Stock Price Prediction

Author(s)	Ayush Kumar
Country	India
Abstract	Predicting stock prices is highly challenging due to the volatile, nonlinear, and multifactorial nature of the financial markets. Various studies have approached stock price prediction using models like Convolutional Neural Networks (CNNs) ,and Artificial Neural Networks (ANNs). However, traditional approaches often struggle with capturing long-term temporal dependencies and are sensitive to market volatility. In our study, we developed a stock price prediction model using Long Short-Term Memory (LSTM) networks, which are well-suited for learning patterns from sequential data. The proposed LSTM-based model achieved a high prediction accuracy of up to 97.2% for certain stocks, significantly outperforming traditional machine learning methods.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-30
DOI	https://doi.org/10.71097/IJSAT.v16.i2.5522
Short DOI	https://doi.org/g9mvsx

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International Journal on Science and Technology