Industry Events
RavenPack | March 15, 2021
RavenPack’s Peter Hafez and Marko Kangrga will be talking about the changing nature of risk and strategies for generating alpha at the 6th edition of the AI & Data Science in Trading Virtual Event, this March.
Generate Alpha. Manage Risk. Optimize Portfolios.
The event brings together experts in the field of AI and advanced data analytic techniques within asset management, whose focus is on finding alpha, managing risk and optimizing portfolios.
RavenPack’s Chief Data Scientist Peter Hafez will kick off the event with a presentation entitled “Understanding Risk Through the Data Mosaic” and will cover the following topics:
The full time-table of events in which Peter and Marko will be participating is below:
11.55am Day 1 Understanding Risk Through the Data Mosaic Peter Ager Hafez
12.15pm Day 1 Birds of a Feather Masterclass Marko Kangrga
12.25pm Day 2 Birds of a Feather Masterclass with topic Marko Kangrga
3.30pm Day 2 Integrating alpha into your ESG portfolio Peter Ager Hafez
4.10pm Day 2 Panel: Journey of embedding quant methods within your fundamental approach for effective analysis Marko Kangrga
About the AI & Data Science in Trading Virtual Event
Delegates to the 6th edition of the AI & Data Science in Trading Virtual Event will be able to join two days of stimulating content online, including keynote speakers and vigorous panel debates. They will have the opportunity to network with other attendees, be part of Live Q&As, and set up one-to-one meetings with colleagues, customers, and suppliers.
See the full agenda here
When : March 15, 2021 11:45 am - 12:00 pm
Where Online event
Peter is a pioneer in the field of applied news analytics, bringing alternative data to banks and hedge funds. He has more than 15 years of experience in quantitative finance with companies such as Standard & Poor's, Credit Suisse First Boston, and Saxo Bank.
Marko is the Head of Quantitative Research for the Americas at RavenPack with over 10 years of experience in the finance industry. He focuses on exploring novel approaches and techniques for combining fundamental drivers with big data quantitative frameworks to identify alpha opportunities from a wide universe of securities across multiple asset classes.
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