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Technische Universität München

Skill, Luck, or Algorithms? Predicting Fund-Level Performance Persistence in Private Equity

Advisor: Philipp Bockshecker, M.Sc.

Type: Master's Thesis
Start: asap 

 

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 industry-standard databases, and an advanced modeling section applying machine learning and backtesting techniques to forecast fund performance.

Objective

Literature Review

  • Summarize the academic literature on performance persistence, shifting the lens toward the underlying mechanics of value creation (operational vs. financial engineering).
  • Review emerging research on "style drift" or "strategy drift" in private markets and its historical impact on Limited Partner (LP) returns.
  • Evaluate the methodological differences between fund-level metrics (e.g., IRR, TVPI) and deal-level metrics (e.g., Gross MOIC, holding periods).

Data Engineering & Empirical Analysis

  • Use Preqin as the primary dataset to study performance persistence at the fund and manager level.
  • Apply established econometric techniques to analyze the relationship between a fund’s performance and that of its predecessors.
  • Control for fund characteristics such as size, sequence number, vintage, strategy, and region.
  • Test robustness using alternative performance measures such as IRR, Multiple on Invested Capital (MOIC), classical Public Market Equivalent (PME), direct alpha, and modern generalized metrics (e.g., Generalized PME and recent risk-adjusted alpha frameworks).
  • Explore potential data enrichment by leveraging PitchBook (to capture granular deal characteristics, financing structures, and add-on acquisition histories) and Orbis (to integrate firm-level accounting data and financial statements of the underlying portfolio companies).

Machine Learning, Backtesting, and Advanced Methods

  • Develop predictive models using machine learning techniques (such as random forests, gradient boosting, or neural networks) to forecast future fund performance.
  • Identify and engineer relevant input features from the constructed dataset, including manager and fund characteristics as well as macroeconomic indicators.
  • Conduct backtesting to evaluate model performance, out-of-sample prediction accuracy, and the stability of forecasting signals.
  • 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 quantitative, data-driven research.
  • Solid programming and analytical skills in Python or R.
  • Familiarity with econometrics and introductory machine learning concepts (e.g., cross-validation, feature importance).
  • Ability to work independently to clean, merge, and analyze large and complex financial datasets.
Resources

Core Literature: Performance Persistence & Manager Skill

  • 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.
  • Braun, R., Dorau, N., Jenkinson, T., & Urban, D. (2025). Size, returns, and value: Do private equity firms allocate capital according to manager skill? Journal of Finance.
  • Braun, R., Jenkinson, T., & Stoff, I. (2017). How Persistent is Private Equity Performance? Journal of Financial Economics, 123(2), 273–291.
  • Korteweg, A. G., & Sorensen, M. (2017). Skill and Luck in Private Equity Performance. Journal of Financial Economics, 124(3), 535–562.

Advanced Methodology: Risk Adjustment & Generalized Metrics 

  • Korteweg, A. G., & Nagel, S. (2024). Risk-Adjusted Returns of Private Equity Funds: A New Approach. Review of Financial Studies.
  • Korteweg, A. G., & Nagel, S. (2016). Risk-Adjusting the Returns to Private Equity. Journal of Finance, 71(3), 1437–1470.

Foundational Context: Returns & Capital Flows 

  • Kaplan, S. N., & Schoar, A. (2005). Private Equity Performance: Returns, Persistence, and Capital Flows. Journal of Finance, 60(4), 1791–1823.
  • Harris, R. S., Jenkinson, T., & Kaplan, S. N. (2014). Private Equity Performance: What Do We Know? Journal of Finance, 69(5), 1851–1882.
Contact & Application

If you are interested in writing your thesis on this topic, please indicate this in your application. Please note that this topic can be expanded and/or taken in other directions depending on the student's own interests and ideas.

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