no code implementations • 12 Jan 2024 • Mohammed Salek, Damien Challet, Ioane Muni Toke
In this study, we adapt the latent/revealed order book framework to the specifics of equity auctions.
no code implementations • 9 Jan 2024 • Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez, Damien Challet
We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to 2023, reposted on large financial media, to determine how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach.
no code implementations • 29 Sep 2023 • Christian Bongiorno, Damien Challet
The Average Oracle, a simple and very fast covariance filtering method, is shown to yield superior Sharpe ratios than the current state-of-the-art (and complex) methods, Dynamic Conditional Covariance coupled to Non-Linear Shrinkage (DCC+NLS).
no code implementations • 4 Aug 2023 • Damien Challet, Vincent Ragel
We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or with highly disparate time scales.
no code implementations • 26 May 2023 • Victor Le Coz, Iacopo Mastromatteo, Damien Challet, Michael Benzaquen
Trading pressure from one asset can move the price of another, a phenomenon referred to as cross impact.
no code implementations • 13 Jan 2023 • Mohammed Salek, Damien Challet, Ioane Muni Toke
Using high-quality data, we report several statistical regularities of equity auctions in the Paris stock exchange.
no code implementations • 21 Jun 2022 • Christian Bongiorno, Damien Challet
On the other hand, the asymptotic distribution of Transfer Entropy between two time series is known.
no code implementations • 12 Jan 2022 • Jérémi Assael, Laurent Carlier, Damien Challet
We systematically investigate the links between price returns and Environment, Social and Governance (ESG) scores in the European equity market.
no code implementations • 14 Dec 2021 • Christian Bongiorno, Damien Challet
Portfolio optimization requires sophisticated covariance estimators that are able to filter out estimation noise.
no code implementations • 10 Mar 2021 • Damien Challet, Christian Bongiorno, Guillaume Pelletier
We apply the knockoff procedure to factor selection in finance.
no code implementations • 18 May 2020 • Christian Bongiorno, Damien Challet
We introduce a $k$-fold boosted version of our Boostrapped Average Hierarchical Clustering cleaning procedure for correlation and covariance matrices.
no code implementations • 30 Jan 2020 • Christian Bongiorno, Damien Challet
We introduce a method to predict which correlation matrix coefficients are likely to change their signs in the future in the high-dimensional regime, i. e. when the number of features is larger than the number of samples per feature.
1 code implementation • 11 Sep 2019 • Baptiste Barreau, Laurent Carlier, Damien Challet
We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame.
2 code implementations • 6 May 2015 • Damien Challet
The total duration of drawdowns is shown to provide a moment-free, unbiased, efficient and robust estimator of Sharpe ratios both for Gaussian and heavy-tailed price returns.