Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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Using machine learning for stock picking

Alun Biffin (Van Lanschot Kempen), David Dogon (Van Lanschot Kempen)
14:0514:45 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Average rating: ****.
(4.45, 11 ratings)

Who is this presentation for?

  • Anyone looking to apply machine learning methodology to the finance space



Prerequisite knowledge

  • Familiarity with basic machine learning concepts

What you'll learn

  • Learn how machine learning can revolutionize the typical stock picking process while also offering more general insights into the process of delivering data science projects in sectors typically associated with more traditional, human-intensive analysis


Gaining exposure to small-cap companies may appeal to investors for a number of reasons. These companies have shown strong historic returns compared to their large-cap counterparts and offer a lower risk for longer investment horizons. Moreover, the relative scarcity of small-cap-focused funds compared to the propensity of stocks available signifies greater opportunity in a stock universe that tends to be less thoroughly researched.

Even after considering the various constraints in choosing companies eligible for inclusion in such a portfolio—whether these be business fundamental (e.g., market cap or liquidity), geographical (e.g., excluding emerging markets), or ethical (e.g., excluding companies based on the United Nations Global Compact) considerations—the available investment universe is huge.

Alun Biffin and David Dogon explain how machine learning methods can revolutionize the stock-picking process by processing vast amounts of fundamental and technical data as well as leveraging novel data sources to provide intelligent stock recommendations. By algorithmically empowering this process of filtering thousands of small-cap stocks down to a handful of potential candidates, portfolio managers can devote their time to carry out the high-level business analysis required before deciding to include a stock in their portfolio. Moreover, the volume and variety of data used to train the model means that portfolio managers are alerted to investment opportunities that might otherwise have laid outside their usual field of research.

Photo of Alun Biffin

Alun Biffin

Van Lanschot Kempen

Alun Biffin is a data scientist at Van Lanschot Kempen, where he applies machine learning to real-life business problems ranging from analyzing millions of web hits for online retailer to predicting customer behavior at one of the Netherland’s largest private banks. Previously, Alun was a Marie Curie fellow at the Paul Scherrer Institute, Switzerland, where he designed and conducted groundbreaking experiments on quantum magnets at cutting-edge facilities in Europe, the US, and Japan. He has presented his work at international workshops and conferences and published three papers as first author. His work has been cited over 100 times. He was also a recipient of the highly selective ASI Data Science Fellowship, London, in the summer of 2018. Alun holds a PhD in condensed matter physics from the University of Oxford.

Photo of David Dogon

David Dogon

Van Lanschot Kempen

David Dogon is a member of the data science team at Van Lanschot Kempen, where he primarily focuses on investments and asset management. David is driven by an interest in the insights and predictive power from data. A bit of an adventurer, he has performed research toward a PhD degree in mechanical engineering at TU Eindhoven in the Netherlands, holds a master’s degree in mechanical engineering from Columbia University in New York, and holds a bachelor’s degree in chemical engineering, which he completed in Cape Town, the same city where he was born.