Rewriting History |
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| Authors: | ALEXANDER LJUNGQVIST CHRISTOPHER MALLOY FELICIA MARSTON |
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| Affiliation: | 1. Ljungqvist is from the New York University Stern School of Business and the Centre for Economic Policy Research, London;2. Malloy is from Harvard Business School;3. and Marston is from the University of Virginia McIntire School of Commerce. Thanks for helpful comments go to Campbell Harvey (the Editor);4. two anonymous reviewers;5. Viral Acharya;6. Brad Barber;7. Nick Barberis;8. Lauren Cohen;9. Jennifer Juergens;10. Jay Ritter;11. Kent Womack;12. and seminar participants at the U.S. Securities and Exchange Commission, the 2006 UNC‐Duke Corporate Finance Conference, the 2007 AFA Conference in Chicago, AQR, Oppenheimer Funds, Barclays Global Investors, University of Sydney, University of New South Wales, University of Auckland, Simon Fraser University, University of Virginia, University of Illinois, Dartmouth College, USC, UCLA, Yale University, Tilburg University, London Business School, Wharton, Harvard Business School, and University of Michigan. We are grateful to Ruiming Lin, Pedro Saffi, and Yili Zhang for excellent research assistance. We gratefully acknowledge the contribution of Thomson Financial for providing broker recommendations data, available through the Institutional Brokers Estimate System. Malloy thanks the Economic and Social Research Council for financial support. We are also grateful to many industry insiders for patiently answering our questions. |
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| Abstract: | We document widespread changes to the historical I/B/E/S analyst stock recommendations database. Across seven I/B/E/S downloads, obtained between 2000 and 2007, we find that between 6,580 (1.6%) and 97,582 (21.7%) of matched observations are different from one download to the next. The changes include alterations of recommendations, additions and deletions of records, and removal of analyst names. These changes are nonrandom, clustering by analyst reputation, broker size and status, and recommendation boldness, and affect trading signal classifications and back-tests of three stylized facts: profitability of trading signals, profitability of consensus recommendation changes, and persistence in individual analyst stock-picking ability. |
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