Enhancing stock price prediction in the Indonesian market: a concave lstm approach with RunReLU

• Autorzy: Diqi Mohammad , Ordiyasa I Wayan

Identyfikatory

DOI

10.14313/JAMRIS/3‐2024/24

• Języki publikacji: EN

Abstrakty

EN

This study addresses the pressing need for improved stock price prediction models in the financial markets, focusing on the Indonesian stock market. It introduces an innovative approach that utilizes the custom activation function RunReLU within a concave long short-term memory (LSTM) framework. The primary objective is to enhance prediction accuracy, ultimately assisting investors and market participants in making more informed decisions. The research methodology used historical stock price data from ten prominent companies listed on the Indonesia Stock Exchange, covering the period from July 6, 2015, to October 14, 2021. Evaluation metrics such as RMSE, MAE, MAPE, and R2 were employed to assess model performance. The results consistently favored the RunReLU- based model over the ReLU-based model, showcasing lower RMSE and MAE values, higher R2 values, and notably reduced MAPE values. These findings underscore the practical applicability of custom activation functions for financial time series data, providing valuable tools for enhancing prediction precision in the dynamic landscape of the Indonesian stock market.

Słowa kluczowe

EN

stock price prediction; concave LSTM; Run-ReLU; Indonesian stock exchange; financial forecasting

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2024
• Tom: Vol. 18, No. 3
• Strony: 69--77
• Opis fizyczny: Bibliogr. 26 poz., rys.

Twórcy

autor

Diqi Mohammad

• Dept. of Informatics, Universitas Respati Yogyakarta, Yogyakarta, 55281, Indonesia

• https://orcid.org/0000-0002-9012-9080

autor

Ordiyasa I Wayan

• Dept. of Informatics, Universitas Respati Yogyakarta, Yogyakarta, 55281, Indonesia

