Performance-Oriented analytical study to evaluate the effectiveness of artificial intelligence techniques in stock market prediction

Abstract

The stock market is one of the most complex and dynamic components of the modern financial system, characterized by uncertainty, volatility, and rapid information flow. Accurate prediction of stock price movements has always been a challenging task due to the influence of numerous interconnected factors such as economic indicators, corporate performance, investor behavior, and global political events. Traditional methods of stock market analysis, including fundamental and technical analysis, rely heavily on historical data and predefined assumptions, which often limit their predictive accuracy in real-world scenarios. [5] Artificial Intelligence (AI) has emerged as a powerful paradigm capable of overcoming these limitations by enabling intelligent, data-driven decision-making. AI techniques such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) can process large volumes of structured and unstructured data, identify hidden patterns, and adapt to changing market conditions. In recent years, AI-driven models have gained significant attention for applications such as stock price forecasting, algorithmic trading, risk assessment, and portfolio optimization. [6] This dissertation investigates the role of Artificial Intelligence in stock market analysis and prediction. The study proposes a hybrid analytical framework that integrates time-series forecasting models with sentiment analysis derived from financial news and social media data. Historical stock prices are analyzed using deep learning models, while NLP techniques are employed to quantify market sentiment. [7] The combined output enhances prediction accuracy and provides [8] deeper insights into market behavior [9].