Authors : Edmund Kofi Yeboah; Daniel Duah; Joseph Kobi; Benjamin Yaw Kokroko

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/4487at8r

Scribd : https://tinyurl.com/3r2rduzu

DOI : https://doi.org/10.38124/ijisrt/26jan589

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The incorporation of artificial intelligence in the management of portfolios has radically changed the approaches to investments especially to retail investors in the emerging markets. The present review explores the existing situation of AI applications in portfolio optimization, being interested in machine learning methods, deep learning, and robo-advisory systems. The study summarizes conclusions of 40 peer-reviewed articles published since 2015 and examines how AI technologies can help retail investors to solve the distinctive problems of the emerging economies, such as market instability, information asymmetry, and the lack of professional financial services. The major findings include that deep learning models (including Long Short-Term Memory networks and Convolutional Neural Networks) are found to be better at prediction accuracy than the traditional statistical techniques, and have been used in asset allocation, risk management, and automated portfolio rebalancing. This report unveils that the robo-advisory platforms have grown exponentially, and the market estimates the growth of the market by 11.83 billion dollars in 2024 to 62.64 billion dollars in 2034. Nevertheless, there are still major obstacles, such as the problem of data quality, the lack of transparency in algorithms, and the insufficient development of regulatory frameworks that do not follow recent technological progress. This study is relevant to the understanding of the way AI democratizes advanced investment policies and what aspects need attention to achieve a fair distribution and responsible application in the new market conditions.

Keywords : Artificial Intelligence, Portfolio Optimization, Machine Learning, Deep Learning, Robo-Advisors, Retail Investors, Neural Networks, Predictive Analytics, Risk Management, Financial Inclusion.

References :

