Stakeholders Module

??? example "single_round_dilution"

from startup_valuation.stakeholders import single_round_dilution

result = single_round_dilution(
    ownership_before=1.0, investment=5_000_000, post_money=50_000_000,
)
print(f"Ownership after: {result.value:.0%}")  # 90%

??? example "multi_round_dilution"

from startup_valuation.stakeholders import multi_round_dilution

rounds = [
    {"investment": 2_000_000, "post_money": 20_000_000},
    {"investment": 5_000_000, "post_money": 50_000_000},
    {"investment": 10_000_000, "post_money": 100_000_000},
]
result = multi_round_dilution(ownership_before=1.0, rounds=rounds)
print(f"Final ownership: {result.value:.0%}")  # 76.5%

??? example "acquisition_value"

from startup_valuation.stakeholders import acquisition_value

result = acquisition_value(
    standalone_value=100_000_000, synergies=30_000_000,
    integration_costs=5_000_000,
)
print(f"Acquisition value: ${result.value:,.0f}")  # $125,000,000

??? example "opm_common_stock"

from startup_valuation.stakeholders import opm_common_stock

result = opm_common_stock(
    enterprise_value=100_000_000, liquidation_preference=50_000_000,
    time_to_exit=5.0, volatility=0.40, risk_free_rate=0.04,
)
print(f"Common stock value: ${result.value:,.0f}")

??? example "pwerm"

from startup_valuation.stakeholders import pwerm

scenarios = [
    {"probability": 0.20, "common_value": 80_000_000},
    {"probability": 0.60, "common_value": 40_000_000},
    {"probability": 0.20, "common_value": 0},
]
result = pwerm(scenarios)
print(f"PWERM value: ${result.value:,.0f}")  # $40,000,000

??? example "liquidation_value"

from startup_valuation.stakeholders import liquidation_value

assets = {"cash": 5_000_000, "ar": 10_000_000, "inventory": 8_000_000}
recovery_rates = {"cash": 1.0, "ar": 0.80, "inventory": 0.40}
result = liquidation_value(assets, recovery_rates)
print(f"Liquidation: ${result.value:,.0f}")  # $16,200,000

??? example "risk_adjusted_synergy"

from startup_valuation.stakeholders import risk_adjusted_synergy

result = risk_adjusted_synergy(
    revenue_synergies=20_000_000, cost_synergies=10_000_000,
    prob_revenue=0.40, prob_cost=0.80, discount_rate=0.10, years=3,
)
print(f"Risk-adjusted synergy: ${result.value:,.0f}")

??? example "intrinsic_option_value"

from startup_valuation.stakeholders import intrinsic_option_value

result = intrinsic_option_value(
    strike_price=10, fair_market_value=25, shares=100_000,
)
print(f"Intrinsic value: ${result.value:,.0f}")  # $1,500,000

??? example "probability_weighted_employee_value"

from startup_valuation.stakeholders import probability_weighted_employee_value

scenarios = [
    {"probability": 0.10, "value_per_share": 50},
    {"probability": 0.60, "value_per_share": 20},
    {"probability": 0.30, "value_per_share": 0},
]
result = probability_weighted_employee_value(scenarios, shares=10_000)
print(f"Expected value: ${result.value:,.0f}")  # $170,000

??? example "vesting_adjusted_value"

from startup_valuation.stakeholders import vesting_adjusted_value

result = vesting_adjusted_value(
    total_value=1_000_000, vested_fraction=0.25,
    annual_vest_rate=0.25, retention_prob=0.80, years_remaining=3,
)
print(f"Vesting-adjusted: ${result.value:,.0f}")

??? example "cash_equity_breakeven"

from startup_valuation.stakeholders import cash_equity_breakeven

result = cash_equity_breakeven(
    salary_reduction=50_000, equity_value=500_000,
    tax_rate=0.30, discount_rate=0.20, years=4,
)
print(f"Breakeven equity: {result.value:.0%}")

??? example "max_asset_based_loan"

from startup_valuation.stakeholders import max_asset_based_loan

result = max_asset_based_loan(
    cash=5_000_000, accounts_receivable=10_000_000,
    inventory=8_000_000, equipment=20_000_000, real_estate=50_000_000,
)
print(f"Max loan: ${result.value:,.0f}")

??? example "venture_debt_dilution"

from startup_valuation.stakeholders import venture_debt_dilution

result = venture_debt_dilution(
    loan_amount=5_000_000, warrant_coverage=0.20, post_money=50_000_000,
)
print(f"Dilution: {result.value:.2%}")  # 2.00%

??? example "common_stock_discount"

from startup_valuation.stakeholders import common_stock_discount

result = common_stock_discount(
    enterprise_value=100_000_000, liquidation_preference=50_000_000,
    time_to_exit=5.0, volatility=0.40, risk_free_rate=0.04,
)
print(f"Common discount: {result.value:.0%}")

startup_valuation.stakeholders

Stakeholder-specific valuation methods.

