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"""
Valuation engine for stock analysis.
Implements DCF, comparable multiples, and scenario analysis.
"""
import pandas as pd
import numpy as np
from typing import Dict, List, Optional, Tuple
class ValuationEngine:
"""Performs stock valuation using multiple methodologies"""
def __init__(self, financial_data: Dict, analysis_results: Dict):
"""
Initialize valuation engine
Args:
financial_data: Complete dataset from FinancialDataFetcher
analysis_results: Results from FinancialAnalyzer
"""
self.ticker = financial_data.get('ticker')
self.metrics = financial_data.get('metrics', {})
self.statements = financial_data.get('financial_statements', {})
self.sector_metrics = financial_data.get('sector_metrics', {})
self.analysis = analysis_results
def calculate_intrinsic_value_dcf(self,
growth_rate: float = 0.10,
terminal_growth: float = 0.02,
discount_rate: float = 0.10,
years: int = 5) -> Dict:
"""
Calculate intrinsic value using DCF method
Args:
growth_rate: Expected FCF growth rate
terminal_growth: Perpetual growth rate
discount_rate: WACC / required return
years: Forecast period
Returns:
DCF valuation results
"""
results = {
'method': 'DCF',
'assumptions': {
'growth_rate': growth_rate,
'terminal_growth': terminal_growth,
'discount_rate': discount_rate,
'forecast_years': years
}
}
try:
# Get current FCF
current_fcf = self.metrics.get('free_cash_flow', 0)
if current_fcf <= 0:
results['error'] = 'Negative or zero FCF - DCF not applicable'
return results
# Project FCF
projected_fcf = []
pv_fcf = []
for year in range(1, years + 1):
fcf = current_fcf * ((1 + growth_rate) ** year)
pv = fcf / ((1 + discount_rate) ** year)
projected_fcf.append(fcf)
pv_fcf.append(pv)
# Terminal value
terminal_fcf = projected_fcf[-1] * (1 + terminal_growth)
terminal_value = terminal_fcf / (discount_rate - terminal_growth)
pv_terminal = terminal_value / ((1 + discount_rate) ** years)
# Enterprise value
enterprise_value = sum(pv_fcf) + pv_terminal
# Equity value
net_debt = self.metrics.get('total_debt', 0) - self.metrics.get('total_cash', 0)
equity_value = enterprise_value - net_debt
# Per share value
shares_outstanding = self.metrics.get('shares_outstanding', 1)
fair_value_per_share = equity_value / shares_outstanding if shares_outstanding > 0 else 0
current_price = self.metrics.get('current_price', 0)
upside = ((fair_value_per_share - current_price) / current_price) * 100 if current_price > 0 else 0
results.update({
'current_fcf': current_fcf,
'pv_cash_flows': sum(pv_fcf),
'pv_terminal_value': pv_terminal,
'enterprise_value': enterprise_value,
'equity_value': equity_value,
'fair_value_per_share': fair_value_per_share,
'current_price': current_price,
'upside_percent': upside,
'recommendation': 'BUY' if upside > 15 else 'HOLD' if upside > -10 else 'SELL'
})
except Exception as e:
results['error'] = f'DCF calculation error: {str(e)}'
return results
def calculate_relative_valuation(self) -> Dict:
"""
Calculate valuation using comparable multiples
Returns:
Relative valuation results
"""
results = {
'method': 'Comparable Multiples'
}
current_price = self.metrics.get('current_price', 0)
# P/E based valuation
if self.metrics.get('trailing_pe') and self.metrics.get('eps_trailing'):
sector_pe = self.sector_metrics.get('trailing_pe_median')
if sector_pe:
eps = self.metrics.get('eps_trailing')
fair_value_pe = eps * sector_pe
pe_upside = ((fair_value_pe - current_price) / current_price) * 100 if current_price > 0 else 0
results['pe_valuation'] = {
'company_pe': self.metrics.get('trailing_pe'),
'sector_pe_median': sector_pe,
'eps': eps,
'fair_value': fair_value_pe,
'current_price': current_price,
'upside_percent': pe_upside
}
# PEG based valuation
if self.metrics.get('peg_ratio') and self.metrics.get('eps_forward'):
sector_peg = self.sector_metrics.get('peg_ratio_median')
earnings_growth = self.analysis.get('growth_analysis', {}).get('earnings_growth_ttm', 0)
if sector_peg and earnings_growth:
eps_forward = self.metrics.get('eps_forward')
fair_pe = sector_peg * (earnings_growth * 100)
fair_value_peg = eps_forward * fair_pe
peg_upside = ((fair_value_peg - current_price) / current_price) * 100 if current_price > 0 else 0
results['peg_valuation'] = {
'company_peg': self.metrics.get('peg_ratio'),
'sector_peg_median': sector_peg,
'earnings_growth': earnings_growth,
'fair_pe': fair_pe,
'fair_value': fair_value_peg,
'current_price': current_price,
'upside_percent': peg_upside
}
# P/B based valuation
if self.metrics.get('price_to_book') and self.metrics.get('book_value_per_share'):
sector_pb = self.sector_metrics.get('price_to_book_median')
if sector_pb:
book_value = self.metrics.get('book_value_per_share')
fair_value_pb = book_value * sector_pb