Bibliografia

• [1] B. Li, X. Gui, and Q. Zhou, “Construction of Development Momentum Index of Financial Technology by Principal Component Analysis in the Era of Digital Economy,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/2244960.
• [2] Y. Zhao, “A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/6210627.
• [3] A. H. Dhafer et al., “Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/9208640.
• [4] S. K. Kumar et al., “Stock Price Prediction Using Optimal Network Based Twitter Sentiment Analysis,” Intell. Autom. Soft Comput., vol. 33, no. 2, pp. 1217–1227, 2022, doi: 10.32604/iasc.2022.024311.
• [5] Z. Bao, Q. Wei, T. Zhou, X. Jiang, and T. Watanabe, “Predicting stock high price using forecast error with recurrent neural network,” Appl. Math. Nonlinear Sci., vol. 6, no. 1, pp. 283–292, 2021, doi: 10.2478/amns.2021.2.00009.
• [6] G. A. Altarawneh, A. B. Hassanat, A. S. Tarawneh, A. Abadleh, M. Alrashidi, and M. Alghamdi, “Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods,” Economies, vol. 10, no. 2, 2022, doi: 10.3390/economies10020043.
• [7] S. Hansun, A. Suryadibrata, and D. R. Sandi, “Deep Learning Approach in Predicting Property and Real Estate Indices,” Int. J. Adv. Soft Comput. its Appl., vol. 14, no. 1, pp. 60–71, 2022, doi: 10.15849/IJASCA.220328.05.
• [8] D. S. N. Ulum and A. S. Girsang, “Hyperparameter Optimization of Long-Short Term Memory using Symbiotic Organism Search for Stock Prediction,” Int. J. Innov. Res. Sci. Stud., vol. 5, no. 2, pp. 121–133, 2022, doi: 10.53894/ijirss.v5i2.415.
• [9] 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.
• [10] W. Lu, J. Li, J. Wang, and S. Wu, “a Novel Model for Stock Closing Price Prediction Using Cnn-Attention-Gru-Attention,” Econ. Comput. Econ. Cybern. Stud. Res., vol. 56, no. 3, pp. 251–264, 2022, doi: 10.24818/18423264/56.3.22.16.
• [11] M. Ratchagit and H. Xu, “A Two-Delay Combination Model for Stock Price Prediction,” Mathematics, vol. 10, no. 19, 2022, doi: 10.3390/math10193447.
• [12] M. Mohtasham Khani, S. Vahidnia, and A. Abbasi, “A Deep Learning-Based Method for Forecasting Gold Price with Respect to Pandemics,” SN Comput. Sci., vol. 2, no. 4, pp. 1–12, 2021, doi: 10.1007/s42979-021-00724-3.
• [13] A. Ntakaris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, Mid-price prediction based on machine learning methods with technical and quantitative indicators, vol. 15, no. 6 June. 2020. doi: 10.1371/journal.pone.0234107.
• [14] S. Mishra, T. Ahmed, V. Mishra, S. Bourouis, and M. A. Ullah, “An Online Kernel Adaptive Filtering-Based Approach for Mid-Price Prediction,” Sci. Program., vol. 2022, 2022, doi: 10.1155/2022/3798734.
• [15] M. A. Ledhem, “Deep learning with small and big data of symmetric volatility information for predicting daily accuracy improvement of JKII prices,” J. Cap. Mark. Stud., vol. 6, no. 2, pp. 130–147, 2022, doi: 10.1108/jcms-12-2021-0041.
• [16] N. Deepika and M. Nirapamabhat, “An optimized machine learning model for stock trend anticipation,” Ing. des Syst. d’Information, vol. 25, no. 6, pp. 783–792, 2020, doi: 10.18280/isi.250608.
• [17] M. K. Daradkeh, “A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction,” Electronics, vol. 11, no. 2, 2022, doi: 10.3390/electronics11020250.
• [18] X. Teng, T. Wang, X. Zhang, L. Lan, and Z. Luo, “Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/6737951.
• [19] 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.
• [20] K. E. Rajakumari, M. S. Kalyan, and M. V. Bhaskar, “Forward Forecast of Stock Price Using LSTM Machine Learning Algorithm,”Int. J. Comput. Theory Eng., vol. 12, no. 3, pp. 74–79, 2020, doi: 10.7763/IJCTE.2020.V12.1267.
• [21] L. Li and B. M. Muwafak, “Adoption of deep learning Markov model combined with copula function in portfolio risk measurement,” Appl. Math. Nonlinear Sci., vol. 7, no. 1, pp. 901–916, 2022, doi: 10.2478/amns.2021.2.00112.
• [22] M. C. Lee, J. W. Chang, S. C. Yeh, T. L. Chia, J. S. Liao, and X. M. Chen, “Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies,” Neural Comput. Appl., vol. 34, no. 16, pp. 13267–13279, 2022, doi: 10.1007/s00521-021-06828-4.
• [23] M. Diqi, “TwitterGAN: robust spam detection in twitter using novel generative adversarial networks,”Int. J. Inf. Technol., vol. 15, no. 6, pp. 3103–3111, 2023, doi: 10.1007/s41870-023-01352-1.
• [24] E. K. Ampomah, G. Nyame, Z. Qin, P. C. Addo, E. O. Gyamfi, and M. Gyan, “Stock market prediction with gaussian naïve bayes machine learning algorithm,” Inform., vol. 45, no. 2, pp. 243–256, 2021, doi: 10.31449/inf.v45i2.3407.
• [25] A. Y. Fathi, I. A. El-Khodary, and M. Saafan, “A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting,” Rev. d’Intelligence Artif., vol. 35, no. 6, pp. 483–488, 2021, doi: 10.18280/ria.350606.
• [26] J. Zhang, “Forecasting of Musical Equipment. Demand Based on a Deep Neural Network,” Mob. Inf. Syst., vol. 2022, 2022, doi: 10.1155/2022/6580742.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).