This article examines the relationship between carbon disclosure, equity returns, and investor awareness in the Chinese A-share market. The study uses firm-disclosed and proprietary vendor-estimated carbon emissions data for A-share listed companies and investigates whether the presentation of carbon risks affects the cross-sectional equity returns of Chinese domestically listed firms. The results indicate that main-board listed companies with higher unscaled carbon emissions tend to earn higher equity returns, even after controlling for factors such as size, book-to-market ratio, momentum, and firm characteristics. Moreover, the observed carbon risk premium associated with carbon-emitting companies decreases as investor awareness improves following the launch of policy agendas promoting carbon neutrality in China. These findings support the research hypothesis that investors seek higher returns for equity investments in carbon-emission-intensive companies to compensate for the higher carbon risks associated with such firms. The study also highlights the importance of carbon disclosure, as companies generally disclose their ESG information when they have improved performance in reducing their carbon emissions.
We use machine learning methods to predict which patents end up in court using the population of US patents granted between 2002 and 2005. We show that patent characteristics have significant predictive power, particularly value indicators and patent-owner characteristics. Furthermore, we analyze the predictive performance concerning the number of observations used to train the model, which patent characteristics to use, and which predictive model to choose. We find that extending the set of patent characteristics has the biggest positive impact on predictive performance. The model choice matters as well. More sophisticated machine learning methods provide additional value relative to a simple logistic regression. This result highlights the existence of non-linearities among and interactions across the predictors. Our results provide practical advice to anyone building patent litigation models, e.g., for litigation insurance or patent management more generally.
This paper analyzes how the plaintiff selects her lawyer based on lawyers’ confidence in their trial-effort productivity. The plaintiff’s lawyer works on a contingent fee and makes litigation decisions on the plaintiff’s behalf. When the lawyer’s preferences are decisive at both the settlement and the trial stage, the plaintiff must anticipate that a more confident lawyer evaluates settlement compared to trial differently and implies different equilibrium trial effort levels. When the lawyer implements the plaintiff’s ideal settlement demand, only the influence of the confidence level on trial effort levels is relevant. In both cases, the plaintiff prefers an overconfident lawyer but would be harmed by excessive overconfidence. In many circumstances, the optimal confidence level maximizes the plaintiff’s trial payoff. However, when the lawyer’s preferences are decisive at both the settlement and trial stage, the plaintiff may choose an even more confident lawyer to raise the settlement level her lawyer demands from the defendant.

