Reported earnings and analyst forecasts as competing sources of information: A new approach
AbstractWe apply a new methodology, modified Granger causality tests, to further analyze the information flows between earnings and forecasts. Our application focuses on the dynamic interaction between reported earnings and analystsâ€™ forecasts. Based on long time series of analyst earnings forecasts and reported earnings, we provide formal and compelling evidence of bi-directional â€˜causalityâ€™. Further, we report that the lag structure in information flows is longer than has been documented in the previous literature. This is consistent with our expectation that, in addition to past earnings reports, the forecasts themselves make a significant contribution to the information that is reflected in future earnings. However, the presence of feedback also suggests that past earnings reports, as well as past forecasts, are incorporated into later forecasts. Collectively, our findings imply that the information in earnings reports has inherent positive value and that forecasts do not fully substitute for earnings releases.
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Bibliographic InfoArticle provided by Australian School of Business in its journal Australian Journal of Management.
Volume (Year): 37 (2012)
Issue (Month): 3 (December)
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Web page: http://www.agsm.edu.au
Analyst forecasts; Granger causality; information flows; reported earnings;
Other versions of this item:
- H.M. Anderson & H. Chan & R. Faff & Y.K. Ho, 2007. "Reported Earnings and Analyst Forecasts as Competing Sources of Information: A New Approach," ANU Working Papers in Economics and Econometrics 2007-488, Australian National University, College of Business and Economics, School of Economics.
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- M41 - Business Administration and Business Economics; Marketing; Accounting - - Accounting - - - Accounting
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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