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 propose a new modeling approach for the cross section of returns. Our method,
Instrumented Principal Component Analysis (IPCA), allows for latent factors and time …

We propose a nonparametric method to study which characteristics provide incremental
information for the cross-section of expected returns. We use the adaptive group LASSO to …

We take up Cochrane's (2011) challenge to identify the firm characteristics that provide
independent information about average US monthly stock returns by simultaneously …

In this paper, we employ a novel dimension reduction approach, the scaled principal
component analysis (s-PCA), to improve the oil price predictability with technical indicators …

This paper proposes a novel supervised learning technique for forecasting: scaled principal
component analysis (sPCA). The sPCA improves the traditional principal component …

We investigate how transaction costs change the number of characteristics that are jointly
significant for an investor's optimal portfolio and, hence, how they change the dimension of …

We propose an investor attention index based on proxies in the literature and find that it
predicts the stock market risk premium significantly, both in sample and out of sample …

Missing data is a prevalent, yet often ignored, feature of company fundamentals. In this
paper, we document the structure of missing financial data and show how to systematically …

This research investigates the impact of climate challenges on financial markets by
introducing an innovative approach to measure climate risk, specifically the aggregate …