I'm wondering about the viability of this task. I'm looking to form something similar to an investment portfolio, which aggregates different financial securities and calculates the expected aggregated return and standard deviation.

As opposed to using the historical returns and s.d. of the securities, however, I want to fit multiple regression models to sample data sets using an array of independent variables, unique to each security, to predict future returns using R Stat's

Code:

`predict()`

The purpose of this exercise is to minimize the effects of low the R-squares for each individual regression for each security. It should minimize the fact that the R-square value for the individual regressions is extremely low, thus leading to a very poor comparison between out-of-sample realized returns and those found using R's

Code:

`predict()`

My questions are (A) is this statistically viable? And (B) regardless if it's statistically viable, is there precedent to this, and is anyone aware of a function in R to do so?

Any help is much appreciated, thank you so much!