Beyond Time Diversification: Clustering Private Equity Vintages for Resilient Portfolio Design

Important: This thesis is part of the ALTQNT Private Equity Flagship Track and can only be pursued after completing the ALTQNT Data Phase.

 

Advisor: Dr. Sara Boni & Dr. Max Knicker
Type: Master's Thesis

 

Overview

Private Equity (PE) portfolios are traditionally diversified across vintage years, under the assumption that each vintage reflects a distinct stage of the economic cycle. However, this time-based diversification strategy has proven limited: macro-financial conditions and structural factors such as region, industry focus, and fund size often spill over across years, producing correlated outcomes among seemingly distinct vintages.

This thesis builds on recent advances in data-driven portfolio design and machine learning clustering techniques to rethink how investors can achieve cycle-resilient diversification. Specifically, it investigates whether grouping vintages by economic and structural similarity—rather than calendar year—can yield more stable performance and enhance the robustness of PE portfolios.

Using fund-level cash flow data, the project will develop an empirical framework to cluster PE vintages based on macroeconomic and structural attributes, apply established PE performance models (Yale, Buchner, and Nowcasting), and evaluate the resilience of each cluster under adverse macro scenarios. The goal is to show that segment-based diversification offers a more effective strategy than traditional time-based approaches, providing new insights for both financial economists and institutional investors.

Objective

1. Motivation and Research Question
   1. While traditional PE portfolio construction emphasizes time diversification, economic and structural features may better capture underlying performance drivers. This project aims to empirically identify behaviorally coherent clusters of PE vintages and assess whether diversification across these clusters improves risk-adjusted outcomes and cycle resilience.
2. Core Research Questions
   1. Can PE vintages be clustered based on observable characteristics (region, industry focus, fund size, and macro regime at inception) and cash-flow behavior?
   2. Do such clusters explain differences in performance persistence, risk exposure, and resilience better than calendar-year vintages?
   3. How can cluster-based segmentation improve portfolio design and stress testing for institutional investors

Methodology

The thesis will replicate and extend the clustering framework proposed in Boni & Knicker (2025), focusing on methodological refinement and empirical validation.

Key steps include:

• Constructing a fund-level dataset from Preqin, covering capital calls, distributions, and fund attributes.
• Applying unsupervised learning (e.g., Self-Organizing Maps, hierarchical clustering, or k-means) to group vintages by similarity in cash-flow patterns.
• Testing supervised approaches (e.g., gradient boosting) to predict cluster membership from observable characteristics.
• Evaluating the resilience of each cluster using macro stress scenarios and PE performance models (Yale, Buchner, and Nowcasting).
• Comparing diversification efficiency between traditional vintage-year portfolios and cluster-based portfolios.

Expected Contribution

This thesis will:

1. Develop a data-driven clustering framework for segmenting PE vintages based on structural and macro-financial similarity.
2. Provide empirical evidence on the limitations of calendar-year diversification and the advantages of behavior-based segmentation.
3. Demonstrate the practical relevance of clustering for portfolio construction and stress testing.
4. Offer a methodological contribution bridging machine learning and financial economics in the context of private markets.

Requirements

• Interest in private markets, portfolio construction, and financial econometrics.
• Knowledge of machine learning and time-series methods.
• Strong data analysis skills in Python or MATLAB.
• Motivation to engage with large, high-dimensional datasets

Resources

• Axelson, U., Jenkinson, T., Strömberg, P., & Weisbach, M. S. (2013). Borrow Cheap, Buy High? The Determinants of Leverage and Pricing in Buyouts. Journal of Finance, 68(6): 2223–2267.
• Brown, G. W., Ghysels, E., & Gredil, O. R. (2022). Nowcasting Net Asset Values: The Case of Private Equity. Review of Financial Studies, 36(3): 945–986.
• Buchner, A., Kaserer, C., & Wagner, N. (2010). Modeling the Cash Flow Dynamics of Private Equity Funds. Journal of Alternative Investments.
• Harris, R. S., Jenkinson, T., & Kaplan, S. N. (2014). Private Equity Performance: What Do We Know? Journal of Finance, 69(5): 1851–1882.
• Phalippou, L. (2020). Private Equity Laid Bare. Oxford University Press.
• Sorensen, M., Wang, N., & Yang, J. (2014). Valuing Private Equity. Review of Financial Studies, 27(7): 1977–2021.

Contact & Application

If you are interested in writing your thesis on this topic, please indicate “ALTQNT Private Equity Flagship Track” in your thesis application. The specific topic can be aligned with your supervisor before the start of the research phase and can be further expanded or adapted based on your interests and ideas.