#!/usr/bin/env python3 """ Step 3: SentinelOne Valuation Analysis This script applies precedent transaction multiples to SentinelOne's financials to calculate implied valuation ranges, share prices, and takeout premiums. Valuation Multiples (from Step 2): - Conservative: 9.2x (Median ex-Wiz) - Base Case: 10.0x (Mean ex-Wiz) - Optimistic: 14.2x (75th Percentile) """ import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.patches as mpatches from pathlib import Path from datetime import datetime # Constants SESSION_DIR = Path("/app/sandbox/session_20260203_085757_d926b2c7d1fb") RESULTS_DIR = SESSION_DIR / "results" FIGURES_DIR = SESSION_DIR / "figures" # Valuation multiples from Step 2 MULTIPLES = { "Conservative": 9.2, "Base Case": 10.0, "Optimistic": 14.2 } def load_sentinelone_data(): """Load SentinelOne's financial data from screening results.""" print("=" * 60) print("Step 3: SentinelOne Valuation Analysis") print("=" * 60) print("\n[1/6] Loading financial data...") df = pd.read_csv(RESULTS_DIR / "financial_screening_data.csv") # Filter for SentinelOne (ticker: S) s_data = df[df["Ticker"] == "S"].iloc[0] financials = { "company": s_data["Company"], "ticker": s_data["Ticker"], "current_price": s_data["Current_Price"], "market_cap": s_data["Market_Cap"], "enterprise_value": s_data["Enterprise_Value"], "ltm_revenue": s_data["LTM_Revenue"], "revenue_growth": s_data["Revenue_Growth"], "ev_revenue_multiple": s_data["EV_Revenue_Multiple"] } print(f" Company: {financials['company']} ({financials['ticker']})") print(f" Current Price: ${financials['current_price']:.2f}") print(f" Market Cap: ${financials['market_cap']/1e9:.2f}B") print(f" Enterprise Value: ${financials['enterprise_value']/1e9:.2f}B") print(f" LTM Revenue: ${financials['ltm_revenue']/1e9:.3f}B") print(f" Revenue Growth: {financials['revenue_growth']*100:.1f}%") print(f" Current EV/Revenue: {financials['ev_revenue_multiple']:.2f}x") return financials def derive_balance_sheet_proxies(financials): """Derive Net Debt and Shares Outstanding from available data.""" print("\n[2/6] Deriving balance sheet proxies...") # Net Debt = EV - Market Cap # Negative value indicates net cash position net_debt = financials["enterprise_value"] - financials["market_cap"] # Shares Outstanding = Market Cap / Current Price shares_outstanding = financials["market_cap"] / financials["current_price"] financials["net_debt"] = net_debt financials["shares_outstanding"] = shares_outstanding cash_status = "Net Cash" if net_debt < 0 else "Net Debt" print(f" Net Debt: ${net_debt/1e9:.3f}B ({cash_status} of ${abs(net_debt)/1e9:.3f}B)") print(f" Shares Outstanding: {shares_outstanding/1e6:.1f}M") return financials def calculate_implied_valuations(financials): """Apply valuation multiples to calculate implied EV, share prices, and premiums.""" print("\n[3/6] Calculating implied valuations...") ltm_revenue = financials["ltm_revenue"] net_debt = financials["net_debt"] shares_outstanding = financials["shares_outstanding"] current_price = financials["current_price"] valuation_results = [] print(f"\n LTM Revenue: ${ltm_revenue/1e9:.3f}B") print(f" Valuation Multiples Applied: {list(MULTIPLES.keys())}") print("\n Scenario Results:") print(" " + "-" * 70) for scenario, multiple in MULTIPLES.items(): # Implied Enterprise Value implied_ev = ltm_revenue * multiple # Implied Market Cap = Implied EV - Net Debt implied_market_cap = implied_ev - net_debt # Implied Share Price = Implied Market Cap / Shares Outstanding implied_share_price = implied_market_cap / shares_outstanding # Takeout Premium (percentage upside vs current price) takeout_premium = (implied_share_price - current_price) / current_price * 100 result = { "Scenario": scenario, "EV_Revenue_Multiple": multiple, "Implied_EV_B": implied_ev / 1e9, "Implied_Market_Cap_B": implied_market_cap / 1e9, "Implied_Share_Price": implied_share_price, "Current_Price": current_price, "Takeout_Premium_Pct": takeout_premium } valuation_results.append(result) print(f" {scenario}:") print(f" Multiple: {multiple}x | Implied EV: ${implied_ev/1e9:.2f}B | " f"Share Price: ${implied_share_price:.2f} | Premium: {takeout_premium:+.1f}%") print(" " + "-" * 70) return pd.DataFrame(valuation_results) def create_football_field_chart(valuation_df, financials): """Generate a football field chart visualizing valuation ranges.""" print("\n[4/6] Generating football field chart...") # Set up the figure fig, ax = plt.subplots(figsize=(12, 6)) current_price = financials["current_price"] # Get valuation ranges conservative = valuation_df[valuation_df["Scenario"] == "Conservative"]["Implied_Share_Price"].values[0] base_case = valuation_df[valuation_df["Scenario"] == "Base Case"]["Implied_Share_Price"].values[0] optimistic = valuation_df[valuation_df["Scenario"] == "Optimistic"]["Implied_Share_Price"].values[0] # Colors colors = { "current": "#34495e", "conservative": "#3498db", "base": "#2ecc71", "optimistic": "#e74c3c", "range": "#95a5a6" } # Y-positions for horizontal bars y_positions = { "Precedent\nTransaction\nRange": 1, } bar_height = 0.4 # Draw the valuation range bar (conservative to optimistic) ax.barh(y=1, width=optimistic - conservative, left=conservative, height=bar_height, color=colors["range"], alpha=0.3, edgecolor=colors["range"]) # Draw scenario markers for scenario, price, color, marker in [ ("Conservative (9.2x)", conservative, colors["conservative"], "s"), ("Base Case (10.0x)", base_case, colors["base"], "D"), ("Optimistic (14.2x)", optimistic, colors["optimistic"], "^") ]: ax.scatter(price, 1, marker=marker, s=200, c=color, zorder=5, edgecolors='white', linewidths=2) ax.annotate(f"${price:.2f}", (price, 1 + 0.35), ha='center', va='bottom', fontsize=10, fontweight='bold') # Draw current price line ax.axvline(x=current_price, color=colors["current"], linestyle='--', linewidth=2, label=f'Current Price (${current_price:.2f})') # Add current price annotation ax.annotate(f"Current\n${current_price:.2f}", xy=(current_price, 0.5), xytext=(current_price - 3, 0.3), fontsize=10, fontweight='bold', color=colors["current"], arrowprops=dict(arrowstyle='->', color=colors["current"], lw=1.5)) # Formatting ax.set_xlim(0, max(optimistic, current_price) * 1.15) ax.set_ylim(0, 2) ax.set_yticks([1]) ax.set_yticklabels(["Precedent\nTransaction\nRange"], fontsize=11) ax.set_xlabel("Share Price ($)", fontsize=12, fontweight='bold') # Add premium annotations premiums = valuation_df.set_index("Scenario")["Takeout_Premium_Pct"] ax.text(conservative, 0.55, f"+{premiums['Conservative']:.0f}%", ha='center', fontsize=9, color=colors["conservative"]) ax.text(base_case, 0.55, f"+{premiums['Base Case']:.0f}%", ha='center', fontsize=9, color=colors["base"]) ax.text(optimistic, 0.55, f"+{premiums['Optimistic']:.0f}%", ha='center', fontsize=9, color=colors["optimistic"]) # Title ax.set_title("SentinelOne (S) Valuation Football Field\nImplied Share Prices Based on Precedent Transaction Multiples", fontsize=14, fontweight='bold', pad=20) # Legend legend_elements = [ mpatches.Patch(facecolor=colors["conservative"], edgecolor='white', label='Conservative (9.2x)'), mpatches.Patch(facecolor=colors["base"], edgecolor='white', label='Base Case (10.0x)'), mpatches.Patch(facecolor=colors["optimistic"], edgecolor='white', label='Optimistic (14.2x)'), plt.Line2D([0], [0], color=colors["current"], linestyle='--', linewidth=2, label=f'Current (${current_price:.2f})') ] ax.legend(handles=legend_elements, loc='upper right', fontsize=9, framealpha=0.9) # Add summary box summary_text = (f"LTM Revenue: ${financials['ltm_revenue']/1e9:.2f}B\n" f"Current EV/Rev: {financials['ev_revenue_multiple']:.1f}x\n" f"Implied Range: ${conservative:.2f} - ${optimistic:.2f}") ax.text(0.02, 0.98, summary_text, transform=ax.transAxes, fontsize=9, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5)) # Grid and spine styling ax.grid(axis='x', alpha=0.3) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['left'].set_visible(False) plt.tight_layout() # Save