This paper evaluates the predictability of monthly stock return using out-of-sample (multi-step ahead and dynamic) prediction intervals. Past studies have exclusively used point forecasts, which are of limited value since they carry no information about the intrinsic predictive uncertainty...

We investigate whether stock returns of international markets are predictable from a range of fundamentals including key financial ratios (dividend-price ratio, dividend-yield, earnings-price ratio, dividend-payout ratio), technical indicators (price pressure, change in volume), and short-term...

This paper analyzes the forecasting benefits of dimension reducing of predictors prior to estimating random forest (RF) model for macroeconomic time series in high-dimensional data. For that, we propose sparse RF (sRF) models based on a two-step procedure where we consider three classes of...