This paper provides global evidence supporting the hypothesis that expected return models are enhanced by the inclusion of variables that describe the evolution of book-to-market-changes in book value, changes in price, and net share issues. This conclusion is supported using data representing...

This paper examines the evidence regarding predictability in the market risk premium using artificial neural networks (ANNs), namely the Elman Network (EN) and the Higher Order Neural network (HONN), univariate ARMA and exponential smoothing techniques, such as Single Exponential Smoothing (SES)...

Even in large equity markets, the dividend-price ratio is significantly related with the growth of future dividends. In order to uncover this relationship, we use monthly dividends and a mixed data sampling technique which allows us to cope with within-year seasonality. We reduce the effect of...

In this paper, we extend the literature on crash prediction models in three main ways. First, we explicitly relate crash prediction measures and asset pricing models. Second, we present a simple, effective statistical significance test for crash prediction models. Finally, we propose a...

Motivated by recent US evidence, we evaluate the predictive power of changes in the weight of large firms in the aggregate stock market ("Goliath vs David" (GVD)) for Swiss stock market returns and bond market returns. Previous research suggests that the asset return dynamics in the US and...

This study discusses the trading behavior of foreign investors with respect to economic uncertainty in the South Korean stock market from a time-varying perspective. We employ a news-based measure of economic uncertainty along with the model of time-varying parameter vector autoregression with...

This paper studies whether investor sentiment can predict future Mexican stock market returns. Furthermore, we examine the dynamic correlation between sentiment and returns. Lastly, we examine whether sentiment innovations influence unexpected returns. We find that sentiment has significant...