Chapter 13: Valuation for Different Stakeholders

Classes

Functions

single_round_dilution(ownership_before, investment, post_money)

Calculate founder ownership after a single funding round.

Formula: Ownership After = Ownership Before × (1 - Investment / Post-Money)

Example

result = single_round_dilution(1.0, 5_000_000, 20_000_000) result.value 0.75

Source code in src/startup_valuation/stakeholders.py

def single_round_dilution(
    ownership_before: float,
    investment: float,
    post_money: float,
) -> ValuationResult:
    """Calculate founder ownership after a single funding round.

    Formula: Ownership After = Ownership Before × (1 - Investment / Post-Money)

    Example:
        >>> result = single_round_dilution(1.0, 5_000_000, 20_000_000)
        >>> result.value
        0.75
    """
    if post_money <= 0:
        raise ValueError("post_money must be positive")
    ownership_after = ownership_before * (1 - investment / post_money)
    return ValuationResult(
        value=ownership_after,
        method="Single Round Dilution",
        inputs={"ownership_before": ownership_before, "investment": investment, "post_money": post_money},
        chapter="13",
    )

multi_round_dilution(initial_ownership, investments, post_money_vals)

Calculate founder ownership after multiple funding rounds.

Formula: Ownership After n Rounds = Π(1 - Investmentᵢ / Post-Moneyᵢ)

Example

result = multi_round_dilution(1.0, [2M, 5M, 10M, 20M], [10M, 25M, 60M, 150M])

After 4 rounds: ~36%

Source code in src/startup_valuation/stakeholders.py

def multi_round_dilution(
    initial_ownership: float,
    investments: list[float],
    post_money_vals: list[float],
) -> ValuationResult:
    """Calculate founder ownership after multiple funding rounds.

    Formula: Ownership After n Rounds = Π(1 - Investmentᵢ / Post-Moneyᵢ)

    Example:
        >>> result = multi_round_dilution(1.0, [2M, 5M, 10M, 20M], [10M, 25M, 60M, 150M])
        >>> # After 4 rounds: ~36%
    """
    if len(investments) != len(post_money_vals):
        raise ValueError("investments and post_money_vals must have same length")

    ownership = initial_ownership
    for inv, pm in zip(investments, post_money_vals):
        ownership *= 1 - inv / pm

    return ValuationResult(
        value=ownership,
        method="Multi-Round Dilution",
        inputs={"investments": investments, "post_money_vals": post_money_vals},
        assumptions=["No option pool refresh between rounds"],
        chapter="13",
    )

acquisition_value(standalone_value, revenue_synergies, cost_synergies, integration_costs, prob_revenue=0.4, prob_cost=0.8)

Calculate acquisition value with risk-adjusted synergies.

Formula: Acquisition V = Standalone V + PV(Revenue Syn × P_rev + Cost Syn × P_cost) - Integration Costs

Example

result = acquisition_value(100_000_000, 20_000_000, 15_000_000, 10_000_000, 0.4, 0.8) result.value 120000000.0

Source code in src/startup_valuation/stakeholders.py

def acquisition_value(
    standalone_value: float,
    revenue_synergies: float,
    cost_synergies: float,
    integration_costs: float,
    prob_revenue: float = 0.4,
    prob_cost: float = 0.8,
) -> ValuationResult:
    """Calculate acquisition value with risk-adjusted synergies.

