The stock market faces persistent challenges, including inefficiencies, volatility, and barriers to entry, which hinder its accessibility and reliability for investors. This paper explores how artificial intelligence (AI) can address these challenges by revolutionizing research, automation, and prediction in the stock market. The primary research question addressed in this study is: “How can artificial intelligence enhance market efficiency, investor decision-making, and accessibility in the stock market, and what are the associated ethical and regulatory challenges?” Using a systematic literature review methodology, this paper examines data processing techniques such as big data analytics and machine learning, which are critical for developing AI-accelerated analysis models like real-time and sentiment analysis. It also evaluates AI’s role in automation and prediction through portfolio management, predictive analysis, and risk mitigation, emphasizing advanced machine learning techniques, including deep learning, reinforcement learning, random forest, and generative AI. The findings indicate that AI significantly improves market efficiency, enhances decision-making accuracy, and increases accessibility for a broader range of investors. However, ethical and regulatory challenges, including accountability, equality, and data protection, remain critical considerations for successful implementation. The paper concludes that while AI has the potential to transform the stock market into a more efficient and inclusive environment, achieving this requires responsible integration and robust oversight.
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