Abstract
Digitalization in the stock market and the use of innovative data processing and analysis technologies have led to the emergence of new approaches to the formation of investment portfolios of different types of investors. The purpose of the article was to identify modern approaches to the formation of investment portfolios of different types of investors (retail, professional) in the context of the development of new technological sectors. It is proposed to consider an investment portfolio as a set of financial assets owned by an investor for a certain period of time and with a certain level of profitability depending on the level of risks taken. It is determined that the use of digital solutions in the formation of an investment portfolio contributes to a more rational approach to the selection of assets, reduces transaction costs for the acquisition of assets and increases the level of diversification of acquired financial assets. Under the influence of the digitalization of the economy and the emergence of new technological companies and startups, investors apply a dynamic approach to the formation of a highly diversified investment portfolio, combining in it the assets of exchange-traded investment funds, shares of companies from high-tech sectors and alternative assets (cryptocurrencies). A high level of prevalence of passive investment strategies through exchange-traded funds, an increase in the level of investment in shares of technology companies in new sectors of the economy, and a tendency to invest in high-risk and high-yield digital assets were revealed. It was found that digitalization and data analytics on digital platforms allow the development of personalized investment solutions using the recommendations of robo-advisors. The latter allows for a more balanced approach to investment based on rational, well-founded decisions made on the basis of data analytics. It was found that the use of algorithmic trading is a common phenomenon, which provides high liquidity in the stock market and minimizes risks for professional investors.
References
1. Briere, M. (2023). Retail investors’ behavior in the digital age: How digitalization is impacting investment decisions. Social Science Research Network. https://doi.org/10.2139/ssrn.4506007
2. Chowdhury, E. K. (2023). Impact of blockchain on stock market (MPRA Paper No. 118189). https://ideas.repec.org//p/pra/mprapa/118189.html
3. Das, S. R., Khadilkar, H., Mittal, S., Ostrov, D., Radhakrishnan, A., & Srivastav, R. (2026). A meta reinforcement learning approach to goals-based wealth management. The Journal of Finance and Data Science, (12), Article 100186. https://doi.org/10.1016/j.jfds.2026.100186
4. Das, S. R., Ostrov, D., Radhakrishnan, A., & Srivastav, R. (2022). Dynamic optimization for multi-goals wealth management. Journal of Banking & Finance, (140), Article 106192. https://doi.org/10.1016/j.jbankfin.2021.106192
5. Dasgupta, S., & Banerjee, A. (2020). Quantum annealing algorithm for expected shortfall based dynamic asset allocation. arXiv. https://doi.org/10.48550/arXiv.1909.12904
6. Economic sector performance dashboards. (n.d.). Yahoo Finance. https://finance.yahoo.com/sectors/
7. Husseın, H. (2023). The impact of diversification in traditional and digital financial tools on reducing risks and improving returns of the investment portfolio. Journal of Business in the Digital Age, 6(1), 42–52. https://doi.org/10.46238/jobda.1286651
8. Jayawardhana, A., & Colombage, S. R. N. (2024). Portfolio diversification possibilities of cryptocurrency: Global evidence. Applied Economics, 56(47), 5618–5633. https://doi.org/10.1080/00036846.2023.2257928
9. Lang, J., Zielinski, S., & Feld, S. (2022). Strategic portfolio optimization using simulated, digital, and quantum annealing. Applied Sciences, 12(23), Article 12288. https://doi.org/10.3390/app122312288
10. Ma, Y., Ahmad, F., Liu, M., & Wang, Z. (2020). Portfolio optimization in the era of digital financialization using cryptocurrencies. Technological Forecasting and Social Change, (161), Article 120265. https://doi.org/10.1016/j.techfore.2020.120265
11. Mattesi, M., Asproni, L., Mattia, C., & Rossi, C. (2024). Diversifying investments and maximizing Sharpe ratio: A novel quadratic unconstrained binary optimization formulation. Quantum Reports, 6(2), 244–262. https://doi.org/10.3390/quantum6020018
12. Metzler, D., Neuss, N., & Torno, A. (2022). The Digitization of Investment Management – An Analysis of Robo-Advisor Business Models. In Wirtschaftsinformatik 2022 Proceedings. https://aisel.aisnet.org/wi2022/finance_and_blockchain/finance_and_blockchain/2 (In Ukrainian)
13. Petukhina, A., Trimborn, S., Härdle, W. K., & Elendner, H. (2021). Investing with cryptocurrencies – evaluating their potential for portfolio allocation strategies. Quantitative Finance, 21(11), 1825–1853. https://doi.org/10.1080/14697688.2021.1880023
14. Pirogova, O., & Loubochkin, M. (2023). Digitalization in the global stock market in the post coronavirus era. In XV International Scientific Conference “INTERAGROMASH 2022” (pp. 1605–1613). Springer. https://doi.org/10.1007/978-3-031-21432-5
15. Rehman, M. U., & Vinh Vo, X. (2020). Cryptocurrencies and precious metals: A closer look from diversification perspective. Resources Policy, (66), Article 101652. https://doi.org/10.1016/j.resourpol.2020.101652
16. Sharma, P., Gupta, S., & Kapoor, D. (2024). Blockchain technology in the stock market: A deep dive into enhancing efficiency and security. In Revolutionizing the Global Stock Market: Harnessing Blockchain for Enhanced Adaptability (pp. 44–59). IGI Global. https://doi.org/10.4018/979-8-3693-1758-7.ch003
17. Tahvildari, M. T. (2025). Analysis of investor profiling and portfolio recommendations by robo-advisors in Germany. Global Business Finance Review, 30(8), 19–33. https://doi.org/10.17549/gbfr.2025.30.8.19
18. Trimborn, S., Li, M., & Härdle, W. K. (2020). Investing with cryptocurrencies—a liquidity constrained investment approach. Journal of Financial Econometrics, 18(2), 280–306. https://doi.org/10.1093/jjfinec/nbz016
19. Usmanovich Burkhanov, A., & Mansur Qizi Eshmamatova, M. (2021). The ways for improvement of investment strategy in the period of digital economy. In The 5th International Conference on Future Networks & Distributed Systems (pp. 655–662). ACM. https://doi.org/10.1145/3508072.3508202
20. Vandana, Garima, & Singh, S. (2026). AI-based predictive modelling of digitalization effects on investment decisions of Gen Z investors. In 2026 5th International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 1–6). IEEE. https://doi.org/10.1109/ICIPTM69057.2026.11466023
21. Xu, H., Dasgupta, S., Pothen, A., & Banerjee, A. (2023). Dynamic asset allocation with expected shortfall via quantum annealing. Entropy, 25(3), Article 541. https://doi.org/10.3390/e25030541
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