AUTOMATED INVESTMENT DECISIONS BY RETAIL INVESTORS IN THE STOCK MARKET IN THE CONTEXT OF USING MODERN ARTIFICIAL INTELLIGENCE TOOLS

Abstract

The article examines the role of artificial intelligence in the system of automated investment decisions in the stock market, taking into account international experience and the Ukrainian context. It is substantiated that modern artificial intelligence tools are no longer limited to technical forecasting of price series but form a broader decision-support architecture that includes investor profiling, processing of market and financial data, risk assessment, portfolio recommendation, suitability control, and explanation of the proposed action. In this approach, an automated investment system is considered not as an autonomous substitute for an investor or a financial adviser, but as an infrastructure component that increases the speed, formalization, and traceability of investment decision-making. The purpose of the article is to develop a conceptual and applied approach to the use of artificial intelligence in the system of automated investment decisions of a retail investor in the stock market and to determine the conditions for its adaptation to the Ukrainian market and regulatory environment. The methodological basis of the article combines comparative analysis of international regulatory practices, a synthesis of academic research on machine learning, robo-advising, deep learning and reinforcement learning in finance, and the economic interpretation of official statistical data from the EU, the United States, Canada, and Ukraine. As a result of the study, a structural model of an automated investment system is proposed. It includes an input data block, information quality checks, an AI analytical module, an economic suitability filter, a control and explanation layer, and a recommendation output block. The article shows that the international experience of the United States, the European Union, Canada, and IOSCO is gradually shifting from technological enthusiasm to requirements for governance, accountability, disclosure, and continuous supervision of models. For Ukraine, the article substantiates the feasibility of using artificial intelligence primarily as a decisionsupport system rather than a fully autonomous trading mechanism, given the structure of the organized market, the dominance of debt instruments, uneven liquidity, and the need to strengthen institutional trust. The practical significance of the results lies in the development of methodological guidelines for brokers, investment advisers, fintech companies, and regulators that may be used in designing automated investment modules, robo-advising services, risk-control systems, and educational tools for retail investors. Further research should focus on empirical testing of the proposed approach using Ukrainian and international market data, comparing the effectiveness of different model classes, developing criteria for the explainability of investment recommendations, and defining minimum requirements for auditing algorithmic decisions in the financial sector.