| June 23, 2021
RavenPack and CAIA Hong Kong team up for an insightful webinar where Peter Hafez, Chief Data
Scientist at RavenPack and Inna Grinis, Senior Data Scientist, RavenPack will speak with Yingwen
Chin, Partner at CAIA, about Using NLP to Empower Asian Investors.
Join our expert speakers are they discuss how Natural Language Processing (NLP) is an evolving
field within machine learning that is empowering financial market participants by extracting
value from textual content at scale. The technology has been instrumental in the continued
adoption of alternative data in the investment industry, and is contributing to the ever
expanding data mosaic available to hedge funds and asset managers. We'll provide concrete
examples of how media attention, sentiment, and actionable events are driving financial markets
across multiple asset classes and investment horizons.
See the full agenda
June 23, 2021
4:30 pm - 5:30 pm Hong Kong Time
Chief Data Scientist
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.
Senior Data Scientist
Inna is a Senior Data Scientist and Quantitative Researcher at RavenPack, specialised
in global macro investment strategies and macroeconomics. Prior to joining RavenPack in
July 2020, Inna worked for three years at Goldman Sachs in London, in the Global
Portfolio Solutions (GPS) Group and the Investment Strategy Group, as a macro
researcher. Her main projects there included thematic research on the impact of
demographics on inflation and asset prices, and building an in-house nowcasting
infrastructure. Inna holds a PhD and an MRes in Economics from the London School of
Economics, a BA from Cambridge, and is a CFA Charterholder.
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High inflation has returned in developed markets after decades of lying low. In our latest paper, we show how to build an inflation-based asset allocation strategy using sentiment data and we illustrate that sentiment-based strategies outperform models that depend merely on past observed inflation values.
This year's RavenPack Research Symposium brought two intense days of knowledge sharing in London and New York, from 25 top experts in natural language processing, quantitative investing and machine learning. Together, we explored how firms can leverage new language models to generate alpha, better manage risk and respond to calls for more sustainable investment practices.
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