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Quantitative investing strategies

Managing innovative, quantitative strategies since 1999

Our quantitative strategies are built for clients seeking an investment approach that eliminates human biases while still benefiting from human oversight.
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Our quantitative franchise

Pictet Asset Management’s independence and focus on long-term thinking fosters creativity. This allows our quantitative team the time and freedom to develop innovative investment approaches underpinned by four pillars:

  • Innovative research

    We prioritise innovation and publish our multi-year research in renowned peer-reviewed journals.

  • Advanced technology

    Dedicated servers at the Pictet datacentre give us the power to deploy advanced technology in our investment process. 

  • Customized solutions

    We tailor our offering to meet the unique needs of our clients through customized equity mandates and multi asset solutions.

  • 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

  • 80%

    PhD holders in the investment team

  • 16

    years

    Average years' industry experience, incl. 10 at Pictet

As of end of August 2026.

What makes our stock selection model different

AI-powered stock selection

  • 10 years of research, hundreds of signals.
  • Finds patterns traditional quant models miss.

Factor-neutral alpha

  • Stock-specific insights across market regimes.
  • Focus on an underexploited one-month horizon.

Explainable returns

  • Clear attribution for every forecast.
  • Human oversight and discipline.
One AI model, multiple flavours

The strategy is available across different geographic allocations, in ETFs and mandate formats.

F = False, T= True.

Any questions?

photograph of Ben Becker at Pictet Office 712 5th Ave, New York, NY 10019

Ben Becker, Head of ETF Distribution.

Speak to us

Drop us your question or schedule a 15-minute ETF consultation with Ben Becker, Head of ETF distribution.

Your relationship team

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 customization of our flagship strategies or through dedicated multi-asset solutions. Please contact us for more information.

Glossary

  • 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.

  • 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).

  • 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.

  • 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.

  • 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.

  • 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.  

  • 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.

  • 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.  

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