We propose a global expected business cycle condition factor (GEBC) relying on OECD leading economic indicators of 18 stock markets through Principal Component Analysis (PCA) approach, and show that this index is a powerful predictor for stock returns around the globe both in- and out-of-sample....

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...

Our study tries to explore whether the financial strength proxied by F-score can predict the returns in Chinese stock market and its economic explanations. Results show that the financially stronger firms can generate higher expected raw returns and abnormal returns in Fama-French five-factor...

This paper investigates the predictive ability of international volatility risk for the daily aggregate Chinese stock market returns. We employ the innovations in implied volatility indices of seven major international markets as our international volatility risk proxies. We find that...

This paper proposes a two-state predictive regression model and shows that stock market 12-month return (TMR), the time-series momentum predictor of Moskowitz, Ooi, and Pedersen (2012), forecasts the aggregate stock market negatively in good times and positively in bad times. The out-of-sample...

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...

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...