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Machine Learning, News Analytics, and Stock Selection

Yin Luo - Managing Director, Global Head of Quantitative Strategy - Deutsche Bank | June 16, 2016

In this presentation, Yin Luo (Deutsche Bank) finds news sentiment data adds significant incremental predictive power to his machine learning based global stock selection models.

Big data and machine learning have generated tremendous interest in empirical finance research.

In this presentation, Yin Luo, Global Head of Quantitative Strategy at Deutsche Bank, examines a unique news analytics database provided by Ravenpack. He applies a suite of innovative machine learning algorithms, including adaBoost, spline regression, and other boosting/bagging techniques on both traditional and unstructured news data in predicting stock returns.

He finds news sentiment data adds significant incremental predictive power to his machine learning based global stock selection models.

Presentation held at the RavenPack 4th Annual Research Symposium, New York, June 16th 2016.

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