I have recently began work on some high frequency financial tick data. I have been told to 'normalize' the data as much as possible and run linear regressions through them. In fact, the data doesn't seem to be anymore linear after I did transformations on them (box-cox/log etc) I understand the linear regressions bit, but most financial data is non-normal anyway, so why bother normalizing?
As an aside, you're right that empirically markets have not exhibited normal returns. In fact, Mandelbrot explains in this article that the Pareto distribution is more realistic. It was published in 1963, but more recently he talks in this book about how the data have continued to demonstrate this pattern. The point is that you may read or hear about normalization techniques that rest on assumptions like normality that may not always be suited to the problem at hand. At the risk of editorializing, the assumption of normality has been subtly embedded in a lot of financial research and it may sometimes be misleading, so make sure to check the assumptions underlying what you're being told.
got an answer from one of my pals, thought it might be interesting to share it here. The reason why we often use the normal distribution is because the distribution will be stable regardless of the number of samples (central limit theorem).
Imagine you had a normal distribution after transforming x amount of samples, and across time, u get more variables and we will want them to stay in the 'normal' distribution shape by exploiting the central limit theorem.
However, if you have exponential/Pareto (or any other) distributions, the distribution will tend to a normal distribution once the sample size is large enough due to the central limit theorem. This way, we can have a consistent model regardless of the time/sample size.
Hope this helps, and if anyone have different ideas about this, please do comment here!