• R.Harini
• Dr.S.Rethinavalli

Abstract: Forecasting stock market volatility is crucial for managing risk, optimizing portfolios, and executing algorithmic trades. Conventional models like GARCH and stochastic volatility frameworks often fail to adapt to rapid market changes. While machine learning has enhanced predictive accuracy, fixed AI models struggle to adjust to dynamic financial environments. This research investigates trend-switching AI as an adaptive method to improve volatility forecasting by dynamically choosing predictive models based on market conditions and corporate financial analysis. The proposed system incorporates market regime detection techniques, such as Hidden Markov Models, Kalman Filters, and clustering algorithms, to categorize market states. Moreover, it includes corporate financial indicators—like earnings reports, debt ratios, and liquidity metrics—to fine-tune predictive models. The AI-powered switching mechanism utilizes meta-learning and reinforcement learning to select the most suitable forecasting model for various volatility regimes. This combined approach enables real-time adaptation, ensuring better responsiveness to sudden market shifts. Empirical tests show that trend-switching AI significantly surpasses traditional machine learning methods, minimizing prediction errors and enhancing model resilience across turbulent market conditions. The results indicate that combining corporate analysis with AI-driven adaptive modeling improves share market volatility prediction. Future studies should address real-time computational efficiency, incorporate sentiment analysis, and explore multi-asset applications to enhance financial forecasting strategies.

Trend-Switching AI, Adaptive Forecasting Models, Reinforcement Learning in Finance, Financial Time Series Forecasting, Dynamic Model Selection, Meta-Learning for Prediction.

"Utilizing AI for Volatility Prediction in the Share Market through a Corporate Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.c787-c791, February-2025, Available :http://www.jetir.org/papers/JETIR2502292.pdf

"Utilizing AI for Volatility Prediction in the Share Market through a Corporate Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppc787-c791, February-2025, Available at : http://www.jetir.org/papers/JETIR2502292.pdf