J.P. Morgan: Using RavenPack Sentiment for Cross Asset Style Timing
Peter Hafez, Chief Data Scientist, RavenPack | October 25, 2018
equities global macro third party
Our Chief Data Scientist comments a recent J.P. Morgan white paper which proposes a framework for style timing in cross-asset risk, using various Machine Learning techniques to generate views on expected returns. He highlights where RavenPack data provide the most value.Featured Articles
Recent Articles
Popular Articles
Empirical Research Partners’ Take on Big Data: Media Sentiment - Useful Around the World
Empirical Research Partners and Peter Hafez, Chief Data Scientist at RavenPack | October 01, 2018
Intraday News Patterns: Implementation of Sentiment Strategies
RavenPack | August 08, 2018
CitiBank's take on CAPEX announcement strategies using RavenPack data
Citi Research and Peter Hafez, Chief Data Scientist, RavenPack | July 26, 2018
J.P. Morgan: Using RavenPack Sentiment for Cross Asset Style Timing
Peter Hafez, Chief Data Scientist, RavenPack | October 25, 2018
equities global macro third party
Our Chief Data Scientist comments a recent J.P. Morgan white paper which proposes a framework for style timing in cross-asset risk, using various Machine Learning techniques to generate views on expected returns. He highlights where RavenPack data provide the most value.Empirical Research Partners’ Take on Big Data: Media Sentiment - Useful Around the World
Empirical Research Partners and Peter Hafez, Chief Data Scientist at RavenPack | October 01, 2018
RavenPack Analytics For Equity Trading
RavenPack | April 20, 2017
Earnings Sentiment Consistently Outperforms Consensus
RavenPack | March 16, 2016
Thematic Alpha Streams Improve Equity Portfolio Performance
RavenPack | September 22, 2015
Discover the RavenPack Platform
Choose from over 180 curated datasets covering a variety of different topics.
Explore Now