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@article{185975,
        author = {Niranjan Mane and Swayam Mishra and Anuj Sonawane and Om Bhoir},
        title = {A Review on AI-Based Fundamental Stock Analysis in the Indian Stock Market},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2916-2919},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185975},
        abstract = {This survey reviews recent developments in the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for fundamental stock analysis in the Indian stock market. The focus lies on models that use company financial ratios, quarterly results, and balance sheet data to predict future stock performance. Ten key studies from 2019–2025 are reviewed to compare data preprocessing methods, AI architectures, feature engineering, evaluation metrics, and limitations. Findings indicate that ensemble learning, hybrid deep models, and transformer-based frameworks enhance predictive accuracy. The paper also highlights persistent challenges — such as limited Indian datasets, interpretability issues, and real-time integration — and provides directions for future work, including Explainable AI (XAI) and API-based automation.},
        keywords = {Artificial Intelligence, Machine Learning, Deep Learning, Fundamental Analysis, Financial Ratios, Stock Market Prediction, Indian Stock Market, Data-Driven Investment, Company Valuation, Forecasting Models, Explainable AI, Ensemble Learning, Financial Data Analytics, Predictive Modeling, Automated Stock Analysis.},
        month = {October},
        }