Subject. This article discusses the effectiveness of using artificial intelligence in investment strategies in the Russian and American stock markets. Objectives. The article aims to assess the effectiveness of using artificial intelligence in building investment portfolios and managing assets, relying on key metrics of investment performance. Methods. For the study, we used the methods of analysis and comparative assessment of the effectiveness of investment strategies and financial asset portfolio management, as well as infographics. Results. The article finds that regarding the Russian market, only one out of the five strategies examined has a statistically significant positive alpha coefficient. At the same time, the index of hedge funds using artificial intelligence in the American market also does not show a significant advantage over the broader market. Conclusions and Relevance. The article concludes that the implementation of artificial intelligence in investment strategies currently does not significantly increase the return on the investment portfolio to outperform the benchmark, however, this may change with the alteration of the time horizon for using such strategies. The results of the study are significant for advancing academic research on the effects of the use of artificial intelligence in the financial market. They can be applied later in the development and optimization of investment strategies using artificial intelligence, as well as for assessing their effectiveness for investors.
Chia-Hung W., Yingping Z., Jinchen Y. Two-stage stock portfolio optimization based on AI-powered price prediction and mean-CVaR models. Expert Systems with Applications, 2024, vol. 255, Part B, 124555. URL: Link
Alzaman C. Optimizing portfolio selection through stock ranking and matching: A reinforcement learning approach. Expert Systems with Applications, 2025, vol. 269, 126430. URL: Link
Akdogan Y.M., Anbar A. More than just sentiment: Using social, cognitive, and behavioral information of social media to predict stock markets with artificial intelligence and big data. Borsa Istanbul Review, 2024, vol. 24, sup. 2, pp. 61–82. URL: Link
Kim A., Muhn M., Nikolaev V. Financial Statement Analysis with Large Language Models. URL: Link
Zulkifley M.A., Munir A.F., Abd Sukor M.E., Mohd Shafiai M.H. A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence. Computers, Materials & Continua, 2023, vol. 75, no. 2, pp. 4395–4418. URL: Link
Zhu Q., Han C., Liu S. et al. Artificial intelligence-driven financial innovation: A robo-advisor system for robust returns across diversified markets. Expert Systems with Applications, 2025, vol. 274, 126881. URL: Link
Chopra R., Sharma G.D., Pereira V. Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction. Technovation, 2024, vol. 135, 103067. URL: Link
Ahmed S., Alshater M.M., El Ammari A., Hammami H. Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 2022, vol. 61, 101646. URL: Link
Chhajer P., Shah M., Kshirsagar A. The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2022, vol. 2, 100015. URL: Link
Gupta S., Nachappa S., Paramanandham N. Stock market time series forecasting using comparative machine learning algorithms. Procedia Computer Science, 2025, vol. 252, pp. 893–904. URL: Link
Najem R., Fakhouri Amr M., Bahnasse A., Talea M. Advancements in Artificial Intelligence and Machine Learning for Stock Market Prediction: A Comprehensive Analysis of Techniques and Case Studies. Procedia Computer Science, 2024, vol. 231, pp. 198–204. URL: Link
Lin C.Y., Lobo Marques J.A. Stock market prediction using artificial intelligence: A systematic review of systematic reviews. Social Sciences & Humanities Open, 2024, vol. 9, 100864. URL: Link
Gunnarsson E.S., Isern H.R., Kaloudis A. et al. Prediction of realized volatility and implied volatility indices using AI and machine learning: A review. International Review of Financial Analysis, 2024, vol. 93, 103221. URL: Link
Muhammad D., Ahmed I., Naveed K., Bendechache M. An explainable deep learning approach for stock market trend prediction. Heliyon, 2024, vol. 10, iss. 21, e40095. URL: Link
Luo J., Zhu G., Xiang H. Artificial Intelligent based day-ahead stock market profit forecasting. Computers and Electrical Engineering, 2022, vol. 99, 107837. URL: Link
Song Y. Enhancing stock market trend reversal prediction using feature-enriched neural networks. Heliyon, 2024, vol. 10, iss. 2, e124126. URL: Link
Buczynski W., Cuzzolin F., Sahakian B. A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life. International Journal of Data Science and Analytics, 2021, vol. 11, pp. 221–242. URL: Link
Vakhrushev V.D. [Problems and prospects of using and regulating artificial intelligence systems in the stock market]. Ustoichivoe razvitie nauki i obrazovaniya = Sustainable Development of Science and Education, 2022, no. 6, pp. 29–38. (In Russ.) EDN: UKJLNH
Natal'son A.V. [The use of artificial intelligence for risk analysis in the securities market]. Ekonomika i upravlenie: problemy i resheniya = Economics and Management: Problems and Solutions, 2024, vol. 6, no. 3, pp. 145–150. (In Russ.) EDN: PRFGBE
Kulikov M.M. [Possibilities of using artificial intelligence tools and neural networks for stock market forecasting]. Vestnik Yuzhno-Rossiiskogo gosudarstvennogo tekhnicheskogo universiteta (NPI). Seriya: Sotsial'no-ekonomicheskie nauki = Bulletin of the South Russian State Technical University. Series: Socio-Economic Sciences, 2024, vol. 17, no. 2, pp. 130–137. (In Russ.) EDN: SSICUL
Bakhtiyarov R.R. [The possibilities of artificial intelligence for securities market analysis]. Vestnik nauki = Bulletin of Science, 2024, vol. 1, no. 5, pp. 496–500. (In Russ.) URL: Link
Kolos I.V., Skibenko G.G., Tomashevskaya E.Yu., Shchetinina D.V. [Providing diagnostics of stock market patterns with artificial intelligence tools]. Ekonomika: teoriya i praktika = Economics: Theory and Practice, 2024, no. 2, pp. 37–43. (In Russ.) EDN: CKWYWQ
Dubkov G.I. [Investigation of the possibilities of using artificial intelligence in making investment decisions on the stock market]. Uchenye zapiski Mezhdunarodnogo bankovskogo instituta = Academic Notes of the International Banking Institute, 2024, no. 1, pp. 49–62. (In Russ.)
Bezsmertnaya E.R., Kolganova E.A. [Modification of the Fama-French three-factor model and its application to assess the effectiveness of portfolio management of investment funds in Russia]. Finansy: teoriya i praktika = Finance: Theory and Practice, 2023, vol. 27, no. 2, pp. 17–27. (In Russ.) EDN: CSWBBT