    Formula: Acquisition V = Standalone V + PV(Revenue Syn × P_rev + Cost Syn × P_cost) - Integration Costs

    Example:
        >>> result = acquisition_value(100_000_000, 20_000_000, 15_000_000, 10_000_000, 0.4, 0.8)
        >>> result.value
        120000000.0
    """
    risk_adj_synergy = revenue_synergies * prob_revenue + cost_synergies * prob_cost
    return ValuationResult(
        value=standalone_value + risk_adj_synergy - integration_costs,
        method="Acquisition Value",
        inputs={
            "standalone_value": standalone_value,
            "revenue_synergies": revenue_synergies,
            "cost_synergies": cost_synergies,
            "integration_costs": integration_costs,
        },
        chapter="13",
    )

opm_common_stock(enterprise_value, liquidation_preference, time_to_exit, volatility, risk_free_rate=0.04)

Value common stock using Option Pricing Method (OPM).

Formula: C = V × N(d₁) - K × e^(-rT) × N(d₂)

Parameters:

Name	Type	Description	Default
enterprise_value	float	Total enterprise value (V).	required
liquidation_preference	float	Liquidation preference (K).	required
time_to_exit	float	Expected time to exit in years (T).	required
volatility	float	Volatility of enterprise value (σ).	required
risk_free_rate	float	Risk-free rate (r).	0.04

Returns:

Type	Description
ValuationResult	ValuationResult with common stock value.

Example

result = opm_common_stock(100_000_000, 40_000_000, 3, 0.60) round(result.value / 1_000_000, 0) 65000000.0

Source code in src/startup_valuation/stakeholders.py

def opm_common_stock(
    enterprise_value: float,
    liquidation_preference: float,
    time_to_exit: float,
    volatility: float,
    risk_free_rate: float = 0.04,
) -> ValuationResult:
    """Value common stock using Option Pricing Method (OPM).

    Formula: C = V × N(d₁) - K × e^(-rT) × N(d₂)

    Args:
        enterprise_value: Total enterprise value (V).
        liquidation_preference: Liquidation preference (K).
        time_to_exit: Expected time to exit in years (T).
        volatility: Volatility of enterprise value (σ).
        risk_free_rate: Risk-free rate (r).

    Returns:
        ValuationResult with common stock value.

    Example:
        >>> result = opm_common_stock(100_000_000, 40_000_000, 3, 0.60)
        >>> round(result.value / 1_000_000, 0)
        65000000.0
    """
    d1 = (math.log(enterprise_value / liquidation_preference) + (risk_free_rate + volatility**2 / 2) * time_to_exit) / (
        volatility * math.sqrt(time_to_exit)
    )
    d2 = d1 - volatility * math.sqrt(time_to_exit)

    common_value = (
        enterprise_value * stats.norm.cdf(d1)
        - liquidation_preference * math.exp(-risk_free_rate * time_to_exit) * stats.norm.cdf(d2)
    )

    return ValuationResult(
        value=common_value,
        method="OPM (Common Stock)",
        inputs={
            "enterprise_value": enterprise_value,
            "liquidation_preference": liquidation_preference,
            "time_to_exit": time_to_exit,
            "volatility": volatility,
        },
        assumptions=["Common stock valued as call option on enterprise value"],
        chapter="13",
    )

pwerm(scenarios)

Calculate common stock value using PWERM.

Formula: Expected Common V = Σ pᵢ × CommonValueᵢ

Parameters:

Name	Type	Description	Default
scenarios	list[dict[str, float]]	List of dicts with 'probability' and 'common_value'.	required

Example

scenarios = [ ... {"probability": 0.20, "common_value": 92_000_000}, ... {"probability": 0.50, "common_value": 55_000_000}, ... {"probability": 0.20, "common_value": 4_000_000}, ... {"probability": 0.10, "common_value": 0}, ... ] result = pwerm(scenarios) round(result.value / 1_000_000, 1) 46.7

Source code in src/startup_valuation/stakeholders.py

def pwerm(
    scenarios: list[dict[str, float]],
) -> ValuationResult:
    """Calculate common stock value using PWERM.

    Formula: Expected Common V = Σ pᵢ × CommonValueᵢ

    Args:
        scenarios: List of dicts with 'probability' and 'common_value'.

