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Technical University of Munich

Performance Persistence in Private Equity: A Data-Driven Empirical Analysis

Advisor: Philipp Bockshecker, M.Sc.

Type: Bachelor's Thesis
Start: asap

 

Overview

Performance persistence describes the tendency of private equity (PE) fund managers who have performed well in the past to continue doing so in subsequent funds. Persistent performance would indicate the presence of manager skill rather than pure luck and is therefore highly relevant for investors selecting fund managers.

However, measuring persistence is challenging due to data limitations, the use of internal rate of return (IRR) instead of time-weighted returns, and differences in market conditions across fund vintages. Earlier studies found clear signs of persistence, while more recent research suggests that it has declined as the market became more competitive and transparent. 

This bachelor’s thesis examines performance persistence in private equity using Preqin fund-level data. The goal is to analyze whether past fund performance helps predict future results and how persistence varies across strategies, regions, and time periods. 

Objective

  1. Literature Review
    1. Summarize the main studies on performance persistence in private equity.
    2. Review mechanisms that may generate persistence, such as manager skill, network advantages, or superior deal sourcing.
    3. Discuss how persistence varies over time and across fund types (e.g., buyout vs. venture capital funds).
       
  2. Empirical Analysis
    1. Replicate and extend established persistence regressions by relating fund performance measures to those of predecessor funds.
    2. Control for fund characteristics, including size, sequence number, geography, vintage, and strategy.
    3. Compare different performance metrics such as Internal Rate of Return (IRR), Public Market Equivalent (PME), and Direct Alpha.
       
  3. Advanced Topics (Optional, Based on Interest)
    1. Apply Bayesian methods or quantile regressions to capture heterogeneous persistence patterns.
    2. Use machine learning approaches to predict persistence based on fund-level characteristics.
    3. Examine how capital inflows, limited partner concentration, or macroeconomic conditions influence persistence.

Requirements

  • Interest in private markets, performance measurement, and empirical finance.
  • Strong analytical and statistical skills and motivation to work with quantitative data.
  • Programming experience in Python or R is highly recommended.
  • Ability to work independently with attention to detail and data quality.

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 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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