We utilize ridge regression to create a novel set of characteristics-based "ridge factors". We propose Bayesian Average Stochastic Discount Factors (SDFs) based on these ridge factors, addressing model uncertainty in line with asset pricing theory. This approach shrinks the relative contribution of low-variance principal portfolios, avoiding model selection and presumption of a "true model". Our results demonstrate that ridge factor principal portfolios can achieve greater sparsity while maintaining prediction accuracy. Additionally, our Bayesian average SDF produces a higher Sharpe ratio for the tangency portfolio compared to other models.