Evolutionary Learning of Private Equity Recommitment Strategies

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.