Reference : Proximal Policy Optimisation for a Private Equity Recommitment System
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/48722
Proximal Policy Optimisation for a Private Equity Recommitment System
English
Kieffer, Emmanuel mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Pinel, Frederic mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Meyer, Thomas mailto [SimCorp Luxembourg SA, Luxembourg]
Gloukoviezoff, Georges mailto [European Investment Bank, Luxembourg]
Lucius, Hakan mailto [European Investment Bank, Luxembourg]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
2021
Springer CCIS series
Yes
International
BNAIC/BeneLearn 2021 AI & ML conference
from 10-11-2021 to 12-11-2021
Luxembourg
[en] Reinforcement learning ; Private Equity ; Control system
[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.
University of Luxembourg: High Performance Computing - ULHPC
European Investment Bank - EIB
Toward A.I. Recommitment Strategies for ESG integration in Private Equity
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/48722

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