Strategy - S&P500 (v7)

3 September 2025 - Written by ML

Highlights

• This report is the first in a series on US market portfolio construction. The report is published in R and contains python script for producing results. Updated with consistent table formatting.

Aim

• In first instance a python script looks up and extracts tickers that are components of the S&P500 (identified from wikipedia page that contains the list of constituent stocks). Each stock`s Global Industry Classification Standard (GICS) group is extracted at the same time.

• Yahoo finance is then used to extract for each ticker financial data that include for instance share price, earnings, shares outstanding, return on eauity.

• Various computations are then performed on each ticker and tables are output in CSV form.

• Code has been created with a mixture of models including Claude, kimi.ai, deep seek, mistral.ai, chatgpt, and, cursor.

Script

###############################################################################
# Script Name: get_tickers_8.py
# pip install yfinance pandas requests beautifulsoup4 lxml
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
from io import StringIO
from datetime import datetime

# Convert exchange-style tickers (e.g., BRK.B) to Yahoo format (BRK-B)
def normalize_symbol_for_yahoo(symbol: str) -> str:
    if not isinstance(symbol, str):
        return symbol
    return symbol.replace('.', '-').strip()

def get_enhanced_earnings_data(symbol: str):
    """Enhanced function to get comprehensive earnings data."""
    try:
        tk = yf.Ticker(symbol)
        
        # Get price data
        price = None
        try:
            hist = tk.history(period="5d")
            if hist is not None and not hist.empty and 'Close' in hist:
                price = hist['Close'].dropna().iloc[-1]
        except Exception:
            pass

        if price is None:
            fast_info = getattr(tk, 'fast_info', {}) or {}
            price = fast_info.get('last_price')
        if price is None:
            info = getattr(tk, 'info', {}) or {}
            price = info.get('regularMarketPrice') or info.get('previousClose')

        # Get comprehensive earnings data
        info = getattr(tk, 'info', {}) or {}
        fast_info = getattr(tk, 'fast_info', {}) or {}
        
        # Basic earnings
        trailing_eps = info.get('trailingEps')
        if (trailing_eps is None or trailing_eps == 0) and price not in (None, 0):
            pe = fast_info.get('trailing_pe') or info.get('trailingPE')
            if pe not in (None, 0):
                try:
                    trailing_eps = float(price) / float(pe)
                except Exception:
                    trailing_eps = None
        
        # Enhanced earnings measures
        earnings_data = {
            'price': price,
            'trailing_eps': trailing_eps,
            'forward_eps': info.get('forwardEps'),
            'trailing_pe': info.get('trailingPE'),
            'forward_pe': info.get('forwardPE'),
            'earnings_growth': info.get('earningsAnnualGrowth'),
            'revenue_growth': info.get('revenueGrowth'),
            'profit_margins': info.get('profitMargins'),
            'roe': info.get('returnOnEquity'),
            'price_to_book': info.get('priceToBook'),
            'price_to_sales': info.get('priceToSalesTrailing12Months'),
            'debt_to_equity': info.get('debtToEquity'),
            'current_ratio': info.get('currentRatio'),
            'shares_outstanding': info.get('sharesOutstanding')
        }
        
        # Calculate additional derived metrics
        if earnings_data['trailing_eps'] and earnings_data['forward_eps']:
            try:
                earnings_data['eps_growth_rate'] = (
                    (earnings_data['forward_eps'] - earnings_data['trailing_eps']) / 
                    abs(earnings_data['trailing_eps']) * 100
                )
            except:
                earnings_data['eps_growth_rate'] = None
        
        if earnings_data['trailing_pe'] and earnings_data['forward_pe']:
            try:
                earnings_data['pe_ratio_spread'] = (
                    earnings_data['trailing_pe'] - earnings_data['forward_pe']
                )
            except:
                earnings_data['pe_ratio_spread'] = None
        
        return earnings_data
        
    except Exception as e:
        print(f"Error getting data for {symbol}: {e}")
        return {}

def analyze_earnings_quality(earnings_data):
    """Analyze the quality and consistency of earnings data."""
    quality_score = 0
    quality_notes = []
    
