Issue: № 9, 2025
Doi: https://doi.org/10.37634/efp.2025.9.19
The paper is devoted to the study of methodological approaches to investment analysis with a focus on comparing classical mathematical methods and modern artificial intelligence tools. The relevance of the topic is justified by the fact that the increasing uncertainty in financial markets requires the application of not only traditional models but also advanced forecasting algorithms. The purpose of the paper is to identify the advantages and limitations of classical and innovative methods, to define their areas of application, and to outline the prospects of their integration in investment activities. The first part of the study examines traditional tools of investment analysis, including Markowitz portfolio theory, the Kelly criterion, NPV and PI indicators, the Monte Carlo method, as well as DCF and CAPM models. Their strengths, fields of application, as well as risks and limitations are identified. Table 1 systematizes the key characteristics of classical methods, which allows a clear demonstration of their differences. The second part of the paper is devoted to artificial intelligence as a tool for financial analytics. The main areas of its implementation in investment practice are described, such as automated portfolio management, market dynamics forecasting, and big data analysis. Statistical data on the growth of the artificial intelligence market in the financial sector from 2019 to 2030 are presented, confirming the rapid dynamics of its expansion. In addition, Table 2 provides a comparative analysis of classical methods and AI-based solutions, highlighting their common features and fundamental differences. The conclusions emphasize that classical methods remain the foundation of investment analysis, but their combination with artificial intelligence opens new opportunities for improving forecasting accuracy, enhancing risk management efficiency, and increasing the flexibility of decision-making. Such synergy enables the creation of more resilient and adaptive investment strategies under the conditions of modern financial markets.
Keywords : investment analysis, mathematical models, portfolio theory, Monte Carlo method, artificial intelligence, risk management, financial technologies
References:
1. CFI, Modern Portfolio Theory, URL: https://corporatefinanceinstitute.com/resources/career-map/sell-side/capital-markets/modern-portfolio-theory-mpt/
2. Will Kenton, What is the Kelly Criterion, URL : https://www.investopedia.com/terms/k/kellycriterion.asp
3. Kushal Agarwal, The Monte Carlo Simulation: Understanding the Basics, URL: https://www.investopedia.com/articles/investing/112514/monte-carlo-simulation-basics.asp
4. Niekrasova L.A., Polishchuk A.S. Innovative Technologies of the Digital Economy as a Direction of Investment in the Development of Ukraine. Economic journal of Odesa polytechnic university. 2022. № 4 (22). pp. 14-20. URL: https://economics.net.ua/ejopu/2022/No4/14.pdf
5. Kenson Investments, The AI-Powered Hedge Fund: How Machine Learning is Reshaping Investment Strategies, URL: https://kensoninvestments.com/resources/the-ai-powered-hedge-fund-how-machine-learning-is-reshaping-investment-strategies
6. Taylor I. New AI Technology Spurs Excitement and Concerns Among Private-Credit Managers, URL: https://www.wsj.com/articles/new-ai-technology-spurs-excitement-and-concerns-among-private-credit-managers-f3a2e65f?mod=author_content_page_6_pos_3