This article proposes a hierarchical clustering-based asset allocation method, which uses
graph theory and machine learning techniques. Hierarchical clustering refers to the formation …

… Exploiting the same basic idea in a different way, Raffinot [2017a] proposes a hierarchical …
The ”Hierarchical 1/N” proposed in Raffinot [2017a] finds a diversified weighting by distributing …

Ensemble machine learning algorithms (random forest and boosting) are applied to quickly
and accurately detect economic turning points in the United States and in the Eurozone over …

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 propose a linearization of rule-based algorithms that reveals the most important interactions
between characteristics and macroeconomic variables when explaining future stock …

Interest rates are unreliable indicators of appropriate monetary policy; low nominal rates do
not indicate easy money. This paper attempts to assess the stance of monetary policy without …

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, …

Asset returns are not correlated with the business cycle but are primarily caused by the
economic cycles. To validate this claim, economic cycles are first rigorously defined, namely the …

To nowcast output gap turning points, probabilistic indicators are created from a simple and
transparent machine-learning algorithm known as Learning Vector Quantization. The real-…

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 …