Institutional Datasets: How To Build Ravenpack News Signals

Introduction

Ravenpack is probably the most famous dataset outside of fundamentals. Occasionally, Ravenpack salespeople will proudly say that “RavenPack is used in practically all quant funds”, and I really don’t think that’s as big a selling point as that people salesperson must think it is.

Evaluating datasets is a lot like having a really attractive partner. You want the hottest, most good looking, most charming, highly intelligent partner, but you also want it to be exclusive. Value diminishes rapidly as a function of access.

So yeah, Ravenpack used to be an amazing dataset, dishing out 2-3 sharpe signals on the most trivial implementations of their news sentiment data. Today, you’ll get a quarter of that performance for trivial implementations - alpha decay is very real.

So, why are we covering Ravenpack? For two reasons, it is still a useful dataset because you will want to create news sentiment factors from Ravenpack data, and if you are smart about construction, you can still extract meaningful alpha from Ravenpack.

Also, wouldn’t you want to read more about how RavenPack actually comes up with the sentiment scores and how to construct signals from them ;)?