    # Check data completeness
    available_fields = sum(1 for v in earnings_data.values() if v is not None)
    total_fields = len(earnings_data)
    completeness = available_fields / total_fields
    
    if completeness >= 0.8:
        quality_score += 3
        quality_notes.append("High data completeness")
    elif completeness >= 0.6:
        quality_score += 2
        quality_notes.append("Moderate data completeness")
    else:
        quality_score += 1
        quality_notes.append("Low data completeness")
    
    # Check earnings consistency
    if earnings_data.get('trailing_eps') and earnings_data.get('forward_eps'):
        if earnings_data['forward_eps'] > earnings_data['trailing_eps']:
            quality_score += 2
            quality_notes.append("Forward EPS > Trailing EPS (growth expected)")
        else:
            quality_score += 1
            quality_notes.append("Forward EPS <= Trailing EPS")
    
    # Check P/E consistency
    if earnings_data.get('trailing_pe') and earnings_data.get('forward_pe'):
        if earnings_data['forward_pe'] < earnings_data['trailing_pe']:
            quality_score += 2
            quality_notes.append("Forward P/E < Trailing P/E (valuation improving)")
        else:
            quality_score += 1
            quality_notes.append("Forward P/E >= Trailing P/E")
    
    # Check growth metrics
    if earnings_data.get('earnings_growth'):
        if earnings_data['earnings_growth'] > 0:
            quality_score += 1
            quality_notes.append("Positive earnings growth")
        else:
            quality_score += 0
            quality_notes.append("Negative earnings growth")
    
    if earnings_data.get('revenue_growth'):
        if earnings_data['revenue_growth'] > 0:
            quality_score += 1
            quality_notes.append("Positive revenue growth")
        else:
            quality_score += 0
            quality_notes.append("Negative revenue growth")
    
    return {
        'quality_score': quality_score,
        'completeness': completeness,
        'notes': quality_notes
    }

print("Fetching S&P 500 constituents from Wikipedia...")
# 1) get the official S&P 500 list (symbol, company name, sector) ------------
url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
headers = {"User-Agent": "Mozilla/5.0 (compatible; S&P500Fetcher/1.0)"}
resp = requests.get(url, headers=headers, timeout=30)
resp.raise_for_status()
sp500_tbl = pd.read_html(StringIO(resp.text))[0]          # first table on the page
sp500_tbl = sp500_tbl[['Symbol', 'Security', 'GICS Sector', 'GICS Sub-Industry', 'CIK']]
sp500_tbl['Yahoo_Symbol'] = sp500_tbl['Symbol'].apply(normalize_symbol_for_yahoo)
print(f"Loaded {len(sp500_tbl)} tickers.")

# 2) helper to fetch enhanced earnings data via yfinance ----
def get_enhanced_quote_data(symbol: str):
    try:
        earnings_data = get_enhanced_earnings_data(symbol)
        if not earnings_data:
            return None, None, None, None, None, None, None, None, None, None, None
        
        # Extract key metrics
        price = earnings_data.get('price')
        trailing_eps = earnings_data.get('trailing_eps')
        forward_eps = earnings_data.get('forward_eps')
        trailing_pe = earnings_data.get('trailing_pe')
        forward_pe = earnings_data.get('forward_pe')
        eps_growth_rate = earnings_data.get('eps_growth_rate')
        roe = earnings_data.get('roe')
        shares_outstanding = earnings_data.get('shares_outstanding')
        
        # Analyze data quality
        quality_analysis = analyze_earnings_quality(earnings_data)
        quality_score = quality_analysis['quality_score']
        data_completeness = quality_analysis['completeness']
        
        return (price, trailing_eps, forward_eps, trailing_pe, forward_pe, 
                eps_growth_rate, roe, shares_outstanding, quality_score, data_completeness)
    except Exception:
        return None, None, None, None, None, None, None, None, None, None, None

