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Primary Article

Modeling Earnings Expectations Based on Clusters of Analyst Forecasts

Haim A. Mozes and Patricia A. Williams
The Journal of Investing Summer 1999, 8 (2) 25-38; DOI: https://doi.org/10.3905/joi.1999.319412
Haim A. Mozes
Associate professor of accounting at Fordham University's Graduate School of Business in New York. He holds a Ph.D. in accounting and an M.S. in statistics/ operations research from NYU's Stern School. His research interests are in earnings forecasting models, analyst forecast behavior, and in the valuation and design of deferred compensation.
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Patricia A. Williams
Associate professor of accounting at Fordham University's Graduate School of Business. She holds a doctorate from Boston University, an MBA from Southern Methodist University, and an M.A. in French from Middlebury College. Her research interests are management and analyst earnings forecasts and international financial reporting.
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Abstract

This article introduces an approach to modeling market expectations that captures the benefits of both timelines and aggregation. The intuition behind the earnings expectation measure, referred to as the cluster mean, is that analysts' forecasts arrive at the market in a sequence of clearly distinguishable forecast clusters. At any time, the cluster mean expectation is simply the mean of all the forecasts issued in the most recent cluster. The tests show the cluster mean forecast has greater forecast accuracy, higher correlation with security returns, and lower serial correlation in forecast revisions than the consensus forecast, both for the entire tests sample and for a number of different subsamples.

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Vol. 8, Issue 2
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Modeling Earnings Expectations Based on Clusters of Analyst Forecasts
Haim A. Mozes, Patricia A. Williams
The Journal of Investing May 1999, 8 (2) 25-38; DOI: 10.3905/joi.1999.319412

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Modeling Earnings Expectations Based on Clusters of Analyst Forecasts
Haim A. Mozes, Patricia A. Williams
The Journal of Investing May 1999, 8 (2) 25-38; DOI: 10.3905/joi.1999.319412
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