Robo-Advisors and AI-Driven Funds: Catalysts in the Dynamic Evolution of Asset Management
DOI:
https://doi.org/10.32890/ijbf2025.20.2.4Keywords:
AI-driven fund, asset management, investment, Artificial Intelligence, robo-advisorAbstract
Integrating Artificial Intelligence (AI) into investment finance has been transformative; however, its dynamic evolution in asset management remains underexplored. This study aims to comprehend and investigate the presence of AI in Malaysian asset management, its evolution in relation to current traditional practices, and its performance. A mixed-methods approach was employed. Qualitative text analysis was conducted on 702 fund reports and official AMC documents to identify the presence of AI technologies and their role in transforming traditional practices. Quantitative methods were utilised to evaluate performance: user ratings of robo-advisor applications were analysed to measure adoption and satisfaction, while Welch's t-test compared the annual returns of AI-driven and human-managed equity funds to assess performance. This study identified two prominent AI in asset management: robo-advisors and AI-driven funds. Robo-advisors automate investor profiling and fund recommendations, whereas AI-driven funds use algorithms for autonomous trading decisions. AI adoption varies among asset management companies (AMCs), with differing levels of integration. This transformation has supplanted traditional unit trust consultants (UTCs) with robo-advisors and human fund managers with AI-driven funds. Preliminary findings indicate high user satisfaction with robo-advisors because of their efficacy and convenience. AI-driven funds yield annual returns comparable to those of human-managed funds in the equity category, demonstrating their proficiency in managing complex investment strategies. These findings illuminate AI's transformative potential in asset management, suggesting it could replace traditional roles and achieve competitive performance. This study lays the groundwork for future research on AI's long-term impact and scalability in the industry, enriching the understanding of AI's evolving role in finance.
References
Abdullah, F., Hassan, T., & Mohamad, S. (2007). Investigation of performance of Malaysian Islamic unit trust funds: Comparison with conventional unit trust funds. Managerial Finance, 33(2), 142–153. https://doi.org/10.1108/03074350710715854
Ahmed, S., Alshater, M. M., Ammari, A. El, & Hammami, H. (2022). Artificial Intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646. https://doi.org/10.1016/j.ribaf.2022.101646
Al Janabi, M. A. M. (2022). Optimization algorithms and investment portfolio analytics with machine learning techniques under time-varying liquidity constraints. Journal of Modelling in Management, 17(3), 864–895. https://doi.org/10.1108/JM2-10-2020-0259
Al Rahahleh, N., & Bhatti, M. I. (2023). Empirical comparison of Shariah compliant vs conventional mutual fund performance. International Journal of Emerging Markets, 18(10), 4504–4523. https://doi.org/10.1108/IJOEM-05-2020-0565
Al-Natour, S., & Turetken, O. (2020). A comparative assessment of sentiment analysis and star ratings for consumer reviews. International Journal of Information Management, 54, 102132. https://doi.org/10.1016/j.ijinfomgt.2020.102132
Alaminos, D., Salas, M. B., & Fernández-Gámez, M. Á. (2024). Hybrid genetic algorithms in agent-based artificial market model for simulating fan tokens trading. Engineering Applications of Artificial Intelligence, 131, 107713. https://doi.org/10.1016/j.engappai.2023.107713
Amirzadeh, R., Nazari, A., & Thiruvady, D. (2022). Applying Artificial Intelligence in cryptocurrency markets: A survey. Algorithms, 15(11), 428. https://doi.org/10.3390/a15110428
Artiga Gonzalez, T., Dyakov, T., Inhoffen, J., & Wipplinger, E. (2024). Crowding of international mutual funds. Journal of Banking & Finance, 164, 107202. https://doi.org/10.1016/j.jbankfin. 2024.107202
Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis. NursingPlus Open, 2, 8–14. https://doi.org/10.1016/j.npls.2016.01.001
Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025–1044. https://doi.org/10.1287/mnsc.2018.3253
Bhatia, A., Chandani, A., & Chhateja, J. (2020). Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context. Journal of Behavioral and Experimental Finance, 25, 100281. https://doi.org/10.1016/j.jbef.2020.100281
Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Brumen, B., Zajc, A., & Bošnjak, L. (2023). Permissions vs. privacy policies of apps in Google Play Store and Apple App Store. https://doi.org/10.3233/FAIA220507
Cabrera, D., Rubilar, R., & Cubillos, C. (2019). Resilience in the decision-making of an artificial autonomous system on the stock market. IEEE Access, 7, 145246–145258. https://doi.org/10.1109/ACCESS.2019.2945471
Capponi, A., Ólafsson, S., & Zariphopoulou, T. (2022). Personalized robo-advising: Enhancing investment through client interaction. Management Science, 68(4), 2485–2512. https://doi.org/10.1287/mnsc.2021.4014
Chang, Y. H., & Lee, M. S. (2017). Incorporating Markov decision process on genetic algorithms to formulate trading strategies for stock markets. Applied Soft Computing Journal, 52, 1143–1153. https://doi.org/10.1016/j.asoc.2016.09.016
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
Chen, Y., & Hao, Y. (2018). Integrating principle component analysis and weighted support vector machine for stock trading signals prediction. Neurocomputing, 321, 381–402. https://doi.org/10.1016/j.neucom.2018.08.
