We reaffirm the stylized fact that bond risk premia are time-varying with macroeconomic condition, even with real-time macro data instead of commonly used final revised data. While real-time data are noisier and render standard forecasts insignificant, we find that, with four efficient...

We adopt a portfolio perspective and apply a subset combination approach to consolidate the joint predictability of a large number of firm characteristics in the Chinese A-share market. Our approach incorporates higher moments of stock return distribution and imposes shrinkage on the predictive...

This paper studies the intertemporal relation between U.S. volatility risk and international equity risk premia. We show that a common volatility risk factor constructed from the option-implied U.S. forward variances positively and significantly predicts future stock market returns of the...

Using the long-term wavelet component of monthly S&P 500 excess returns as supervision information, we employ a machine learning method to extract the common predictive information of 14 prevalent macroeconomic variables, and construct a new macroeconomic index aligned for predicting stock...

We compare several representative sophisticated model averaging and variable selection techniques of forecasting stock returns. When estimated traditionally, our results confirm that the simple combination of individual predictors is superior. However, sophisticated models improve dramatically...