Development of a method and software for forecasting and risk management in the financial markets of securities

Abstract

This research focuses on developing a novel method and software tool to improve forecasting accuracy and manage risk in securities financial markets. The primary goal is to design a solution integrating advanced statistical models with machine learning algorithms to predict market trends and minimize financial risks. We also want to improve prediction accuracy and enhance risk mitigation strategies by integrating machine learning techniques and stochastic modeling. The methodology involves collecting data historical market trends, analyzing them through advanced algorithms, and implementing predictive models to manage financial risks effectively. Initial results demonstrate significant improvements in prediction accuracy and risk mitigation, contributing to better decision-making processes in volatile financial environments. The results indicate a significant improvement in prediction accuracy and the optimization of risk management strategies, demonstrating the practical applicability of the developed method and software. The proposed system has been validated through backtesting and stress-testing scenarios using real-world market data.