Unlocking Insights: Building a Proprietary PE Dataset from GP Reports
Advisor: Nicolas Pardon, M.Sc.
Type: Master Thesis
Start: January 2024
Overview
Private Equity (PE) research often suffers from a lack of comprehensive, structured data. This thesis aims to bridge this gap by creating a proprietary PE dataset derived from actual General Partner (GP) reports. Students will convert unstructured data from global provisioning services (e.g., Excel, PDF, presentations) into a structured format. The resulting dataset will enable diverse academic research opportunities, such as constructing value bridges or analyzing cash flow patterns.
Objective
- Develop a structured dataset from unstructured GP reports
- Conduct academic research on the newly created dataset with flexibility in topic focus
- Example topics: creating a value bridge, understanding cash flow patterns
Requirements
- Passion for Private Equity and Venture Capital
- Interest in empirical work using basic Python code for data manipulation and statistics
Set-up & supervision
- Access to a variety of GP reports in multiple formats (Excel, PDF, presentations)
- Potential need to gather additional data for comprehensive analysis
- Regular meetings with the supervisor for insights into the PE industry, research skills, and statistical analysis
If you are interested in writing your master thesis on this topic, please indicate so in your application.