Managing innovative, quantitative strategies since 1999
Our quantitative franchise
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Innovative research
We prioritise innovation and publish our multi-year research in renowned peer-reviewed journals.
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Advanced technology
Dedicated servers at the Pictet datacentre give us the power to deploy advanced technology in our investment process.
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Customised solutions
We tailor our offering to meet the unique needs of our clients through customised equity mandates and multi asset solutions.
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Quantitative discipline
A single, fully integrated platform for research, investment, and tech enhances efficiency and resilience.
A diversified and experienced team grounded in academic research
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80%
PhD holders in the investment team
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16
years
Average years' industry experience, incl. 10 at Pictet
As of end of June 2026.
Flagship strategies
Our team has developed two distinct investment approaches, a traditional quantitative strategy, and one using AI to select stocks. These two equity approaches are designed to target different market opportunities, making them complementary.
AI and traditional quantitative models seem to be equally good at capturing long-term market inefficiencies. However, we believe AI is better at finding hidden effects and complex patterns over the short-time scale.
Our AI Enhanced strategy
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Fully integrated machine‑learned stock‑selection model
Built from a decade of research and trained on hundreds of features, the model uncovers patterns and interactions that traditional quant approaches cannot detect.
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Consistent stock-specific factor-neutral alpha
Returns are driven by stock-specific insights, making the strategy resilient across market regimes.
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Transparency to attribute & understand the drivers of returns
The clear attribution for every forecast enables investors to understand the economic logic behind model‑driven decisions.
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One AI model, multiple flavours
The strategy is available across different geographic blocs, in Mutual Funds, ETFs and Mandate formats.
An ensemble of boosted decision trees, for illustrative purpose only.
Insights from the team
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Stéphane Daul, one of the architects behind our AI model
In this short interview, Stéphane Daul talks about the genesis of AI to select stocks at Pictet Asset Management, and how he views the future of asset management.
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Alexandra Nagy, from quantum physicist to portfolio manager
Meet our portfolio manager, Alexandra Nagy, and discover how she went from working at the CERN to co-author our team’s foundational research paper on stock return predictions.
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Enhanced index: the best of active and passive investments
Learn more about enhanced index strategies, the “Goldilocks” of investing—not too passive, not too aggressive, but leading to incremental, repeatable gains that add up over time.
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Cutting through the AI noise: our quant team examines the latest trends
Can we use LLMs to select stocks? Is the future of quantitative investment agentic? Our investment team answers all the questions you might have on AI’s latest trends.
Do you need something else?
Decades of expertise and extensive resources enable us to offer you a wide range of solutions that can best fit your specific investment needs. Whether it be through the customisation of our flagship strategies or through dedicated multi-asset solutions. Please contact us for more information.
Glossary
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Algorithm
A set of step-by-step instructions or rules a computer follows to solve a problem or make decisions. In investing, algorithms can analyse data and help decide which stocks to buy or sell.
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Investment factors
These are specific characteristics or drivers that explain why certain stocks tend to perform better over time, such as value (cheap vs. expensive stocks), momentum (stocks trending upwards), or quality (financially strong companies).
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Machine learning
A type of advanced technology where computers learn patterns from data and improve their decision-making over time without being explicitly programmed for every task. It’s like teaching a computer to learn from experience.
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Non-linear relationships
More complex connections where changes in one variable do not result in proportional changes in another. The relationship could be curved, fluctuate, or change depending on other factors, making it less predictable.
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Interactions
This refers to situations where the effect of one investment factor or variable depends on the level of another. In other words, two or more factors work together in a way that influences the investment outcome differently than if each acted alone.
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Overfitting
When a computer model learns not just the general pattern but also the random noise in past data, making it too tailored to historical information. This often causes poor results when applied to new, unseen data. Think of it as memorising the answers instead of understanding the subject.
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Black box
A system or model where the inputs and outputs are visible, but the internal workings are hidden or too complex to understand. In investing, a "black box" approach means you don’t fully know how decisions are made inside the tool.
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AI transparency
The degree to which the processes and decisions made by artificial intelligence are open, understandable, and explainable. Higher transparency helps investors trust and feel confident about how AI-driven decisions are made.