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"""DCF valuation engine — Gordon Growth Model + EV/EBITDA."""
import numpy as np
import pandas as pd
def compute_historical_growth_rate(fcf_series: pd.Series) -> float | None:
"""
Return the median YoY FCF growth rate from historical data, capped at [-0.5, 0.5].
Returns None if there is insufficient data.
"""
historical = fcf_series.sort_index().dropna().values
if len(historical) < 2:
return None
growth_rates = []
for i in range(1, len(historical)):
if historical[i - 1] != 0:
g = (historical[i] - historical[i - 1]) / abs(historical[i - 1])
growth_rates.append(g)
if not growth_rates:
return None
raw = float(np.median(growth_rates))
return max(-0.50, min(0.50, raw))
def run_dcf(
fcf_series: pd.Series,
shares_outstanding: float,
wacc: float = 0.10,
terminal_growth: float = 0.03,
projection_years: int = 5,
growth_rate_override: float | None = None,
) -> dict:
"""
Run a DCF model and return per-year breakdown plus intrinsic value per share.
Args:
fcf_series: Annual FCF values (yfinance order — most recent first).
shares_outstanding: Diluted shares outstanding.
wacc: Weighted average cost of capital (decimal, e.g. 0.10).
terminal_growth: Perpetuity growth rate (decimal, e.g. 0.03).
projection_years: Number of years to project FCFs.
growth_rate_override: If provided, use this growth rate instead of
computing from historical FCF data (decimal, e.g. 0.08).
Returns:
dict with keys:
intrinsic_value_per_share, total_pv, terminal_value_pv,
fcf_pv_sum, years, projected_fcfs, discounted_fcfs,
growth_rate_used
"""
if fcf_series.empty or shares_outstanding <= 0:
return {}
historical = fcf_series.sort_index().dropna().values
if len(historical) < 2:
return {}
if growth_rate_override is not None:
growth_rate = max(-0.50, min(0.50, growth_rate_override))
else:
growth_rates = []
for i in range(1, len(historical)):
if historical[i - 1] != 0:
g = (historical[i] - historical[i - 1]) / abs(historical[i - 1])
growth_rates.append(g)
raw_growth = float(np.median(growth_rates)) if growth_rates else 0.05
growth_rate = max(-0.50, min(0.50, raw_growth))
base_fcf = float(historical[-1])
projected_fcfs = []
for year in range(1, projection_years + 1):
fcf = base_fcf * ((1 + growth_rate) ** year)
projected_fcfs.append(fcf)
discounted_fcfs = []
for i, fcf in enumerate(projected_fcfs, start=1):
pv = fcf / ((1 + wacc) ** i)
discounted_fcfs.append(pv)
fcf_pv_sum = sum(discounted_fcfs)
terminal_fcf = projected_fcfs[-1] * (1 + terminal_growth)
terminal_value = terminal_fcf / (wacc - terminal_growth)
terminal_value_pv = terminal_value / ((1 + wacc) ** projection_years)
total_pv = fcf_pv_sum + terminal_value_pv
intrinsic_value_per_share = total_pv / shares_outstanding
return {
"intrinsic_value_per_share": intrinsic_value_per_share,
"total_pv": total_pv,
"terminal_value_pv": terminal_value_pv,
"fcf_pv_sum": fcf_pv_sum,
"years": list(range(1, projection_years + 1)),
"projected_fcfs": projected_fcfs,
"discounted_fcfs": discounted_fcfs,
"growth_rate_used": growth_rate,
"base_fcf": base_fcf,
}
def run_ev_ebitda(
ebitda: float,
total_debt: float,
total_cash: float,
shares_outstanding: float,
target_multiple: float,
) -> dict:
"""
Derive implied equity value per share from an EV/EBITDA multiple.
Steps:
implied_ev = ebitda * target_multiple
net_debt = total_debt - total_cash
equity_value = implied_ev - net_debt
price_per_share = equity_value / shares_outstanding
Returns {} if EBITDA <= 0 or any required input is missing/invalid.
"""
if not ebitda or ebitda <= 0:
return {}
if not shares_outstanding or shares_outstanding <= 0:
return {}
if not target_multiple or target_multiple <= 0:
return {}
implied_ev = ebitda * target_multiple
net_debt = (total_debt or 0.0) - (total_cash or 0.0)
equity_value = implied_ev - net_debt
return {
"implied_ev": implied_ev,
"net_debt": net_debt,
"equity_value": equity_value,
"implied_price_per_share": equity_value / shares_outstanding,
"target_multiple_used": target_multiple,
}
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