International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The stock market is one of the most dynamic and unpredictable financial systems, influenced by a wide range of economic, political, and psychological factors. Stock prices fluctuate primarily due to supply and demand, making it challenging to accurately forecast market movements. Traditional methods of analysis, such as fundamental and technical analysis, have been used extensively, but the rise of machine learning and deep learning techniques offers new opportunities for more precise predictions. This research focuses on the application of machine learning models, particularly Long Short-Term Memory (LSTM) networks, for predicting stock market trends. The study leverages historical stock price data, technical indicators, and sentiment analysis from news and social media to train and evaluate the LSTM model. Experimental results demonstrate that LSTM networks can capture complex temporal dependencies in stock price movements, offering improved prediction accuracy compared to conventional methods. Furthermore, the research explores the integration of ensemble models and hybrid approaches combining LSTM with other machine learning algorithms to enhance prediction reliability. The study also discusses challenges such as overfitting, data preprocessing, and feature selection, providing insights into practical implementation for real-world stock market forecasting. This approach can assist investors, financial analysts, and traders in making informed decisions, optimizing investment strategies, and minimizing financial risks.

Keywords: Stock Market Prediction, Machine Learning, Deep Learning, LSTM Networks, Financial Forecasting, Technical Analysis, Sentiment Analysis, Time Series Prediction, Investment Strategies, Ensemble Models, Hybrid Models.

Downloads: | DOI: 10.17148/IJARCCE.2025.141004