Commment l'évolution de l'automatisation et des technologies influence-t-elle la perception et l'adoption des stratégies de gestion active et passive?

Abstract

This master’s thesis explores how automation and technologies, particularly artificial intelligence (AI), big data, and advanced algorithms, are reshaping asset management, both in active and passive strategies. Against a backdrop of sustained growth in passive investment and increasing pressure on active managers to deliver performance and reduce costs, understanding the role of technology has become a priority for the industry.

First, the thesis presents a literature review where the theoretical foundations of modern finance are examined, notably Markowitz’s mean-variance theory and Sharpe’s Capital Asset Pricing Model. It also reviews the principles, advantages, and limitations of both active and passive management, including the rapid rise of exchange-traded funds (ETFs) and innovations such as smart beta. It finally explores the growing influence of AI and machine learning in portfolio construction, risk management, and operational efficiency.

Second, the thesis presents a study using a qualitative approach, based on interviews and questionnaires with four experienced professionals from diverse sectors, including private banking, asset management firms, and fintech companies. This methodology enabled an in-depth exploration of their perceptions, adoption strategies, and views on the opportunities and risks associated with technological integration.

The study reveals a shared view that technology is primarily an enabler rather than a replacement for human decision-making. While some participants see AI as a transformative tool for personalisation and operational efficiency, others remain cautious, perceiving it as an auxiliary resource with limited long-term impact on investment performance.

In conclusion, this research highlights that technological integration in asset management is evolving progressively, with the most significant potential lying in models that combine human expertise with advanced technological tools. Such hybrid approaches may shape the future of asset management, enhancing competitiveness, efficiency, and adaptability in an increasingly complex market environment.