We develop a new class of tree-based models (P-Tree) for analyzing (unbalanced) panel data utilizing global (instead of local) split criteria that incorporate economic guidance to guard against overfitting while preserving interpretability. We grow a P-Tree top-down to split the cross section of...

We propose an alternative approach to the linear factor model to estimate and decompose asset risk premia in empirical asset pricing. To resolve the high-dimensional sort difficulty in forming characteristic-based benchmark portfolios, we introduce a benchmark combination model (BCM) that...

We introduce a class of interpretable tree-based models (P-Tree) for analyzing (unbalanced) panel data, with iterative and global (instead of recursive and local) split criteria. We apply P-Tree to split the cross section of asset returns under the no-arbitrage condition, generating a stochastic...

This paper documents substantial evidence of return predictability and investment gains for individual corporate bonds via machine learning. The forecast-implied long-short and market-timing strategies deliver significant risk-adjusted returns over transaction costs. Random Forest has the best...

This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a...