Is the Value Relevance of Earnings Information Really Decreasing Over Time?

Previous studies state that the value relevance of earnings information has declined over time, based on decreasing ERCs and R2s. This paper demonstrates that measurement error bias is a major factor that drives these results when using earnings changes as a proxy for unexpected earnings. The variance of measurement error in earnings changes as a proxy for unexpected earnings is found to increase over time using a latent variable model. After controlling for the impact of measurement error, trends of ERCs and R2s estimated using the model are significantly closer to zero and in fact not significantly different from zero. Consistent with these results, the analysis with quarterly earnings, firm-specific models, as well as OLS estimation using analyst forecasts does not provide any evidence of declining value relevance of earnings information over time.This paper provides an explanation for the low magnitude of OLS ERCs observed in previous literature by showing substantial measurement errors in using either earnings changes or analyst forecasts to calculate unexpected earnings. After controlling for measurement errors with a latent variable model, this paper considerably improves ERC estimation and makes it economically more reasonable.By ¡§observing¡¨ the properties of (unobservable) market earnings expectations, future research, using the latent variable model, allows analysis of a number of accounting research topics from new perspectives.