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Earnings Management and the Long-Term Market Performance of Initial Public Offerings

Siew Hong Teoh
University of California - Paul Merage School of Business

T.J. Wong
Chinese University of Hong Kong (CUHK) - School of Accountancy

Gita Rao
Colonial Management Associates


May 1994


Abstract:     
We examine empirically whether earnings management as measured by discretionary accounting accruals explain post-issue stock return underperformance for IPO firms. We find that high discretionary accounting accruals are related to negative abnormal stock returns with high statistical significance. For example, a trading strategy of a short position in IPO firms with high discretionary accruals and a long position in IPOs with low discretionary accruals result in a mean (median) excess return of 102% (83.5%) in the 36-month period beginning after the first fiscal year end of the IPO. The evidence is consistent with Ritter's [1991] conjecture that investors are systematically overoptimistic about the growth prospects of IPO firms. The high discretionary accounting accruals seem to be associated with initial overoptimism of investors with subsequent revelations about the appropriateness of the accruals causing a subsequent downward revision in stock prices.

JEL Classifications: G14, M41

Working Paper Series

Date posted: September 01, 1999 ; Last revised: September 01, 1999

Suggested Citation

Teoh, Siew Hong, Wong, T.J. and Rao, Gita R., Earnings Management and the Long-Term Market Performance of Initial Public Offerings (May 1994). Available at SSRN: http://ssrn.com/abstract=5354


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Contact Information

Siew Hong Teoh (Contact Author)
University of California - Paul Merage School of Business ( email )
Irvine, CA 92697-3125
United States
949 824 9952 (Phone)
Gita R. Rao
Colonial Management Associates
One Financial Center
12 Floor
Boston, MA 02111
United States
617-772-3461 (Phone)
Tak Jun Wong
Chinese University of Hong Kong (CUHK) - School of Accountancy ( email )
Shatin, N.T. Hong Kong
852.2609.7750 (Phone)
Feedback to SSRN (Beta)


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