The Handbook of News Analytics in Finance

6

Relating news analytics to stock returns

David Leinweber and Jacob Sisk

ABSTRACT

News analytics measure the relevance, sentiment, novelty, and volume of news. They combine natural language analysis of content with historical and news metadata. Signals from analytics of this type have been shown to be predictive of volatility. Aggregation and filtering of news events can also generate alpha signals for portfolio management. Filters use thresholds set using both absolute and relative measures. This detects investor behavior associated with accumulation of information and changes in sentiment. The analytics described are used to generate investment signals. In practice, they would be combined with forecasts from other quantitative or research sources (e.g., factor, momentum, and earnings). In this chapter, we analyze investment signals derived only from news, an important distinction. Event studies on a broad universe of US equities (segmented by sector and capitalization class) are shown for the period 2003–2008. US portfolio simulation results are shown for these signals applied over 2006–2009. The portfolio simulation, like the event studies, is based on a “pure news” signal, without mixing in other quant signals, which can confuse the question of alpha from news. Both the event studies and portfolio simulation show evidence of exploitable alpha using news analytics.