We examine firms' trade‐offs in their voluntary disclosure decisions following negative media coverage of climate change incidents. By combining a keyword discovery algorithm and a fine‐tuned BERT model, we identify “hard” and “soft” climate disclosures on Twitter. Our findings indicate that firms tend to issue climate tweets as a rapid response to negative climate incidents. Additionally, firms with a history of hard climate change disclosure, as measured by ESG reports, are more likely to issue climate‐related responses than firms without such a history. Furthermore, we show that prior hard disclosure is associated with hard responses when the incident receives moderate media attention, but with soft responses when the incident receives low media attention. Our findings provide empirical insights for dynamic disclosure theory by illustrating how prior disclosure shapes firms' response strategies to negative media coverage.

We conduct a thorough analysis on the role played by the unobserved systematic risk factor in default prediction. We find that this latent factor outweighs the observed systematic risk factors and can substantially improve the in-sample predictive accuracy at the firm, rating group, and aggregate levels. Thus it might be helpful to include the unobserved systematic risk factor when simulating portfolio credit losses. However, we also find that this factor only marginally improves out-of-sample model performance. Therefore, although the models we investigated all show reasonably good ability to rank order firms by default risk, accurate prediction of default rate remains challenging even when the unobserved systematic risk factor is considered.

Does government debt impede firm innovation? We address this question by examining the effects of the debt accumulated by local government financing vehicles (LGFVs) across Chinese prefectures between 2006 and 2012 on industrial firms’ R&D spending and patents. We find that government debt reduces firms’ R&D expenditures and lowers firms’ number of new patents. One plausible explanation is that government debt raises firms’ capital costs, which limits innovation activities. Consistently, we find that the innovations of firms that are more likely to be financially constrained – small firms and firms with low cash flow – are more affected by the expansion of government debt. Our results imply that although government deficit spending may stimulate the economy in the short run, it could have negative repercussions for economic productivity in the longer run.

We develop a non-linear Markov error correction approach to examine the general co-integration relation between the H- and A-prices of cross-listed Chinese stock issuers across the period January 1999 to March 2009. We unravel three important dimensions of this relation. These pertain to (i) the long-run expectation of the H- (to A-price) discount; (ii) the level of short-run co-movement in prices; and (iii) the magnitude of error corrections. Findings point to significant improvements in all three areas. Policy and corporate governance change appears to be the principal force driving the efficiency gains. Weakening informational asymmetries underlie much of the change in the markets’ relative pricing. In contrast, sentiment effects strongly underpin the contemporaneous response and error correction adjustments. Finally, the escalating Global Financial Crisis of 2008 appears to have not only bolstered the A- and H-markets’ short-term pricing dynamic but also temporarily increased the long-term H-share discount.