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Using Machine Learning to Predict Stock Returns from Sustainability Data
The researchers critically examine the connection between corporate sustainability—specifically, material aspects of sustainability as defined by the Sustainability Accounting Standards Board (SASB)—and stock returns. The original hypothesis by Khan, Serafeim, and Yoon (KSY) suggested that sustainability measures, when aligned with SASB’s materiality framework, could predict stock returns. This study further explores this relationship through a "model uncertainty analysis" and the application of machine learning to assess the practicality of using historical sustainability data in forecasting stock returns.
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