Performance evaluation of stock price prediction models using EMAGRU

• Autorzy: Erizal, Erizal , Diqi, Mohammad
• Języki publikacji: EN

Abstrakty

EN

Stock price prediction is an exciting issue and is very much needed by investors and business people to develop their assets. The main difficulties in predicting stock prices are dynamic movements, high volatility, and noises caused by company performance and external influences. The traditional method investors use is the technical analysis based on statistics, valuation of previous stock portfolios, and news from the mass media and social media. Deep learning can predict stock price movements more accurately than traditional methods. As a solution to the issue of stock prediction, the authors offer the Exponential Moving Average Gated Recurrent Unit (EMAGRU) model and demonstrate its utility. The EMAGRU architecture contains two stacked GRUs arranged in parallel. The inputs and outputs are the EMA10 and EMA20, formed from the closing prices over ten years. The authors also combine the AntiReLU and ReLU activation functions into the model so that EMAGRU has 6 model variants. The proposed model produces low losses and high accuracy. RMSE, MEPA, MAE, and R^2 are 0.0060, 0.0064, 0.0050, and 0.9976 for EMA10, and 0.0050, 0.0058, 0.0045, and 0.9982 for EMA20, respectively.

Słowa kluczowe

EN

stock prediction; deep learning; GRU; EMA; ReLU; AntiReLU

• Czasopismo: Applied Computer Science
• Rocznik: 2023
• Tom: Vol. 19, no 3
• Strony: 160--173
• Opis fizyczny: Bibliogr. 31 poz., fig., tab.

Twórcy

autor

Erizal, Erizal

• Universitas Respati Yogyakarta, Faculty of Science and Technology, Department of Information System, Indonesia,

autor

Diqi, Mohammad

• Universitas Respati Yogyakarta, Faculty of Science and Technology, Department of Informatics, Indonesia,

