We analyze 18 quadrillion models for the joint pricing of corporate bond and stock returns. Only a handful of factors, behavioural and nontradable, are robust sources of priced risk. Yet, the true latent stochastic discount factor is dense in the space of observable factors. A Bayesian Model Averaging Stochastic Discount Factor (BMA-SDF), combining the corporate bond and stock factor zoos, explains risk premia better than all existing models, both in- and out-of-sample. We show that multiple factors are noisy proxies for common underlying sources of risk, and the BMA-SDF aggregates them optimally. The SDF, as well as its conditional mean and volatility, are persistent, track the business cycle and times of heightened economic uncertainty, and predict future asset returns. Finally, we show that stock factors price the credit component of corporate bond excess returns well, while the Treasury component is priced almost exclusively by the bond factors.