20 citations · 42 across the 7 of their papers we have counts for
25 papers
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…
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…
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…
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…
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…
Omega and Sharpe ratio
Eric Benhamou, Beatrice Guez, Nicolas Paris1
Omega ratio, defined as the probability-weighted ratio of gains over losses at a given level of expected return, has been advocated as a better performance indicator compared to Sh…