Affiliations: [a] Department of Computer Science, Bar Ilan University, Ramat Gan 52900, Israel | [b] ClearForest Ltd., 6 Yoni Netanyahu St., Or Yehuda 60376, Israel | [c] University of Houston, 334 Melcher Hall Houston, TX 77204-6021, USA | [d] Stern School of Business Administration, New York University, 311 Tisch Hall, 40 W. 4th St. NY NY 10012, USA | [e] Graduate School of Management, University of California at Davis, One Shields Avenue, AOB IV Room 134, Davis, CA 95616-8609, USA
Abstract: This study explores a system to retrieve and classify the reasons for late mandatory SEC (Securities and Exchange Commission) filings. From the source documents, the system identifies the reasons for the late filing and classifies them into one or more of seven categories. The system can be used by potential investors who have to track a large number of filings concentrated within a day or two. Our results indicate that the SEC filings may be quite ambiguous, with experienced raters disagreeing on one category for a training sample of 600 filings in about 30% of the cases. However, allowing classifications into more than one category using document level information yields accuracy of about 90% in a test sample of 200 filings. We also show that the stock market reactions to over 9,000 late filings vary in an intuitive way according to the classified reasons.
Keywords: Computerized text classification, computerized categorization, late filings, accuracy of categorization algorithms
