Investment Strategies

Event studies can inform investment strategies in various ways. Commonly discussed forms include the discovery of patterns in market responses to event types or the application as a signal in quantitative investment strategies. These two types of contributions give way to event-driven and other signal-driven quantitative strategies as described below.  

Event-driven investment strategies

Event-driven investment strategies try to exploit market inefficiencies that occur in the context of corporate or market events. Event types that are commonly in scope of such strategies are:

  • Mergers and acquisitions
  • Earnings announcements
  • Restructuring announcements
  • Share buybacks
  • Special dividends
  • Spinoffs
  • Announcements of economic indicators (e.g., unemployment rate, interest rate changes)

Event-driven funds invest in almost all liquid asset classes, i.e. equities, fixed-income instruments, and derivatives. The strategies can be differentiated in terms of their investment horizon. High-Frequency investors have typically an investment horizon of less than five minutes and aim to profit from fast information processing. Merger-arbitrage funds in contrast invest often over time periods spanning a horizon of several weeks up to several months, seeking to capture the spreads in the transaction bid and the trading price after a merger or acquisition announcement. Also, these two variants differ in the approach applied. While high-frequency traders tend to rely on news feeds and quantitative models, merger-arbitrage funds may pick individual M&A events and apply the knowledge about response patterns to acquisitions in a more discrete manner.

Other signal-driven quantitative strategies

Besides certain types of event-driven strategies, there are other quantitative approaches to make use of event studies. Return, volume, and volatility event studies, when applied recurrently, provide strong signals for shifts in the evolution of stocks or other asset types.When used as a monitoring instrument, abnormal returns, trading volumes, and volatilities can thus be used as input variables for decisions on tactical asset allocation changes or the trading of individual instruments (e.g., bottom-fishing strategies). While potential applications are literally countless, the approach of identifying these strategies follows the general approach for designing quantitative investment strategies: 

  1. Based on an investment idea/thesis, the instrument universe is identified
  2. Corresponding data, including the abnormal stock metrics from event studies, is acquired
  3. The strategy is back-tested against this historical data
  4. The strategy is tested with out-of-sample data and evaluated along typical performance and risk metrics
  5. Once considered beneficial, the strategy is implemented and continuously monitored