# 3) populate the dataframe with enhanced data ---------------------------------------------------
print("Fetching enhanced earnings data from Yahoo Finance...")
prices, trailing_eps_list, forward_eps_list, trailing_pe_list, forward_pe_list = [], [], [], [], []
eps_growth_list, roe_list, shares_list, quality_scores, completeness_scores = [], [], [], [], []

total = len(sp500_tbl)
for i, sym in enumerate(sp500_tbl['Yahoo_Symbol'], start=1):
    (price, trailing_eps, forward_eps, trailing_pe, forward_pe, 
     eps_growth_rate, roe, shares, quality_score, completeness) = get_enhanced_quote_data(sym)
    
    prices.append(price)
    trailing_eps_list.append(trailing_eps)
    forward_eps_list.append(forward_eps)
    trailing_pe_list.append(trailing_pe)
    forward_pe_list.append(forward_pe)
    eps_growth_list.append(eps_growth_rate)
    roe_list.append(roe)
    shares_list.append(shares)
    quality_scores.append(quality_score)
    completeness_scores.append(completeness)
    
    if i % 25 == 0 or i == total:
        print(f"Processed {i}/{total} tickers...")

sp500_tbl['Latest_Price'] = prices
sp500_tbl['Trailing_EPS'] = trailing_eps_list
sp500_tbl['Forward_EPS'] = forward_eps_list
sp500_tbl['Trailing_PE'] = trailing_pe_list
sp500_tbl['Forward_PE'] = forward_pe_list
sp500_tbl['EPS_Growth_Rate'] = eps_growth_list
sp500_tbl['Return_on_Equity'] = roe_list
sp500_tbl['Shares_Outstanding'] = shares_list
sp500_tbl['Data_Quality_Score'] = quality_scores
sp500_tbl['Data_Completeness'] = completeness_scores

# Calculate market capitalization
sp500_tbl['Market_Cap'] = sp500_tbl.apply(
    lambda row: row['Latest_Price'] * row['Shares_Outstanding'] 
    if pd.notna(row['Latest_Price']) and pd.notna(row['Shares_Outstanding']) 
    and row['Latest_Price'] > 0 and row['Shares_Outstanding'] > 0 
    else None, axis=1
)

# Calculate trailing earnings for each ticker (trailing EPS × shares outstanding)
sp500_tbl['Trailing_Earnings'] = sp500_tbl.apply(
    lambda row: row['Trailing_EPS'] * row['Shares_Outstanding'] 
    if pd.notna(row['Trailing_EPS']) and pd.notna(row['Shares_Outstanding']) 
    and row['Trailing_EPS'] is not None and row['Shares_Outstanding'] > 0 
    else None, axis=1
)

# Format market cap for better readability (in billions)
sp500_tbl['Market_Cap_Billions'] = sp500_tbl['Market_Cap'].apply(
    lambda x: round(x / 1e9, 2) if pd.notna(x) and x > 0 else None
)

# 4) inspect / save -----------------------------------------------------------
print(sp500_tbl.head())
print(f"\nMarket Cap Summary:")
print(f"Total companies with market cap data: {sp500_tbl['Market_Cap'].notna().sum()}")
print(f"Average market cap: ${sp500_tbl['Market_Cap'].mean() / 1e9:.2f}B")
print(f"Median market cap: ${sp500_tbl['Market_Cap'].median() / 1e9:.2f}B")

# Display enhanced earnings data summary
print(f"\nEnhanced Earnings Data Summary:")
print(f"Companies with trailing EPS: {sp500_tbl['Trailing_EPS'].notna().sum()}")
print(f"Companies with forward EPS: {sp500_tbl['Forward_EPS'].notna().sum()}")
print(f"Companies with trailing P/E: {sp500_tbl['Trailing_PE'].notna().sum()}")
print(f"Companies with forward P/E: {sp500_tbl['Forward_PE'].notna().sum()}")
print(f"Companies with ROE data: {sp500_tbl['Return_on_Equity'].notna().sum()}")
print(f"Companies with trailing earnings: {sp500_tbl['Trailing_Earnings'].notna().sum()}")
print(f"Average data quality score: {sp500_tbl['Data_Quality_Score'].mean():.1f}/10")
print(f"Average data completeness: {sp500_tbl['Data_Completeness'].mean():.1%}")

# 5) Create GICS Sector Summary Table -----------------------------------------
print("\nCreating GICS Sector Summary Table...")

