• PEAD.txt: Post-Earnings-Announcement Drift Using Text

    Vitaly Meursault, Pierre Jinghong Liang, Bryan R. Routledge, and Madeline Marco Scanlon

    Federal Reserve Research: philadelphia We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings-announcement drift (PEAD.txt) larger than the classic PEAD. The magnitude of PEAD.txt is considerable even in recent years when the classic PEAD […]

  • Advancing Fairness in Lending Through Machine Learning

    Vitaly Meursault, Daniel Moulton, Larry Santucci, and Nathan Schor, with web adaptation by Kali Aloisi

    Federal Reserve Research: Philadelphia Advances in machine learning (ML) provide the opportunity to improve predictions that may expand credit access to more applicants. However, there is concern that gains from advanced models could accrue unequally between demographic groups or do little to reduce existing disparities in credit access. This research explores an approach using ML […]