Published on: April 2026
A MACHINE LEARNING APPROACH FOR STOCK PRICE TREND PREDICTION USING LSTM
Saiteja Veerabomma MD. Feroz Khan Basani Tanush Reddy N. Chandra Kiran Reddy
DR. R. R. S. Ravi Kumar
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Abstract
Stock market prediction is a complex and challenging task due to the dynamic nature of financial markets and the influence of multiple economic and behavioral factors. Accurate prediction of stock price trends can assist investors in making informed investment decisions and managing financial risks effectively. This project focuses on developing an intelligent system for predicting stock price trends using historical market data, technical indicators, and deep learning techniques. The proposed system collects historical stock price data and analyzes important attributes such as opening price, closing price, highest price, lowest price, and trading volume. Technical indicators including Moving Average (MA), Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD) are calculated to better capture market patterns and trends. A Long Short-Term Memory (LSTM) neural network is implemented to learn sequential patterns in time-series stock data. The trained model predicts whether the stock price will move upward or downward based on historical patterns. Performance is evaluated using accuracy, precision, recall, and Mean Squared Error (MSE). A Streamlit-based web dashboard enables real-time visualization of actual and predicted stock prices.
How to Cite this Paper
Veerabomma, S., Khan, M. F., Reddy, B. T. & Reddy, N. C. K. (2026). A Machine Learning Approach for Stock Price Trend Prediction using LSTM. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.808
Veerabomma, Saiteja, et al.. "A Machine Learning Approach for Stock Price Trend Prediction using LSTM." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.808.
Veerabomma, Saiteja,MD. Khan,Basani Reddy, and N. Reddy. "A Machine Learning Approach for Stock Price Trend Prediction using LSTM." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.808.
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References
[1] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.[2] 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.
[3] J. Brownlee, Deep Learning for Time Series Forecasting. Machine Learning Mastery, 2018.
[4] F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[5] Yahoo Finance, "Historical stock data," Available: https://finance.yahoo.com/
[6] G. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art," International Journal of Forecasting, vol. 14, no. 1, pp. 35–62, 1998.
[7] F. Chollet, Deep Learning with Python. Manning Publications, 2018.
[8] W. McKinney, Python for Data Analysis. O'Reilly Media, 2017.
[9] Streamlit Documentation, "Build beautiful web apps for machine learning and data science," Available: https://docs.streamlit.io/[10] D. Kingma and J. Ba, "Adam: A method for stochastic optimization," International Conference on Learning Representations (ICLR), 2015.
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- •Published on: Apr 28 2026
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