021, doi: 10.1109/ACCESS.2021.3096825.
[4] S. Ravikumar and P. Saraf, “Prediction of Stock Prices using Machine Learning (Regression, Classification) Algorithms,” in 2020 International Conference for Emerging Technology (INCET), Jun. 2020, pp. 1–5, doi: 10.1109/INCET49848.2020.9154061.
[5] M. A. Istiake Sunny, M. M. S. Maswood, and A. G. Alharbi, “Deep Learning-Based Stock Price Prediction Using LSTM and Bi-Directional LSTM Model,” in 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), Oct. 2020, pp. 87–92, doi: 10.1109/NILES50944.2020.9257950.
[6] K. Kumar and M. T. U. Haider, “Enhanced Prediction of Intra-day Stock Market Using Metaheuristic Optimization on RNN–LSTM Network,” New Gener. Comput., vol. 39, no. 1, pp. 231–272, Apr. 2021, doi: 10.1007/s00354-020-00104-0.
[7] A. S. Saud and S. Shakya, “Analysis of look back period for stock price prediction with RNN variants: A case study on banking sector of NEPSE,” Procedia Comput. Sci., vol. 167, pp. 788–798, 2020, doi: 10.1016/j.procs.2020.03.419.
[8] “Comparative-Analysis-of-RNNs-in-Stock-Price-Prediction.” https://github.com/ehfahad/Comparative-Analysis-of-RNNs-in-Stock-Price-Prediction/blob/main/Comparative-Analysis-of-RNNs-in-Stock-Price-Prediction.zip (accessed Sep. 05, 2021).
[9] PT. Bursa Efek Indonesia, “Kep-00061/BEI/07-2021,” Jakarta, 2021. [Online]. Available: https://www.idx.co.id/media/10022/peraturan_ii_a_perdagangan_efek_bersifat_ekuitas.pdf.
[10] Suyanto DR.ST.M.Sc, K. Nur Ramadhani S.T., M.T, and S. Mandala Ph.D, Modernisasi Machine Learning Untuk Big Data, 1st ed. Bandung: Informatika Bandung, 2019.
[11] Y. Wang, W. Liao, and Y. Chang, “Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting,” Energies, vol. 11, no. 8, p. 2163, Aug. 2018, doi: 10.3390/en11082163.
[12] B. Santosa and A. Umam, Data Mining dan Big Data Analytics, 2nd ed. Yogyakarta: Penebar Media Pustaka, 2018.
[13] D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, p. e623, Jul. 2021, doi: 10.7717/peerj-cs.623.
[14] “Google Colab.” https://colab.research.google.com/ (accessed Sep. 05, 2021).
[15] “Yahoo Finance.” https://finance.yahoo.com/.
[16] “Daftar Penerapan Saham BEI.” https://github.com/nausya/tesis/tree/main/indosaham (accessed Jan. 02, 2021)
Published
2022-06-02
How to Cite
ANDRIYANTO, Dwi; RIANTO, Yan.
Optimization Of Recurrent Neural Network In Indonesia Stock Exchange Price Prediction Modeling.
JISICOM (Journal of Information System, Informatics and Computing), [S.l.], v. 6, n. 1, p. 1-10, june 2022.
ISSN 2597-3673.
Available at: <https://journal.stmikjayakarta.ac.id/index.php/jisicom/article/view/744>. Date accessed: 04 oct. 2023.
doi: https://doi.org/10.52362/jisicom.v6i1.744.
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
.png)
.png)
