Dr. Robert Kosowski, Head of Quantitative Research, Unigestion
| May 30, 2019
Robert discusses the benefits of using machine learning to forecast equity beta. In addition to common fundamental variables and historical beta he also incorporates equity sentiment data into the set of predictor variables. Watch the highlights below, you can request access to slides and the full video.
Many recent machine learning studies related to equity portfolio management focus on forecasting expected returns despite the relatively small signal to noise ratio in such exercises. Forecasting risk such as stock beta is important in many practical applications.
This session was held at the
RavenPack Research Symposium held in London on May 23, 2019
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We consider incorporating sentiment signals from news, earnings call transcripts, and insider transactions to
boost the risk-adjusted returns, and revive factor performance.
We find stronger, more predictable market reactions when the words of company executives agree with their actions.
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