Reference : Learning private equity recommitment strategies for institutional investors
Scientific journals : Article
Engineering, computing & technology : Computer science
Finance
http://hdl.handle.net/10993/54569
Learning private equity recommitment strategies for institutional investors
English
Kieffer, Emmanuel mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Meyer, Thomas mailto [Simcorp Luxembourg SA]
Gloukoviezoff, Georges mailto [European Investment Bank]
Lucius, Hakan mailto [European Investment Bank]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
7-Feb-2023
Frontiers in Artificial Intelligence in Finance
Yes
International
[en] Private Equity ; Evolutionary learning ; Recommitment strategies
[en] Keeping strategic allocations at target level to maintain high exposure to private equity is a complex but essential task for investors who need to balance against the risk of default. Illiquidity and cashflow uncertainty are critical challenges especially when commitments are irrevocable. In this work, we propose to use a trustworthy and explainable A.I. approach to design recommitment strategies. Using intensive portfolios simulations and evolutionary computing, we show that efficient and dynamic recommitment strategies can be brought forth automatically.
European Investment Bank - EIB
STAREBEI
http://hdl.handle.net/10993/54569

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