Stock price prediction

Registration ID: IJNRD_303335

Published ID: IJNRD2504160

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NITISH KUMAR, SHOBHIT KUMAR

Accurate stock price prediction is a significant challenge in financial markets due to their inherent volatility and complexity, which are influenced by various factors such as economic indicators, market sentiment, and geopolitical events. This paper proposes a novel approach to stock price prediction by integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN), aiming to improve prediction accuracy beyond traditional methods. We leverage a comprehensive dataset comprising historical stock prices and various technical indicators, including Moving Averages (MA), Relative Strength Index (RSI), and Bollinger Bands, to capture both historical trends and market signals. The data preprocessing phase involved handling missing values, normalizing data, and creating lagged features that reflect previous stock price movements, which are crucial for time series forecasting. The proposed architecture begins with an LSTM layer to effectively model the temporal dependencies within the sequential data, allowing the model to remember long-term trends. This is followed by several convolutional layers that extract spatial features from the input time series, enhancing the model’s ability to recognize patterns and fluctuations in stock prices. The hybrid LSTMCNN model is trained using a robust backpropagation algorithm, ensuring convergence and optimal performance. Extensive experiments are conducted on diverse stock market datasets, demonstrating the model's superior predictive power compared to standalone LSTM and CNN models, as well as traditional statistical methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to evaluate the model's accuracy, highlighting its potential in real-world applications. The integration of LSTM and CNN not only captures the temporal and spatial dynamics but also mitigates the risk of overfitting, leading to more reliable stock price predictions. Our findings suggest that this innovative approach can significantly aid investors and financial analysts in making informed decisions.

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NITISH KUMAR & SHOBHIT KUMAR (April-2025). Stock price prediction. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), b363-b367. https://ijnrd.org/papers/IJNRD2504160.pdf

Volume 10 Issue 4, April-2025

Pages : b363-b367

Paper Reg. ID: IJNRD_303335

Published Paper Id: IJNRD2504160

Research Area: Science and Technology

Author Type: Indian Author

Country: Greater Noida, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2504160.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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