We are the first to study the interplay between corporate diversification and debt maturity, both theoretically and empirically. Our models predict that diversification mitigates the debt-overhang problem, making long-term debt more attractive in the presence of rollover costs. Using data on 30,135 firms from 1978 to 2022, we find that multi-division firms have debt maturities at least 1 year longer than stand-alone firms, especially when facing debt overhang. Consistent with our predictions, the excess value of Berger and Ofek (1995) and Mansi and Reeb (2002) increases with debt maturity, suggesting that traditional measures of the diversification discount could be misleading.

This paper explores the impact of product market competition on the positive relation between labor mobility (LM) and future returns. We develop a production-based model and formalize the intuition that low exposure to systematic risk in a concentrated industry limits LM’s amplifying effect on operating leverage. Therefore, the model predicts a stronger positive relation between LM and expected returns for firms in competitive industries. Consistent with the model’s prediction, we empirically find that LM predicts returns only among firms in competitive industries. This evidence suggests that the intensity of competition in firms’ product market potentially drives the positive LM-return relation.

We examine the effect of voluntary climate risk disclosure on Credit Default Swap (CDS) premiums. We develop a structural model, in which climate-related disclosures serve as an information source reducing climate change uncertainty. The model predicts a negative relation between the informativeness of climate risk disclosure and the CDS premium, and asymmetric effects of positive and negative disclosure tone on the CDS premium. Using climate risk measures quantified from earnings call transcripts, we provide evidence supporting these predictions with causal inference. Our study suggests that climate risk is priced in the CDS market, where investors pay attention to climate risk disclosures.

Based on a reduced-form model of credit risk, we explore mispricing in the credit default swaps (CDS) spreads of North American companies and its economic content. Specifically, we develop a trading strategy using the model to trade out of sample market-neutral portfolios across the term structure of CDS contracts. Our empirical results show that the trading strategy exhibits abnormally large returns, confirming the existence and persistence of a mispricing. The aggregate returns of the trading strategy are positively related to the square of market-wide credit and liquidity risks, indicating that the mispricing is more pronounced when the market is more volatile. When implemented on the Markit data, the strategy shows significant economic value even after controlling for realistic transaction costs.

We introduce in the theory of Gabaix and Maggiori (2015) a network structure to capture the complexity of the balance sheets of financial intermediaries, using the Leontief inverse-based centrality. We use this framework in a multi-country world with imperfect financial markets to study how currency risk premia are connected to financiers’ risk bearing capacity. Guided by the theory, we construct a Centrality Based Characteristic (CBC), based on the centrality of the trade imbalance network and variance–covariance matrix of currency returns. Sorting currencies on CBC generates a high Sharpe ratio, and the resulting excess returns reflect a novel source of predictability.

There have been 128 defaults among U.S. CDS reference entities between 2001 and 2020. Within this sample, the five-year CDS spread is a significant predictor of corporate default in models with equity market covariates and firm attributes. This finding holds for forecast horizons up to 12 months, among financial and non-financial firms, within and without the great financial crisis, and is robust to the inclusion of corporate bond and equity options market information. A decomposition of the CDS spread into liquidity, physical default, and risk premium components shows that most of its predictive power for corporate default comes from the physical default component, both in- and out-of-sample. These results confirm the relevance of information contained in single-name CDS pricing to corporate default prediction.