Corporate Sustainability: A Model Uncertainty Analysis of Materiality

Description

For decades, scholars searched for a connection between a corporation's current performance with respect to sustainability and the future returns of its stock. In 2016, Khan, Serafeim, and Yoon published an apparent breakthrough in this quest: guidance on materiality from the Sustainability Accounting Standards Board allowed the construction of corporate sustainability scales that reliably predicted stock returns. Their finding had an immediate and broad impact, but it remains, in its authors' own words, just ``first evidence.'' Here, we further explore the relationship between material sustainability and stock returns by performing a ``model uncertainty analysis.'' We reproduce the original estimate but conclude that it is a statistical artifact. We then use machine learning to explore the practicality of employing historical associations to determine which aspects of sustainability are material to investors. We conclude that, for one popular source of data on corporate sustainability, accurate guidance on materiality may be difficult to achieve.

Highlights

• Materiality • Social and financial performance • Research methods • Epistemology • Model uncertainty • Replication

Objectives

• Apply critical analysis skills to evaluate the limitations of empirical models in determining the relationship between corporate sustainability and stock returns, as evidenced by conducting a model uncertainty analysis. • Apply machine learning techniques to explore and assess the practicality of utilizing historical associations in determining the materiality of sustainability factors influencing investors' decisions.