Ricardo De la O, University of Southern California
Xiao Han, City University of London
Abstract: What explains cross-sectional dispersion in stock valuation ratios? We find that 75% of dispersion in price-earnings ratios is reflected in differences in future returns, while only 25% is reflected in differences in future earnings growth. This holds at both the portfolio-level and the firm-level. We reconcile these conclusions with previous literature which has found a strong relation between prices and future profitability. Our results support models in which the cross-section of price-earnings ratios is driven mainly by discount rates or mispricing rather than future earnings growth. Evaluating six models of the value premium, we find that most models struggle to match our results, however, models with long-lived differences in risk exposure or gradual learning about parameters perform the best. The lack of earnings growth differences at long horizons provides new evidence in favor of long-run return predictability. We also show a similar dominance of predicted returns for explaining the dispersion in return surprises.
Discussant: Lars Lochstoer, University of California-Los Angeles
Abstract: We investigate a comprehensive sample of 78,509 equity reports to understandhow professionals perform valuations. By directly observing measures of short- andmedium-term growth expectations, terminal growth expectations, and discount rates,we study the drivers of fluctuations in expected valuations. We find that both growthexpectations and discount rates play crucial roles. Our analysis reveals that discountrate calculations align with theoretical recommendations, track other professionals’estimates, and vary substantially over time, both in the aggregate and within firms.Equity betas explain four times more of the discount rate process than equity riskpremia. The slope of the security market line obtained using analyst equity beta isequal to 7.9%. The partial correlation between discount rates and growth expectationsis small, at 0.03. Lastly, terminal growth rates respond to macroeconomic factors, suchas monetary policies and GDP growth, but not to inflation.
Discussant: Theis Ingerslev Jensen, Yale University
Wang Renxuan, China Europe International Business School
Abstract: Professional house price forecast data are consistent with a rational model where agents must learn about the parameters of the house price growth process and the underlying state of the housing market. Slow learning about the long-run mean generates overreaction to forecast revisions and a modest response of forecasts to lagged realizations. Heterogeneity in signals and priors about the long-run mean helps the model account for cross-sectional dispersion in forecasts. Introducing behavioral biases helps improve the model's predictions for short-horizon overreaction and dispersion. Using a cross-section of forecasters and a term structure of forecasts are crucial for inference.
