Most popular strategies

Emerging Markets Debt

Global Equity

International Equity

U.S. Equity

Funds

Strategies

Insights

People

Pages

Meet Sandro

Sandro Felicioni

Quantitative Investment Manager

20 years investment experience

8 years with Vontobel

Zurich

Vontobel Asset Management AG

Genferstrasse 27

CH-8022 Zurich

Switzerland

Sandro Felicioni joined Vontobel in December 2018. As a Quantitative Investment Manager within the Asset Allocation team of the Multi Asset Boutique, he applies statistical analysis and machine learning techniques to large data sets in order to extract insights that drive business decisions and develop investment strategies. He is responsible for the models behind the AI Powered Global Equity strategy. He also collaborates with portfolio managers and other stakeholders to identify opportunities for automation and optimization of investment processes.

Prior to joining Vontobel, he worked as a Data Scientist at Elca Informatik between 2015 and 2018 where he developed various machine learning models for the Health Care sector. He started his professional career in 2007 as a Software Engineer at UBS.

Sandro holds a Master’s degree in Computer Science from ETH Zurich with a major in Machine Learning.

Related perspectives

Multi Asset Boutique

Sep 25 2025 Viewpoint

The Power of Many – Part II

How can machine learning ensembles be built in practice? From Bagging and Boosting to Stacking, this article explores different ensemble strategies and the role of model diversity. We also examine how ensembles are being applied in financial forecasting, drawing on both academic research and industry experience. Discover how combining models tends to deliver stronger, more resilient predictions in finance.

Multi Asset Boutique

Sep 18 2025 Viewpoint

The Power of Many – Part I

Why rely on one model when many do better? By combining multiple forecasts, ensembles tend to deliver more accurate and resilient results. This first article in our two-part series explores the theory behind model ensembles, linking classic forecasting research with today’s financial machine learning. Discover why combining models consistently outperforms relying on a single predictor.

Multi Asset Boutique

Apr 10 2025 White Paper

The AI factor

Are traditional factors falling behind? In this Expl(AI)ning Quanta Byte, we explore how AI can breathe new life into factor investing. Discover the concept of AI-powered factors—a smarter, more adaptive approach that turns static models into predictive engines.

Multi Asset Boutique

2025-02-17_Quanta Byte -good old factors_teaser.jpg

Feb 18 2025 White Paper

Good old factors

Factor investing has long explained market behavior, but its flaws—timing dependencies, rigid linearity, and backward-looking assumptions—limit predictive power. Can AI overcome these? This Expl(AI)ning episode dissects factor evolution, their pitfalls, and how machine learning enables dynamic models. Dive into quantitative investing’s future.

Multi Asset Boutique

Dec 18 2024 Viewpoint

Will AI ever crack investing?

AI has dominated games like chess and Go, but the markets are far more complex. In our latest Expl(AI)ning article, we explore why investing’s complexity remains unmatched, where AI stands today, and what’s next for finance.

Multi Asset Boutique

2024-11-04_-architecture-and-data-trumpmaths_teaser.jpg

Nov 05 2024 Viewpoint

Architecture and data trump maths

Gain an intuition on how complex AI architectures actually work. You will see that one model cannot fit all kinds of data. The key to solving problems with AI and surpassing mathematical challenges is to use the right model and the appropriate, high-quality data.