# Group by GICS Sector and calculate summary statistics
sector_summary = sp500_tbl.groupby('GICS Sector').agg({
    'Symbol': 'count',  # Count of tickers
    'Market_Cap': 'sum',  # Sum of market capitalization
    'Trailing_Earnings': 'sum',  # Sum of trailing earnings
    'Data_Quality_Score': 'mean',  # Average data quality score
    'Data_Completeness': 'mean'  # Average data completeness
}).rename(columns={
    'Symbol': 'Ticker_Count',
    'Market_Cap': 'Total_Market_Cap',
    'Trailing_Earnings': 'Total_Sector_Earnings',
    'Data_Quality_Score': 'Avg_Data_Quality',
    'Data_Completeness': 'Avg_Data_Completeness'
})

# Convert market cap to billions for better readability
sector_summary['Total_Market_Cap_Billions'] = sector_summary['Total_Market_Cap'].apply(
    lambda x: round(x / 1e9, 2) if pd.notna(x) and x > 0 else 0
)

# Convert sector earnings to billions for better readability
sector_summary['Total_Sector_Earnings_Billions'] = sector_summary['Total_Sector_Earnings'].apply(
    lambda x: round(x / 1e9, 2) if pd.notna(x) and x > 0 else 0
)

# Calculate sector P/E ratio (sector market cap ÷ sector earnings)
sector_summary['Sector_PE_Ratio'] = sector_summary.apply(
    lambda row: round(row['Total_Market_Cap'] / row['Total_Sector_Earnings'], 2) 
    if pd.notna(row['Total_Market_Cap']) and pd.notna(row['Total_Sector_Earnings']) 
    and row['Total_Sector_Earnings'] > 0 else None, axis=1
)

# Calculate percentage share of total market cap
total_market_cap = sector_summary['Total_Market_Cap'].sum()
sector_summary['Market_Cap_Percentage'] = sector_summary['Total_Market_Cap'].apply(
    lambda x: round((x / total_market_cap * 100), 2) if pd.notna(x) and x > 0 and total_market_cap > 0 else 0
)

# Sort by market cap (descending)
sector_summary = sector_summary.sort_values('Total_Market_Cap', ascending=False)

# Display the summary table
print("\nGICS Sector Summary Table:")
print("=" * 140)
print(f"{'GICS Sector':<25} {'Ticker Count':<15} {'Market Cap ($B)':<20} {'Sector Earnings ($B)':<25} {'Sector P/E':<15} {'% of Total':<15}")
print("=" * 140)
for sector, row in sector_summary.iterrows():
    ticker_count = int(row['Ticker_Count'])
    market_cap_b = row['Total_Market_Cap_Billions']
    sector_earnings_b = row['Total_Sector_Earnings_Billions']
    sector_pe = f"{row['Sector_PE_Ratio']:.1f}" if pd.notna(row['Sector_PE_Ratio']) else "N/A"
    percentage = row['Market_Cap_Percentage']
    print(f"{sector:<25} {ticker_count:<15} ${market_cap_b:<19,.2f} ${sector_earnings_b:<24,.2f} {sector_pe:<15} {percentage:<14.2f}%")

# Calculate and display totals
total_tickers = sector_summary['Ticker_Count'].sum()
total_market_cap_b = total_market_cap / 1e9
total_sector_earnings = sector_summary['Total_Sector_Earnings'].sum()
total_sector_earnings_b = total_sector_earnings / 1e9
overall_pe = total_market_cap / total_sector_earnings if total_sector_earnings > 0 else None
overall_pe_str = f"{overall_pe:.1f}" if overall_pe is not None else "N/A"

print("=" * 140)
print(f"{'TOTAL':<25} {total_tickers:<15} ${total_market_cap_b:<19,.2f} ${total_sector_earnings_b:<24,.2f} {overall_pe_str:<15} {'100.00':<14}%")

# Save sector summary to CSV
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
sector_summary_path = f"gics_sector_summary_{timestamp}.csv"
sector_summary.to_csv(sector_summary_path)
print(f"\nSaved GICS sector summary to {sector_summary_path}")

# 6) Create GICS Sector Constituents Table (sorted by market cap) ------------
print("\nCreating GICS Sector Constituents Table...")

