Ricerc@Sapienza

The proposed research deals with the challenge of developing new risk measurement and management tools in the field of Socially Responsible Investments (SRIs), also known as Environmental, Social and Corporate Governance (ESG) investments to assess the level of Corporate Social Responsibility (CSR). In the last decade, investors as well as governments have been increasingly incorporating ESG information into the analysis of investments. For instance, the European Investment Bank contributed for 55 trillion on 'green' projects. The literature is only at the dawn. Rating agencies have recently started to rate companies according to their SRI business model. Such ratings are obtained by analyzing different features such as emissions, environmental product innovations, human rights, and the companies' structure. The understanding of risk profiles of investments becomes crucial for guidance and benchmarking. However, common metrics to assess financial performances still rely on financial accounting tools well-know impact assessment analysis and the need to develop new instruments is called for.
The target of this research is twofold: first we investigate the relationship between structural features of each company (balance sheet data and fundamental information of the company) and the assigned ESG rating using a machine learning approach, promising to assess the accuracy of the current ESG score thanks to the ability of detecting hidden (non-linear) relationship between the scores and balance sheet data.
Second, we study how the ESG rating affects the companies' performance in terms of stocks' return conditional volatility. What drives stock returns' volatility is a largely debated topic. We assume that companies' commitment in the CSR may provide an additional factor to explain conditional volatility dynamics and to capture the financial market reaction to the use of ESG ratings in response to the increasing investors¿ attention to SRI.