Bibliografia

• [1] Chen, W., Jiang, M., Zhang, W. G., & Chen, Z. (2021). A novel graph convolutional feature based convolutional neural network for stock trend prediction. Information Sciences, 556, 67-94. https://doi.org/10.1016/j.ins.2020.12.068
• [2] Chen, W., Zhang, H., Mehlawat, M. K., & Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, 106943. https://doi.org/10.1016/j.asoc.2020.106943
• [3] Chiniforoush, N., & Latif Shabgahi, G. (2021). A novel method for forecasting surface wind speed using wind-direction based on hierarchical markov model. International Journal of Engineering, 34(2), 414-426. https://doi.org/10.5829/ije.2021.34.02b.13
• [4] Chun, J., Ahn, J., Kim, Y., & Lee, S. (2021). Using deep learning to develop a stock price prediction model based on individual investor emotions. Journal of Behavioral Finance, 22(4), 480-489. https://doi.org/10.1080/15427560.2020.1821686
• [5] Diqi, M. (2022). StockTM: Accurate stock price prediction model using LSTM. International Journal of Informatics and Computation, 4(1), 1-10. https://doi.org/10.35842/ijicom.v4i1.50
• [6] Diqi, M., Hiswati, M. E., & Nur, A. S. (2022). StockGAN: Robust stock price prediction using GAN algorithm. International Journal of Information Technology, 14(5), 2309–2315. https://doi.org/10.1007/s41870-022-00929-6
• [7] Diqi, M., Mulyani, S. H., & Pradila, R. (2023). DeepCov: Effective prediction model of COVID-19 using CNN algorithm. SN Computer Science, 4, 396. https://doi.org/10.1007/s42979-023-01834-w
• [8] Gao, Y., Wang, R., & Zhou, E. (2021). Stock prediction based on optimized LSTM and GRU models. Scientific Programming, 2021, 4055281. https://doi.org/10.1155/2021/4055281
• [9] Jabeen, A., Afzal, S., Maqsood, M., Mehmood, I., Yasmin, S., Niaz, M. T., & Nam, Y. (2021). An LSTM based forecasting for major stock sectors using COVID sentiment. Computers, Materials and Continua, 67(1), 1191-1206. https://doi.org/10.32604/cmc.2021.014598
• [10] Ji, X., Wang, J., & Yan, Z. (2021). A stock price prediction method based on deep learning technology. International Journal of Crowd Science, 5(1), 55-72. https://doi.org/10.1108/IJCS-05-2020-0012
• [11] Jin, Z., Yang, Y., & Liu, Y. (2020). Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 32(13), 9713-9729. https://doi.org/10.1007/s00521-019-04504-2
• [12] Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 13(7), 3433-3456. https://doi.org/10.1007/s12652-020-01839-w
• [13] Ko, C.- R., & Chang, H.- T. (2021). LSTM-based sentiment analysis for stock price forecast. PeerJ Computer Science, 7, e408. https://doi.org/10.7717/peerj-cs.408
• [14] Li, X., Wu, P., & Wang, W. (2020). Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong. Information Processing and Management, 57(5), 102212. https://doi.org/10.1016/j.ipm.2020.102212
• [15] Lin, H.- C., Chen, C., Huang, G.- F., & Jafari, A. (2021). Stock price prediction using generative adversarial networks. Journal of Computer Science, 17(3), 188-196. https://doi.org/10.3844/JCSSP.2021.188.196
• [16] Liwei, T., Li, F., Yu, S., & Yuankai, G. (2021). Forecast of LSTM-XGBoost in stock price based on bayesian optimization. Intelligent Automation and Soft Computing, 29(3), 855-868. https://doi.org/10.32604/iasc.2021.016805
• [17] Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN-LSTM-based model to forecast stock prices. Complexity, 2020, 6622927. https://doi.org/10.1155/2020/6622927
• [18] Lv, J., Wang, C., Gao, W., & Zhao, Q. (2021). An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model. Computational Intelligence and Neuroscience, 2021, 8128879. https://doi.org/10.1155/2021/8128879
• [19] Manjunath, C., Marimuthu, B., & Ghosh, B. (2021). Deep learning for stock market index price movement forecasting using improved technical analysis. International Journal of Intelligent Engineering and Systems, 14(5), 129-141. https://doi.org/10.22266/ijies2021.1031.13
• [20] Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., Salwana, E., & Shahab, S. (2020). Deep learning for stock market prediction. Entropy, 22(8), 840. https://doi.org/10.3390/E22080840
• [21] Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53(4), 3007-3057. https://doi.org/10.1007/s10462-019-09754-z
• [22] Qiu, J., Wang, B., & Zhou, C. (2020). Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS ONE, 15(1), e0227222. https://doi.org/10.1371/journal.pone.0227222
• [23] Radojičić, D., & Kredatus, S. (2020). The impact of stock market price fourier transform analysis on the gated recurrent unit classifier model. Expert Systems with Applications, 159, 113565. https://doi.org/10.1016/j.eswa.2020.113565
• [24] Saud, A. S., & Shakya, S. (2021). 3-Way gated recurrent unit network architecture for stock price prediction. Indian Journal of Computer Science and Engineering, 12(2), 421-427. https://doi.org/10.21817/indjcse/2021/v12i2/211202011
• [25] Savadi Hosseini, M., & Ghaderi, F. (2020). A Hybrid deep learning architecture using 3D CNNs and GRUs for human action recognition. International Journal of Engineering, 33(5), 959-965. https://doi.org/10.5829/ije.2020.33.05b.29
• [26] Shahi, T. B., Shrestha, A., Neupane, A., & Guo, W. (2020). Stock price forecasting with deep learning: a comparative study. Mathematics, 8(9), 1441. https://doi.org/10.3390/math8091441
• [27] Ta, V.- D., Liu, C.- M., & Tadesse, D. A. (2020). Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 10(2), 437. https://doi.org/10.3390/app10020437
• [28] Thormann, M.- L., Farchmin, J., Weisser, C., Kruse, R.- M., Safken, B., & Silbersdorff, A. (2021). Stock price predictions with LSTM neural networks and twitter sentiment. Statistics, Optimization and Information Computing, 9(2), 268-287. https://doi.org/10.19139/soic-2310-5070-1202
• [29] Wang, C.- C., Chien, C.- H., & Trappey, A. J. C. (2021). On the application of ARIMA and LSTM to predict order demand based on short lead time and on-time delivery requirements. Processes, 9(7), 1157. https://doi.org/10.3390/pr9071157
• [30] Zhang, S., & Fang, W. (2021). Multifractal behaviors of stock indices and their ability to improve forecasting in a volatility clustering period. Entropy, 23(8), 1018. https://doi.org/10.3390/e23081018
• [31] Zhao, J., Zeng, D., Liang, S., Kang, H., & Liu, Q. (2021). Prediction model for stock price trend based on recurrent neural network. Journal of Ambient Intelligence and Humanized Computing, 12, 745-753. https://doi.org/10.1007/s12652-020-02057-0

Identyfikatory

DOI

10.35784/acs-2023-30