J.P. Morgan: Using RavenPack Sentiment for Cross Asset Style Timing
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.
Energy Futures Trading with Machine Learning & Event Detection
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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