We show short selling in corporate bonds forecasts future bond returns. Short selling predicts bond returns where private information is more likely, in high-yield bonds, particularly after Lehman Brothers’ collapse of 2008. Short selling predicts returns following both high and low past bond returns. This, together with short selling increasing following past buying order imbalances, suggests short sellers trade against price pressures as well as trade on information. Short selling predicts bond returns both in the individual bonds that are shorted and in other bonds by the same issuer. Past stock returns and short selling in stocks predict bond returns but do not eliminate bond short selling predicting bond returns. Bond short selling does not predict the issuer’s stock returns. These results show bond short sellers contribute to efficient bond prices and that short sellers’ information flows from stocks to bonds but not from bonds to stocks.

The capital asset pricing model (CAPM) performs poorly overall, as market risk (beta) is weakly related to 24-h returns. This is because stock prices behave very differently with respect to their sensitivity to beta when markets are open for trading versus when they are closed. Stock returns are positively related to beta overnight, whereas returns are negatively related to beta during the trading day. These day-night relations hold for beta-sorted portfolios and individual stocks in the US and internationally as well as for industry and book-to-market portfolios and cash flow and discount rate beta-sorted portfolios. In addition to the change in slope of returns with respect to beta, the implied risk-free rate differs significantly between night and day. Consistent with this, returns on US Treasury futures differ significantly between night and day.

We examine the network of trading relationships between insurers and dealers in the over‐the‐counter (OTC) corporate bond market. Regulatory data show that one‐third of insurers use a single dealer, whereas other insurers have large dealer networks. Execution prices are nonmonotone in network size, initially declining with more dealers but increasing once networks exceed 20 dealers. A model of decentralized trade in which insurers trade off the benefits of repeat business and faster execution quantitatively fits the distribution of insurers' network size and explains the price–network size relationship. Counterfactual analysis shows that regulations to unbundle trade and nontrade services can decrease welfare.