activity
20182020
most citedKalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets

2 citations · 2 across the 3 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.LG2020

Bridging the gap between Markowitz planning and deep reinforcement learning

Eric Benhamou, David Saltiel, Sandrine Ungari +1

While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum vari…

cs.LG2020

AAMDRL: Augmented Asset Management with Deep Reinforcement Learning

Eric Benhamou, David Saltiel, Sandrine Ungari +2

Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations? Through trading bots, we illustrate how De…

q-fin.PM2020

Time your hedge with Deep Reinforcement Learning

Eric Benhamou, David Saltiel, Sandrine Ungari +1

Can an asset manager plan the optimal timing for her/his hedging strategies given market conditions? The standard approach based on Markowitz or other more or less sophisticated fi…

q-fin.PM2020

Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning

Eric Benhamou, David Saltiel, Jean-Jacques Ohana +1

Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving). However, it remains an open question whether DRL ca…

cs.LG2020

Estimating Individual Treatment Effects through Causal Populations Identification

Céline Beji, Michaël Bon, Florian Yger +1

Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one…