pb_upside = ((fair_value_pb - current_price) / current_price) * 100 if current_price > 0 else 0
results['pb_valuation'] = {
'company_pb': self.metrics.get('price_to_book'),
'sector_pb_median': sector_pb,
'book_value_per_share': book_value,
'fair_value': fair_value_pb,
'current_price': current_price,
'upside_percent': pb_upside
}
# Calculate average upside from available methods
upsides = []
if 'pe_valuation' in results:
upsides.append(results['pe_valuation']['upside_percent'])
if 'peg_valuation' in results:
upsides.append(results['peg_valuation']['upside_percent'])
if 'pb_valuation' in results:
upsides.append(results['pb_valuation']['upside_percent'])
if upsides:
avg_upside = sum(upsides) / len(upsides)
results['average_upside'] = avg_upside
results['recommendation'] = 'BUY' if avg_upside > 15 else 'HOLD' if avg_upside > -10 else 'SELL'
return results
def scenario_analysis(self) -> Dict:
"""
Perform bull/base/bear scenario valuation
Returns:
Scenario analysis results
"""
# Base case: use historical/current growth rates
base_growth = self.analysis.get('growth_analysis', {}).get('revenue_growth_ttm', 0.08)
base_growth = max(0.05, min(base_growth, 0.20)) # Cap between 5% and 20%
scenarios = {}
# Bear case: 20% lower growth, higher risk (lower valuation)
bear_growth = base_growth * 0.8
scenarios['bear'] = self.calculate_intrinsic_value_dcf(
growth_rate=bear_growth,
discount_rate=0.12 # Higher discount rate = higher risk = lower valuation
)
# Base case
scenarios['base'] = self.calculate_intrinsic_value_dcf(
growth_rate=base_growth,
discount_rate=0.10
)
# Bull case: 20% higher growth, lower risk (higher valuation)
bull_growth = base_growth * 1.2
scenarios['bull'] = self.calculate_intrinsic_value_dcf(
growth_rate=bull_growth,
discount_rate=0.08 # Lower discount rate = lower risk = higher valuation
)
# Summary
results = {
'scenarios': scenarios,
'current_price': self.metrics.get('current_price', 0)
}
# Calculate price ranges
bear_price = scenarios['bear'].get('fair_value_per_share', 0)
base_price = scenarios['base'].get('fair_value_per_share', 0)
bull_price = scenarios['bull'].get('fair_value_per_share', 0)
results['price_range'] = {
'bear': bear_price,
'base': base_price,
'bull': bull_price,
'range': bull_price - bear_price
}
# Risk/reward assessment
current_price = results['current_price']
if current_price > 0:
downside = ((bear_price - current_price) / current_price) * 100
upside = ((bull_price - current_price) / current_price) * 100
results['risk_reward'] = {
'downside_percent': downside,
'upside_percent': upside,
'risk_reward_ratio': abs(upside / downside) if downside != 0 else 0,
'assessment': 'Favorable' if upside > abs(downside) else 'Unfavorable'
}
return results
def calculate_margin_of_safety(self) -> Dict:
"""
Calculate margin of safety
Returns:
Margin of safety metrics
"""
results = {}
current_price = self.metrics.get('current_price', 0)
if current_price <= 0:
return {'error': 'Invalid current price'}
# Based on DCF
dcf_result = self.calculate_intrinsic_value_dcf()
if 'fair_value_per_share' in dcf_result:
intrinsic_value = dcf_result['fair_value_per_share']
margin = ((intrinsic_value - current_price) / intrinsic_value) * 100 if intrinsic_value > 0 else 0
results['dcf_margin_of_safety'] = {
'intrinsic_value': intrinsic_value,
'current_price': current_price,
'margin_percent': margin,
'assessment': self._assess_margin_of_safety(margin)
}
# Based on book value
book_value = self.metrics.get('book_value_per_share', 0)
if book_value > 0:
margin = ((book_value - current_price) / book_value) * 100
results['book_value_margin'] = {
'book_value': book_value,
'current_price': current_price,
'margin_percent': margin
}
return results
def _assess_margin_of_safety(self, margin: float) -> str:
"""Assess margin of safety"""
if margin >= 30:
return "Excellent - Strong margin of safety"
elif margin >= 20:
return "Good - Adequate margin of safety"
elif margin >= 10:
return "Fair - Minimal margin of safety"
elif margin >= 0:
return "Weak - Little to no margin of safety"
else:
return "Overvalued - Negative margin of safety"
def generate_valuation_report(self) -> Dict:
"""
Generate comprehensive valuation report
Returns:
Complete valuation analysis
"""
return {
'ticker': self.ticker,
'current_price': self.metrics.get('current_price', 0),
'dcf_valuation': self.calculate_intrinsic_value_dcf(),
'relative_valuation': self.calculate_relative_valuation(),
'scenario_analysis': self.scenario_analysis(),
'margin_of_safety': self.calculate_margin_of_safety()
}
if __name__ == "__main__":
print("This module is meant to be imported and used with data from data_fetcher.py and financial_analyzer.py")
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