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@article{194480,
        author = {Shubhangi S. Gaikwad and Samiksha L. Gurav and Arati B. Farakte and Pradnya M. Bansode and Jasmin Shaikh},
        title = {Predicting Stock Prices from Historical Data Using LSTM Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4169-4173},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194480},
        abstract = {Stock price prediction is a challenging task due to the highly dynamic and nonlinear nature of financial markets. Prices are influenced by multiple factors such as company performance, economic conditions, and investor sentiment, making accurate forecasting difficult using traditional statistical methods. This paper presents a machine learning and deep learning-based approach for predicting stock prices using historical market data.
The proposed system utilizes algorithms such as Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks to analyze past stock trends and forecast future prices. Historical stock data including opening price, closing price, high, low, and trading volume is collected and preprocessed using normalization and feature engineering techniques to improve prediction accuracy.
The LSTM model is particularly effective in capturing long-term dependencies and complex patterns in time-series data. The system compares model performance using evaluation metrics such as RMSE and accuracy measures. Experimental results demonstrate that deep learning-based models provide better prediction performance compared to traditional methods.
This system can assist investors, financial analysts, and organizations in making informed investment decisions, reducing risks, and understanding market trends through data-driven forecasting.},
        keywords = {Stock Price Prediction, Machine Learning, LSTM, Deep Learning, Time Series Analysis, Financial Forecasting.},
        month = {March},
        }