LSTM PRICE MOVEMENT PREDICTION FOR STOCK MARKET
DOI:
https://doi.org/10.29121/granthaalayah.v13.i3.2025.6058Keywords:
Fashion, Technology, Trends, Research, Web Design, WebsiteAbstract [English]
The prediction of stock market prices has historically posed significant challenges due to the complex, chaotic, and dynamic characteristics of financial markets. Conventional models frequently fail to adequately capture the intricate patterns required for precise forecasting. This paper investigates the application of machine learning techniques, with a particular emphasis on Recurrent Neural Networks (RNN) and their advanced variant, Long Short-Term Memory (LSTM) networks, for the purpose of predicting future stock prices. LSTM models are specifically engineered to overcome the limitations associated with RNNs, particularly in managing long-term dependencies and addressing issues such as vanishing gradients, thereby rendering them particularly effective for time-series forecasting.The research centers on the development of an LSTM-based model aimed at predicting stock price fluctuations utilizing historical price data alongside technical analysis indicators. A series of experiments are conducted to evaluate the model's performance across various metrics, focusing on its predictive accuracy and the influence of different training epochs on model optimization. The findings indicate that the LSTM model substantially enhances prediction accuracy in comparison to other machine learning methodologies and traditional investment strategies. This study underscores the potential of sophisticated neural network architectures in yielding more dependable predictions within the inherently volatile realm of stock market forecasting.
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Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016). Deep Learning for Stock Prediction using Numerical and Textual Information. Proceedings of the International Conference on Information Systems (ICIS). https://doi.org/10.1109/ICIS.2016.7550882 DOI: https://doi.org/10.1109/ICIS.2016.7550882
Bini, B. S., & Mathew, T. (2015). Clustering and Regression Techniques for Stock Prediction. Proceedings of the International Conference on Computational Intelligence and Data Analytics. https://doi.org/10.1016/j.protcy.2016.05.104 DOI: https://doi.org/10.1016/j.protcy.2016.05.104
Chaturvedi, P., Daniel, A. K., & Narayan, V. (2023). A Novel Heuristic for Maximizing Lifetime of Target Coverage in Wireless Sensor Networks. Advanced Wireless Communication and Sensor Networks, 227-242. https://doi.org/10.1201/9781003326205-20 DOI: https://doi.org/10.1201/9781003326205-20
Desai, R., & Gandhi, S. (2014). Stock Market Prediction using Data Mining. Journal of Predictive Analytics, 7 (2), 76-89.
Khare, K., Darekar, O., Gupta, P., & Attar, V. Z. (2017). Short tErm Stock Price Prediction using Deep Learning. Proceedings of the International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). https://doi.org/10.1109/RTEICT.2017.8256643 DOI: https://doi.org/10.1109/RTEICT.2017.8256643
Li, J., Bu, H., & Wu, J. (2017). Sentiment-Aware Stock Market Prediction: A Deep Learning Method. Journal of Computational Finance, 5 (3), 89-102.
Li, X., Yang, L., Xue, F., & Zhou, H. (2017). Time Series Prediction of Stock Price using Deep Belief Networks with Intrinsic Plasticity. Journal of Financial Analytics, 10 (2), 56-67. DOI: https://doi.org/10.1109/CCDC.2017.7978707
Lotlikar, S., et al. (2017). Stock Prediction using Clustering and Regression Techniques. Journal of Financial Analytics, 8 (3), 198-210.
Mali, P., & Karchalkar, H. (2017). Open Price Prediction of Stock Market using Regression Analysis. Journal of Business Intelligence, 12 (4), 145-159. DOI: https://doi.org/10.17148/IJARCCE.2017.6578
Mall, P. K., et al. (2023). A Comprehensive Review of Deep Neural Networks for Medical Image Processing: Recent Developments and Future Opportunities. Healthcare Analytics, 4, 100216. https://doi.org/10.1016/j.health.2023.100216 DOI: https://doi.org/10.1016/j.health.2023.100216
Mall, P. K., et al. (2023). Rank-Based Two-Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification. Journal of Medical Imaging, 15 (2), 76-90.
