Come on, don't create a trial of nitpicking. I am not saying returns are a law of nature meant to teach us normality.
My point is that, for all intent and purposes, you should assume normal distribution of returns.
If you don't, you're obviously on either end of the spectrum: not knowing the subject at all, or nitpicking expertise on the internet.
The subject of the matter here is convincing someone that risk adjusted measures should be considered when comparing portfolios. This is the basic underlying modelisation that 99.99% of the finance world makes, "compare sharpes", "compare volatility adjusted returns".
I'm stating 1+1=2 and you're arguing it doesn't hold in Z/2.
> At best, using a normal distribution is something that undergrads use as a tool to learn about the stock market and make some simplifying assumptions for pedagogical purposes
Implicit normality assumptions are everywhere. I encourage you to think hardly about your model and question whether anything you do would work on non normal distributions, you will most likely find that you have millions of these assumptions in your linear combinations, sample renormalization, regressions, sharpe weighters and optimizations.
Now of course you could refine that with students, lognormals, and whatever, but this is more _refinement_ than anything.
My point is that, for all intent and purposes, you should assume normal distribution of returns.
If you don't, you're obviously on either end of the spectrum: not knowing the subject at all, or nitpicking expertise on the internet.
The subject of the matter here is convincing someone that risk adjusted measures should be considered when comparing portfolios. This is the basic underlying modelisation that 99.99% of the finance world makes, "compare sharpes", "compare volatility adjusted returns".
I'm stating 1+1=2 and you're arguing it doesn't hold in Z/2.
> At best, using a normal distribution is something that undergrads use as a tool to learn about the stock market and make some simplifying assumptions for pedagogical purposes
Implicit normality assumptions are everywhere. I encourage you to think hardly about your model and question whether anything you do would work on non normal distributions, you will most likely find that you have millions of these assumptions in your linear combinations, sample renormalization, regressions, sharpe weighters and optimizations.
Now of course you could refine that with students, lognormals, and whatever, but this is more _refinement_ than anything.