Description:
ESG assets, the center of today's investment interest, grew into one of the largest parts of the investment universe. Features of ESG stocks are presented distinctively from a financial and non-financial perspective. Thus, potential investors might perceive them as a new (sub-)asset class. For this reason, we follow a multivariate statistical approach to address whether stocks similar in terms of their ESG characteristics provide unique characteristics in terms of return, risk, and liquidity. We employ the ESG ratings of seven major data providers and their artificial structural combinations to determine the individual strength and the effect of shared information in the aggregated (ESG) and dimensional (E-S-G) perspectives. The empirical classification of S\&P 1200 stocks regarding their ESG and various risk-return-liquidity variables shows no general systematic effect for seven consecutive years, including the period of the COVID-19 pandemic. Nevertheless, the unidimensional scores, particularly Governance, are more powerful than multidimensional indicators. Moreover, we demonstrate that the artificial structural scores considerably improve the quality of the model, implying the financial importance of core ESG information and its usefulness within financial decision-making. These results are supported by a robustness check on different time settings exploring ESG as a leading and concurrent indicator