In this study, the fundamental value (V) of the sample stocks is calculated with the use of the residual income model, using forecasts for the cost of equity, book value, dividend payout ratio and return on equity (ROE) and its usefulness is examined in predicting cross-sectional stock returns. Specifically, the main inputs of the residual valuation model are forecasted using an AR1 time series model and the resulting estimate of the fundamental value over the market value, i.e. the value-to-price (V/P) ratio is used to investigate issues related to market efficiency and the predictability of cross-sectional stock returns.
Furthermore, a value risk factor is constructed in such manner as to obtain a monotonic relation between risk and expected returns. We do so by creating a zero-investment portfolio that takes long positions in stocks that have high V/P ratios and short positions in stocks with low V/P ratios. If the value risk factor is a priced factor it should reduce the mean pricing error (absolute value of the intercept) of the other classical asset pricing models (CAPM, Fama-French 3 factor, Carhart 4 factor models).
The findings of the present research are related to the finance literature on the predictability of stock returns and asset pricing models. Research in the area has focused either on accounting-based ratios or additional risk factors that exhibit increased predictive power for stock returns. Our results suggest that rather than attempting to produce a better risk proxy, superior return prediction may result from adopting a more complete valuation approach.
A dataset from the two largest Eurozone economies (Germany and France) is utilized, from 1999 to 2010, and the objective is examined under different macroeconomic conditions, thus, the implications of the results of the present study will be useful for investment professionals (asset managers, institutional investors, economists), retail investors and academics.