2. Private Equity Shadow Pricing Approach

With the factor model calibrated prices, we shadow price the privateMetrics® Universe of eligible private companies.
Universe excludes publicly listed companies, entirely government-owned companies, infrastructure companies, inactive companies, and other ineligible companies.
When data is missing for non-key factors, we impute such values based on its historical financials or country-activity median values.
Outliers in the data are winsorised or trimmed, to obtain a meaningful universe.

In this page, we detail our methodology to shadow price the companies in the privateMetrics® Universe based on the factor model which is calibrated to private market transactions. Specifically, this page covers:

Filtering the universe for eligible companies
Treatment of missing data
Treatment of outliers

Universe Filters

As described in the BMU & PEU determination, the privateMetrics® universe is defined to accommodate a broad global coverage, and hence from that perspective, we implement the below filters:

The company should not be dissolved, liquidated, or declared bankrupt at the time of consideration.
Is a for-profit private company with a capital structure that does not have any security (equity or bond) traded publicly at the time of consideration.
If it is a subsidiary of another private company, only the parent is eligible for inclusion provided their financial information is available.
Is not entirely government-owned or controlled.
Has non-negative sales with the average sales in the past being over USD 1 million and at least two fiscal years of financial accounting data that is accessible.
Has a business and industrial activity description.
Exclude companies that are specifically available to be included in the infrastructure indices of inframetrics.
Information on the key factors that are used to price the private company using our factor model approach is available. Missing information is imputed provided there is sufficient information in the sector, year, and country available and the factor being imputed is not one of the primary factors affecting its valuation.

Missing Factors

In our data collection process, despite the due diligence exercised, there arise scenarios where we are unable to obtain data for a specific factor. In such cases, we impute the missing values. For performing imputations, we follow a specific hierarchy of methods.

First, when available, the past standardised historical values are used. For example, if EBITDA for 2023 is required, but EBITDA/Revenue for 2022 is available, we apply the past year EBITDA/Revenue on 2023 Revenue to generate EBITDA.

If past historical financials are not available then cross-sectional sample medians of standardised values (e.g., ratio or percentages) are used focusing on the country & activity of the private company with missing information.

Note that key factors such as size and PECCS™ dummies are not imputed, and private companies without data on these factors are not priced.

Outlier Treatments

Extreme outliers can affect the model and also skew the indices and analytics. To meaningfully tackle outliers and not allow them to affect our model, indices, and analytics, we perform the below:

Some factors that are prone to outliers such as EBITDA margin are winsorised, i.e., extreme values are replaced by fixed upper and lower percentiles from the distribution.
Transactions included in the model calibration are trimmed to exclude outliers.
Valuations that are estimated to be extreme both in terms of levels ($ value) or ratios (Price-to-sales) are excluded, and such companies are eliminated from the entire privateMetrics® Universe.