Agile risk management methodology for decision-making in startup projects based on stock price forecasting

The article is devoted to the study of the issues of risk management during decision-making in startup projects, in particular in conditions of high uncertainty and volatility of financial markets. To improve the efficiency of risk management, a method of forecasting stock prices based on modern machine learning models, such as Support Vector Regression, Random Forest and Gradient Boosting, is proposed. Experimental studies are conducted using historical financial data collected through the Yahoo Finance API, which were cleaned, normalized and supplemented with technical analysis indicators. The metrics of mean square error (MSE) and coefficient of determination (R²) are used to assess the accuracy of forecasts. The experiments have shown that the use of ensemble models and stack techniques provides high quality forecasting. Based on the results, a web application has been developed to integrate forecasts into the decision-making process in startup projects. The application allows investors and managers to analyze market trends, assess risks and make informed investment decisions. The use of the proposed system helps minimize risks and increase the stability of financial results of startup projects.