AI-DRIVEN FINANCIAL TECHNOLOGIES AND THEIR ROLE IN PORTFOLIO MANAGEMENT AND RISK REDUCTION
Abstract
Artificial intelligence is one of the most important technological innovations in modern finance because it improves investment research, portfolio construction, and risk management by providing rapid information processing, predictive modelling, and automated decision support. Financial institutions, asset managers, hedge funds and fintech platforms are increasingly deploying AI-powered technologies such as machine learning, natural language processing, robo-advisory systems and predictive analytics to improve investment decision-making and to more efficiently manage risk. These technologies are typically touted as answers to the complexity, speed and instability of financial markets. But as they become more widely adopted, the true effect of AI-enabled financial technology on portfolio management and risk mitigation is up for discussion. Many of the existing literature papers point to possible advantages such as better forecasting, more effective diversification, enhanced risk monitoring and reduced behavioural bias. However, fewer papers critically assess if these technologies lead to more robust and sustainable portfolio outcomes in practice.
This thesis looks at the function of financial technology powered by AI in portfolio management and risk management. It contends that AI can enhance specific dimensions of investment practice, notably in data-intensive analysis, signal detection, dynamic portfolio monitoring and risk identification, but that such gains are neither general nor automated. Their success depends on the quality of data, model design, institutional governance, market conditions and the capacity of users to evaluate and dispute the results of algorithms. The thesis is sceptical of the view that more advanced technology will necessarily lead to better investing judgements. Instead, it considers AI-based finance as a socio-technical evolution driven by institutional, behavioural and market factors. It is expected that the study will contribute to the finance literature by more directly tying technological adoption to the quality of portfolio management and realities of financial risk control.
References
• Agrawal, A., Gans, J. and Goldfarb, A. (2018) Prediction machines: The simple economics of artificial intelligence. Boston, MA: Harvard Business Review Press.
• Boukherouaa, E.B., AlAjmi, K., Deodoro, J., Farias, A., Ravikumar, R. and Tsai, J. (2021) Powering the digital economy: Opportunities and risks of artificial intelligence in finance. Washington, DC: International Monetary Fund.
• D’Acunto, F., Prabhala, N. and Rossi, A.G. (2019) ‘The promises and pitfalls of robo-advising’, The Review of Financial Studies, 32(5), pp. 1983–2020.
• Gu, S., Kelly, B. and Xiu, D. (2020) ‘Empirical asset pricing via machine learning’, The Review of Financial Studies, 33(5), pp. 2223–2273.
• Jabeur, S.B., Mefteh-Wali, S. and Viviani, J.-L. (2023) ‘Artificial intelligence in finance: A bibliometric review and research agenda’, Research in International Business and Finance, 66, 102033.
• Jiang, Z., Kelly, B. and Xiu, D. (2020) ‘(Re-)Imag(in)ing price trends’, The Journal of Finance, 75(6), pp. 3183–3249.
• Kahneman, D. (2011) Thinking, fast and slow. London: Penguin.
• Lo, A.W. (2017) Adaptive markets: Financial evolution at the speed of thought. Princeton, NJ: Princeton University Press.
• Markowitz, H. (1952) ‘Portfolio selection’, The Journal of Finance, 7(1), pp. 77–91.
• Rudin, C. (2019) ‘Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead’, Nature Machine Intelligence, 1(5), pp. 206–215.
• Sironi, P. (2016) FinTech innovation: From robo-advisors to goal based investing and gamification. Chichester: Wiley.
