ESG Confusion and Stock Returns: Tackling the Problem of Noise

Abstract

Existing measures of ESG (environmental, social, and governance) performance – ESG ratings – are noisy and, therefore, standard regression estimates of the effect of ESG performance on stock returns are biased. We address this as a classical errors-in-variables problem and develop a noise-correction procedure in which we instrument ESG ratings with ratings of other ESG rating agencies. With this procedure, the median increase in the regression coefficients is a factor of 2.3. This relative increase is stable across horizons over which stock returns are measured. In simulations, our noise-correction procedure outperforms alternative approaches such as simple averages or principal component analysis.

Keywords: measurement error, instrumental variables, sustainable investing, ESG ratings

JEL Classification: C26, G12, Q56