# Create a list to store all rows for the constituents table
constituents_data = []

# For each GICS sector, get all tickers sorted by market cap (descending)
for sector in sector_summary.index:
    # Get all tickers for this sector
    sector_tickers = sp500_tbl[sp500_tbl['GICS Sector'] == sector].copy()
    
    # Sort by market cap (descending) - largest to smallest
    sector_tickers_sorted = sector_tickers.sort_values('Market_Cap', ascending=False, na_position='last')
    
    # Add each ticker to the constituents data
    for _, row in sector_tickers_sorted.iterrows():
        constituents_data.append({
            'GICS_Sector': sector,
            'Ticker': row['Symbol'],
            'Company_Name': row['Security'],
            'Market_Cap': row['Market_Cap'],
            'Market_Cap_Billions': row['Market_Cap_Billions'],
            'Latest_Price': row['Latest_Price'],
            'Trailing_EPS': row['Trailing_EPS'],
            'Forward_EPS': row['Forward_EPS'],
            'Trailing_PE': row['Trailing_PE'],
            'Forward_PE': row['Forward_PE'],
            'Trailing_Earnings': row['Trailing_Earnings'],
            'EPS_Growth_Rate': row['EPS_Growth_Rate'],
            'Return_on_Equity': row['Return_on_Equity'],
            'Data_Quality_Score': row['Data_Quality_Score'],
            'Data_Completeness': row['Data_Completeness'],
            'GICS_Sub_Industry': row['GICS Sub-Industry']
        })

# Create the constituents dataframe
constituents_df = pd.DataFrame(constituents_data)

# Save the constituents table to CSV
constituents_path = f"gics_sector_constituents_{timestamp}.csv"
constituents_df.to_csv(constituents_path, index=False)
print(f"Saved GICS sector constituents table to {constituents_path}")

# Display a sample of the constituents table
print(f"\nSample of GICS Sector Constituents Table (first 20 rows):")
print("=" * 160)
print(f"{'GICS Sector':<20} {'Ticker':<8} {'Company':<25} {'Market Cap':<12} {'Trail EPS':<10} {'Fwd EPS':<10} {'Trail Earn':<12} {'ROE':<8} {'Quality':<8}")
print("=" * 160)
for _, row in constituents_df.head(20).iterrows():
    sector = row['GICS_Sector'][:19]  # Truncate if too long
    ticker = row['Ticker']
    company = row['Company_Name'][:24]  # Truncate if too long
    market_cap_b = f"${row['Market_Cap_Billions']:.1f}B" if pd.notna(row['Market_Cap_Billions']) else "N/A"
    trailing_eps = f"${row['Trailing_EPS']:.2f}" if pd.notna(row['Trailing_EPS']) else "N/A"
    forward_eps = f"${row['Forward_EPS']:.2f}" if pd.notna(row['Forward_EPS']) else "N/A"
    trailing_earnings = f"${row['Trailing_Earnings']/1e6:.1f}M" if pd.notna(row['Trailing_Earnings']) else "N/A"
    roe = f"{row['Return_on_Equity']:.1f}%" if pd.notna(row['Return_on_Equity']) else "N/A"
    quality = f"{row['Data_Quality_Score']:.0f}/10" if pd.notna(row['Data_Quality_Score']) else "N/A"
    print(f"{sector:<20} {ticker:<8} {company:<25} {market_cap_b:<12} {trailing_eps:<10} {forward_eps:<10} {trailing_earnings:<12} {roe:<8} {quality:<8}")

print(f"\nTotal rows in constituents table: {len(constituents_df)}")

output_path = f"sp500_latest_{timestamp}.csv"
sp500_tbl.to_csv(output_path, index=False)
print(f"\nSaved results to {output_path}")

Constituent highlights

• S&P 500 constituents cover 503 companies with a combined market capitalization of $56.7 trillion.

• Total Market Cap: $56.7 trillion

• Average Company Size: $112.8 billion

• Data Quality Score: 7.4/10

• Data Completeness: 92.1%

Portfolio Weights: All tables now include portfolio weight columns showing each company’s and sector’s percentage allocation within the S&P 500 index.

