Using U.S. census survey data on CEOs’ residence in their formative years, I document a negative relation between CEOs’ endowed family wealth and managerial performance. Consistent with the view that CEOs born into low-income families face higher entry barriers but may possess greater levels of ability that enable them to become CEOs, I find that CEOs born into less privileged families outperform those from higher-wealth families. The outperformance of CEOs from less wealthy families is not driven by risk taking or omitted variables. Overall, my results suggest that CEOs’ social endowment provides a useful signal for their managerial ability.

We provide a comprehensive account of the evolution of the currency composition of sovereign and corporate external borrowing by emerging markets from 2003 to 2017. We show that a higher reliance on foreign currency debt by the corporate sector is associated with higher sovereign default risk. We introduce local currency sovereign debt and private sector currency mismatch into a standard sovereign debt model to examine how the currency composition of corporate borrowing affects the sovereign’s incentive to inflate or default. A calibration of the model generates the empirical patterns of sovereign credit risk.

We examine how political corruption affects corporate investment. We find firms in more corrupt states invest less than firms in less corrupt states. Our results are robust to using alternative investment measures, alternative corruption measures, and different regression specifications. Further, we find that the negative effect of corruption became insignificant after the enactment of Dodd–Frank Whistleblower Provision. The impact of the Dodd–Frank Whistleblower Provision is stronger in states with higher corruption. Our findings suggest that political corruption hinders investment, but the changes in legal environments can help firms reduce the decline in investments in highly corrupt states.

We exploit information in sovereign CDS spreads and the international trade network to provide causal evidence of the propagation of global economic shocks. We show that trade links are an important source of shock transmission using the natural experiments of the Japanese tsunami and the COVID-19 lockdown in China. We then confirm more general and gradual information flows along the trade network by showing extensive country-level credit/equity cross-sectional return predictability. News about country fundamentals flows primarily from importers to exporters, depends on both direct and indirect links in the trade network, and is magnified by the exporting country's financial vulnerability.

We use Word2vec to develop a financial sentiment dictionary from 3.1 million Chinese-language financial news articles. Our dictionary maps semantically similar words to a subset of human-expert generated financial sentiment words. In validation tests, our dictionary scores the sentiment of articles consistently with human reading of full articles. In return association tests, our dictionary outperforms and subsumes previous Chinese financial sentiment dictionaries, such as direct translations of Loughran and McDonald’s (2011, Journal of Finance, 66, 35–65) English-language financial dictionary. We also generate a list of politically related positive words that is unique to China; we find that this list has a weaker association with returns than does the list of other positive words. We demonstrate that state media uses more politically related positive and fewer negative words, and exhibits a sentiment bias. This bias renders the state media’s sentiment as less return-informative. Our findings demonstrate that dictionary-based sentiment analysis exhibits strong language and domain specificity.

Unlike traditional asset categories (e.g., industry classifications) that are generally defined clearly, some groups of stocks are tied to certain loosely defined “concepts” (e.g., e-commerce). When investors find it difficult to analyze ambiguous concept-oriented information, information diffuses slowly, creating “concept momentum”. Based on unique concept data in the Chinese stock market, this study constructs a concept-momentum strategy that involves buying stocks from past winning concepts and selling stocks from past losing concepts, which can generate pronounced abnormal returns. Neither risk factors, firm-level momentum, nor industry-level momentum can explain concept momentum. Furthermore, we find that both the underreaction and cross-stock lead-lag effect channels can cause slow information diffusion and drive concept momentum. Moreover, the concept momentum effect is stronger for relatively ambiguous concepts, for concepts that attract less investor attention, and following high-sentiment periods.