Machine learning (ML) is progressively reshaping the fields of quantitative finance and
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, …

This paper quantifies the impact of stock-specific news sentiment on future financial returns.
Daily predictive regressions yield significant t-statistics for 7% at most of our sample of more …

The aim of this paper is to study the performance of carbon-based portfolios when all emissions
scopes are accounted for. We formalize low-carbon mean-variance portfolio strategies …

We show that the introduction of a leverage constraint improves the practical implementation
of characteristics-based portfolios. The addition of the constraint leads to significantly lower …

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 build on a one parameter family of weighting schemes arising from $$L^2$$ L 2 -constrained
portfolio optimization problems. The parameter allows to fine tune the trade-off …

We propose a detailed and comprehensive examination of the two main regression-based
techniques used to detect herding among investors. We also introduce a novel approach …

Sustainable investing has recently gained traction throughout the world. This trend has
multiple sources, which span from genuine ethical concerns to hopes of performance boosting, …

In this paper, we document the importance of memory in machine learning (ML)-based models
relying on firm characteristics for asset pricing. We find that predictive algorithms perform …

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 …