Winners Keep Winning? Performance Persistence and Forecasting in Private Equity

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

 

Advisor: Philipp Bockshecker, M.Sc.
Type: Master's Thesis

 

Overview

Performance persistence, meaning the continued outperformance of private equity (PE) fund managers across successive funds, remains one of the most debated topics in private markets research. If persistence reflects genuine skill rather than luck or favorable market timing, investors can use it to identify top-performing managers and improve portfolio allocation decisions.

While earlier studies found strong persistence, more recent work suggests that its strength varies across fund types, periods, and market environments. Traditional empirical approaches often rely on linear models and fund-level data, which may not fully capture nonlinear relationships or the influence of complex interactions between fund characteristics, manager experience, and market conditions.

This master’s thesis examines performance persistence in private equity in three phases: a comprehensive literature review, an empirical analysis using a proprietary dataset, and an advanced modeling section applying machine learning and backtesting techniques to forecast fund performance.

Objective

1. Literature Review
   1. Summarize the main academic findings on performance persistence in private equity.
   2. Discuss potential sources of persistence such as manager skill, access to superior deal flow, or reputation effects.
   3. Review existing empirical methodologies and identify their strengths and limitations.
   4. Highlight recent developments that link persistence to broader market dynamics and capital flows.
       
2. Empirical Analysis
   1. Use a granular proprietary dataset to study persistence at the fund and manager level.
   2. Apply established econometric techniques to analyze the relationship between a fund’s performance and that of its predecessors.
   3. Control for fund characteristics such as size, sequence number, vintage, strategy, and region.
   4. Test robustness using alternative performance measures such as IRR, Multiple on Invested Capital (MOIC), Public Market Equivalent (PME), and direct alpha.
   5. Compare results with benchmarks derived from Preqin data to ensure consistency.
       
3. Machine Learning, Backtesting, and Advanced Methods
   1. Develop predictive models using machine learning techniques such as random forests, gradient boosting, or neural networks to forecast future fund performance.
   2. Identify and engineer relevant input features from the dataset, including manager and fund characteristics as well as macroeconomic indicators.
   3. Conduct backtesting to evaluate model performance, out-of-sample prediction accuracy, and the stability of forecasting signals.
   4. Assess how well machine learning models improve upon traditional econometric approaches and discuss the implications for investor decision-making.

Requirements

• Strong interest in private equity, empirical finance, and data-driven research.
• Solid programming and analytical skills in Python or R.
• Familiarity with econometrics and introductory machine learning concepts.
• Ability to work independently with large and complex datasets.

Resources

• Braun, R., Jenkinson, T., & Stoff, I. (2017). How Persistent is Private Equity Performance? Journal of Financial Economics, 123(2), 273–291.
• Harris, R. S., Jenkinson, T., & Kaplan, S. N. (2014). Private Equity Performance: What Do We Know? Journal of Finance, 69(5), 1851–1882.
• Harris, R. S., Jenkinson, T., Kaplan, S. N., & Stucke, R. (2023). Has Persistence Persisted in Private Equity? Evidence from Buyout and Venture Capital Funds. Journal of Corporate Finance, 81, 102361.
• Kaplan, S. N., & Schoar, A. (2005). Private Equity Performance: Returns, Persistence, and Capital Flows. Journal of Finance, 60(4), 1791–1823.
• Korteweg, A. G., & Sorensen, M. (2017). Skill and Luck in Private Equity Performance. Journal of Financial Economics, 124(3), 535–562.

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.