Optimization of Stock Movements Based on Historical Data for Stock Index Prediction Using Deep Learning Models
Keywords:
Deep Learning Models, Stock Index Prediction, Jakarta Composite Index, Historical DataAbstract
Investors, analysts, and intellectuals are consistently known for predicting stock movements. Therefore, this research focuses on the importance of simplicity, relying solely on stock data that includes open, high, low, close, and volume prices. The objective is to forecast stock movements on the Indonesia Stock Exchange (IDX). To achieve the desired result, three well-known Deep Learning architectures were used namely, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Recurrent Neural Networks (RNN). Furthermore, the architectures assisted in creating a series of prediction models based on the datasets. The dataset was sourced from the index of the Indonesian Stock Exchange, known as the Jakarta Composite Index. Through experimentation, efficiency, reliability, and susceptibility to fluctuations in data for each architecture were assessed. Consequently, the results showed that historical data alone could be used to create a stock prediction model, particularly when approached correctly. Among the three architectures explored, there was an observation that RNN achieved the highest level of prediction accuracy as the research signified the importance of simplicity in modeling. Based on the findings, further research could develop streamlined and effective stock prediction models that rely on minimal data.
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A. H. Manurung, W. Budiharto, and H. Prabowo, “Algorithm and modeling of stock prices forecasting based on long short-term memory (LSTM),” ICIC Express Lett., vol. 12, no. 12, pp. 1277–1283, 2018, doi: 10.24507/icicel.12.12.1277.
M. Sharaf, E. E. D. Hemdan, A. El-Sayed, and N. A. El-Bahnasawy, “StockPred: a framework for stock Price prediction,” Multimed. Tools Appl., vol. 80, no. 12, pp. 17923–17954, 2021, doi: 10.1007/s11042-021-10579-8.
X. Zhang, N. Gu, J. Chang, and H. Ye, “Predicting stock price movement using a DBN-RNN,” Appl. Artif. Intell., vol. 35, no. 12, pp. 876–892, 2021, doi: 10.1080/08839514.2021.1942520.
W. Khan, M. A. Ghazanfar, M. A. Azam, A. Karami, K. H. Alyoubi, and A. S. Alfakeeh, “Stock market prediction using machine learning classifiers and social media, news,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 7, pp. 3433–3456, 2022, doi: 10.1007/s12652-020-01839-w.
S. K. Chandar, “Convolutional neural network for stock trading using technical indicators,” Autom. Softw. Eng., vol. 29, no. 1, pp. 1–14, 2022, doi: 10.1007/s10515-021-00303-z.
H. Liu and Z. Long, “An improved deep learning model for predicting stock market price time series,” Digit. Signal Process. A Rev. J., vol. 102, p. 102741, 2020, doi: 10.1016/j.dsp.2020.102741.
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, 2020, doi: 10.1007/s00521-019-04504-2.
S. Das and S. Mishra, “Advanced deep learning framework for stock value prediction,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 10, pp. 2358–2367, 2019, doi: 10.35940/ijitee.B2453.0881019.
Y. Li and Y. Pan, “A novel ensemble deep learning model for stock prediction based on stock prices and news,” Int. J. Data Sci. Anal., vol. 13, no. 2, pp. 139–149, 2022, doi: 10.1007/s41060-021-00279-9.
T. T. Nguyen and S. Yoon, “A novel approach to short-term stock price movement prediction using transfer learning,” Appl. Sci., vol. 9, no. 22, 2019, doi: 10.3390/app9224745.
M. Smorada, A. Lukáčková, Z. Hajduová, Ľ. Šrenkel, and J. Havier, “Predictive Financial Analysis as a Tool for Business Management,” TEM J., vol. 12, no. 1, pp. 147–155, 2023, doi: 10.18421/TEM121-20.
X. Ji, J. Wang, and Z. Yan, “A stock price prediction method based on deep learning technology,” Int. J. Crowd Sci., vol. 5, no. 1, pp. 55–72, 2021, doi: 10.1108/IJCS-05-2020-0012.
