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Deep Reinforcement Learning (DRL) for portfolio allocation

Benhamou, Éric; Saltiel, David; Ohana, Jean-Jacques; Atif, Jamal; Laraki, Rida, Deep Reinforcement Learning (DRL) for portfolio allocation, in Dong, Yuxiao; Ifrim, Georgiana; Mladenić, Dunja, Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, Proceedings, Part V, Springer, p. 527-531. 10.1007/978-3-030-67670-4_32

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2021_Chapter_DeepReinforcement.pdf (966.6Kb)
Type
Communication / Conférence
Conference title
European Conference, ECML PKDD 2020
Conference date
2020-09
Conference city
Ghent
Conference country
Belgium
Book title
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, Proceedings, Part V
Book author
Dong, Yuxiao; Ifrim, Georgiana; Mladenić, Dunja
Publisher
Springer
ISBN
978-3-030-67669-8
Number of pages
577
Pages
527-531
Publication identifier
10.1007/978-3-030-67670-4_32
Metadata
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Author(s)
Benhamou, Éric
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Saltiel, David
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Ohana, Jean-Jacques
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Atif, Jamal
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Laraki, Rida cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
Deep reinforcement learning (DRL) has reached an unprecedent level on complex tasks like game solving (Go [6], StarCraft II [7]), and autonomous driving. However, applications to real financial assets are still largely unexplored and it remains an open question whether DRL can reach super human level. In this demo, we showcase state-of-the-art DRL methods for selecting portfolios according to financial environment, with a final network concatenating three individual networks using layers of convolutions to reduce network’s complexity. The multi entries of our network enables capturing dependencies from common financial indicators features like risk aversion, citigroup index surprise, portfolio specific features and previous portfolio allocations. Results on test set show this approach can overperform traditional portfolio optimization methods with results available at our demo website.
Subjects / Keywords
Deep reinforcement learning; Portfolio selection; Convolutional networks; Index surprise; Risk aversion

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