A Study on the Potential Impact of AI-Driven Tools on Stock Market Prediction: Opportunities, Challenges and Regulatory Implications

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@article{1722342,
      author = {Dr. Farhana Anjum},
      title = {A Study on the Potential Impact of AI-Driven Tools on Stock Market Prediction: Opportunities, Challenges and Regulatory Implications},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {2690-2699},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722342.pdf},
      abstract = {The rapid development of Artificial Intelligence (AI) has significantly influenced the financial sector, particularly in the areas of stock market analysis, prediction, investment management, risk assessment, and algorithmic trading. Traditional stock market forecasting methods primarily rely on historical prices, financial ratios, technical indicators, fundamental analysis, and econometric techniques. AI-driven tools, in contrast, can process large volumes of structured and unstructured data and identify complex patterns that may not be easily detected through conventional methods. Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) are increasingly being applied to financial decision-making. This paper examines the potential impact of AI-driven tools on stock market prediction, with particular emphasis on their applications, benefits, limitations, and regulatory implications. The study adopts a descriptive and conceptual research approach based on secondary data obtained from academic literature, regulatory publications, scholarly books, and documented applications of AI in financial markets. The study finds that AI can improve the speed of data processing, support multifactor analysis, identify market sentiment, automate trading decisions, and enhance certain aspects of portfolio and risk management. However, AI-based prediction should not be considered a guarantee of consistently accurate returns. Challenges such as data quality, bias, overfitting, model instability, lack of Explainability, cybersecurity threats, algorithmic herding, and systemic risk may reduce the reliability of AI systems.
The paper concludes that AI should primarily be regarded as a decision-support technology rather than an infallible forecasting mechanism. Responsible implementation requires high-quality data, rigorous model validation, human oversight, transparency, cybersecurity safeguards, investor awareness, and adaptive regulatory frameworks. The future of AI-driven stock market prediction is therefore likely to depend on an effective combination of technological innovation, human judgement, ethical standards, and financial regulation.},
      keywords = {artificial intelligence, machine learning, deep learning, stock market prediction, algorithmic trading, sentiment analysis, financial markets, risk management},
      month = {May},
      doi = {https://doi.org/10.64388/IREV8I11-1722342}
  }