Resumo:
As a crucial input to many valuation models, earnings forecasts are important to many practitioners and academics. Unfortunately, there is a large sample of firms that analysts do not cover, and analysts’ earnings forecasts are less accurate than a random walk at long horizons. Recent work by Hou, van Dijk, and Zhang (2012) and Li and Mohanram (2014) suggested the use of cross-sectional models to produce earnings forecasts. Several studies immediately used these models because of the obvious advantage that forecasts can be formed for a sample that is much greater than the sample of firms covered by analysts. Unfortunately, these models also produce earnings forecasts significantly worse than random walk forecasts. We present a simple and intuitive modification to these models – the use of quantile rather than OLS regressions in the prediction model – that produces earnings forecasts significantly better than a random walk. Subsequent analysis suggests that this simple modification produces earnings forecasts that lead to more accurate return forecasts, and better represents market expectations.
Easton, Peter D. and Kelly, Peter and Neuhierl, Andreas, Beating a Random Walk (August 7, 2018). Available at SSRN: https://ssrn.com/abstract=3040354 or http://dx.doi.org/10.2139/ssrn.3040354
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