We propose a latent variables approach within a present-value model to estimate the expected returns and expected …-dividend ratios and dividend growth rates to predict future returns and dividend growth rates. We find that returns and dividend … growth rates are predictable with R-squared values ranging from 8.2% to 8.9% for returns and 13.9% to 31.6% for dividend …

We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the … specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use … data, we find that daily realized power (involving 5-minute absolute returns) is the best predictor of future volatility …

good earnings news, suggesting these studies are not just measuring an indexing effect. We develop a regression …

of one-period expected returns and expected-return news. As a result, the R2 from a regression of returns on cash …Stock returns are correlated with contemporaneous earnings growth, dividend growth, future real activity, and other … cash-flow proxies. The correlation between cash-flow proxies and stock returns may arise from association of cash …

to predict stock or bond market returns. Recent research argues that these results may be driven by an aggregate time …

expected level of performance persistence. To identify the causal impact of fund size on future returns, we exploit the fact … that small differences in returns can cause discrete changes in Morningstar ratings that, in turn, generate discrete … differences in size. Despite robust evidence that Morningstar ratings increase fund size, our regression discontinuity estimates …

ratios for such shifts. The forecasting relationship of adjusted price ratios and future returns is statistically significant …

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions … for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor … variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series …

moves the focus of attention from the distribution of returns directly to the behavior of the quantile. We postulate a … variety of dynamic processes for updating the quantile and use regression quantile estimation to determine the parameters of … methodologies developed so far give satisfactory solutions. Interpreting Value at Risk as a quantile of future portfolio values …

When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression … substantially from the standard regression setting. Bayesian posterior distributions for the regression parameters are obtained …