Comparables Module

??? example "pe_ratio"

from startup_valuation.comparables import pe_ratio

result = pe_ratio(market_cap=1_000_000_000, net_income=100_000_000)
print(f"P/E ratio: {result.value:.1f}x")  # 10.0x

??? example "ps_ratio"

from startup_valuation.comparables import ps_ratio

result = ps_ratio(market_cap=500_000_000, revenue=50_000_000)
print(f"P/S ratio: {result.value:.1f}x")  # 10.0x

??? example "ev_ebitda"

from startup_valuation.comparables import ev_ebitda

result = ev_ebitda(enterprise_value=1_000_000_000, ebitda=150_000_000)
print(f"EV/EBITDA: {result.value:.1f}x")  # 6.7x

??? example "ev_revenue"

from startup_valuation.comparables import ev_revenue

result = ev_revenue(enterprise_value=500_000_000, revenue=50_000_000)
print(f"EV/Revenue: {result.value:.1f}x")  # 10.0x

??? example "regression_adjusted_multiple"

from startup_valuation.comparables import regression_adjusted_multiple

result = regression_adjusted_multiple(
    intercept=5.0, growth_rate=0.30, growth_coefficient=10.0,
    market_maturity=0.5, maturity_coefficient=-2.0,
)
print(f"Adjusted multiple: {result.value:.1f}x")  # 7.0x

??? example "target_valuation_multiple"

from startup_valuation.comparables import target_valuation_multiple

result = target_valuation_multiple(multiple=8.0, metric=50_000_000)
print(f"Valuation: ${result.value:,.0f}")  # $400,000,000

startup_valuation.comparables

Market comparables and regression-adjusted multiples.

Chapter 5: Market Comparables

Classes

Functions

pe_ratio(market_cap, net_income)

Calculate P/E ratio.

Formula: P/E = Market Cap / Net Income

Source code in src/startup_valuation/comparables.py

def pe_ratio(market_cap: float, net_income: float) -> ValuationResult:
    """Calculate P/E ratio.

    Formula: P/E = Market Cap / Net Income
    """
    if net_income <= 0:
        raise ValueError("net_income must be positive for P/E ratio")
    return ValuationResult(
        value=market_cap / net_income,
        method="P/E Ratio",
        inputs={"market_cap": market_cap, "net_income": net_income},
        chapter="5",
    )

ps_ratio(market_cap, revenue)

Calculate P/S ratio.

Formula: P/S = Market Cap / Revenue

Example

result = ps_ratio(500_000_000, 100_000_000) result.value 5.0

Source code in src/startup_valuation/comparables.py

def ps_ratio(market_cap: float, revenue: float) -> ValuationResult:
    """Calculate P/S ratio.

    Formula: P/S = Market Cap / Revenue

    Example:
        >>> result = ps_ratio(500_000_000, 100_000_000)
        >>> result.value
        5.0
    """
    if revenue <= 0:
        raise ValueError("revenue must be positive")
    return ValuationResult(
        value=market_cap / revenue,
        method="P/S Ratio",
        inputs={"market_cap": market_cap, "revenue": revenue},
        chapter="5",
    )

ev_ebitda(enterprise_value, ebitda)

Calculate EV/EBITDA ratio.

Formula: EV/EBITDA = Enterprise Value / EBITDA

Source code in src/startup_valuation/comparables.py

def ev_ebitda(enterprise_value: float, ebitda: float) -> ValuationResult:
    """Calculate EV/EBITDA ratio.

    Formula: EV/EBITDA = Enterprise Value / EBITDA
    """
    if ebitda <= 0:
        raise ValueError("ebitda must be positive")
    return ValuationResult(
        value=enterprise_value / ebitda,
        method="EV/EBITDA",
        inputs={"enterprise_value": enterprise_value, "ebitda": ebitda},
        chapter="5",
    )

ev_revenue(enterprise_value, revenue)

Calculate EV/Revenue ratio.