Cherednik, I. (2021). Artificial Intelligence approach to momentum risk-taking. International Journal of Financial Studies, 9(4), https://doi.org/10.3390/ijfs9040058
Chung, D., Jeong, P., Kwon, D., & Han, H. (2023). Technology acceptance prediction of robo-advisors by machine learning. Intelligent Systems with Applications, 18, 200197. https://doi.org/10.1016/ j.iswa.2023.200197
Cofnas, A. (2018). The future of forex trading: Algorithms, Artificial Intelligence, and social forex trading (pp. 91–96). Springer. https://doi.org/10.1007/978-3-319-92913-2_8
Coleman, L. (2016). Applied investment theory. Springer International Publishing. https://doi.org/10.1007/978-3-319-43976-1
Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications, Inc. https://lccn.loc.gov/2017037536
Deprez, L., Antonio, K., & Boute, R. (2021). Pricing service maintenance contracts using predictive analytics. European Journal of Operational Research, 290(2), 530–545. https://doi.org/10. 1016/j.ejor.2020.08.022
Durán-Santomil, P., Otero-González, L., Domingues, R., & Leite, P. (2023). Can managers' characteristics explain European bond mutual fund performance? Finance Research Letters, 58, 104626. https://doi.org/10.1016/j.frl.2023.104626
Dhiman, N., Jamwal, M., & Kumar, A. (2023). Enhancing value in customer journey by considering the (ad)option of Artificial Intelligence tools. Journal of Business Research, 167, 114142. https://doi.org/10.1016/j.jbusres.2023.114142
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Elmahjub, E. (2023). Artificial Intelligence (AI) in Islamic ethics: Towards pluralist ethical benchmarking for AI. Philosophy & Technology, 36(4), 73. https://doi.org/10.1007/s13347-023-00668-x
Evans, R., Gil‐Bazo, J., & Lipson, M. (2024). Mutual fund performance and manager assets: The negative effect of outside holdings. Financial Management, 53(1), 3–29. https://doi.org/10.1111/fima.12443
Fernández-Loría, C., Provost, F., & Han, X. (2022). Explaining data-driven decisions made by AI Systems: The counterfactual approach. MIS Quarterly, 45(3), 1635–1660. https://doi.org/10. 25300/MISQ/2022/16749
Flemming, K., Booth, A., Garside, R., Tunçalp, Ö., & Noyes, J. (2019). Qualitative evidence synthesis for complex interventions and guideline development: Clarification of the purpose, designs and relevant methods. BMJ Global Health.
Frederick, A. (2019). Investment: An introduction to analysis and management. Prentice-Hall.
Galloppo, G. (2021). Asset allocation strategies for mutual funds. Springer International Publishing. https://doi.org/10.1007/978-3-030-76128-8
Grassa, R. (2015). Shariah supervisory systems in Islamic finance institutions across the OIC member countries: An investigation of regulatory frameworks. Journal of Financial Regulation and Compliance, 23(2), 135–160. https://doi.org/10.1108/JFRC-02-2014-0011
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
Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial Intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. https://doi.org/10.1016/j.techfore.2020.120392
Hammond, P. B., Maurer, R., & Mitchell, O. (2023). Pension Funds and Sustainable Investment (P. B. Hammond, R. Maurer, & O. Mitchell, Eds.). Oxford University PressOxford. https://doi.org/10.1093/oso/9780192889195.001.0001
Helms, N., Hölscher, R., & Nelde, M. (2022). Automated investment management: Comparing the design and performance of international robo‐managers. European Financial Management, 28(4), 1028–1078. https://doi.org/10.1111/eufm.12333
Herbert, B. M., & Michael, J. L. (2023). Basic finance: An introduction to financial institutions, investments, and management (13th ed.). Cengage Learning.