Mall, P. K., et al. (2023). Rank-Based Two-Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification: An Approach Toward Tagging Unlabeled Medical Dataset. Journal of Scientific & Industrial Research (JSIR), 82 (8), 818-830. https://doi.org/10.56042/jsir.v82i08.3396 DOI: https://doi.org/10.56042/jsir.v82i08.3396
Mall, P. K., et al. (2024). Self-Attentive CNN + BERT: An Approach for Analysis of Sentiment on Movie Reviews using Word Embedding. International Journal of Intelligent Systems & Applications Engineering, 12 (12s), 612-623.
Mehta, V. (2017). Stock Price Prediction using Regression and Artificial Neural Networks. Journal of Financial Engineering, 11 (4), 112-124.
Narayan, V., Daniel, A. K., & Chaturvedi, P. (2023). E-FEERP: Enhanced Fuzzy-Based Energy-Efficient Routing Protocol for Wireless Sensor Network. Wireless Personal Communications, 131 (1), 371-398. https://doi.org/10.1007/s11277-023-10434-z DOI: https://doi.org/10.1007/s11277-023-10434-z
Narayan, V., et al. (2023). A Comprehensive Review of Various Approaches for Medical Image Segmentation and Disease Prediction. Wireless Personal Communications, 132 (3), 1819-1848. https://doi.org/10.1007/s11277-023-10682-z DOI: https://doi.org/10.1007/s11277-023-10682-z
Narayan, V., et al. (2023). Extracting Business Methodology: Using Artificial Intelligence-Based Method. Semantic Intelligent Computing and Applications, 16, 123. https://doi.org/10.1515/9783110781663-007 DOI: https://doi.org/10.1515/9783110781663-007
Navale, G. S., Dudhwala, N., & Jadhav, K. (2016). Prediction of Stock Market using Data Mining and Artificial Intelligence. International Journal of Computer Applications, 162 (6), 45-52. https://doi.org/10.5120/ijca2016907635 DOI: https://doi.org/10.5120/ijca2016907635
Nigade, B., Pawar, A., et al. (2017). Stock Trend Prediction using Regression Analysis - A Data Mining Approach. Journal of Business Intelligence, 12 (3), 198-210.
Nigade, B., et al. (2017). Comparative Study of Stock Prediction System using Regression Techniques. Journal of Computational Finance, 8 (2), 134-148.
Polanyi, A. S., Adele, K. S., & Jimoh, R. G. (2011). Stock Trend Prediction using Regression Analysis - A Data Mining Approach. Journal of Business Intelligence, 5 (4), 230-245.
Prasanna, S., & Ezhilmaran, D. (2013). An Analysis on Stock Market Prediction using Data Mining Techniques. Journal of Financial Technology, 6 (4), 112-125.
Roondiwala, M., Patel, H., & Varma, S. (2017). Predicting Stock Prices using LSTM. Journal of Economic Modeling, 34 (1), 23-30. https://doi.org/10.21275/ART20172755 DOI: https://doi.org/10.21275/ART20172755
Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock Price Prediction using LSTM, RNN and CNN-Sliding Window Model. Proceedings of the International Conference on Advances in Computing, Communications, and Informatics (ICACCI). https://doi.org/10.1109/ICACCI.2017.8126078 DOI: https://doi.org/10.1109/ICACCI.2017.8126078
Suthar, A. B., Patel, H. R., & Parikh, S. M. (2012). A Comparative Study on Financial Stock Market Prediction Models. Journal of Economic Analysis, 9 (1), 56-72.
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Copyright (c) 2025 Sugam Agrawal, Saurabh Bhadauriya, Dr. Vipul Narayan
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