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...

In this paper, we propose two asymmetry measures for stock returns. Unlike the popular skewness measure, our measures are based on the distribution function of the data rather than just the third central moment. We present empirical evidence that greater upside asymmetries calculated using our...

We provide an entropy approach for measuring asymmetric comovement between the return on a single asset and the market return. This approach yields a model-free test for stock return asymmetry, generalizing the correlation-based test proposed by Hong, Tu, and Zhou (2007). Based on this test, we...

Starting from the twelve distinct risk factors in four well-established asset pricing models, a pool we refer to as the winners, we construct and compare 4,095 asset pricing models and find that the model with the risk factors, Mkt, SMB, MOM, ROE, MGMT, and PEAD, performs the best in terms of...

We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional...

We propose a 4-factor model for the Chinese stock market by adding a trend factor into the market, size, and value of Liu, Stambaugh, and Yuan's (2019) 3-factor model. Because of up to 80% of individual trading, the trend factor captures salient relevant price and volume trends, and earns a...

We use machine learning to estimate sparse principal components (PCs) for 120 monthly macro variables spanning 1960:02 to 2018:06 from the FRED-MD database. For comparison, we also extract the first ten conventional PCs from the macro variables. Each of the conventional PCs is a linear...

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...

Using a large number of predictors and based on an extended iterated combination approach of Lin, Wu, and Zhou (2017), we document both statistical and economic significance of Treasury bond return predictability. Macroeconomic and aggregate liquidity variables contain predictive information for...

This paper conducts a comprehensive study on predicting the cross section of Chinese stock market returns with a large panel of 75 individual firm characteristics. We use not only the traditional Fama-MacBeth regression, but also “big-data” econometric methods: principal component analysis...