Forward PE Investing:
Growth-Adjusted Valuation

Python Implementation

import yfinance as yf
import pandas as pd

def calculate_forward_pe(ticker, growth_rate):
    """
    Calculate Forward PE for a given stock.
    
    Args:
        ticker: Stock ticker symbol (e.g., 'AAPL')
        growth_rate: Expected EPS growth rate (decimal)
    
    Returns:
        dict with PE metrics
    """
    
    # Fetch stock data
    stock = yf.Ticker(ticker)
    info = stock.info
    
    # Get current PE
    current_pe = info.get('trailingPE', None)
    
    if current_pe is None:
        return {'error': 'PE data not available'}
    
    # Calculate Forward PE
    forward_pe = current_pe / (1 + growth_rate)
    
    # Get additional info
    price = info.get('currentPrice', 0)
    eps = price / current_pe if current_pe > 0 else 0
    
    return {
        'ticker': ticker,
        'price': price,
        'trailing_eps': eps,
        'current_pe': current_pe,
        'growth_rate': growth_rate * 100,
        'forward_pe': forward_pe
    }

# Example usage
stocks = {
    'NVDA': 0.90,  # 90% expected growth
    'TSLA': 0.60,  # 60% expected growth
    'META': 0.25,  # 25% expected growth
}

results = []
for ticker, growth in stocks.items():
    result = calculate_forward_pe(ticker, growth)
    results.append(result)
    
    print(f"\n{ticker}:")
    print(f"  Traditional PE: {result['current_pe']:.1f}")
    print(f"  Forward PE: {result['forward_pe']:.1f}")
    print(f"  Growth Rate: {result['growth_rate']:.0f}%")

# Create DataFrame for analysis
df = pd.DataFrame(results)
print("\n", df)