When analysts or academics want to assess the risks that a company faces, they usually look at macroeconomic factors or internal firm metrics such as a declining sales trend to calculate those risks. But research from Wharton doctoral candidate Alejandro Lopez-Lira takes a different approach.
He asked this question: What if, instead of letting the outside world tell us what risks a company faces, we let the company tell us itself? After all, a company knows its business best. Lopez-Lira used machine learning to read through the annual reports of all U.S. public companies to find out which risks they identified as the most serious ones they face. And the results can be surprising.
His findings are in the paper, “Risk Factors That Matter: Textual Analysis of Risk Disclosures for the Cross-Section of Returns.” His research was supported the Mack Institute and the Rodney L. White Center for Financial Research.Knowledge@Wharton - 03/22/2019