    Example:
        >>> scenarios = [
        ...     {"probability": 0.20, "common_value": 92_000_000},
        ...     {"probability": 0.50, "common_value": 55_000_000},
        ...     {"probability": 0.20, "common_value": 4_000_000},
        ...     {"probability": 0.10, "common_value": 0},
        ... ]
        >>> result = pwerm(scenarios)
        >>> round(result.value / 1_000_000, 1)
        46.7
    """
    total_prob = sum(s["probability"] for s in scenarios)
    if abs(total_prob - 1.0) > 0.01:
        raise ValueError(f"scenario probabilities must sum to 1.0, got {total_prob}")

    expected_value = sum(s["probability"] * s["common_value"] for s in scenarios)
    return ValuationResult(
        value=expected_value,
        method="PWERM",
        inputs={"scenarios": scenarios},
        chapter="13",
    )

common_stock_discount(preferred_value, common_value)

Calculate common stock discount.

Formula: Discount = (Preferred V - Common V) / Preferred V

Example

result = common_stock_discount(100_000_000, 65_000_000) result.value 0.35

Source code in src/startup_valuation/stakeholders.py

def common_stock_discount(preferred_value: float, common_value: float) -> ValuationResult:
    """Calculate common stock discount.

    Formula: Discount = (Preferred V - Common V) / Preferred V

    Example:
        >>> result = common_stock_discount(100_000_000, 65_000_000)
        >>> result.value
        0.35
    """
    if preferred_value <= 0:
        raise ValueError("preferred_value must be positive")
    return ValuationResult(
        value=(preferred_value - common_value) / preferred_value,
        method="Common Stock Discount",
        inputs={"preferred_value": preferred_value, "common_value": common_value},
        chapter="13",
    )

liquidation_value(assets, recovery_rates)

Calculate liquidation value.

Formula: Liquidation V = Σ(Asset Value × Recovery Rate)

Example

assets = {"cash": 5_000_000, "ar": 3_000_000, "equipment": 2_000_000} rates = {"cash": 1.0, "ar": 0.80, "equipment": 0.30} result = liquidation_value(assets, rates) result.value 8000000.0

Source code in src/startup_valuation/stakeholders.py

def liquidation_value(
    assets: dict[str, float],
    recovery_rates: dict[str, float],
) -> ValuationResult:
    """Calculate liquidation value.

    Formula: Liquidation V = Σ(Asset Value × Recovery Rate)

    Example:
        >>> assets = {"cash": 5_000_000, "ar": 3_000_000, "equipment": 2_000_000}
        >>> rates = {"cash": 1.0, "ar": 0.80, "equipment": 0.30}
        >>> result = liquidation_value(assets, rates)
        >>> result.value
        8000000.0
    """
    total = sum(assets.get(k, 0) * recovery_rates.get(k, 0) for k in set(assets) | set(recovery_rates))
    return ValuationResult(
        value=total,
        method="Liquidation Value",
        inputs={"assets": str(assets), "recovery_rates": str(recovery_rates)},
        chapter="13",
    )

venture_debt_dilution(warrant_coverage, loan_amount, post_money)

Calculate venture debt warrant dilution.

Formula: Warrant Dilution = Warrant Coverage × Loan Amount / Post-Money

Example

result = venture_debt_dilution(0.10, 3_000_000, 40_000_000) result.value 0.0075

Source code in src/startup_valuation/stakeholders.py

def venture_debt_dilution(
    warrant_coverage: float,
    loan_amount: float,
    post_money: float,
) -> ValuationResult:
    """Calculate venture debt warrant dilution.

    Formula: Warrant Dilution = Warrant Coverage × Loan Amount / Post-Money

    Example:
        >>> result = venture_debt_dilution(0.10, 3_000_000, 40_000_000)
        >>> result.value
        0.0075
    """
    if post_money <= 0:
        raise ValueError("post_money must be positive")
    return ValuationResult(
        value=warrant_coverage * loan_amount / post_money,
        method="Venture Debt Dilution",
        inputs={"warrant_coverage": warrant_coverage, "loan_amount": loan_amount, "post_money": post_money},
        chapter="13",
    )

risk_adjusted_synergy(revenue_synergies, cost_synergies, prob_revenue=0.4, prob_cost=0.8, discount_rate=0.1, years=3)

Calculate risk-adjusted synergy value.

Formula: PV = Σ[Rev_Syn × P_rev + Cost_Syn × P_cost] / (1+r)^t

Parameters:

Name	Type	Description	Default
revenue_synergies	float	Annual revenue synergies.	required
cost_synergies	float	Annual cost synergies.	required
prob_revenue	float	Probability of achieving revenue synergies.	0.4
prob_cost	float	Probability of achieving cost synergies.	0.8
discount_rate	float	Discount rate.	0.1
years	int	Years to realize synergies.	3

Returns:

Type	Description
ValuationResult	ValuationResult with risk-adjusted synergy value.

