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research article

Analysis of Large Market Data Using Neural Networks: A Causal Approach

Divernois, Marc Aurèle  
•
Etesami, Jalal
•
Filipovic, Damir  
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2023
IEEE Journal on Selected Areas in Information Theory

We develop a data-driven framework to identify the interconnections between firms using an information-theoretic measure. This measure generalizes Granger causality and is capable of detecting nonlinear relationships within a network. Moreover, we develop an algorithm using recurrent neural networks and the aforementioned measure to identify the interconnections of high-dimensional nonlinear systems. The outcome of this algorithm is the causal graph encoding the interconnections among the firms. These causal graphs can be used as preliminary feature selection for another predictive model or for policy design. We evaluate the performance of our algorithm using both synthetic linear and nonlinear experiments and apply it to the daily stock returns of U.S. listed firms and infer their interconnections.

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Type
research article
DOI
10.1109/JSAIT.2024.3351549
Scopus ID

2-s2.0-85188502071

Author(s)
Divernois, Marc Aurèle  
•
Etesami, Jalal
•
Filipovic, Damir  
•
Kiyavash, Negar  
Date Issued

2023

Published in
IEEE Journal on Selected Areas in Information Theory
Volume

4

Start page

833

End page

847

Subjects

Directed information

•

Granger causality

•

recurrent neural network

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CSF  
BAN  
Available on Infoscience
January 16, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/242926
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