This paper proposes a novel portfolio strategy over individual stocks based on subset combination of a large number of characteristics documented to predict return. Akin to the forecast combination literature, we exploit all characteristics by combining parametric rules that include a particular...

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...

Markowitz's mean-variance portfolio optimization is either inefficient or impossible when the number of assets becomes relatively large. To overcome this difficulty, we propose several component-wise boosting learning methods that, in a linear regression specification, can iteratively select the...

While numerous studies have analyzed the asset allocation issue of US stock market from various angles, much less attention has been paid to the asset allocation issue of Chinese stock market. This article investigates the asset allocation in Chinese stock market from a perspective of...

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...

This paper constructs a dual-sparse optimal mean-variance factor portfolio to improve the interpretation of the factor portfolio in a set of 187 anomaly portfolios. The dual-sparse method limits the nonzero elements of principal components (PCs) and number of factor invested in cross-section. We...