Example

result = risk_adjusted_synergy(20_000_000, 15_000_000, 0.4, 0.8, 0.10, 3) round(result.value / 1_000_000, 1) 49.7

Source code in src/startup_valuation/stakeholders.py

def risk_adjusted_synergy(
    revenue_synergies: float,
    cost_synergies: float,
    prob_revenue: float = 0.4,
    prob_cost: float = 0.8,
    discount_rate: float = 0.10,
    years: int = 3,
) -> ValuationResult:
    """Calculate risk-adjusted synergy value.

    Formula: PV = Σ[Rev_Syn × P_rev + Cost_Syn × P_cost] / (1+r)^t

    Args:
        revenue_synergies: Annual revenue synergies.
        cost_synergies: Annual cost synergies.
        prob_revenue: Probability of achieving revenue synergies.
        prob_cost: Probability of achieving cost synergies.
        discount_rate: Discount rate.
        years: Years to realize synergies.

    Returns:
        ValuationResult with risk-adjusted synergy value.

    Example:
        >>> result = risk_adjusted_synergy(20_000_000, 15_000_000, 0.4, 0.8, 0.10, 3)
        >>> round(result.value / 1_000_000, 1)
        49.7
    """
    annual_synergy = revenue_synergies * prob_revenue + cost_synergies * prob_cost
    pv = sum(annual_synergy / ((1 + discount_rate) ** t) for t in range(1, years + 1))

    return ValuationResult(
        value=pv,
        method="Risk-Adjusted Synergy",
        inputs={
            "revenue_synergies": revenue_synergies,
            "cost_synergies": cost_synergies,
            "prob_revenue": prob_revenue,
            "prob_cost": prob_cost,
        },
        assumptions=["Synergies are realized evenly over projection period"],
        chapter="13",
    )

intrinsic_option_value(strike_price, fair_market_value, shares)

Calculate intrinsic value of equity options.

Formula: Intrinsic Value = max(FMV - Strike, 0) × Shares

Parameters:

Name	Type	Description	Default
strike_price	float	Option strike price per share.	required
fair_market_value	float	Current fair market value per share.	required
shares	int	Number of option shares.	required

Returns:

Type	Description
ValuationResult	ValuationResult with intrinsic option value.

Example

result = intrinsic_option_value(1.0, 5.0, 100_000) result.value 400000.0

Source code in src/startup_valuation/stakeholders.py

def intrinsic_option_value(
    strike_price: float,
    fair_market_value: float,
    shares: int,
) -> ValuationResult:
    """Calculate intrinsic value of equity options.

    Formula: Intrinsic Value = max(FMV - Strike, 0) × Shares

    Args:
        strike_price: Option strike price per share.
        fair_market_value: Current fair market value per share.
        shares: Number of option shares.

    Returns:
        ValuationResult with intrinsic option value.

    Example:
        >>> result = intrinsic_option_value(1.0, 5.0, 100_000)
        >>> result.value
        400000.0
    """
    intrinsic = max(fair_market_value - strike_price, 0) * shares
    return ValuationResult(
        value=intrinsic,
        method="Intrinsic Option Value",
        inputs={
            "strike_price": strike_price,
            "fair_market_value": fair_market_value,
            "shares": shares,
        },
        assumptions=["FMV reflects current 409A valuation"],
        chapter="13",
    )

probability_weighted_employee_value(scenarios)

Calculate employee option value using probability-weighted scenarios.

Formula: E[V] = Σ pᵢ × OptionValueᵢ

Parameters:

Name	Type	Description	Default
scenarios	list[dict[str, float]]	List of dicts with 'probability', 'fmv', 'strike', 'shares'.	required

Returns:

Type	Description
ValuationResult	ValuationResult with expected employee option value.

Example

scenarios = [ ... {"probability": 0.20, "fmv": 10.0, "strike": 1.0, "shares": 50000}, ... {"probability": 0.50, "fmv": 5.0, "strike": 1.0, "shares": 50000}, ... {"probability": 0.30, "fmv": 1.0, "strike": 1.0, "shares": 50000}, ... ] result = probability_weighted_employee_value(scenarios) result.value 225000.0

Source code in src/startup_valuation/stakeholders.py

def probability_weighted_employee_value(
    scenarios: list[dict[str, float]],
) -> ValuationResult:
    """Calculate employee option value using probability-weighted scenarios.

