This paper examines the ability of the (C)CAPM—comprising the CAPM and the consumption CAPM—to explain the cross-section of average stock returns. Unlike previous studies, the authors use a conditional linear factor model for the pricing kernel, reflecting time-varying risk premia. A key variable, cay, proxies for fluctuations in the log consumption-aggregate wealth ratio and is crucial for summarizing conditional expectations of excess returns. The authors demonstrate that conditional factor models explain a substantial fraction of cross-sectional return variation, outperforming unconditional (C)CAPM specifications and performing similarly to the three-factor Fama-French model. The conditional consumption CAPM using aggregate consumption data explains nearly 70% of the cross-sectional variation in returns on Fama-French portfolios, with little residual size or book-to-market effects. The paper also shows that cay is a strong predictor of excess returns on common stock market indexes, suggesting it is a good proxy for the consumption-aggregate wealth ratio. The study finds that the scaled multifactor (C)CAPM performs well in explaining cross-sectional returns, with the scaled consumption CAPM explaining about 75% of the variation. The results support the view that the value premium is due to true nondiversifiable risk rather than mispricing. The paper concludes that the choice of conditioning variable is crucial, with cay providing better explanatory power than other variables. The study also highlights the importance of time-varying risk premia in asset pricing models.This paper examines the ability of the (C)CAPM—comprising the CAPM and the consumption CAPM—to explain the cross-section of average stock returns. Unlike previous studies, the authors use a conditional linear factor model for the pricing kernel, reflecting time-varying risk premia. A key variable, cay, proxies for fluctuations in the log consumption-aggregate wealth ratio and is crucial for summarizing conditional expectations of excess returns. The authors demonstrate that conditional factor models explain a substantial fraction of cross-sectional return variation, outperforming unconditional (C)CAPM specifications and performing similarly to the three-factor Fama-French model. The conditional consumption CAPM using aggregate consumption data explains nearly 70% of the cross-sectional variation in returns on Fama-French portfolios, with little residual size or book-to-market effects. The paper also shows that cay is a strong predictor of excess returns on common stock market indexes, suggesting it is a good proxy for the consumption-aggregate wealth ratio. The study finds that the scaled multifactor (C)CAPM performs well in explaining cross-sectional returns, with the scaled consumption CAPM explaining about 75% of the variation. The results support the view that the value premium is due to true nondiversifiable risk rather than mispricing. The paper concludes that the choice of conditioning variable is crucial, with cay providing better explanatory power than other variables. The study also highlights the importance of time-varying risk premia in asset pricing models.