We selectively review recent advancements in research on predictive models of earnings
and returns. We discuss why applying statistical, econometric, and machine learning …

We examine return predictability with machine learning in 46 stock markets around the
world. We calculate 148 firm characteristics and use them to feed a repertoire of different …

We employ a repertoire of machine learning models to investigate the cross-sectional return
predictability in cryptocurrency markets. While all methods generate substantial economic …

Deep learning (DL) has made its way into many disciplines ranging from health care to self-
driving cars. In financial markets, we see a rich literature for DL applications. Particularly …

We employ machine learning techniques to examine cross-sectional variation in country
equity returns by aggregating information across multiple market characteristics. Our models …

We propose an asset allocation strategy that engineers optimal weights before feeding them
to a supervised learning algorithm. In contrast to the traditional approaches, the machine is …

We reverse-engineer the equilibrium construction process of asset prices in order to obtain
returns which depend on firm characteristics, possibly in a linear fashion. One key …

The supervised portfolios approach is an effective asset allocation strategy that engineers
optimal weights before feeding them to a supervised learning algorithm. Yet, supervised …

We employ machine learning techniques to determine what matters more for stock return
predictability: market data or accounting information. Market data clearly dominates—it …

Many finance time-series analyses often track a matrix of variables at each time and study
their co-evolution over a long time. The matrix time series is overly sparse, involves complex …