The Handbook of News Analytics in Finance

10.4 A BAYESIAN FRAMEWORK FOR NEWS INCLUSION

The methods used in the two preceding papers were similar in one particularly important respect. Both papers assume that there are two states of the world, the regular state as defined by the parameters of an orthogonal factor model derived from historical observation, and the “now” state as adjusted to reflect the conditioning information derived from option-implied volatility, news flows, or both. An important improvement is to consider the potential states of the world in a probabilistic Bayesian fashion so as to derive the most efficient risk forecast for any given time horizon. Once we embark down this road, we must also address the mathematical implications of serial correlation when forecasting over differing horizons.

Both the issue of optimal use of conditioning information in risk models and the impact of serial correlation are addressed in Shah (2008, 2009). He states, “Forecasting long term behavior requires intentionally restraining news. A priori, one cannot know whether the effects of events being reported upon are transient (more likely) or shifts in regime (less likely), so a sane model integrates innovations more cautiously. For a long term investor, reacting to every passing bump is an exercise akin to driving cross country in a go-kart: the trading turnover would be battering. Being well informed, however, is certainly advantageous. Indeed, the leveraged investor's longevity hinges on skillfully navigating passing ...