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.

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Energy Futures Trading with Machine Learning & Event Detection

RavenPack | May 02, 2018

We show how combining all models produce solid risk-adjusted returns with lower average bias and without the need to select one particular model.

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Big Data and AI Strategies - Machine Learning and Alternative Data Approach to Investing

J.P. Morgan Global Quantitative & Derivatives Strategy Team | May 29, 2017

third party global macro

The new J.P. Morgan report provides a framework for machine learning and big data investing and shows how to trade Global Macro strategies using RavenPack Analytics.

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