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Reported Earnings and Analyst Forecasts as Competing Sources of Information: A New Approach

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  • H.M. Anderson
  • H. Chan
  • R. Faff
  • Y.K. Ho

Abstract

We study information flows between earnings and forecasts, using suitably adapted Granger causality tests. This approach complements existing cross-sectional studies by abstracting from stock market reactions to information, and focussing on dynamic interactions between information flows instead. We find bi-directional causality in timeseries of analyst earnings forecasts and reported earnings, supporting our expectation that forecasts contribute to information that is reflected in future reports. Further, our evidence of feedback suggests that past reports and forecasts are both reflected in future forecasts, implying that the information in reports has inherent value, and that forecasts do not fully substitute for reports.

Suggested Citation

  • 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.
    • Handle: RePEc:acb:cbeeco:2007-488
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    2. Camillo Lento & Julie Cotter & Irene Tutticci, 2016. "Does the market price the nature and extent of earnings management for firms that beat their earnings benchmark?," Australian Journal of Management, Australian School of Business, vol. 41(4), pages 633-655, November.

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    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - 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; State Space Models

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