Forecasting Stock Prices with Long Short-Term Memory (LSTM) Networks: A Deep Learning Approach

Velmurugan, Ramprakash; Stezhko, Nadiia; Sophika, Senthur; Nair, Safaa

doi:10.25673/122136

Proceedings of International Conference on Applied Innovation in IT · 2025/08/29 · Vol. 13 · Issue 4 · pp. 333–339

Forecasting Stock Prices with Long Short-Term Memory (LSTM) Networks: A Deep Learning Approach

Ramprakash Velmurugan, Nadiia Stezhko, Senthur Sophika, Safaa Jasim Mohammed and Sujitha Vijayalekshmi Sadhasivan Nair

📄 Download PDF DOI: 10.25673/122136

Abstract

Stock price prediction is essential yet not easy because of the high volatility of the stock prices, non-linearity, and non-stationarity of the financial markets. In this case, the current research examines a robust architecture developed by LSTM networks, a type of deep learning architecture renowned for its effectiveness in analyzing sequence data and its resistance to the gradient vanishing problem. The overall goal is to improve predictive performance in given settings by overcoming the known contemporary issues, which include the inability of positive models to accommodate random variance and other complex market dynamics. The proposed model improves the results further than prior research in terms of skillful noise reduction, feature normalization, and dynamic walk-forward validation, achieving better and more accurate stock price prediction. The historical price information of stocks forms the core of model training and evaluation. Outcomes are measured based on RMSE and MAE, and through these measures, LSTM proves to be superior to conventional approaches. As an entirely original concept, this method serves as a verifiable and practical asset, providing a valuable lens for examining market trends more closely and making informed decisions.

Keywords

Stocks Price Prediction Long-Short Term Memory Network Deep Learning (DL) Walk-Forward Validation.

References

R. Iacomin, “Stock market prediction,” in Proc. 19th Int. Conf. on System Theory, Control and Computing (ICSTCC), Cheile Gradistei, Romania, 2015, pp. 200–205.
X. Zheng and B. M. Chen, Stock Market Modeling and Forecasting, vol. 442. London, UK: Springer, 2013.
V. K. Saini, R. Kumar, A. S. Al-Sumaiti, A. Sujil, and E. Heydarian-Forushani, “Learning based short-term wind speed forecasting models for smart grid applications: An extensive review and case study,” Electric Power Systems Research, vol. 222, p. 109502, 2023.
S. H. Chen, T. Lux, and M. Marchesi, “Testing for non-linear structure in an artificial financial market,” Journal of Economic Behavior & Organization, vol. 46, no. 3, pp. 327–342, 2001.
R. K. Behera, M. Jena, S. K. Rath, and S. Misra, “Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data,” Information Processing & Management, vol. 58, no. 1, p. 102435, 2021.
B. Gautam, S. Kandel, M. Shrestha, and S. Thakur, “Comparative analysis of machine learning models for stock price prediction: Leveraging LSTM for real-time forecasting,” Journal of Computer and Communications, vol. 12, no. 8, pp. 52–80, 2024.
J. Sen and S. Mehtab, “Long-and-short-term memory (LSTM) networks: Architectures and applications in stock price prediction,” in Emerging Computing Paradigms: Principles, Advances and Applications, 2022, pp. 143–160.
H. Hadhood, Stock Trend Prediction Using Deep Learning Models LSTM and GRU with Non-Linear Regression, Master’s thesis, Itä-Suomen yliopisto, 2022.
G. Ding and L. Qin, “Study on the prediction of stock price based on the associated network model of LSTM,” International Journal of Machine Learning and Cybernetics, vol. 11, no. 6, pp. 1307–1317, 2020.
V. Akgiray, “Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts,” Journal of Business, vol. 62, no. 1, pp. 55–80, 1989.
B. Lev, Winning Investors Over: Surprising Truths About Honesty, Earnings Guidance, and Other Ways to Boost Your Stock Price. Boston, MA, USA: Harvard Business Press, 2012.
M. L. Thormann, J. Farchmin, C. Weisser, R. M. Kruse, B. Säfken, and A. Silbersdorff, “Stock price predictions with LSTM neural networks and Twitter sentiment,” Statistics, Optimization & Information Computing, vol. 9, no. 2, pp. 268–287, 2021.
M. Beniwal, A. Singh, and N. Kumar, “Forecasting long-term stock prices of global indices: A forward-validating genetic algorithm optimization approach for support vector regression,” Applied Soft Computing, vol. 145, p. 110566, 2023.
D. Shah, H. Isah, and F. Zulkernine, “Stock market analysis: A review and taxonomy of prediction techniques,” International Journal of Financial Studies, vol. 7, no. 2, p. 26, 2019.
L. Zheng and H. He, “Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA,” Expert Systems with Applications, vol. 183, p. 115384, 2021.