We survey the nascent literature on machine learning in the study of financial markets. We
highlight the best examples of what this line of research has to offer and recommend …

We show that two important issues in empirical asset pricing—the presence of weak factors
and the selection of test assets—are deeply connected. Since weak factors are those to …

Companies always try to balance the risk and return on their investments, finances, and
daily operations. This study presents the moderating role of ownership status, company size …

No, it can help. To support our argument, we show that economic forecast models driven by
latent factors are not sparse. This fact allows us to establish a compelling result that …

First and foremost, I extend my deepest gratitude to my advisor, Prof. Dacheng **u, who has
guided me since my master's studies. Dacheng has not only been an exceptional academic …

This article establishes a new and comprehensive estimation and inference theory for
principal component analysis (PCA) under the weak factor model that allow for cross …

This paper studies the principal component (PC) method-based estimation of weak factor
models with sparse loadings. We uncover an intrinsic near-sparsity preservation property for …

In this paper, we extend the recent Gaussian autoregressive conditional beta (Gaussian-
ACB) model from the literature on score-driven models. In the new asset pricing model …

In the age of big data, PCA and PLS are widely used in finance for dimension deduction to
identify a few predictive factors. In this paper, we make a surprising discovery that the …

The disruption of supply chain due to Covid-19 and the war in Ukraine, render the prediction
of agricultural output a determinant factor of economic life. We consider the predictability of …