An Examination of the Statistical Significance and Economic Implications of Model-Based and Analyst Earnings Forecasts

54 Pages Posted: 7 Jan 2013

See all articles by Kevin Ow Yong

Kevin Ow Yong

Peking University

Mark E. Evans

Wake Forest University

Kenneth Njoroge

University of Oregon

Date Written: December 31, 2012

Abstract

We address the demand for model-based earnings forecasts by proposing a cross-sectional model which incorporates three salient ideas. First, firm performance converges to expected levels over time; second, amounts from current financial statements are robust predictors of future performance; and third, ordinary least squares (OLS) estimation is unreliable in samples including extreme values. Accordingly, we estimate a cross-sectional earnings forecasting model based on least absolute deviations analysis (LAD), and include profitability drivers derived from financial statements as predictors. In terms of statistical significance, we find that these forecasts are more accurate than forecasts from three extant prediction models and consensus analysts’ forecasts. In terms of economic implications, we find that forecasts from our model have greater predictive ability for future abnormal returns than consensus analysts’ forecasts. Overall, our results are important because they document the usefulness of a cross-sectional earnings forecasting model for a broad range of diverse firms, including those with little or no analyst coverage.

Keywords: Earnings Forecasts, Financial Statement Analysis, Security Analysts

JEL Classification: M40, G11, G17

Suggested Citation

Ow Yong, Kevin and Evans, Mark E. and Njoroge, Kenneth, An Examination of the Statistical Significance and Economic Implications of Model-Based and Analyst Earnings Forecasts (December 31, 2012). Available at SSRN: https://ssrn.com/abstract=2197145 or http://dx.doi.org/10.2139/ssrn.2197145

Kevin Ow Yong

Peking University ( email )

University Town
Shenzhen, 518055
China

Mark E. Evans (Contact Author)

Wake Forest University ( email )

P.O. Box 7659
Winston-Salem, NC 27109-7285
United States

Kenneth Njoroge

University of Oregon ( email )

1280 University of Oregon
Eugene, OR 97403
United States

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