Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{170188,
        author = {Vijiyalakshmi J and Milan L and Vipin B and Vineeth S},
        title = {STOCK MARKET PREDECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3866-3870},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170188},
        abstract = {Stock market prediction is a challenging yet crucial goal influenced by economic indicators, market sentiment, and external events. Recent advances in machine learning (ML) and artificial intelligence (AI) have improved the analysis of financial data, uncovering patterns for trend forecasting. Techniques like supervised learning, sentiment analysis using NLP, and deep learning models such as LSTMs excel in capturing temporal and contextual dynamics.
However, challenges like market volatility, data quality, and unforeseen events persist. Integrating alternative data sources (e.g., social media trends) and ensuring ethical AI use are key areas of focus. While predictive models offer valuable insights, their success depends on continuous innovation and robust validation.},
        keywords = {},
        month = {December},
        }