Improving practices of price and earnings estimations
Kim, Ja Ryong
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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.