Could you share a bit about your professional journey and what first drew you to the world of quantitative investing?
From an early age, I was captivated by statistics, particularly in sports like football and cricket, where data is integral to understanding performance. At university, my dissertation focused on the efficiency of football betting markets, which opened the door to my first role at Barclays Global Investors (BGI). There, I supported quantitative portfolio managers and researchers, initially handling tasks such as alpha checking and analyst surveys – many of which are now automated. This experience provided a strong foundation in quantitative investing. Over time, my responsibilities expanded to include client portfolio management, defending a significant book of institutional mandates, launching and growing hedge funds, and driving the integration of ESG considerations into investment products.
Having spent time at Blackrock, what were some of the most valuable lessons or experiences you gained there?
On the investment side, I learned the critical importance of collaboration. Research projects benefit immensely from diverse perspectives, and investment teams from having an open dialogue with their colleagues working on different asset classes. Fostering this collaborative environment requires a supportive company culture and clear top-down incentives – something I am committed to cultivating within my own team. On the product side, I learned to focus on solving client problems – sometimes by improving existing solutions rather than simply being first to market. Building a diversified platform also proved key, both for growth and resilience when certain strategies inevitably underperformed. That is why we should always be looking ahead, recognising that today’s success has a shelf life, and that the next engine of growth needs to be built before the current one fades.
What motivated your decision to join Pictet Asset Management, and how did you know it was the right next step for you?
I was drawn by the chance to help shape and grow a business with deep roots in quantitative investing, though not widely recognised outside a handful of markets. I saw strong potential in the team and opportunities to fill product gaps, especially in core equity. Pictet’s robust distribution network meant that with the right products and performance, success was within reach.
And more personally, moving to Geneva offered a new adventure for my family and the chance to enjoy a city I’d always loved visiting – the prospect of running alongside the lake before a day of meetings was especially appealing.
In your view, what distinguishes Pictet Asset Management from other firms in the industry?
It embodies the Swiss cultural mix of heritage and innovation. That heritage is reflected in the long-term thinking, allowing innovation to flourish over time without creating short pressure which could lead to inferior outcome. Our AI strategy benefitted from this culture, with the team spending many years researching and refining the process before we brought it to market, resulting in better outcomes for clients.
Geneva also provides an excellent base for managing quantitative strategies, while remaining somewhat under the radar. Switzerland’s top universities excel in STEM fields but are often underutilised by US and UK quant firms. Pictet has leveraged this, building strong links with EPFL in Lausanne and benefiting from proximity to France’s talented mathematicians, as well as the CERN physics lab, which has served as a finishing school for several of our team members.
You manage a 35-person team spanning very different mindsets – physicists, computer scientists, traditional portfolio managers, engineers. How does it work?
Diversity is particularly important for me. It helps us avoid groupthink when researching new signals or models. For example, as we have refined our machine learning models, there is a spectrum of views within the team about the balance between economic intuition and trusting the relationships identified by robust, data-driven models.
Ultimately, it is this blend of perspectives and expertise that drives our innovation and success. By fostering an environment where different viewpoints are valued and debated, we are able to challenge assumptions, uncover new opportunities, and build more resilient investment strategies for our clients.
What's the most common misconception about using AI you encounter, and how do you address it?
A common misconception is that all quant teams use AI in the same way, leading to crowded trades. In reality, approaches vary widely – some use AI to enhance factor models, others to process alternative data, and some, like us, to train stock forecasting models. Even within these groups, methods and investment horizons differ, and recent datahttps://www.ft.com/content/4300b622-42b2-4fbb-bfcf-016e1b112bf9 shows that correlations between quant managers have actually decreased as AI adoption has grown.
Looking ahead, what are you most optimistic about in the future of quantitative investing and your role at Pictet Asset Management?
Our team is at the forefront of using AI within the investment process to make stock forecasts. There is exciting ongoing work to advance those efforts, across data, modelling, portfolio construction and hardware. We are also exploring broader applications of AI to enhance our processes, such as using language models to accelerate non-critical coding tasks or to document our research more efficiently. I am particularly excited about the potential for agentic AI to query our proprietary models, which will improve our interpretation of forecasts and performance.