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As the title already says, should log returns, instead of simple returns, be used in regression analysis? In this case, I want to analyse the impact of specific factors (Dividend yield etc.) on the return of the Dow Jones stock index.
I know that Fama-French have developed their 3 factor model using only simple returns, but I often hear quants using log returns because those are closer to iid properties than simple returns and, after all, the dependent variable in a regression needs to be iid.
As you pointed out, not necessarily:
I know that Fama-French have developed their 3 factor model using only simple returns
That's because of a very common misconception:
and, after all, the dependent variable in a regression needs to be iid.
In fact, the dependent variable does not have to be normally distributed or i.i.d. for that matter. That assumption only applies to the residuals. And even despite this, in practice, it's very difficult to meet those modeling assumptions.
The primary reason for using log returns is that they are time additive and easier to compute compound returns on.
