Computerized retrieval and classification: An application to reasons for late filings with the securities and exchange commission

Ronen Feldman, Benjamin Rosenfeld, Ron Lazar, Joshua Livnat, Benjamin Segal

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish
Pages (from-to)183-195
Number of pages13
JournalIntelligent Data Analysis
Volume10
Issue number2
DOIs
StatePublished - 2006

Keywords

  • Computerized text classification
  • accuracy of categorization algorithms
  • computerized categorization
  • late filings

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