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RavenPack: Enhancing Equity Trading Models with Corporate & Macro Sentiment Abnormality

First Published 28th October 2014

Ravenpack presents latest research on how to improve the performance of common trading strategies using RavenPack News Analytics (RPNA).

Peter Hafez, director of Quantitative Research, RavenPack

In this study, a set of indicators is created that capture abnormal sentiment for different news topics as they relate to a country or economy. Indicators are tested in long-only and long/short strategies on the S&P 500 and find they outperform traditional price-driven models.

Specifically, findings show:

  • Since September 2007, both news-based strategies have Information Ratios (IRs) of 1.45 versus a buy-and-hold S&P 500 strategy IR of 0.23 and a pure return-driven model IR of 0.73
  • Maximum drawdown in news-based models is reduced by over 70% on the S&P 500 compared to a buy & hold strategy
  • Abnormality in aggregate corporate news is a key driver of outperformance

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