    Formula: E[V] = Σ pᵢ × OptionValueᵢ

    Args:
        scenarios: List of dicts with 'probability', 'fmv', 'strike', 'shares'.

    Returns:
        ValuationResult with expected employee option value.

    Example:
        >>> scenarios = [
        ...     {"probability": 0.20, "fmv": 10.0, "strike": 1.0, "shares": 50000},
        ...     {"probability": 0.50, "fmv": 5.0, "strike": 1.0, "shares": 50000},
        ...     {"probability": 0.30, "fmv": 1.0, "strike": 1.0, "shares": 50000},
        ... ]
        >>> result = probability_weighted_employee_value(scenarios)
        >>> result.value
        225000.0
    """
    total_prob = sum(s["probability"] for s in scenarios)
    if abs(total_prob - 1.0) > 0.01:
        raise ValueError(f"scenario probabilities must sum to 1.0, got {total_prob}")

    expected_value = sum(
        s["probability"] * max(s["fmv"] - s["strike"], 0) * s["shares"]
        for s in scenarios
    )
    return ValuationResult(
        value=expected_value,
        method="Probability-Weighted Employee Option Value",
        inputs={"scenarios": scenarios},
        chapter="13",
    )

vesting_adjusted_value(total_value, vested_fraction, annual_vest_rate=0.25, retention_prob=0.8, years_remaining=3)

Calculate option value adjusted for vesting schedule.

Formula: Adjusted V = V_vested + Σ[V_unvested × VestRate × Retention^t]

Parameters:

Name	Type	Description	Default
total_value	float	Total option value (intrinsic).	required
vested_fraction	float	Fraction already vested (0-1).	required
annual_vest_rate	float	Annual vesting rate (typically 0.25 for 4-year).	0.25
retention_prob	float	Annual probability of staying employed.	0.8
years_remaining	int	Years of unvested options remaining.	3

Returns:

Type	Description
ValuationResult	ValuationResult with vesting-adjusted value.

Example

result = vesting_adjusted_value(400_000, 0.25, 0.25, 0.8, 3) round(result.value / 1000, 0) 246.0

Source code in src/startup_valuation/stakeholders.py

def vesting_adjusted_value(
    total_value: float,
    vested_fraction: float,
    annual_vest_rate: float = 0.25,
    retention_prob: float = 0.8,
    years_remaining: int = 3,
) -> ValuationResult:
    """Calculate option value adjusted for vesting schedule.

    Formula: Adjusted V = V_vested + Σ[V_unvested × VestRate × Retention^t]

    Args:
        total_value: Total option value (intrinsic).
        vested_fraction: Fraction already vested (0-1).
        annual_vest_rate: Annual vesting rate (typically 0.25 for 4-year).
        retention_prob: Annual probability of staying employed.
        years_remaining: Years of unvested options remaining.

    Returns:
        ValuationResult with vesting-adjusted value.

    Example:
        >>> result = vesting_adjusted_value(400_000, 0.25, 0.25, 0.8, 3)
        >>> round(result.value / 1000, 0)
        246.0
    """
    vested_value = total_value * vested_fraction
    unvested_value = total_value * (1 - vested_fraction)

    adjusted_unvested = 0.0
    for t in range(1, years_remaining + 1):
        vest_portion = annual_vest_rate * unvested_value
        retention = retention_prob ** t
        adjusted_unvested += vest_portion * retention

    return ValuationResult(
        value=vested_value + adjusted_unvested,
        method="Vesting-Adjusted Option Value",
        inputs={
            "total_value": total_value,
            "vested_fraction": vested_fraction,
            "annual_vest_rate": annual_vest_rate,
            "retention_prob": retention_prob,
            "years_remaining": years_remaining,
        },
        assumptions=["Retention probability compounds annually"],
        chapter="13",
    )

cash_equity_breakeven(salary_reduction, equity_value, tax_rate=0.3, discount_rate=0.2, years=4)

Calculate breakeven for cash vs equity tradeoff.

