Complex Disclosures And Investor Divergence At Earnings Announcements

Stella Park, University of Pennsylvania


I examine how complex prior disclosures influence investors’ pricing of current period earnings. To do so, I focus on firms’ disclosure of product innovations in voluntary new product announcements (NPAs) as prior complex disclosures that complement quarterly earnings releases. Using machine learning algorithms, I extract five different types of product-related information to measure NPA complexity. I find that when the complexity of prior NPAs increases, investors are more likely to differ in their abilities to use them to complement information released at the current earnings announcement, resulting in greater divergence.