Portfolio optimization through risk-averse linear models

We establish a novel framework for minimum deviation portfolio optimization by directly connecting a risk-averse stochastic problem (RASP) to linear regression models. By leveraging a set of coherent risk measures and scoring functions, our methodology generalizes classical models while enabling alternative formulations that account for tail risk and asymmetric scoring functions. Using S&P 100 stock data, we empirically illustrate our approach. Nonparametric hypothesis tests indicate significant differences in risk and risk-adjusted performance across RASP specifications.