Yes, there are estimates for the aggregate number of transactions that have occurred within the Private Equity (PE) market over the preceding decade, as well as related details about transaction data within the privateMetrics database.

Details on the number of PE transactions:

• Annual Buyout Transactions: Globally, the number of buyout transactions is approximately 4,000 to 6,000 annually.
• Frequency of Transactions: Between 2013 and 2022, there were over 300 transactions observed per year, translating to more than 25 transactions every month.

Explanation of privateMetrics’ approach to transaction data:

• Transaction Definition: A transaction is defined as a purely private deal between two non-state entities for a 100% equity acquisition. IPOs are not considered, as the focus is on capturing private market prices where private asset investors are active.
• Data Sources: The data used to calibrate the asset pricing model consists of a representative set of 10,000 private market transactions (entries and exits) since 2013. Data is collected from PitchBook, Capital IQ, and fund manager annual reports and processed via artificial intelligence.
• Data Validation: Each transaction is analyzed and validated before inclusion in the transaction dataset. Key information, including risk factors and PECCS or TICCS classes, must be available.
• Factor Model Calibration: The baseline factor model is constructed using 5,438 private market transactions that happened globally between 1999 and 2022.
• Monthly Updates: The model is calibrated every month based on new observed transactions, making the indices reflect newly obtained factor prices from these updates.
• Kalman Filter: The model employs a dynamic estimation process using a Kalman filter, retaining the memory of past factor prices while allowing new transactions to influence the relationship.
• PE Universe (PEU): The PEU consists of companies that PE funds typically target, based on size and profitability ranges that vary by sector.

Challenges and considerations in using transaction data:

• Data Limitations: There is less reported data for actual entry and exit transactions, especially after market slowdowns. This implies that much of the reported valuation data is model-based.
• Relying on Models: In private markets, most data is modeled because too few transactions take place to have access to robust observed data. Using raw reported data or listed proxies introduces biases and noise, leading to large estimation errors.
• Peer Group Issues: Peer group data is almost never robust due to data limitations. The likelihood of a representative peer group benchmark that also includes enough data to be robust is very low.

How privateMetrics addresses these challenges:

• Factor Model: privateMetrics employs a factor model that leverages actual equity transactions in private companies to estimate valuations.
• Shadow Pricing: The approach uses all observed transactions to estimate shadow prices of the entire private equities universe, which are in line with the transactions.
• PECCS Classification: Transactions are classified by PECCS segments, which cover aspects such as activity, lifecycle phase, revenue model, customer model, and value chain.
• Robustness: The model’s robustness is confirmed by the close alignment of average observed transaction prices and model-predicted values (shadow prices), even at the individual market segment level.

Addressing Potential Biases: While the model is based on P/S ratios, it also ensures that predictions for EV/EBITDA are not biased by computing the EV based on the book value of debt and predicted equity valuation. These predictions are compared to transaction-implied ratios.