Why are distributions important? This may as well go down as the silliest questions ever asked on this forum, but having received sound and meaningful answers to a previous question, i thought i will stretch my luck again.
I have been very confused for some time on the importance of statistical distributions especially as they relate to asset returns and even more specifically in asset allocation.
My question to be specific is this:  Assume i have 20 years of S&P 500 monthly returns data, why should i need to assume a certain kind of distribution (i.e Normal/Johnson/Levy flight etc) for my asset allocation decision when i can simply just make my asset allocation decisions based on the historical data i have with me?
 A: Using an assumed distribution (ie. parametric analysis) will reduce the computational cost of your method. I am assuming that you would like to perform a regression or classification task. This means that at some point you are going to estimate the distribution of some data. Nonparametric methods are useful when the data does not conform to a well studied distribution, but they typically take either more time to compute or more memory to store. 
Also if the data are generated by a process that conforms to a distribution, such as they are an average of some uniformly random processes, then using that distribution makes more sense. In the case of averaging a set of uniform variable the correct distribution is probably the Gaussian Distribution. 
A: Complementing James answer: parametric models also (usually) require less samples in order to have a good fit: this may increase their generalization power: that is, they may predicted new data better, even being wrong. Of course, this depends in the situation, the models and the sample sizes.