M. Jarrah and N. Salim, “A recurrent neural network and a discrete wavelet transform to predict the Saudi stock price trends,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 4. pp. 155–162, 2019. doi: 10.14569/ijacsa.2019.0100418.
Y. F. Lin, T. M. Huang, W. H. Chung, and Y. L. Ueng, “Forecasting Fluctuations in the Financial Index Using a Recurrent Neural Network Based on Price Features,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 5, no. 5, pp. 780–791, 2021, doi: 10.1109/TETCI.2020.2971218.
U. Gupta, V. Bhattacharjee, and P. S. Bishnu, “StockNet—GRU based stock index prediction,” Expert Syst. Appl., vol. 207, no. April, p. 117986, 2022, doi: 10.1016/j.eswa.2022.117986.
D. Lien Minh, A. Sadeghi-Niaraki, H. D. Huy, K. Min, and H. Moon, “Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network,” IEEE Access, vol. 6. pp. 55392–55404, 2018. doi: 10.1109/ACCESS.2018.2868970.
K. Kumar and D. P. Gandhmal, “An intelligent indian stock market forecasting system using LSTM deep learning,” Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 2, pp. 1082–1089, 2020, doi: 10.11591/ijeecs.v21.i2.pp1082-1089.
T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European Journal of Operational Research, vol. 270, no. 2. pp. 654–669, 2018. doi: 10.1016/j.ejor.2017.11.054.
Y. Baek and H. Y. Kim, “ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module,” Expert Systems with Applications, vol. 113. pp. 457–480, 2018. doi: 10.1016/j.eswa.2018.07.019.
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, 2019, doi: 10.1002/for.2585.
J. Luo, G. Zhu, and H. Xiang, “Artificial Intelligent based day-ahead stock market profit forecasting,” Comput. Electr. Eng., vol. 99, no. February, p. 107837, 2022, doi: 10.1016/j.compeleceng.2022.107837.
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 Systems with Applications, vol. 103. pp. 25–37, 2018. doi: 10.1016/j.eswa.2018.03.002.
X. Pang, Y. Zhou, P. Wang, W. Lin, and V. Chang, “An innovative neural network approach for stock market prediction,” J. Supercomput., vol. 76, no. 3, pp. 2098–2118, 2020, doi: 10.1007/s11227-017-2228-y.
A. Q. Md et al., “Novel optimization approach for stock price forecasting using multi-layered sequential LSTM,” Appl. Soft Comput., vol. 134, p. 109830, Feb. 2023, doi: 10.1016/j.asoc.2022.109830.
M. Nabipour, P. Nayyeri, H. Jabani, S. Shahab, and A. Mosavi, “Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; A Comparative Analysis,” IEEE Access, vol. 8. pp. 150199–150212, 2020. doi: 10.1109/ACCESS.2020.3015966.
T. Swathi, N. Kasiviswanath, and A. A. Rao, “An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis,” Appl. Intell., pp. 13675–13688, 2022, doi: 10.1007/s10489-022-03175-2.
W. Budiharto, “Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM),” J. Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00430-0.
A. S. Saud and S. Shakya, “3-Way Gated Recurrent Unit Network Architecture for Stock Price Prediction,” Indian J. Comput. Sci. Eng., vol. 12, no. 2, pp. 421–427, 2021, doi: 10.21817/indjcse/2021/v12i2/211202011.
C. Chen, L. Xue, and W. Xing, “Research on Improved GRU-Based Stock Price Prediction Method,” Appl. Sci., vol. 13, no. 15, 2023, doi: 10.3390/app13158813.
M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and S. Shahab, “Deep learning for stock market prediction,” Entropy, vol. 22, no. 8, 2020, doi: 10.3390/E22080840.
M. Li, Y. Zhu, Y. Shen, and M. Angelova, “Clustering-enhanced stock price prediction using deep learning,” World Wide Web, 2022, doi: 10.1007/s11280-021-01003-0.
D. Satria, “Predicting Banking Stock Prices Using Rnn, Lstm, and Gru Approach,” Appl. Comput. Sci., vol. 19, no. 1, pp. 82–94, 2023, doi: 10.35784/acs-2023-06.
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