Intelligent Unmanned AI Detection Model for Financial Volatility in Stock Exchange

At the present days, the popularity of the stock marketing is very high as this is only the legal platform where the investor can doubling their investments within very short span. However, besides the chance of profitability, there is huge risk present due to the extreme volatility. In this context, to reducing the risk of financial this research study has developed a AI detection model so that a predictive future data related to the stock exchange can be obtained through the past data train. In order to predict where prices will go in the future, this model uses machine learning algorithms and NLP techniques to decipher historical market data. Backtesting on simulated data demonstrates that high levels of accuracy, precision, recall, and Fl score may be attained in the prediction of market volatility using the proposed model. The proposed model may be used by investors and financial institutions as a tool for making more informed investment decisions, reducing risk, and identifying untapped investment opportunities. An important contribution to the field of finance, this research shows how machine learning algorithms and natural language processing methods show potential in predicting market volatility.