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Accounting Conservatism, Information Uncertainty and Analysts' Forecasts


Jing Li


Carnegie Mellon University - David A. Tepper School of Business



Abstract:     
This study examines how accounting conservatism may affect the information environment of analysts' earnings forecasts, taking into account the interaction between unconditional and conditional conservatism. Unconditional conservatism preempts conditional conservatism in the later period and reduces the uncertainty in loss recognition associated with bad news. Through a simple analyst forecast model, I demonstrate that: 1) unconditional conservatism is negatively correlated with analysts' forecast errors for good news or mild bad news firms, but positively correlated with analysts' forecast errors for extreme bad news firms; and 2) unconditional conservatism reduces the overall uncertainty in analysts' forecasts. The empirical results are consistent with the predictions. Moreover, the evidence shows that the impact of unconditional conservatism on analysts' forecasts is greater for early forecasts, when the information uncertainty is high, than for late forecasts.

Number of Pages in PDF File: 42

Keywords: unconditional conservatism, conditional conservatism, analysts' forecast error, overall uncertainty in analysts' forecasts

JEL Classification: M41, M44, G29

working papers series


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Date posted: June 11, 2008 ; Last revised: January 29, 2010

Suggested Citation

Li, Jing, Accounting Conservatism, Information Uncertainty and Analysts' Forecasts. Available at SSRN: http://ssrn.com/abstract=1002681 or http://dx.doi.org/10.2139/ssrn.1002681

Contact Information

Jing Li (Contact Author)
Carnegie Mellon University - David A. Tepper School of Business ( email )
5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
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