Source code in src/startup_valuation/comparables.py

def ev_revenue(enterprise_value: float, revenue: float) -> ValuationResult:
    """Calculate EV/Revenue ratio."""
    if revenue <= 0:
        raise ValueError("revenue must be positive")
    return ValuationResult(
        value=enterprise_value / revenue,
        method="EV/Revenue",
        inputs={"enterprise_value": enterprise_value, "revenue": revenue},
        chapter="5",
    )

regression_adjusted_multiple(intercept, growth_rate, growth_coefficient, market_maturity=0, maturity_coefficient=0, stage=0, stage_coefficient=0, geography=0, geography_coefficient=0)

Calculate regression-adjusted valuation multiple.

Formula: Multiple = β₀ + β₁×g + β₂×M + β₃×S + β₄×G + ε

Parameters:

Name	Type	Description	Default
intercept	float	Base multiple (β₀).	required
growth_rate	float	Revenue growth rate (g).	required
growth_coefficient	float	Coefficient for growth (β₁).	required
market_maturity	float	Market maturity indicator (M).	0
maturity_coefficient	float	Coefficient for maturity (β₂).	0
stage	float	Company stage indicator (S).	0
stage_coefficient	float	Coefficient for stage (β₃).	0
geography	float	Geography indicator (G).	0
geography_coefficient	float	Coefficient for geography (β₄).	0

Returns:

Type	Description
ValuationResult	ValuationResult with adjusted multiple.

Example

result = regression_adjusted_multiple(2.5, 0.30, 10, 1, 0.5, 0, -1.5, 0, -0.2) result.value 6.0

Source code in src/startup_valuation/comparables.py

def regression_adjusted_multiple(
    intercept: float,
    growth_rate: float,
    growth_coefficient: float,
    market_maturity: float = 0,
    maturity_coefficient: float = 0,
    stage: float = 0,
    stage_coefficient: float = 0,
    geography: float = 0,
    geography_coefficient: float = 0,
) -> ValuationResult:
    """Calculate regression-adjusted valuation multiple.

    Formula: Multiple = β₀ + β₁×g + β₂×M + β₃×S + β₄×G + ε

    Args:
        intercept: Base multiple (β₀).
        growth_rate: Revenue growth rate (g).
        growth_coefficient: Coefficient for growth (β₁).
        market_maturity: Market maturity indicator (M).
        maturity_coefficient: Coefficient for maturity (β₂).
        stage: Company stage indicator (S).
        stage_coefficient: Coefficient for stage (β₃).
        geography: Geography indicator (G).
        geography_coefficient: Coefficient for geography (β₄).

    Returns:
        ValuationResult with adjusted multiple.

    Example:
        >>> result = regression_adjusted_multiple(2.5, 0.30, 10, 1, 0.5, 0, -1.5, 0, -0.2)
        >>> result.value
        6.0
    """
    multiple = (
        intercept
        + growth_coefficient * growth_rate
        + maturity_coefficient * market_maturity
        + stage_coefficient * stage
        + geography_coefficient * geography
    )

    return ValuationResult(
        value=multiple,
        method="Regression-Adjusted Multiple",
        inputs={
            "intercept": intercept,
            "growth_rate": growth_rate,
            "growth_coefficient": growth_coefficient,
        },
        assumptions=["Regression coefficients are from comparable company analysis"],
        chapter="5",
    )

target_valuation_multiple(multiple, metric)

Calculate target valuation using a comparable multiple.

Formula: Valuation = Multiple × Metric

Example

result = target_valuation_multiple(5.5, 8_000_000) result.value 44000000.0

Source code in src/startup_valuation/comparables.py

def target_valuation_multiple(multiple: float, metric: float) -> ValuationResult:
    """Calculate target valuation using a comparable multiple.

    Formula: Valuation = Multiple × Metric

    Example:
        >>> result = target_valuation_multiple(5.5, 8_000_000)
        >>> result.value
        44000000.0
    """
    return ValuationResult(
        value=multiple * metric,
        method="Target Valuation (Multiple)",
        inputs={"multiple": multiple, "metric": metric},
        assumptions=["Multiple is from comparable companies"],
        chapter="5",
    )