Reinforcement learning; Private Equity; Control system
Résumé :
[en] Recommitments are essential for limited partner investors to maintain a target exposure to private equity. However, recommitting to new funds is irrevocable and expose investors to cashflow uncertainty and illiquidity. Maintaining a specific target allocation is therefore a tedious and critical task. Unfortunately, recommitment strategies are still manually designed and few works in the literature have endeavored to develop a recommitment system balancing opportunity cost and risk of default. Due to its strong similarities to a control system, we propose to “learn how to recommit” with Reinforcement Learning (RL) and, more specifically, using Proximal Policy Optimisation (PPO). To the best of our knowledge, this is the first attempt a RL algorithm is applied to private equity with the aim to solve the recommitment problematic. After training the RL model on simulated portfolios, the resulting recommitment policy is compared to state-of-the-art strategies. Numerical results suggest that the trained policy can achieve high target allocation while bounding the risk of being overinvested.
Centre de recherche :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Sciences informatiques
Auteur, co-auteur :
KIEFFER, Emmanuel ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
PINEL, Frederic ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Meyer, Thomas; SimCorp Luxembourg SA, Luxembourg
Gloukoviezoff, Georges; European Investment Bank, Luxembourg
Lucius, Hakan; European Investment Bank, Luxembourg
BOUVRY, Pascal ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Proximal Policy Optimisation for a Private Equity Recommitment System
Date de publication/diffusion :
2021
Nom de la manifestation :
BNAIC/BeneLearn 2021 AI & ML conference
Lieu de la manifestation :
Luxembourg
Date de la manifestation :
from 10-11-2021 to 12-11-2021
Manifestation à portée :
International
Titre de l'ouvrage principal :
Springer CCIS series
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Intitulé du projet de recherche :
Toward A.I. Recommitment Strategies for ESG integration in Private Equity
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