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Quest - managing innovative, quantitative strategies since 1999

Our Quest strategies are built for clients seeking a quantitative investment approach that eliminates human biases while still benefiting from human oversight. We offer evidence-based and scalable products and solutions by leveraging on the power of data & technology to deliver sustainable investment performance.

Key principles

  • #1 Innovative research

  • #2 Advanced technology

  • #3 Customised solutions

  • #4 Quantitative discipline

We manage USD 13.7bn of AUMExcluding assets managed on behalf of other Pictet AM strategies. Source: Pictet Asset Management, as of 31.12.2023 across an established and innovative range of quantitative strategies and solutions.

Our Quantitative Equities & Solutions Team consists of 20 professionals with an average industry experience of 15+ yearsAs of 31.01.2024.. They have a skill-set spanning model building, feature engineering, AI/ML, risk management, portfolio construction and implementation.

  • Did you know? 80%

    of our Quest investment team hold a PhD

Source: Pictet Asset Management, as of 01.04.2024

Our flagship strategies

We believe that over shorter time-horizons, investor behaviour rather than company fundamentals is the main driver of company share price returns. We think this is best captured by an approach that uses artificial intelligence to process a wide range of higher frequency data. However, reversion to company fundamentals occurs over longer horizons, which is this time captured using traditional factors.

We therefore have two investment engines that operate their approaches accordingly: Quest AI-Driven, which is monthly rebalanced, offering an enhanced index return profile; and Quest Sustainable, which has a wider tracking error target to focus on longer term company fundamentals and deliver a through cycle quality/defensive exposure. Both solutions aim to offer clients long term exposure to global equities.

Quest AI-Driven Equities
Quest Sustainable Equities 
Enhanced index strategyProprietary 4P quantitative framework 
Harnessing the scale and efficiency of AI for stock selectionMulti decade track record 
Based on quantitative researchQuantitative process based on traditional finance factors 
Low tracking error, lower fee and compounding outperformanceQuality defensive profile 
Aims to be a ‘crystal box’ rather than a ‘black box’ESG integration intrinsic to process 
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