Formula: After-Tax Salary Loss = Salary_Reduction × (1 - TaxRate) Breakeven if: Equity_Value / (1+r)^n > After-Tax Loss × n

Parameters:

Name	Type	Description	Default
salary_reduction	float	Annual salary reduction for equity.	required
equity_value	float	Estimated equity value at exit.	required
tax_rate	float	Marginal tax rate.	0.3
discount_rate	float	Discount rate for equity risk.	0.2
years	int	Years to exit.	4

Returns:

Type	Description
ValuationResult	ValuationResult with net benefit (positive = equity is better).

Example

result = cash_equity_breakeven(50_000, 500_000, 0.30, 0.20, 4) round(result.value / 1000, 0) 100.0

Source code in src/startup_valuation/stakeholders.py

def cash_equity_breakeven(
    salary_reduction: float,
    equity_value: float,
    tax_rate: float = 0.30,
    discount_rate: float = 0.20,
    years: int = 4,
) -> ValuationResult:
    """Calculate breakeven for cash vs equity tradeoff.

    Formula: After-Tax Salary Loss = Salary_Reduction × (1 - TaxRate)
    Breakeven if: Equity_Value / (1+r)^n > After-Tax Loss × n

    Args:
        salary_reduction: Annual salary reduction for equity.
        equity_value: Estimated equity value at exit.
        tax_rate: Marginal tax rate.
        discount_rate: Discount rate for equity risk.
        years: Years to exit.

    Returns:
        ValuationResult with net benefit (positive = equity is better).

    Example:
        >>> result = cash_equity_breakeven(50_000, 500_000, 0.30, 0.20, 4)
        >>> round(result.value / 1000, 0)
        100.0
    """
    after_tax_loss = salary_reduction * (1 - tax_rate) * years
    pv_equity = equity_value / ((1 + discount_rate) ** years)
    net_benefit = pv_equity - after_tax_loss

    return ValuationResult(
        value=net_benefit,
        method="Cash-Equity Breakeven",
        inputs={
            "salary_reduction": salary_reduction,
            "equity_value": equity_value,
            "tax_rate": tax_rate,
            "discount_rate": discount_rate,
            "years": years,
        },
        assumptions=["Equity value is realized at exit"],
        chapter="13",
    )

max_asset_based_loan(cash=0, accounts_receivable=0, inventory=0, equipment=0, real_estate=0)

Calculate maximum asset-based loan amount.

Formula: Max Loan = Cash×100% + AR×85% + Inventory×50% + Equipment×60% + RE×75%

Parameters:

Name	Type	Description	Default
cash	float	Cash and equivalents.	0
accounts_receivable	float	Accounts receivable.	0
inventory	float	Inventory value.	0
equipment	float	Equipment value.	0
real_estate	float	Real estate value.	0

Returns:

Type	Description
ValuationResult	ValuationResult with maximum loan amount.

Example

result = max_asset_based_loan(1_000_000, 2_000_000, 500_000, 3_000_000) result.value 5500000.0

Source code in src/startup_valuation/stakeholders.py

def max_asset_based_loan(
    cash: float = 0,
    accounts_receivable: float = 0,
    inventory: float = 0,
    equipment: float = 0,
    real_estate: float = 0,
) -> ValuationResult:
    """Calculate maximum asset-based loan amount.

    Formula: Max Loan = Cash×100% + AR×85% + Inventory×50% + Equipment×60% + RE×75%

    Args:
        cash: Cash and equivalents.
        accounts_receivable: Accounts receivable.
        inventory: Inventory value.
        equipment: Equipment value.
        real_estate: Real estate value.

    Returns:
        ValuationResult with maximum loan amount.

    Example:
        >>> result = max_asset_based_loan(1_000_000, 2_000_000, 500_000, 3_000_000)
        >>> result.value
        5500000.0
    """
    max_loan = (
        cash * 1.0
        + accounts_receivable * 0.85
        + inventory * 0.50
        + equipment * 0.60
        + real_estate * 0.75
    )

    return ValuationResult(
        value=max_loan,
        method="Max Asset-Based Loan",
        inputs={
            "cash": cash,
            "accounts_receivable": accounts_receivable,
            "inventory": inventory,
            "equipment": equipment,
            "real_estate": real_estate,
        },
        assumptions=["Standard advance rates: Cash 100%, AR 85%, Inv 50%, Equip 60%, RE 75%"],
        chapter="13",
    )