Mapping the DNA of Private Equity Cash Flows: A Quantitative Framework for Modeling, Classification, and Simulation

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. Max Knicker
Type: Master's Thesis

 

Overview

Cash flow time series are the fundamental unit of analysis in private markets. They drive everything—from strategic asset allocation to liquidity forecasting and track record benchmarking. Yet, despite their centrality, a comprehensive understanding of their structure, variability, and generative patterns remains underdeveloped in academic and practitioner circles alike.

This thesis addresses that gap by developing a data-driven, quantitative framework to model and classify cash flow time series from private equity funds. The goal is to extract, analyze, and simulate the stylized facts of fund cash flows across strategies, vintages and geographies. This project aims to establish a taxonomy of cash flow behavior and build generative models that can replicate observed patterns.

This foundational work has far-reaching implications for improving downstream applications such as portfolio construction, risk management, and commitment planning.

Objective

• Conduct a comprehensive statistical exploration of private equity cash flow time series
• Identify and model key stylized facts such as autocorrelation, asymmetry, volatility clustering, and drawdown profiles
• Use clustering and dimensionality reduction to build a quantitative classification of cash flow types
• Develop and test a generative model (e.g., probabilistic, ML-based, or simulation-driven) that can replicate and simulate real-world cash flow trajectories
• Lay the groundwork for a “cash flow map” by vintage, region, strategy, and market condition

Requirements

• Strong interest in Quantitative Finance, Time Series Analysis, and Private Markets
• Experience with statistical modeling, machine learning, or generative simulation (e.g., Python)
• Interest in time series concepts (ARIMA, GARCH, HMM, etc.) and modern ML approaches (e.g., generative models, sequence learning)
• Analytical mindset and comfort with high-dimensional data and statistical hypothesis testing

Supervision & Support

• Individual supervision on model design, statistical inference, and evaluation methodology
• Guidance on working with real-world cash flow datasets and interpretation of results
• Optional: opportunity to align the work with ongoing research on forecasting and portfolio analytics

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.