Abstract We build a novel data set of time-stamped corporate decarbonization commitments—green pledges—for U.S. public firms by classifying news articles with large language models and human validation. Firms announcing green pledges tend to be larger and browner than other firms, both within and across industries. Green pledges significantly raise stock prices, consistent with a reduction in the carbon premium, and predict sizable declines in future carbon emissions and emission intensities. These effects tend to be strongest for firms in brown industries. Green pledges thus appear credible, convey relevant new information to investors, and provide meaningful financial incentives to decarbonize. (JEL G14, G32, Q54, Q56)

We evaluate the relative and absolute performance of competing factor-based asset pricing models in international regions and globally. Our holistic analysis controls for model transaction costs and incorporates both right-hand-side tests (based on maximum squared Sharpe ratios) and left-hand-side tests (individual return predictors, composite mispricing proxies). The overall view of the tests shows that recently proposed models tend to perform better than classical models, but otherwise perform comparably. This finding, the performance of the models in some of the LHS tests as well as further results collectively suggest the need for new powerful asset pricing models for global equity markets.