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Learning private equity recommitment strategies for institutional investors.

Emmanuel Kieffer1, Thomas Meyer2, Georges Gloukoviezoff3

  • 1Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.

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|February 24, 2023
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Summary

This study introduces an explainable artificial intelligence (AI) approach to create effective recommitment strategies for private equity investors. The AI method automatically generates dynamic strategies to manage illiquidity and cashflow risks.

Keywords:
artificial intelligenceevolutionary learningoptimizationprivate equityrecommitment strategies

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Area of Science:

  • Finance
  • Artificial Intelligence
  • Risk Management

Background:

  • Maintaining target allocations in private equity (PE) is crucial for investors seeking high exposure.
  • Investors face significant challenges due to PE illiquidity and cashflow uncertainty, particularly with irrevocable commitments.

Purpose of the Study:

  • To develop a trustworthy and explainable AI-driven approach for designing optimal recommitment strategies.
  • To address the complexities of managing PE investments amidst illiquidity and cashflow uncertainties.

Main Methods:

  • Utilized intensive portfolio simulations.
  • Employed evolutionary computing techniques.
  • Developed a trustworthy and explainable artificial intelligence (AI) framework.

Main Results:

  • Demonstrated the ability to automatically generate efficient recommitment strategies.
  • Showcased dynamic strategy generation capabilities.
  • Validated the AI approach through extensive simulations.

Conclusions:

  • The proposed AI approach offers an effective solution for designing dynamic recommitment strategies in private equity.
  • This method helps investors navigate the challenges of illiquidity and cashflow uncertainty.
  • Explainable AI provides a robust tool for strategic financial decision-making.