Learning private equity recommitment strategies for institutional investors

[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.

Disciplines :

Computer science

Author, co-author :

Kieffer, Emmanuel ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

Meyer, Thomas; Simcorp Luxembourg SA

Gloukoviezoff, Georges; European Investment Bank

Lucius, Hakan; European Investment Bank

Bouvry, Pascal ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

External co-authors :

yes

Language :

English

Title :

Learning private equity recommitment strategies for institutional investors

Publication date :

07 February 2023

Journal title :

Frontiers in Artificial Intelligence in Finance

eISSN :

2624-8212

Peer reviewed :

Peer reviewed

Focus Area :

Finance

Name of the research project :

STAREBEI

Funders :

EIB - European Investment Bank [LU]

Available on ORBilu :

since 11 March 2023

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Bibliography

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