Sector Overview

GICS Sector Summary
Market Capitalization and Key Metrics by Sector
GICS Sector	Companies	Total_Market_Cap_Billions	Avg_Market_Cap_Billions	Median_Market_Cap_Billions	Avg Trailing P/E	Avg Forward P/E	Avg ROE	Avg Data Quality	Portfolio Weight (%)	Total Market Cap	Avg Market Cap	Median Market Cap
Information Technology	68	18617.85	273.79191	56.965	54.4	30.9	0.367	7.7	32.8	18.6T	273.8B	57B
Financials	75	7653.66	102.04880	47.810	36.1	18.4	0.186	7.3	13.5	7.7T	102B	47.8B
Consumer Discretionary	51	6231.98	122.19569	36.570	32.4	23.5	0.305	7.5	11.0	6.2T	122.2B	36.6B
Communication Services	24	6049.05	252.04375	36.625	50.1	19.5	0.164	7.0	10.7	6T	252B	36.6B
Health Care	60	5105.90	85.09833	36.735	31.4	19.5	2.614	7.7	9.0	5.1T	85.1B	36.7B
Industrials	78	4676.03	59.94910	47.630	34.8	31.5	0.375	7.5	8.2	4.7T	59.9B	47.6B
Consumer Staples	37	3301.35	89.22568	27.480	22.9	19.1	0.379	7.3	5.8	3.3T	89.2B	27.5B
Energy	22	1673.36	76.06182	46.050	21.1	14.5	0.158	7.2	3.0	1.7T	76.1B	46B
Utilities	31	1288.17	41.55387	28.420	22.3	18.8	0.114	7.4	2.3	1.3T	41.6B	28.4B
Real Estate	31	1103.93	35.61065	26.530	202.9	55.7	0.030	7.0	1.9	1.1T	35.6B	26.5B
Materials	26	1043.57	40.13731	24.575	55.9	18.9	0.120	7.2	1.8	1T	40.1B	24.6B
TOTAL	503	56744.85	5158.62273	4676.030	51.3	24.6	0.438	7.3	100.0	56.7T	5158.6B	4676B
Data source: Yahoo Finance via yfinance API | Generated: September 2025

Sector Total Returns

Sector Total Returns Summary
Performance Metrics and Market Capitalization by Sector (S&P 500 TOTAL at bottom)
GICS Sector	Companies	Market Cap (T$)	Price Return (%)	Dividend Yield (%)	Total Return (%)	Avg Stock Return (%)
Communication Services	24	6.05	41.2	0.77	41.9	29.3
Information Technology	68	18.62	29.4	0.62	30.0	22.6
Consumer Discretionary	51	6.23	25.8	0.68	26.5	24.7
Financials	75	7.65	22.3	1.61	23.9	21.1
Industrials	78	4.68	19.4	1.69	21.1	19.6
Utilities	31	1.29	12.7	3.30	16.0	21.6
Energy	22	1.67	5.6	4.19	9.7	11.3
Consumer Staples	37	3.30	4.5	2.45	6.9	−1.0
Materials	26	1.04	3.6	2.10	5.7	3.6
Real Estate	31	1.10	−1.3	3.60	2.3	1.2
Health Care	60	5.11	−9.8	1.93	−7.8	−4.7
S&P 500 TOTAL	503	56.74	19.5	1.34	20.9	NA
Data source: Sector Performance Analysis | Generated: September 2025

