Thomas Raffinot

Paris Dauphine University | UPD · Faculty of Economics and Social Sciences

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, forward-looking, and non-linear framework. Our empirical...

A well-worked theory of macro-based investment decision is introduced. The theoretical influence of economic cycles on time-varying risk premiums is explained and exhibited. The importance of the turning points of the growth cycle, better known as the output gap, is outlined. To quickly and accurately detect economic turning points, probabilistic i...

Ensemble machine learning algorithms, referred to as random forest (Breiman (2001)) and as boosting (Schapire (1990)), are applied to quickly and accurately detect economic turning points in the United States and in the euro area. The two key features of those algorithms are their abilities to entertain a large number of predictors and to perform e...

Non-parametric methods have been empirically proved to be of great interest in the statistical literature in order to forecast stationary time series, but very few applications have been proposed in the econometrics literature. In this paper, our aim is to test whether non-parametric statistical procedures based on a Kernel method can improve class...

Policy-makers and analysts are continually assessing the state of the economy. But, gross domestic product (GDP) is only available on quarterly basis with a time span of 1-3 months, and sometimes with significant revisions. The aim of this article is to develop a monthly indicator of GDP for Euro-Area based on business surveys and to give the outlo...