The Handbook of News Analytics in Finance

13.3 UPDATING MODEL VOLATILITY USING QUANTIFIED NEWS

There is a strong, yet complex relationship between market sentiment and news. Traders and other market participants digest news rapidly and update their asset positions accordingly. Most traders have access to newswires at their desks. However, whereas raw news is qualitative data, for models to incorporate news directly and automatically we require quantitative inputs.

RavenPack has developed linguistic analytics that process the textual input of news stories to determine quantitative sentiment scores. In particular, they classify individual stories by the market aspects to which they relate; they also assign sentiment indicators that define a story as positive, negative, or neutral. These methods are then applied to derive specific scores about different market entities such as a company or an industry sector. Scores that indicate the relative sentiment for a stock over time have been produced; for further details of how these scores are calculated and more specific details of their methodology, see Section 13.A (appendix on p. 301).

The score for an individual company varies over time, but this time-series is defined over time points with uneven intervals as news stories arrive unexpectedly. We wish to use the information about changing market sentiment to update our beliefs about factor volatility. The score a_nt measures market sentiment about company n at time t (n ∈ {1,…, N₂} denotes the assets for which option-implied ...