Top 20 Companies by Market Cap

Top 20 S&P 500 Companies
Ranked by Market Capitalization
Ticker	Company Name	Sector	Market_Cap_Billions	Portfolio Weight (%)	Price ($)	Trailing P/E	Forward P/E	ROE	Market Cap
NVDA	Nvidia	Information Technology	4154.09	7.3	170.62	48.5	41.4	1.094	4.2T
MSFT	Microsoft	Information Technology	3756.35	6.6	505.35	37.0	33.8	0.333	3.8T
AAPL	Apple Inc.	Information Technology	3538.99	6.2	238.47	36.2	28.7	1.498	3.5T
AMZN	Amazon	Consumer Discretionary	2410.16	4.2	225.99	34.4	36.7	0.248	2.4T
META	Meta Platforms	Communication Services	1598.63	2.8	737.05	26.7	29.1	0.406	1.6T
AVGO	Broadcom	Information Technology	1422.28	2.5	302.39	110.4	49.0	0.190	1.4T
GOOGL	Alphabet Inc. (Class A)	Communication Services	1341.75	2.4	230.66	24.6	25.7	0.348	1.3T
GOOG	Alphabet Inc. (Class C)	Communication Services	1254.87	2.2	231.10	24.7	25.8	0.348	1.3T
TSLA	Tesla, Inc.	Consumer Discretionary	1077.59	1.9	334.09	198.9	103.1	0.082	1.1T
JPM	JPMorgan Chase	Financials	823.58	1.5	299.51	15.4	17.9	0.162	823.6B
WMT	Walmart	Consumer Staples	792.82	1.4	99.44	37.5	36.6	0.234	792.8B
BRK.B	Berkshire Hathaway	Financials	691.33	1.2	501.49	17.2	25.0	0.099	691.3B
LLY	Lilly (Eli)	Health Care	661.43	1.2	737.83	48.3	32.6	0.863	661.4B
ORCL	Oracle Corporation	Information Technology	627.63	1.1	223.45	51.4	31.2	0.824	627.6B
V	Visa Inc.	Financials	596.02	1.1	350.87	34.2	27.7	0.518	596B
MA	Mastercard	Financials	532.33	0.9	593.28	39.9	36.2	1.769	532.3B
NFLX	Netflix	Communication Services	521.04	0.9	1,226.18	52.3	51.6	0.435	521B
XOM	ExxonMobil	Energy	477.10	0.8	111.91	15.9	14.2	0.118	477.1B
JNJ	Johnson & Johnson	Health Care	428.68	0.8	178.00	19.0	16.8	0.302	428.7B
COST	Costco	Consumer Staples	421.21	0.7	949.78	53.8	48.3	0.321	421.2B

Sector Deep Dives

Information Technology

Information Technology Sector - 68 companies, $18.6T market cap
Top 20 Companies by Market Cap
Ticker	Company Name	Sub-Industry	Market_Cap_Billions	Portfolio Weight (%)	Price ($)	Trailing P/E	Forward P/E	ROE	Market Cap
NVDA	Nvidia	Semiconductors	4154.09	7.3	170.62	48.5	41.4	1.094	4.2T
MSFT	Microsoft	Systems Software	3756.35	6.6	505.35	37.0	33.8	0.333	3.8T
AAPL	Apple Inc.	Technology Hardware, Storage & Peripherals	3538.99	6.2	238.47	36.2	28.7	1.498	3.5T
AVGO	Broadcom	Semiconductors	1422.28	2.5	302.39	110.4	49.0	0.190	1.4T
ORCL	Oracle Corporation	Application Software	627.63	1.1	223.45	51.4	31.2	0.824	627.6B
PLTR	Palantir Technologies	Application Software	352.28	0.6	154.90	516.3	329.6	0.152	352.3B
CSCO	Cisco	Communications Equipment	267.97	0.5	67.67	25.9	17.4	0.226	268B
AMD	Advanced Micro Devices	Semiconductors	263.11	0.5	162.13	97.1	31.8	0.047	263.1B
CRM	Salesforce	Application Software	245.17	0.4	256.45	40.1	23.0	0.103	245.2B
IBM	IBM	IT Consulting & Other Services	227.38	0.4	244.10	39.3	23.0	0.227	227.4B
NOW	ServiceNow	Systems Software	190.79	0.3	919.38	115.9	55.1	0.170	190.8B
INTU	Intuit	Application Software	186.53	0.3	668.68	49.0	30.1	0.203	186.5B
TXN	Texas Instruments	Semiconductors	177.95	0.3	195.74	35.8	33.3	0.300	177.9B
ANET	Arista Networks	Communications Equipment	172.67	0.3	137.38	53.9	14.1	0.336	172.7B
QCOM	Qualcomm	Semiconductors	169.71	0.3	157.28	15.2	12.9	0.446	169.7B
ACN	Accenture	IT Consulting & Other Services	158.30	0.3	254.15	20.2	18.1	0.269	158.3B
ADBE	Adobe Inc.	Application Software	147.83	0.3	348.50	22.3	17.0	0.523	147.8B
APH	Amphenol	Electronic Components	135.14	0.2	110.69	44.1	51.2	0.310	135.1B
MU	Micron Technology	Semiconductors	132.86	0.2	118.72	21.4	9.2	0.131	132.9B
PANW	Palo Alto Networks	Systems Software	128.11	0.2	191.53	119.7	26.6	0.175	128.1B

