Green investments remain one of the major challenges for future sustainable and impactful growth of EU countries. Since results about the importance and the sign of climate risk factors and sustainability scores in explaining future stock returns are often mixed, it is crucial to provide investors with a better understanding of the relationship between climate risks and the cross-section of stock returns to encourage conscious investment in environmentally sustainable firms.

Despite recent progress, considerable challenges still limit the potential for available ESG information to support long-term value and climate-related international objectives. More specifically, ESG scores are available for a limited number of companies, and ESG data are available at a low computation frequency (on a quarterly or an annual basis). When available, ESG scores provided by different information providers (e.g., Bloomberg, Reuters, S&P Global) differ one from each other, creating confusion on the investor's side.

Moreover, at the level of the individual company, there could be a tendency to disclose only partial information, emphasizing the environmental dimensions on which the firm performs best and neglecting those where the firm does not do as well (greenwashing).

To fill these gaps, the project aims to investigate in depth the relationship between climate risk and the cross-section of stock returns, aggregating different sources of information to cope with the greenwashing problem and making it possible to deal with uncertainty inherent in the data with appropriate econometric techniques.

The aim of the project is fourfold. First, to propose an innovative theoretical framework for the relationship between climate risk and the cross-section of stock returns. Second, to measure the company’s exposure to climate risk, for a high number of European stocks, by combining different information sources such as ESG ratings, the firm's exposure to climate news, and other climate-related variables at the firm level (such as company financial statements, green bonds issued by the companies, the company's carbon footprint, data on emissions and on possible legal disputes related to environmental aspects). Third, to investigate the climate risk premium by creating portfolios that reflect the exposure to climate risk. Fourth, to evaluate the properties of green and brown portfolios concerning their level of diversification, tail risk and possible spillovers. Fourth, to assess the forecasting performance of climate risk on future stock returns, by means of advanced techniques based on machine learning methods for large datasets characterized by mixed frequencies.

The project results are expected to have important implications for investors, firms and policy makers and more in general for the EU as a whole since they are vital to direct private financial resources to climate-change mitigation and adaptation activities.