Agile risk management methodologies in the life cycle of an intelligent system for forecasting solutions of market share dynamics

Abstract

The article investigates the problem of forecasting market share dynamics using modern machine learning methods. The high volatility of financial markets and a significant level of uncertainty make the use of automated intelligent systems relevant for increasing forecasting accuracy and optimizing investment strategies. The proposed system combines Prophet and LSTM (Long Short-Term Memory) machine learning models for time series analysis, as well as the Monte Carlo method for risk assessment. An algorithm for collecting, cleaning, and preprocessing financial data has been developed, which includes obtaining historical stock prices from the Yahoo Finance platform, normalization, eliminating outliers, and forming training samples. The system architecture consists of modules for collecting and processing data, building forecasting models, and assessing risks. An experimental study of the effectiveness of the proposed methods based on real financial data was conducted. A comparative analysis of forecasting accuracy showed that using LSTM allows achieving an average accuracy of 92.4%, while Prophet demonstrates an accuracy of 88.7%. Risk assessment using the Monte Carlo method allowed us to determine the probability of extreme changes in asset values ​​and their impact on the investment portfolio. The results obtained confirm the feasibility of using the proposed system for forecasting financial markets. Further research will focus on improving the accuracy of the models by integrating additional macroeconomic indicators and improving adaptive mechanisms for setting forecasting parameters.