Financials

Financials Sector - 75 companies, $7.7T market cap
Top 20 Companies by Market Cap
Ticker	Company Name	Sub-Industry	Market_Cap_Billions	Portfolio Weight (%)	Price ($)	Trailing P/E	Forward P/E	ROE	Market Cap
JPM	JPMorgan Chase	Diversified Banks	823.58	1.5	299.51	15.4	17.9	0.162	823.6B
BRK.B	Berkshire Hathaway	Multi-Sector Holdings	691.33	1.2	501.49	17.2	25.0	0.099	691.3B
V	Visa Inc.	Transaction & Payment Processing Services	596.02	1.1	350.87	34.2	27.7	0.518	596B
MA	Mastercard	Transaction & Payment Processing Services	532.33	0.9	593.28	39.9	36.2	1.769	532.3B
BAC	Bank of America	Diversified Banks	370.79	0.7	50.06	14.7	13.7	0.095	370.8B
WFC	Wells Fargo	Diversified Banks	257.91	0.5	80.51	13.8	14.7	0.115	257.9B
MS	Morgan Stanley	Investment Banking & Brokerage	237.10	0.4	148.53	16.8	18.7	0.141	237.1B
AXP	American Express	Consumer Finance	225.26	0.4	323.71	22.7	21.4	0.328	225.3B
GS	Goldman Sachs	Investment Banking & Brokerage	221.16	0.4	730.56	16.1	17.6	0.127	221.2B
C	Citigroup	Diversified Banks	174.94	0.3	95.03	14.0	13.2	0.068	174.9B
SCHW	Charles Schwab Corporation	Investment Banking & Brokerage	174.66	0.3	96.22	25.9	25.3	0.156	174.7B
BLK	BlackRock	Asset Management & Custody Banks	170.26	0.3	1,099.49	26.6	22.4	0.139	170.3B
SPGI	S&P Global	Financial Exchanges & Data	164.63	0.3	539.24	41.4	32.1	0.113	164.6B
PGR	Progressive Corporation	Property & Casualty Insurance	144.68	0.3	246.72	13.9	17.9	0.373	144.7B
COF	Capital One	Consumer Finance	141.54	0.2	221.33	614.8	14.1	0.000	141.5B
BX	Blackstone Inc.	Asset Management & Custody Banks	123.34	0.2	167.34	45.2	28.7	0.288	123.3B
KKR	KKR & Co.	Asset Management & Custody Banks	120.13	0.2	134.83	63.6	22.2	0.075	120.1B
CB	Chubb Limited	Property & Casualty Insurance	110.92	0.2	278.22	12.3	11.8	0.136	110.9B
MMC	Marsh McLennan	Insurance Brokers	100.92	0.2	205.27	24.7	21.8	0.284	100.9B
ICE	Intercontinental Exchange	Financial Exchanges & Data	100.25	0.2	175.13	33.5	26.0	0.111	100.2B

Interactive Data Explorer

For detailed exploration of all 503 companies, use the interactive table below:

Data Quality Assessment

Data Quality Summary
Completeness and Quality of Financial Data
Data Quality Metric	Value
Companies with Trailing EPS	503
Companies with Forward EPS	500
Companies with Trailing P/E	480
Companies with Forward P/E	503
Companies with ROE Data	475
Average Data Quality Score	7.4
Average Data Completeness	92.1%

Key Insights

1. Market Concentration: The top 10 companies represent approximately 40% of the total S&P 500 market capitalization.

2. Sector Dominance: Information Technology leads with $18.6 trillion in market cap, followed by Financials at $7.7 trillion.

3. Data Quality: Excellent data coverage with 92.1% completeness and an average quality score of 7.4/10.

4. Valuation Metrics: Forward P/E ratios are generally lower than trailing P/E ratios, suggesting earnings growth expectations.

5. Geographic Distribution: All companies are US-based, providing exposure to the largest equity market globally.

6. Portfolio Weights: Portfolio weight columns (defined as company market capitalization / total market capitalization) show each company’s and sector’s percentage allocation within the S&P 500 index.

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Report generated on 2025-09-04 using data from Yahoo Finance via yfinance API.