Continuous independent variables that sum up to 100% As I am studying the capital structure of firms, the total of proportions owned by different ownership types (free float, institutional ownership ... etc.) will be 100% of the company shares. In my study each of those ownership types is an independent variable. Hence, when running regressions, one of these variables will be dropped due to collinearity.
Is there any transformations that can be done to these variables in order to have all of them in the model? If not, which variable (highest/ lowest correlated with the dependent variable) is to be dropped?

A "traditional" transformation for $n$ variables that must add up to a constant (usually we use 1 corresponding to 100%), is to work with $n-1$ parameters $\alpha_i \in (-\infty, \infty)$, to define the $n$th one as $\alpha_n = 1 - \sum_{i=1}^n \alpha_i$ ("sum to zero constraint") and to use the softmax function to define
$$w_i = \frac{e^{\alpha_i}}{\sum_{i=1}^n e^{\alpha_i}}.$$
We will then have $\sum_{i=1}^n w_i =1$ and all $w_i$ will be in $(0,1)$.
The $\alpha_i$ for $i=1,\ldots,n-1$ could then each have a set of linear regression terms behind them (or some other kind of model, e.g. if you like a neural network or whatever else).
