NLP and News Sentiment Applications in Finance - Jefferies Asia Forum

RavenPack | September 01, 2020

Watch Marko Kangrga speak at the Jefferies Asia Forum, 2020 about global trends in the investment community & how to leverage NLP to formulate alpha-generating strategies.

Head of Quantitative Research, Marko Kangrga will be presenting NLP and News Sentiment Applications in Finance at the Jefferies Online Asia Forum.

The Jefferies Asia Forum is an opportunity to learn and understand global trends that are impacting investors and the international economy.

In his session, Marko will demonstrate how RavenPack helps buy-side clients leverage NLP to sort through the noise and identify relevant signals to manage risk and formulate alpha generation strategies.

He will show how NLP fits within the alternative data landscape and demonstrate how events and sentiment extracted from thousands of news sources can be tailored to specific use-cases, like:

  • ESG applications
  • Insider transactions
  • Factor modelling
  • Forecasting the U.S. elections.

When :
Monday September 14th, 2020
10:00 - 10:45 am (HKT)

Online Event
Jefferies Asia Forum

RavenPack Presents:

Marko Kangrga

Head of Quantitative Research RavenPack

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.

Previously, as the head trader/investment analyst at an event-driven hedge fund in New York, he was responsible for macro research, idea generation and risk management. Marko has experience in utilizing quantitative methods in portfolio construction, developing hedging strategies and trading structured derivative instruments. He earned a B.S. degree in Finance, summa cum laude, with a minor in Computer Science from the University of Evansville in 2008.

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