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.

We demonstrate that limited participation can arise endogenously in the presence of model uncertainty. Our model generates novel predictions on how limited participation relates to equity premium and diversification discount. When the dispersion in investors?model uncertainty is small, full participation prevails in equilibrium. In this case, equity premium is unrelated to model uncertainty dispersion and a conglomerate trades at a price equal to the sum of its single segment counterparts. When model uncertainty dispersion is large, however, investors with relatively high uncertainty optimally choose to stay sidelined in equilibrium. In this case, equity premium can decrease with model uncertainty dispersion. This is in sharp contrast to the understanding in the existing literature that limited participation leads to higher equity premium. Moreover, when limited participation occurs, a conglomerate trades at a discount relative to its single segment counterparts. The discount increases in model uncertainty dispersion and is positively related to the proportion of investors not participating in the markets.