We provide a comprehensive study on the cross-sectional predictability of corporate bond returns using big data and machine learning. We examine whether a large set of equity and bond characteristics drive the expected returns on corporate bonds. Using either set of characteristics, we find that...

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 constructs a manager sentiment index based on the aggregated textual tone of corporate financial disclosures. We find that manager sentiment is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample R2 of 9.75% and 8.38%,...

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 shows that investors do not fully incorporate cost behavior information into valuation. Firms with higher growth in operating costs generate substantially lower future stock returns and operating performance. An equal-weighted long-short spread portfolio earns an average return of 82...

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 propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices both in- and out-of-sample, and the...

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...