Barro et al. (2022) investigate the quantity of safe assets held in the cross-section of developed countries and find that the average safe-asset ratio (ratio of safe assets to total assets) was 37% in 2015 and has remained relatively stable over time. They also document a crowding-out...

In this chapter, we apply a popular Machine Learning approach (extreme gradient boosted trees) to build enhanced diversified equity portfolios. A simple naïve equally-weighted portfolio of US stocks based on a boosted tree-based signal generates on average an excess return of 3.1% per annum,...

In this article, we investigate the impact of truncating training data when fitting regression trees. We argue that training times can be curtailed by reducing the training sample without any loss in out-of-sample accuracy as long as the prediction model has been trained on the tails of the...

The supervised portfolios approach is an effective asset allocation strategy that engineers optimal weights before feeding them to a supervised learning algorithm. Yet, supervised learning algorithms are often seen as opaque, which undermines trust in those models, thereby limiting their...

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 able to learn risk measures, preferences and constraints beyond simple expected returns, within a flexible,...

In this article, we quantify the demand for sustainability at the sovereign level. We do so by estimating a longitudinal model, at the issue level, that captures net demand in sovereign ESG factors for both equity and fixed income markets. In spite of the surging interest in ethical investing,...

We document the impact of ESG shocks on the stock returns of suppliers and clients of affected firms. The impact is contingent not only on the sign and magnitude of the shock, but also on its interaction with the firm's ESG level. ESG shocks are integrated into prices intra-daily and the...