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Systematic trading using sentiment analysis data

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The use of alternative data in systematic hedge funds has exploded in recent years, enabled by the advancement of machine learning techniques and a drop in the cost of computing power. Strategies using datasets from credit card transactions to satellite images are now increasingly incorporated into quantitative funds. In this talk, I’ll present my research on the predictive power of sentiment analysis data, which was done during my placement at Cantab Capital. In particular, I’ll discuss my analysis of the RavenPack Global Macro dataset, and its applications towards predicting the price of commodities.

This talk is part of the Data Intensive Science Seminar Series series.

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