Building a Reusable ML Framework for Private Equity Valuation

Background

One of the largest investment firms initiated a Data Science project to estimate private company valuations using financial data from public companies. The goal was to build a predictive model that could estimate Enterprise Value (EV) or Market Valuation for private firms based on limited financial inputs, leveraging public data from FactSet.

Challenge

• Limited private data: Only basic financial metrics (Revenue, EBITDA, Net Debt) were available for private companies. • Comp set accuracy: Difficulty in identifying truly comparable public companies due to missing growth rate data and nuanced financial indicators. • Data quality issues: Outliers and currency misalignments in public data required extensive cleaning.

Approach

• Data Collection: Gathered public company financials from Factset and private company data from GP-managed portfolios. • Exploratory Analysis: Cleaned and visualized public data to identify trends and remove outliers. • Feature Engineering: Created financial ratios, growth metrics, and sector-based indicators. • Segmentation: Used clustering (KMeans) to group companies by industry and financial similarity. • Modeling: Trained multiple ML models (Random Forest, GLM, Neural Network) using Kedro framework. • Scoring: Applied trained models to private company data to estimate valuations. • Evaluation: Compared predicted EVs to actuals; assessed accuracy using MAPE and other metrics.

Key results

Benefits

• Established a framework for future valuation modeling using machine learning. • Created a reusable pipeline for data ingestion, transformation, and scoring. • Identified key data gaps and modeling improvements needed for future iterations.

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