Reference : Evolutionary Learning of Private Equity Recommitment Strategies
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/48723
Evolutionary Learning of Private Equity Recommitment Strategies
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
2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Yes
International
IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021)
from 05-12-2021 to 07-12-2021
[en] Private Equity ; Genetic Programming ; Evolutionary Learning
[en] Achieving and maintaining high allocations to Private Equity and keeping allocations at the targeted level through recommitment strategies is a complex task which needs to be balanced against the risk of becoming a defaulting investor. When looking at recommitments we are quickly faced with a combinatorial explosion of the solution space, rendering explicit enumeration impossible. As a consequence, manual management if any is becoming time-consuming and error-prone. For this reason, investors need guidance and decision aid algorithms producing reliable, robust and trustworthy recommitment strategies. In this work, we propose to generate automatically recommitment strategies based on the evolution of symbolic expressions to provide clear and understandable decision rules to Private Equity experts and investors.
To the best of our knowledge, this is the first time a methodology to learn recommitment strategies using evolutionary learning is proposed. Experiments demonstrate the capacity of the proposed approach to generate efficient and robust strategies, keeping a high degree of investment 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/10993/48723

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