Through the analysis of the weekly Commodity Futures Trading Commission reports on 12 US traded agricultural commodities, we revisit the heated debate on the impact of index flows on commodities prices. After introducing a novel stock-to-use proxy that may be used to represent inventory...

The main goal of this paper is to introduce a new financial stress indicator, signaling regime transitions from stability to turbulence. This indicator is based on the combination of a wide range of market prices of risk, properly normalized to make them comparable across markets and time...

The 2008 financial crisis has severely challenged passive forms of investment. In this paper, we compare two systematic investment processes that a global asset allocator may employ to preserve its capital in the face of a turbulent financial environment. The "risk-driven" allocation, derived...

Through the analysis of the weekly CFTC reports on 12 US traded agricultural commodities, we revisit the heated debate on the impact of index flows on commodities prices. After introducing a novel stock-to-use proxy that may be used to represent inventory variations at the intra-month level, we...

Regime changes planning in financial markets is well known to be hard to explain and interpret. Can an asset manager ex-plain clearly the intuition of his regime changes prediction on equity market ? To answer this question, we consider a gradi-ent boosting decision trees (GBDT) approach to plan...

We consider a gradient boosting decision trees (GBDT) approach to predict large S&P 500 price drops from a set of 150 technical, fundamental and macroeconomic features. We report an improved accuracy of GBDT over other machine learning (ML) methods on the S&P 500 futures prices. We show that...

Planning in financial markets is a difficult task as the method needs to dramatically change its behavior when facing very rare black swan events like crises that shift market regime. In order to address this challenge, we present a gradient boosting decision trees (GBDT) approach to predict...

Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go, StarCraft II, Atari Games), and autonomous driving. However, it remains an open question whether DRL can reach human level in applications to financial problems and in particular in detecting...