MODEL OF INFORMATION TECHNOLOGY TO SUPPORT THE INVESTMENT DECISION-MAKING PROCESS BASED ON THE USE OF ARTIFICIAL INTELLIGENCE METHODS - ARGUNOV, VOLODYMYR; PUSAN, ANATOLY (Herald of Khmelnytskyi National University. Technical sciences, 2025)
The object of this study is an information technology system developed to support and optimize investment decision-making processes within modern financial markets. The subject of the research focuses on the integration of market and alternative data sources, as well as their combined use with machine learning and artificial intelligence algorithms to enhance the performance, adaptability, and robustness of investment strategies. The research explores the problem of constructing a comprehensive and high-quality dataset that consolidates heterogeneous financial information — including market quotations, sentiment indicators derived from news and social media, market turbulence measures, and a wide range of technical indicators. The unified dataset serves as the foundation for training reinforcement learning and predictive modeling algorithms, which enable the automation of forecasting, portfolio management, and trading decision-making processes. Based on a detailed analysis of financial market dynamics during periods of volatility and crisis, the study identifies major challenges in implementing intelligent models, such as data incompleteness, overfitting, limited interpretability, and instability of predictive results. The research examines the influence of additional informational variables — such as sentiment and macroeconomic indicators — on the accuracy and resilience of the models. A technological framework for their systematic integration into investment strategy formation is proposed, improving both risk-adjusted performance and decision transparency. To evaluate the practical efficiency of the developed technology, the study employs global financial metrics such as the Sharpe Ratio, Sortino Ratio, Omega Ratio, and Jensen’s Alpha, confirming a measurable improvement in strategy performance and stability. The obtained results demonstrate the potential for further development of adaptive decision-support systems capable of leveraging artificial intelligence for dynamic and resilient financial investment management.