Mitigating Market Volatility Using Machine Learning Techniques

This paper uses advanced machine learning algorithms to control market fluctuations and improve the accuracy of the financial forecast decision-making tool. The application of techniques is then simulated with models and real-time situations to evaluate the technique's effectiveness on market forecasting. By analyzing the data, we show that using machine learning as the approach to build models that are more accurate than the existing ones reduces risks connected with flapping markets. Some of the difficulties observed are described, and the results suggest possible solutions. In conclusion, our results indicate that these techniques are essential for enhancing financial stability in volatile markets.