Edmond LEZMI

Head of Alternative Quant Portfolio Strategy, Amundi Investment Institute

Edmond is the Head of Alternative Quant Portfolio Strategy at Amundi Investment Institute. He is involved in the application of machine learning techniques, notably optimisation, Bayesian optimisation, graph neural networks and generative models.

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30/04/2024

Sustainable Finance

Causality Approach Applied to Clean-Tech Equities

Explore how the Causality Approach unveils the dynamics of clean-tech equities, highlighting how pure players outshine non-pures in financial performance.

Real-Time Macro Information and Bond Return Predictability: A Weighted Group Deep Learning Approach

1/03/2023

Machine Learning & Artificial Intelligence

Time Series Forecasting with Transformer Models and applicat...

Since its introduction in 2017 (Vaswani et al., 2017), the Transformer model has excelled in a wide range of tasks involving natural language processing...

6/04/2022

Portfolio Allocation

Multi-Period Portfolio Optimization and Application to Portf...

This research project is both an update of the analysis on carbon emissions trajectories proposed by Le Guenedal et al. (2020) and a companion study of...

1/12/2021

Asset Pricing & Behavioural Finance

Graph Neural Networks for Asset Management

In this research article, Amundi Quantitative Research explores the use of graph theory and neural networks in asset management.

7/07/2020

Machine Learning & Artificial Intelligence

Improving the Robustness of Trading Strategy Backtesting wit...

In this article, we explore generative models in order to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial...

19/12/2019

Portfolio Allocation

A Note on Portfolio Optimization with Quadratic Transaction ...

In this short note, we consider mean-variance optimized portfolios with transaction costs.

30/09/2019

Asset Pricing & Behavioural Finance

Forecasting Combination: An Application For Exchange Rates

This paper tries to forecast exchange rates by comparing forecasting methods that take into account cointegration and methods that do not.

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