1. Tsagkanos, A., Sharma, C., & Ghosh, B. (2024). Enhancing portfolio management using artificial intelligence: Literature review. Frontiers in Artificial Intelligence, 7, Article 1371502. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1371502/full
2. Mehtab, S., & Sen, J. (2020). Stock price prediction using CNN and LSTM-based deep learning models. 2020 International Conference on Decision Aid Sciences and Application (DASA), 447-453. https://doi.org/10.1109/DASA51403.2020.9317207
3. Allied Market Research. (2023). Robo advisory market size, share, trends & growth | 2032. https://www.alliedmarketresearch.com/robo-advisory-market
4. Banerjee, S. (2025). Portfolio management with the help of AI: What drives retail Indian investors to robo-advisors? Electronic Journal of Information Systems in Developing Countries, 91, Article e12346. https://doi.org/10.1002/isd2.12346
5. Zou, J., Zhao, Q., Jiao, Y., Cao, H., Liu, Y., Yan, Q., Abbasnejad, E., Liu, L., & Shi, J. Q. (2023). Stock market prediction via deep learning techniques: A survey. ACM Computing Surveys, 56(6), 1-34. https://arxiv.org/pdf/2212.12717
6. Market.us. (2024). Robo advisory market size, share, growth & trends report 2032. https://market.us/report/robo-advisory-market/
7. Chen, L., Qiao, Z., Wang, M., Wang, C., Du, R., & Stanley, H. E. (2018). Which artificial intelligence algorithm better predicts the Chinese stock market? IEEE Access, 6, 48625-48633. https://doi.org/10.1109/ACCESS.2018.2859809
8. Business Research Insights. (2025). Robo advisory market size, share, trends & forecast. https://www.businessresearchinsights.com/market-reports/robo-advisory-market-120781
9. Hoseinzade, E., & Haratizadeh, S. (2019). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129, 273-285. https://doi.org/10.1016/j.eswa.2019.03.029
10. Cardillo, G., Gonzalez-Igual, M., & Vergara-Alert, C. (2024). The emerging field of robo advisor: A relational analysis. Heliyon, 10(17), Article e35747. https://doi.org/10.1016/j.heliyon.2024.e35747
11. SNS Insider. (2025). Robo advisory market size & growth, 2033. https://www.snsinsider.com/reports/robo-advisory-market-8255
12. Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review 2005-2019. Applied Soft Computing, 90, Article 106181. https://doi.org/10.1016/j.asoc.2020.106181
13. Khoa, T. D., & Hieu, H. N. (2024). Applying machine learning algorithms to predict the stock price trend in the stock market–The case of Vietnam. Humanities and Social Sciences Communications, 11, Article 338. https://doi.org/10.1057/s41599-024-02807-x
14. Market Data Forecast. (2025). Robo advisory market size, share, trends & analysis, 2033. https://www.marketdataforecast.com/market-reports/robo-advisory-market
15. Lumenalta. (2025). The impact of AI for portfolio management in 2025. https://lumenalta.com/insights/the-impact-of-ai-for-portfolio-management-in-2025
16. Bartram, S. M., Branke, J., & De Rossi, G. (2020). Machine learning for active portfolio management. CFA Institute Research Foundation. https://www.cfainstitute.org/research/foundation/2020/machine-learning-asset-management
17. Bhanusree, K., Sri Ramya, K., & Swathi, S. (2025). Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities. Scientific Reports, 15, Article 2847. https://doi.org/10.1038/s41598-025-87847-4
18. Verified Market Research. (2025). Robo-advisor market size, share, trends & forecast. https://www.verifiedmarketresearch.com/product/robo-advisor-market/
19. Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data. IEEES Access, 8, 150199-150212. https://doi.org/10.1109/ACCESS.2020.3015966
20. Chowdhury, R., Rahman, M. A., Rahman, M. S., & Mahdy, M. R. C. (2021). An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning. Physica A: Statistical Mechanics and its Applications, 551, Article 124569. https://doi.org/10.1016/j.physa.2020.124569
21. LeewayHertz. (2025). AI in portfolio management: Use cases, applications, benefits and development. https://www.leewayhertz.com/ai-for-portfolio-management/
22. Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184, Article 115537. https://doi.org/10.1016/j.eswa.2021.115537
23. Market Research Biz. (2024). Robo advisory market size, share, growth | CAGR of 30.1%. https://marketresearch.biz/report/robo-advisory-market/
24. FTI Consulting. (2025). AI in trading and portfolio management. https://www.fticonsulting.com/insights/articles/artificial-intelligence-trading-portfolio-management
25. Zhang, Y., Wu, L., & Wang, X. (2025). Research on deep learning model for stock prediction by integrating frequency domain and time series features. Scientific Reports, 15, Article 2160. https://doi.org/10.1038/s41598-025-14872-6
26. Talebi, H., Hoang, W., & Gavrilova, M. L. (2022). Multi-scale foreign exchange rates ensemble for classification of trending and volatility market conditions. Digital Finance, 4, 39-62. https://doi.org/10.1007/s42521-021-00041-4
27. Biz4Group. (2025). Top AI tools for trading in 2025: The future of smart investing. https://www.biz4group.com/blog/top-ai-tools-for-trading
28. López de Prado, M. (2018). Advances in financial machine learning. John Wiley & Sons. https://doi.org/10.1002/9781119482086
29. Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1643-1647. https://doi.org/10.1109/ICACCI.2017.8126078
30. Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411-1430. https://doi.org/10.1108/IMDS-08-2018-0368
31. Financial Content. (2025). How artificial intelligence is revolutionizing your investment portfolio. https://markets.financialcontent.com/stocks/article/marketminute-2025-7-8-how-artificial-intelligence-is-revolutionizing-your-investment-portfolio
32. Khansa, A., & Choudhry, M. (2025). Artificial intelligence attitudes and resistance to use robo-advisors: Exploring investor reluctance toward cognitive financial systems. Frontiers in Artificial Intelligence, 8, Article 1623534. https://doi.org/10.3389/frai.2025.1623534
33. Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702. https://doi.org/10.1016/j.ejor.2016.10.031
34. Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669. https://doi.org/10.1016/j.ejor.2017.11.054
35. Finkerr. (2025). Artificial intelligence in investments: Step-by-step guide. https://finkerr.com/artificial-intelligence-in-investments/
36. Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172. https://doi.org/10.1016/j.eswa.2014.10.031
37. The Motley Fool. (2025). 7 AI applications in investing. https://www.fool.com/investing/stock-market/market-sectors/information-technology/ai-stocks/ai-in-investing/
38. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223-2273. https://doi.org/10.1093/rfs/hhaa009
39. Chhajer, P., Shah, M., & Kshirsagar, A. (2022). The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2, Article 100015. https://doi.org/10.1016/j.dajour.2021.100015