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2.3.5 private_equity_comparable

Purpose
Perform a comparable computation for private equities. This involves finding data points which have similar PECCS classifications and factor values, and averaging the metric values.

Parameters

  • metric (string, required):
    The metric for which the comparable has to be evaluated.
    Supported metrics are: "DividendOverRevenue", "EbitdaToSales", "EbitToSales", "EvToEbitda", "EvToSales", "Leverage", "NetDebtToAssets", "NetDebtToEquity", "NetIncomeToSales", "NetOperatingIncome", "PriceToBook", "PriceToEarnings", "PriceToEbitda", "PriceToSales", "ReturnOnAssets", "ReturnOnCapitalEmployed", "ReturnOnEquity", "RevenueGrowth", "Size", "TotalReturns".

  • currency (string, optional):
    The currency of monetary factor inputs, such as size.
    Supported currencies are: "EUR", "GBP", "LCU", "USD". Default value is "USD" if omitted.

  • age_in_months (int, optional):
    The age of the company in months, and the value should be between 24 and 240. Note that the computation extends 6-month before and after the specified age. For instance, 24 would indicate inclusion of companies of age between 18-month and 30-month.
    If this parameter is set, end_date and window_in_years will be ignored.

  • end_date (date, optional):
    The maximum date of the comparable dataset.

  • window_in_years (int, optional):
    The window in years of the comparable dataset. The minimum date of the dataset is calculated as end_date - window_in_years.

  • industrial_activites (list of strings, optional):
    List of industrial activity PECCS codes. See 4.2 Activity Classification for the complete definitions.
    Acceptable values include "AC01", "AC02".

  • revenue_models (list of strings, optional):
    List of revenue model PECCS codes. See 4.4 Revenue Model for the complete definitions.
    Acceptable values include "RM01", "RM02".

  • customer_models (list of strings, optional):
    List of customer model PECCS codes. See 4.5 Customer Model for the complete definitions.
    Acceptable values include "CM01", "CM02".

  • lifecycle_phases (list of strings, optional):
    List of lifecycle phase PECCS codes. See 4.3 Lifecycle Phases for the complete definitions.
    Acceptable values include "LP01", "LP02".

  • value_chain_types (list of strings, optional):
    List of value chain type PECCS codes. See 4.6 Value Chain Types for the complete definitions.
    Acceptable values include "VC01", "VC02".

  • countries (list of strings, optional):
    List of country ISO codes. Values can be obtained by referring to Privatemetrics® Country Codes.

  • size: (float or string, optional)
    Revenue of the company. Represented either as an absolute value in millions of the specified currency (e.g., “USD”, “EUR”) or as a quintile value.
    Acceptable numerical values: 5.6 (represents 5.6 million).
    Supported quintile values are: "Q1", "Q2", "Q3", "Q4", "Q5".

  • growth: (float or string, optional)
    Revenue growth month-on-month. Represented either as a percentage (e.g., "30%") or as a quintile value.
    Acceptable numerical values: 0 to 1 (0.3 represents 30%).
    Supported quintile values are: "Q1", "Q2", "Q3", "Q4", "Q5".

  • leverage: (float or string, optional)
    Total debt over revenue. Represented either as a percentage (e.g., "10%") or as a quintile value.
    Acceptable numerical values: 0 to 1 (0.1 represents 10%).
    Supported quintile values are: "Q1", "Q2", "Q3", "Q4", "Q5".

  • profits: (float or string, optional)
    EBITDA margin. Represented either as a percentage (e.g., "15%") or as a quintile value.
    Acceptable numerical values: 0 to 1 (0.15 represents 15%).
    Supported quintile values are: "Q1", "Q2", "Q3", "Q4", "Q5".

  • country_risk: (list of strings, optional):
    Term spread, specified as a list of country ISO codes. Values can be obtained by referring to Privatemetrics® Country Codes.

  • universe (string, optional):
    Universe of companies in the dataset.
    Supported universes are: "MIU" for market index universe, and "PEU" for PE-backed universe. Refer to1. privateMetrics® Market Indices and 2. PEU and BMU Benchmarks for more information, respectively. Default value is "PEU" if omitted.

  • factor_weight (float, optional):
    A numeric value between 0 and 1. At the extremes, 1 indicates that comparables are purely based on factors, while 0 indicates that comparables are purely based on PECCS. Values between 0 and 1 create a weighted average between the two.

  • type (string, optional):
    Determines how to aggregate the comparables dataset.
    Supported types are "CompanyCount", "Max", "Median", "Min", "ObservationCount" "P25", "P75", "Var975", "Var99", "Vol". Default value is "Mean" if omitted.
    Note: "Var975", "Var99", and "Vol" are only applicable for the TotalReturns metric.

  • intersect_peccs (boolean, optional):
    Intersect or union the PECCS filters when doing the calculation. Default is True.

Example 1: Quintile

PY
response = await service.private_equity_comparable(
    metric="PriceToSales",
    currency="USD",
    # age_in_months=None,
    end_date=date(2023, 10, 31),
    window_in_years=2,
    industrial_activities=["AC01"],
    # revenue_models=None,
    # customer_models=None,
    # lifecycle_phases=None,
    # value_chain_types=None,
    # countries=None,
    # size=None,
    # growth=None,
    leverage="Q1",
    # profits=None,
    # country_risk=None,
    universe="MIU",
    factor_weight=1,
    # type="mean",
    # intersect_peccs=True,
)

#Sample Response
#{
#    "data": {
#        "results": 1.3850760217577789
#    }
#}

Example 2: Absolute value

PY
response = await service.private_equity_comparable(
    metric="PriceToSales",
    currency="USD",
    # age_in_months=None,
    end_date=date(2022, 11, 30),
    window_in_years=2,
    # industrial_activities=None,
    # revenue_models=None,
    # customer_models=None,
    # lifecycle_phases=None,
    # value_chain_types=None,
    countries=["GBR"],
    # size=None,
    growth=0.1,
    # leverage=None,
    profits=0.2,
    # country_risk=None,
    universe="MIU",
    factor_weight=1,
    # type="mean",
    # intersect_peccs=True,
)

#Sample Response
#{
#    "data": {
#        "results": 1.6995594302494075
#    }
#}

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