We develop a novel term-structure model for pricing GDP-linked bonds, hypothetical securities with cash-flows indexed to the level of U.S. GDP. For this purpose, we rely on a term-structure model of equity yields estimated using the prices of dividend swaps, which we assume span GDP growth. Our approach provides a novel way of estimating the relative cost of conventional and GDP-linked bonds, as well as measuring more general market-based expectations of (and risks around) GDP growth. Our model predicts that U.S. GDP-linked bonds would typically have yields lower than those on conventional Treasury bonds with the same maturity in our sample from 2010 to 2017. Positive expected future GDP growth lowers the yield on GDP-linked bonds relative to conventional bonds, which typically more than offsets the estimated GDP risk premium demanded by investors for holding GDP risk.

We examine the role of macroeconomic uncertainty in the cross section of corporate bonds and find a significant uncertainty premium for both investment-grade (IG) (0.40% per month) and non-investment-grade (NIG) (0.81% per month) bonds. The economic-uncertainty premium declines as we progressively remove downgraded bonds, indicating that the premium represents an increase in required returns for bonds with higher credit and macroeconomic risk. The economic-uncertainty premia vary across equities and bonds in a manner consistent with the heterogeneous risk-aversion levels of dominant players in equities (retail investors) versus bonds (institutional investors).

We provide time-series and cross-sectional evidence on the significance of a risk-return tradeoff in the bond and equity markets. We find a significantly positive intertemporal relation between expected return and risk in the bond market. We also propose novel measures of systematic and idiosyncratic risk for individual corporate bonds and find a significantly positive cross-sectional relation between systematic risk and expected bond returns, whereas there is no significant link between idiosyncratic risk and future bond returns. We provide an explanation for the significance of systematic (idiosyncratic) risk based on different investor preferences and informational frictions in the bond (equity) market.

Long-term reversals in corporate bonds are economically and statistically significant in a comprehensive sample spanning the period 1977 to 2017. Such reversals are stronger for bonds with high credit risk and more binding regulatory, capital, and funding liquidity constraints. Bond long-term reversal is not a manifestation of the equity counterpart and is mainly driven by long-term losers. A long-term reversal factor carries a sizable premium and is not explained by long-established equity and bond market factors. Thus, past returns capture investors’ ex-ante risk assessment and the degree of institutional constraints they face, so losing bonds command higher expected returns.

This study investigates the effect of managerial sentiment on corporate disclosure decisions. Using terrorist attacks in the United States as adverse shocks to managerial sentiment, we find that firms located in the metropolitan areas attacked issue more negatively biased earnings forecasts. The effect is stronger for firms with higher operating uncertainty and firms with younger, inexperienced, or less confident executives and it is weaker for firms located in states with increasing violent crime rates. A potential alternative explanation is that managers strategically bias earnings forecasts downward and attribute the poor performance to terrorist attacks. To address this issue, we conduct a battery of additional analyses and find results more consistent with managerial sentiment than strategic attribution. In addition, we show that our results are unlikely to be driven by any economic effects of terrorist attacks. Finally, firms in attacked areas exhibit a more pessimistic tone in 10-K/10-Q filings.

Using a large sample of firms from 43 markets, we find significant time-series variation in firms’ leverage ratios around the world. Industry median leverage ratios and aggregate leverage ratios also change substantially over time. Relative to actual leverage ratios, target leverage ratios estimated from the time-varying target models are much more stable. Variance decomposition shows that leverage instability is largely driven by deviations from the target. A number of firm and market characteristics are related to capital structure instability. We also find evidence consistent with firms using financing activities to adjust their leverage ratios towards the target in global markets.