This article examines whether and to what extent private firms learn from the stock market. Using a large panel data set for the UK, I find that private firms’ investment responds positively to the valuation of public firms in the same industry. The sensitivity increases with price informativeness. To further pin down the information channel, I construct a price noise measure based on public firms’ unrelated minor segments and show that it positively affects the investment of private firms in the major-segment industry. The results are consistent with models featuring learning from noisy signals and are not driven by alternative channels in the absence of learning. My findings suggest that the stock market can have real effects on private firms through an information-spillover channel, even when these firms do not list their shares on the stock exchanges.

We show that callable bonds have both higher yields and lower market prices than matched non-callable bonds of the same issuer-time, reflecting the value of call features to issuers and investors. This “value of callability” as well as the inclusion and the exercise of call rights are jointly determined by issuer credit quality. Critically, our agency-based theoretical and empirical analyses show that callability reduces debt overhang in corporate mergers. Our results help explain the value and increasing prevalence of callable bonds in credit markets.

We model the interaction of financial constraints, capacity constraints, and the response of production and inventory to cost and demand shocks. The model predicts that in response to favorable shocks, financially constrained firms are unable to build inventory as rapidly as are unconstrained firms. However, because the favorable shocks gradually ease the financial constraints, constrained firms continue to build inventory and eventually carry surplus inventory (relative to unconstrained firms) to unfavorable states. This allows them to deplete inventory more aggressively in response to unfavorable shocks. Our empirical evidence provides broad support for the model’s predictions.Received September 3, 2016; editorial decision January 11, 2018 by Editor David Denis.

We provide the first systematic evidence on the link between long‐short anomaly portfolio returns—a cornerstone of the cross‐sectional literature—and the time‐series predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high‐dimensional setting. We find that long‐short anomaly portfolio returns evince statistically and economically significant out‐of‐sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence.