The increase in Environmental, Social, and Governance (ESG) mutual funds raises concerns about “greenwashing,” where misleading environmental claims are made to lure investors. Moreover, such funds charge higher fees than Non-ESG funds with similar ESG ratings. These funds attract investments similar to genuine ESG funds, with retail investors most likely to be misled. While ESG ratings from data providers can be useful, they suffer time delays, making them less helpful for investors. To address this, we develop models that use recent data to predict the “miss-aligned” with over 85% accuracy, providing investors with a new tool to select genuine ESG funds.
Deep Learning Applications in Finance
Huang, S. (Author). 22 Jan 2025
Student thesis: Doctoral Thesis › PhD