Performance Persistence vs. Strategy Drift: A Deal-Level Exploration of Private Equity

Advisor: Philipp Bockshecker, M.Sc.

Type: Master's Thesis
Start: asap 

 

Overview

The debate surrounding private equity performance persistence has historically relied on fund-level return data. However, as the asset class matures and capital inflows reach record highs, top-performing General Partners (GPs) often face pressure to deploy larger pools of capital. This frequently leads to "strategy drift"—where managers deviate from their original, successful mandates by executing larger transactions, drifting into new sectors (e.g., software), or relying heavily on add-on acquisitions rather than organic platform growth.

Does a GP's performance persist because they stick to their core competency, or because they successfully evolve? By bridging fund-level performance data from Preqin with granular, deal-level transaction data from PitchBook, this thesis deconstructs the underlying drivers of returns. It aims to quantify GP strategy drift, assess its impact on performance persistence, and utilize machine learning to predict whether a manager's shift in deal-making behavior will generate alpha or destroy value.

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

• Data Integration: Construct a robust proprietary dataset by mapping Preqin’s fund-level performance and cash-flow data (PME, Direct Alpha) to PitchBook’s deal-level transaction data (target sectors, deal sizes, add-on vs. platform investments, holding periods).
• Quantifying Strategy Drift: Develop a "Style Drift Index" for GPs by measuring period-over-period deviations in their median deal size, sector concentration, and pacing (capital deployment speed).
• Econometric Evaluation: Analyze how varying degrees of strategy drift impact a fund’s likelihood of remaining in the top quartile. Control for macroeconomic regimes (e.g., interest rate changes, dry powder levels).

Machine Learning, Backtesting, and Advanced Methods

• Behavioral Forecasting: Train machine learning models (e.g., Random Forests, XGBoost) using PitchBook deal-flow velocity to predict when a historically top-quartile GP is likely to experience strategy drift in their next fund.
• Return Prediction: Assess whether certain types of drift (e.g., migrating toward tech/software vs. moving up-market in deal size) act as predictive signals for outperformance or underperformance.
• LP Allocation Backtesting: Simulate a fund-of-funds allocation strategy that actively penalizes or rewards GPs based on predicted deal-level style drift, comparing the risk-adjusted returns against a naive persistence-based allocation model.

Requirements

• Strong interest in private markets, corporate finance, and quantitative research.
• Solid programming and analytical skills in Python or R (experience merging disparate datasets using fuzzy matching or API calls is a significant plus).
• Familiarity with applied econometrics and tabular machine learning frameworks (e.g., scikit-learn).
• Ability to synthesize complex financial data from Preqin and PitchBook into actionable insights.

Resources

• Braun, R., Jenkinson, T., & Stoff, I. (2017). How Persistent is Private Equity Performance? Journal of Financial Economics, 123(2), 273–291.
• Cumming, D., Fleming, G., & Schwienbacher, A. (2009). Style Drift in Private Equity. Journal of Business Finance & Accounting, 36(5-6), 645-678.
• 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.
• Koenig, L., & Burghof, H. P. (2022). The Investment Style Drift Puzzle and Risk-Taking in Venture Capital. Review of Corporate Finance, 2(3), 527-585.
• Stojkovski, I., & Braun, R. (2022). Software is Eating the World: Using Machine Learning to Analyze Private Equity Style Drift Toward Software. Available at SSRN.

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.