Article
A STUDY ON COMPARATIVE ANALYSIS OF SHAREPRICE IN VARIOUS SECTOR
The stock market is a complex, dynamic system influenced by numerous factors including economic indicators, sector performance, and investor behavior. Traditional analysis methods often fall short in capturing intricate patterns and predicting stock price movements accurately. This study employs Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to perform a comparative analysis of share price trends across various industry sectors. By leveraging historical stock data, news sentiment, and macroeconomic variables, the study aims to develop robust predictive models that can identify sector-specific price movements and market behavior .Machine learning algorithms such as Random Forest, Support Vector Machines (SVM), and Gradient Boosting are utilized to analyze large volumes of structured financial data, uncovering hidden patterns and relationships among different sectors. Deep learning models, including Long Short-Term Memory (LSTM) networks, are applied for time-series forecasting to capture the sequential dependencies in stock prices. The integration of Natural Language Processing (NLP) techniques further enriches the analysis by incorporating market sentiment from news and social media, providing a comprehensive view of factors impacting share prices.The findings of this study are expected to assist investors, portfolio managers, and financial analysts in making informed decisions by providing sector-wise comparative insights and accurate share price predictions. Additionally, the research demonstrates the potential of AI-driven approaches in enhancing traditional financial analysis, offering scalable and adaptive tools for the evolving landscape of stock market investment.
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