We investigate the cross-sectional factors of Chinese corporate bond returns via the reducedrank regression analysis (RRA) proposed by He et al. (2022). We collect 37 individual bond characteristics in the extant literature using a new dataset and construct 40 factor portfolios. Empirically, we find that the four-factor models created by RRA outperform the traditional factor models, PCA, and PLS factor models, both in-sample and out-of-sample. Among the 40 factors, the bond market factor is the most substantial predictor of future bond returns. In contrast, other factors provide limited incremental information for the cross-sectional pricing. Therefore, it is necessary to find more new bond factors. We further find that stock market anomalies do not improve the explanatory power of the RRA factor models. In particular, stock market anomalies can only partially explain the systematic part of bond returns in the RRA framework and have almost no explanatory power for the idiosyncratic component.