Carbon data isn't the whole story: A price signal approach to measure climate transition risk and improve asset pricing

Abstract

The shift to a green, low-carbon economy is generating new investment strategies and impacting asset prices. In this paper, we isolate price signals related to the low-carbon transition using statistical multi-factor models and the Random Matrix Theory (RMT). This allows us to isolate green and brown stocks, and to build a Brown-minus-Green (BMG) factor which has two purposes. Firstly, the sensitivity of asset prices to this new BMG factor gives us a market measure of climate transition risk that does not involve naturally outdated fundamental climate metrics. Secondly, adding this BMG factor to asset pricing models significantly and robustly improves previous BMG factors used in the literature. We contribute to the nascent literature on climate factors by improving the climate risk measure of enabling activities: we achieve the isolation of companies that allow others to reduce their climate risk and that are challenging to capture when using only greenhouse gas (GHG) emissions or previous BMG factors, due to their high carbon intensities.