This question might be very basic but still I couldn't really find a satisfying answer anywhere. I want to analyse the effect of a repeated event (data release) on the price of a specific asset (I have daily data) thrgouh regression (with a GARCH model for volatility). I have already ran an event study, but I would like to see a different approach.
So I run a linear regression explaining $R_{t+1}$ with several regressors : lagged return $R_{t}$, exogenous variable $E_{t}$ and the z-score of the released data $Z_{t}$ (equal to 0 on non-release days). For the sake of the argument, suppose that the regression coefficients are all significant.
My question is : how can I use these results to build a basic trading strategy ? So far I could think of three approaches, but I would like to know whether some/all are wrong/useless/good :
- Using the coefficient of the lagged return to build basic trend following (if the coeff is > 0 ) or mean-reversion (is it is < 0)
- Using the coefficient of the z-score of the data to determine whether a positive value has a positive or negative effect on the return. Then, for future data, if the coeff is positive, then if $Z_{t}$ is positive, go long, else go short.
- Using all the coefficients to later on forecast the value of $R_{t+1}$ and invest accordingly.
Are these ways to interpret regression results investment-wise flawed or correct ? Are there others ?