Eric Benhamou

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Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting

Model-Free Reinforcement Learning has achieved meaningful results in sta...
04/19/2021 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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Bridging the gap between Markowitz planning and deep reinforcement learning

While researchers in the asset management industry have mostly focused o...
09/30/2020 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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

Deep reinforcement learning (DRL) has reached super human levels in comp...
09/07/2020 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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Variance Reduction in Actor Critic Methods (ACM)

After presenting Actor Critic Methods (ACM), we show ACM are control var...
07/23/2019 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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NGO-GM: Natural Gradient Optimization for Graphical Models

This paper deals with estimating model parameters in graphical models. W...
05/14/2019 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)

Reinforcement learning (RL) is about sequential decision making and is t...
04/12/2019 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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BCMA-ES II: revisiting Bayesian CMA-ES

This paper revisits the Bayesian CMA-ES and provides updates for normal ...
04/02/2019 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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BCMA-ES: A Bayesian approach to CMA-ES

This paper introduces a novel theoretically sound approach for the celeb...
04/02/2019 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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A discrete version of CMA-ES

Modern machine learning uses more and more advanced optimization techniq...
12/27/2018 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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Trade Selection with Supervised Learning and OCA

In recent years, state-of-the-art methods for supervised learning have e...
12/09/2018 ∙ by David Saltiel, et al. ∙ 0 ∙ share

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Feature selection with optimal coordinate ascent (OCA)

In machine learning, Feature Selection (FS) is a major part of efficient...
11/29/2018 ∙ by David Saltiel, et al. ∙ 0 ∙ share

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Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets

In this paper, we revisit the Kalman filter theory. After giving the int...
11/28/2018 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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Gram Charlier and Edgeworth expansion for sample variance

In this paper, we derive a valid Edgeworth expansions for the Bessel cor...
09/18/2018 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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T-statistic for Autoregressive process

In this paper, we discuss the distribution of the t-statistic under the ...
09/11/2018 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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A few properties of sample variance

A basic result is that the sample variance for i.i.d. observations is an...
09/11/2018 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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Seven proofs of the Pearson Chi-squared independence test and its graphical interpretation

This paper revisits the Pearson Chi-squared independence test. After pre...
08/28/2018 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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Connecting Sharpe ratio and Student t-statistic, and beyond

Sharpe ratio is widely used in asset management to compare and benchmark...
08/02/2018 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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Incremental Sharpe and other performance ratios

We present a new methodology of computing incremental contribution for p...
07/13/2018 ∙ by Eric Benhamou, et al. ∙ 0 ∙ share

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Eric Benhamou

Featured Co-authors

Jamal Atif

Sébastien Verel

Anne Auger

David Saltiel

Fabien Teytaud

Rida Laraki

Abhishek Mukhopadhyay

Nicolas Paris

Beatrice Guez

Valentin Melot

Jean-Jacques Ohana

Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting

Bridging the gap between Markowitz planning and deep reinforcement learning

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

Variance Reduction in Actor Critic Methods (ACM)

NGO-GM: Natural Gradient Optimization for Graphical Models

Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)

BCMA-ES II: revisiting Bayesian CMA-ES

BCMA-ES: A Bayesian approach to CMA-ES

A discrete version of CMA-ES

Trade Selection with Supervised Learning and OCA

Feature selection with optimal coordinate ascent (OCA)

Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets

Gram Charlier and Edgeworth expansion for sample variance

T-statistic for Autoregressive process

A few properties of sample variance

Seven proofs of the Pearson Chi-squared independence test and its graphical interpretation

Connecting Sharpe ratio and Student t-statistic, and beyond

Incremental Sharpe and other performance ratios