We study market segmentation effects using data on U.S. railroads that list their bonds in New York and London between 1873 and 1913. This sample provides a unique setting for such analysis because of the precision offered by bond yields in cost of capital estimation, the geography-specific nature of railroad assets, and ongoing substantial technological change. We document a significant reduction in market segmentation over time. While New York bond yields exceeded those in London in the 1870s, this premium disappeared by the early 1900s. However, the segmentation premium persisted in the more remote regions of the United States. Received June 18, 2015; editorial decision October 4, 2017 by Editor Robin Greenwood.

Recent papers have debated whether the negative correlation between measures of firm asset growth and subsequent returns is of little importance since it applies only to small firms, is justified as compensation for risk, or is evidence of mispricing. We show that the asset growth effect is pervasive, and evidence to the contrary arises due to specification choices; that one measure of asset growth, the change in total assets, largely subsumes the explanatory power of other measures; that the ability of asset growth to explain either the cross section of returns or the time series of factor loadings is linked to firm idiosyncratic volatility (IVOL); that the return effect is concentrated around earnings announcements; and that analyst forecasts are systematically higher than realized earnings for faster growing firms. In general, there appears to be no asset growth effect in firms with low IVOL. Our findings are consistent with a mispricing-based explanation for the asset growth effect in which arbitrage costs allow the effect to persist.

We test for firm‐level asset investment effects in returns by examining the cross‐sectional relation between firm asset growth and subsequent stock returns. Asset growth rates are strong predictors of future abnormal returns. Asset growth retains its forecasting ability even on large capitalization stocks. When we compare asset growth rates with the previously documented determinants of the cross‐section of returns (i.e., book‐to‐market ratios, firm capitalization, lagged returns, accruals, and other growth measures), we find that a firm's annual asset growth rate emerges as an economically and statistically significant predictor of the cross‐section of U.S. stock returns.

Nonlinearities can arise in international investment factors because of a pecking order in barriers. When direct barriers are severe, improvements in governance factors such as rule of law and expropriation risk can increase investment. Only when severe barriers are ameliorated can factors such as firm-specific information, transaction costs and hedging motives become more important. Evidence from unconditional quantile regressions provides support for a pecking order hypothesis, as we find that investment factors vary across the distribution. Specifically, our empirical results indicate that access to basic information is important everywhere, governance and familiarity matter where barriers are high, roles for information and hedging motives become more apparent where barriers are moderate, and where there are no barriers small improvements in governance have little effect on investment. Going forward, analysis should incorporate nonlinearities inherent in cross-border barriers and investment.

We study how listing in multiple markets affects the dynamics between firms’ credit default swap (CDS) and stock returns. We find that cross-listing increases (1) the sensitivity of CDS to stock returns, (2) the integration of CDS with world equity and bond markets, and (3) the statistical synchronicity of CDS and stock prices. Our results are stronger for firms with greater media attention, analyst and CDS coverage, and Google search intensity and for listings in familiar markets. We suggest that a firm’s presence in global equity markets comes with an improvement in the credit-equity integration through a reduction of informational frictions. Received April 20, 2017; editorial decision February 12, 2019 by Editor Andrew Karolyi.