Herlambang, A. (2020). The performace comparation in Indonesia: Conventional mutual funds vs Sharia mutual funds. Journal of Business Management Review, 1(5), 295–312. https://doi.org/10.47153/jbmr15.642020
Hilpisch, Y. (2020). Artificial Intelligence in finance. O'Reilly Media.
Hoberg, G., Kumar, N., & Prabhala, N. (2018). Mutual fund competition, managerial skill, and alpha persistence. Review of Financial Studies, 31(5), 1896–1929. https://doi.org/10.1093/rfs/hhx127
Hong, X., Pan, L., Gong, Y., & Chen, Q. (2023). Robo-advisors and investment intention: A perspective of value-based adoption. Information & Management, 60(6), 103832. https://doi.org/10.1016/j. im.2023.103832
Horn, M., & Oehler, A. (2020). Automated portfolio rebalancing: Automatic erosion of investment performance? Journal of Asset Management, 21(6), 489–505. https://doi.org/10.1057/s41260-020-00183-0
Huang, C. F., & Li, H. C. (2017). An evolutionary method for financial forecasting in microscopic high-speed trading environment. Computational Intelligence and Neuroscience. https://doi.org/10. 1155/2017/9580815
Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
Huang, Y., Wan, X., Zhang, L., & Lu, X. (2024). A novel deep reinforcement learning framework with BiLSTM-Attention networks for algorithmic trading. Expert Systems with Applications, 240, 122581. https://doi.org/10.1016/j.eswa.2023.122581
Hung, M.-C., Chen, A.-P., & Yu, W.-T. (2024). AI-driven intraday trading: Applying Machine Learning and market activity for enhanced decision support in financial markets. IEEE Access, 12, 12953–12962. https://doi.org/10.1109/ACCESS.2024.3355446
Hyun Baek, T., & Kim, M. (2023). AI robo-advisor anthropomorphism: The impact of anthropomorphic appeals and regulatory focus on investment behaviors. Journal of Business Research, 164, 114039. https://doi.org/10.1016/j.jbusres.2023.114039
Immenkötter, P. (2024). How can Artificial Intelligence transform asset management? The Economists' Voice. https://doi.org/10.1515/ev-2024-0055
Jain, R., Sharma, D., Behl, A., & Tiwari, A. K. (2023). Investor personality as a predictor of investment intention – mediating role of overconfidence bias and financial literacy. International Journal of Emerging Markets, 18(12), 5680–5706. https://doi.org/10.1108/IJOEM-12-2021-1885
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial Intelligence in healthcare: Past, present and future. In Stroke and Vascular Neurology (Vol. 2, Issue 4, pp. 230–243). BMJ Publishing Group. https://doi.org/10.1136/svn-2017-000101
Jiang, T. (2021). Using machine learning to analyze merger activity. Frontiers in Applied Mathematics and Statistics, 7. https://doi.org/10.3389/fams.2021.649501
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Khan, S., & Rabbani, M. R. (2020, November 17). Chatbot as Islamic Finance Expert (CaIFE): When finance meets Artificial Intelligence. ACM International Conference Proceeding Series. https://doi.org/10.1145/3440084.3441213
Kraiwanit, T., Jangjarat, K., & Atcharanuwat, J. (2022). The acceptance of financial robo-advisors among investors: The emerging market study. Journal of Governance and Regulation, 11(2, special issue), 332–339. https://doi.org/10.22495/jgrv11i2siart12
Lebo, T., Del Rio, N., Fisher, P., & Salisbury, C. (2016). A five-star rating scheme to assess application seamlessness. Semantic Web, 8(1), 43–63. https://doi.org/10.3233/SW-150207
Lee, I., & Shin, Y. J. (2018). Fintech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46. https://doi.org/10.1016/j.bushor.2017.09.003
Lee, N., & Moon, J. (2023). Offline reinforcement learning for automated stock trading. IEEE Access, 11, 112577–112589. https://doi.org/10.1109/ACCESS.2023.3324458
Lessambo, F. I. (2021). International finance. Springer International Publishing. https://doi.org/10. 1007/978-3-030-69232-2
Lin, T. Y., Chen, C. W. S., & Syu, F. Y. (2021). Multi-asset pair-trading strategy: A statistical learning approach. North American Journal of Economics and Finance, 55. https://doi.org/10.1016/j. najef.2020.101295
Linnainmaa, J. T., Melzer, B. T., & Previtero, A. (2021). The misguided beliefs of financial advisors. Journal of Finance, 76(2), 587–621. https://doi.org/10.1111/jofi.12995
Maknickas, A., & Maknickiene, N. (2019). Support system for trading in exchange market by distributional forecasting model. Informatica (Netherlands), 30(1), 73–90. https://doi.org/10.15388/Informatica.2019.198
Maknickienė, N., Maknickas, A., & Martinkutė-Kaulienė, R. (2020). Trading support method based on computational intelligence for speculators in the options market. Journal of International Studies, 13(3), 231–247. https://doi.org/10.14254/2071
Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial Intelligence-empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161, 113838. https://doi.org/10.1016/j.jbusres.2023.113838
Mayo, H. B. (2020). Investments: An introduction. Cengage Learning.
Mhlanga, D. (2020). Industry 4.0 in finance: The impact of Artificial Intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 45. https://doi.org/10. 3390/ijfs8030045
Miguel, A. F., & Chen, Y. (2021). Do machines beat humans? Evidence from mutual fund performance persistence. International Review of Financial Analysis, 78. https://doi.org/10.1016/j.irfa. 2021.101913
Miller, T. (2019). Explanation in Artificial Intelligence: Insights from the social sciences. In Artificial Intelligence (Vol. 267, pp. 1–38). Elsevier B.V. https://doi.org/10.1016/j.artint.2018.07.007
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022). Explainable artificial intelligence: A comprehensive review. Artificial Intelligence Review, 55(5), 3503–3568. https://doi.org/10. 1007/s10462-021-10088-y
Mirabile, K. R. (2016). Hedge fund investing. Wiley. https://doi.org/10.1002/9781119210702
Morgan, H. (2022). Conducting a qualitative document analysis. The Qualitative Report. https://doi.org/10.46743/2160-3715/2022.5044
Naga, P., Balivada, D., Nirmala, S., & Tiruveedi, P. (2024). Decision trees for intuitive intraday trading strategies. https://doi.org/10.48550/arxiv.2405.13959
Nimalendran, M., Rzayev, K., & Sagade, S. (2024). High-frequency trading in the stock market and the costs of options market making. Journal of Financial Economics, 159, 103900. https://doi.org/10.1016/j.jfineco.2024.103900
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). Efficient stock-market prediction using ensemble support vector machine. Open Computer Science, 10(1), 153–163. https://doi.org/10.1515/ comp-2020-0199
Oehler, A., Horn, M., & Wendt, S. (2022). Investor characteristics and their impact on the decision to use a robo-advisor. Journal of Financial Services Research, 62(1–2), 91–125. https://doi.org/10.1007/s10693-021-00367-8
Paiva, F. D., Cardoso, R. T. N., Hanaoka, G. P., & Duarte, W. M. (2019). Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Systems with Applications, 115, 635–655. https://doi.org/10.1016/j.eswa.2018.08.003
Pollet, J. M., & Wilson, M. (2008). How does size affect mutual fund Behavior? The Journal of Finance, 63(6), 2941–2969. https://doi.org/10.1111/j.1540-6261.2008.01417.x
Ratanabanchuen, R., & Saengchote, K. (2020). Institutional capital allocation and equity returns: Evidence from Thai mutual funds' holdings. Finance Research Letters, 32. https://doi.org/10.1016/j.frl.2018.12.033
Rui, C., & Jinjuan, R. (2022). Do AI-powered mutual funds perform better? Finance Research Letters, 47. https://doi.org/10.1016/j.frl.2021.102616
Ronald, W. M., & Edgar, A. N. (2019). Introduction to finance: Markets, investments, and financial management (17th Edition). Wiley.
