Improving practices of price and earnings estimations
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Date
01/07/2015Author
Kim, Ja Ryong
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Abstract
Despite extensive research on price and earnings estimations, there are still puzzling
results that have not been resolved. One of the puzzles in price estimation is that
multiples using earnings forecasts outperform multiples using the residual income
model (Liu, Nissim and Thomas, 2002). This puzzle undermines the validity of theory-based
valuation models, which are originated from valuation theory and have been
developed over the century. The first two projects of this thesis address this puzzle and
explain mathematically how the pricing error of a multiple is determined by the
correlation coefficient between price and a value driver. The projects then demonstrate
that the puzzle in Liu, Nissim and Thomas (2002) is caused by the bad selection of
residual income models and, in fact, the majority of residual income models (i.e. well-chosen
residual income models) actually outperform multiples using earnings
forecasts in pricing error. When models are examined in terms of future return
generation, residual income models again outperform multiples using earnings
forecasts, providing evidence that theory-based valuation models are superior to rule-of-
thumb based multiples in price and intrinsic value estimations.
The third project addresses an issue in earnings estimation by cross-sectional models.
Recently, Hou, van Dijk and Zhang (2012) and Li and Mohanram (2014) introduce
cross-sectional models in earnings estimation and argue that their cross-sectional
models produce better earnings forecasts than analyst forecasts. However, their models
suffer from one fundamental problem of cross-sectional models: the loss of firm-specific
information in earnings estimation (Kothari, 2001). In other words, cross-sectional
models apply the same coefficients (i.e. the same earnings persistence and
future prospects) to all firms to estimate their earnings forecasts. The third project of
this thesis addresses this issue by proposing a new model, a conditional cross-sectional
model, which allows the coefficient on earnings to vary across firms. By allowing
firms to use different earnings coefficients (i.e. different earnings persistence and
future prospects), the project shows that a conditional cross-sectional model improves
a cross-sectional model in all dimensions: a) bias, accuracy and earnings response
coefficient; b) unscaled and scaled earnings estimations; and c) across all forecast
horizons.
The thesis contributes to the price and earnings estimations literature. First, the thesis
addresses the decade-old puzzle in price estimation and rectifies the previous
misunderstanding of valuation model performance. By demonstrating the superiority
of theory-based valuation models over rule-of-thumb based multiples, the thesis
encourages further development of theory-based valuation models. Second, in
earnings estimation, the thesis provides future researchers a new model, which
overcomes the fundamental problem of cross-sectional models in earnings estimation
while keeping their advantages. In sum, the thesis improves the knowledge and
practices of price and earnings estimations.