Recently, I learned that there is something called an event driven strategy:
An event driven strategy is a type of investment strategy that attempts to take advantage of temporary stock mispricing that can occur before or after a corporate event takes place.
Sorta makes sense: the information is flawed or incomplete, the market's interpretation of the information is knuckle-headed, the timing or the diffusion of information is not efficient, the event is leaked either intentionally or inadvertently prior to the event, etc., and so the price ends up out of whack, but eventually comes back into alignment with the "efficient" market and finds its happy place (with respect to whatever irrational exuberance, FUD, or funk d'jour of the market).
The question in my mind is how to apply the Q framework (nicely summarized on A Professional Quant Equity Workflow), to combine event-driven alphas, with a variety of other types of alphas, all on different time scales. For events, one has a set of step functions, whereas for other alpha sources (e.g. fundamentals), the changes are continuous, and thus a regular updating of the portfolio weights makes sense (e.g. daily ranking and re-balancing with a turn-over constraint).
The Q guidance is to dig into the new event-driven FactSet Estimates data, but it is not so clear how to meld it into a framework seemingly designed for continuous ranking and re-balancing. Any thoughts?