Sankofa, N. (2023). Critical method of document analysis. International Journal of Social Research Methodology, 26(6), 745–757. https://doi.org/10.1080/13645579.2022.2113664
Sari, K. N., Sulchan, M., & Mutamimah, M. (2021). Performance comparison of mutual funds and sharia mutual funds. Bukhori: Kajian Ekonomi Dan Keuangan Islam, 1(1), 65–77. https://doi.org/10.35912/bukhori.v1i1.600
Silva, C. S. R., & Fonseca, J. M. (2019). Artificial Intelligence and algorithms in intelligent systems (pp. 308–317). https://doi.org/10.1007/978-3-319-91189-2_30
Seiler, V., & Fanenbruck, K. M. (2021). Acceptance of digital investment solutions: The case of robo advisory in Germany. Research in International Business and Finance, 58, 101490. https://doi.org/10.1016/j.ribaf.2021.101490
Setty, R., Elovici, Y., & Schwartz, D. (2024). Cost‐sensitive machine learning to support startup investment decisions. Intelligent Systems in Accounting, Finance and Management, 31(1). https://doi.org/10.1002/isaf.1548
Smuha, N. A. (2019). The EU approach to ethics guidelines for trustworthy Artificial Intelligence. A Journal of Information Law and Technology. https://www.mmcventures.com/wp-content/ uploads/2019/0
Snow, D. (2019). Machine learning in asset management—part 1: Portfolio construction—trading strategies. The Journal of Financial Data Science, 2(1), 10–23. https://doi.org/10.3905/jfds. 2019.1.021
Sommer, M., Todd, T. M., & Lim, H. (2023). Exploring the relationship between investors' financial literacy and advisor use with securities-based loans. Financial Planning Review, 6(3). https://doi.org/10.1002/cfp2.1166
Stein, R. (2023). Are mutual fund managers good gamblers? Journal of Financial Markets, 64. https://doi.org/10.1016/j.finmar.2022.100787
Surono, S., Juwita, R., Ghofur, R. A., & Anggraini, E. (2021). Comparison of Sharia performance and conventional mutual funds in forming optimal portfolio. Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah, 4(3), 655–672. https://doi.org/10.47467/alkharaj.v4i3.711
Tanos, B. A. (2022). Culture and mutual fund performance. Finance Research Letters, 46. https://doi.org/10.1016/j.frl.2021.102466
Tao, R., Su, C. W., Xiao, Y., Dai, K., & Khalid, F. (2021). Robo advisors, algorithmic trading and investment management: Wonders of ourth industrial revolution in financial markets. Technological Forecasting and Social Change, 163. https://doi.org/10.1016/j.techfore. 2020.120421
Umer Ghani, M., Awais, M., & Muzammul, M. (2019). Stock market prediction using Machine Learning(ML) algorithms. Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), 97–116. https://doi.org/10.14201/ADCAIJ20198497116
Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15(3), 398–405. https://doi.org/10.1111/nhs.12048
Wall, L. D. (2018). Some financial regulatory implications of Artificial Intelligence. Journal of Economics and Business, 100, 55–63. https://doi.org/10.1016/j.jeconbus.2018.05.003
Wang, C., Shi, Y., & Liu, Y.-J. (2023). Financial advisor's covert discrimination against long-term clients. Pacific-Basin Finance Journal, 82, 102174. https://doi.org/10.1016/j.pacfin.2023. 102174
Wang, J., Zhuang, Z., & Feng, L. (2022). Intelligent optimization based multifactor deep learning stock selection model and quantitative trading strategy. Mathematics, 10(4). https://doi.org/10.3390/ math10040566
West, R. M. (2021). Best practice in statistics: Use the Welch t-test when testing the difference between two groups. Annals of Clinical Biochemistry: International Journal of Laboratory Medicine, 58(4), 267–269. https://doi.org/10.1177/0004563221992088
Zhang, Y.-J., Zhang, H., & Gupta, R. (2023). A new hybrid method with data-characteristic-driven analysis for Artificial Intelligence and robotics index return forecasting. Financial Innovation, 9(1), 75. https://doi.org/10.1186/s40854-023-00483-5
Zhou, Y., Li, H., Xiao, Z., & Qiu, J. (2023). A user-centered explainable Artificial Intelligence approach for financial fraud detection. Finance Research Letters, 58, 104309. https://doi.org/10. 1016/j.frl.2023.104309
Zhu, M. (2018). Informative fund size, managerial skill, and investor rationality. Journal of Financial Economics, 130(1), 114–134. https://doi.org/10.1016/j.jfineco.2018.06.002
Zhuang, Z., Hong, X., & Yao, J. (2023). The journey is the reward: A study of corporate site visits and mutual fund performance. Pacific-Basin Finance Journal, 82, 102194. https://doi.org/10.1016/ j.pacfin.2023.102194
Published
Issue
Section
How to Cite
Research impact
Harvested 2026-09-07Counts differ between services because each indexes a different body of literature. None of them is the whole picture.
