Using a comprehensive data set and an array of 27 macroeconomic, stock and bond predictors, we find that corporate bond returns are highly predictable based on an iterated combination model. The large set of predictors outperforms traditional predictors substantially, and predictability...

This paper constructs an investor sentiment measure at both individual bond and aggregate levels, uncovering the first evidence that investor sentiment has strong cross- sectional predictive power for corporate bond returns. High bond investor sentiment leads to low future returns. A portfolio...

This paper exploits different machine learning models to forecast of stock returns. We find that machine learning models substantially improve the performance of various variables in predicting stock returns. Technical indicators generate a significantly better result than the stock...

We document strong evidence of cross-sectional predictability of corporate bond returns based on a set of yield predictors that capture the information in the yields of past 1, 3, 6, 12, 24, 36 and 48 months. Return predictability is economically and statistically significant, and is robust to...

This paper studies the predictive power of the trend strategy in the international stock market. Using data from 49 markets, we find that a trend signal exploiting the short-, intermediate-, and long-term price information can predict stock returns cross-sectionally in the international market....

We propose a news-implied rare disaster risk indicator and study its predictive power on the returns of U.S. Treasury bonds. We find that the predictive power of this factor is both statistically significant and economically important and is not spanned by the current yield curve. The disaster...