In this article, we link the realized accuracy of predictive panels to changes in distributions that occur between the training (in-sample) phase and the testing (out-of-sample) phase. We obtain polynomial upper bounds for the loss of accuracy between training and testing. We model covariate...

We extend the model of Anufriev and Bottazzi (2010) to the case with many assets. We show that under the procedural equilibrium, all assets with nonzero aggregate demand must have the same price returns. Heterogeneity in price returns appears when some assets face zero demand. In this case, the...

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 portfolio strategies by integrating a carbon intensity penalty to a constrained mean-variance optimization framework. To do so, we resort to direct...

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 best when they are trained on long samples, with long-term returns as dependent variables. In addition, we...