figure output_path = FIGURES_DIR / "valuation_football_field.png" plt.savefig(output_path, dpi=300, bbox_inches='tight', facecolor='white') plt.close() print(f" Saved: {output_path}") return output_path def save_valuation_metrics(valuation_df, financials): """Save detailed valuation metrics to CSV.""" print("\n[5/6] Saving valuation metrics...") # Add additional context columns valuation_df["Company"] = financials["company"] valuation_df["Ticker"] = financials["ticker"] valuation_df["LTM_Revenue_B"] = financials["ltm_revenue"] / 1e9 valuation_df["Net_Debt_B"] = financials["net_debt"] / 1e9 valuation_df["Shares_Outstanding_M"] = financials["shares_outstanding"] / 1e6 valuation_df["Analysis_Date"] = datetime.now().isoformat() # Reorder columns for clarity column_order = [ "Company", "Ticker", "Scenario", "EV_Revenue_Multiple", "LTM_Revenue_B", "Implied_EV_B", "Net_Debt_B", "Implied_Market_Cap_B", "Shares_Outstanding_M", "Implied_Share_Price", "Current_Price", "Takeout_Premium_Pct", "Analysis_Date" ] valuation_df = valuation_df[column_order] output_path = RESULTS_DIR / "sentinelone_valuation.csv" valuation_df.to_csv(output_path, index=False) print(f" Saved: {output_path}") print(f" Columns: {list(valuation_df.columns)}") return output_path def update_screening_summary(valuation_df, financials): """Append Valuation Analysis section to screening_summary.md.""" print("\n[6/6] Updating screening summary...") summary_path = RESULTS_DIR / "screening_summary.md" # Build the new section content valuation_section = """ --- ## Step 3: SentinelOne Valuation Analysis ### Objective Apply precedent transaction multiples to SentinelOne's financials to calculate implied enterprise values, share prices, and potential takeout premiums. ### Input Data | Metric | Value | |--------|-------| | LTM Revenue | ${ltm_rev:.2f}B | | Current Share Price | ${current_price:.2f} | | Market Cap | ${market_cap:.2f}B | | Enterprise Value | ${ev:.2f}B | | Net Debt (Cash) | ${net_debt:.3f}B | | Shares Outstanding | {shares:.1f}M | | Current EV/Revenue | {ev_rev:.2f}x | ### Valuation Scenarios Using the multiples derived from precedent transaction analysis (Step 2): | Scenario | Multiple | Implied EV | Implied Share Price | Takeout Premium | |----------|----------|------------|---------------------|-----------------| {scenario_rows} ### Visual Summary ![Football Field Chart](../figures/valuation_football_field.png) ### Key Findings 1. **Current Valuation Discount**: SentinelOne trades at {ev_rev:.1f}x EV/Revenue, significantly below the precedent transaction median of 9.2x - 10.0x, suggesting potential undervaluation relative to takeout peers. 2. **Takeout Premium Range**: Based on precedent multiples, a potential acquirer would likely pay: - **Conservative Case**: ${conservative_price:.2f}/share (+{conservative_prem:.0f}% premium) - **Base Case**: ${base_price:.2f}/share (+{base_prem:.0f}% premium) - **Optimistic Case**: ${optimistic_price:.2f}/share (+{optimistic_prem:.0f}% premium) 3. **Implied Enterprise Value**: The EV range spans ${conservative_ev:.1f}B to ${optimistic_ev:.1f}B, representing a {ev_upside_low:.0f}% to {ev_upside_high:.0f}% increase over current trading levels. 4. **Strategic Premium Potential**: Given SentinelOne's strong growth profile ({growth:.1f}% YoY) and AI-native security platform, a strategic acquirer (similar to Cisco/Splunk or Google/Mandiant) could justify multiples toward the upper end of the range. ### Outputs Generated - `results/sentinelone_valuation.csv` - Detailed valuation metrics by scenario - `figures/valuation_football_field.png` - Visual comparison of valuation ranges ### Next Steps - **Step 4**: Acquirer Financial Profile Analysis - Assess potential acquirers' capacity to fund the transaction - **Step 5**: Accretion/Dilution Analysis - Model deal impact on acquirer EPS """.format( ltm_rev=financials["ltm_revenue"]/1e9, current_price=financials["current_price"], market_cap=financials["market_cap"]/1e9, ev=financials["enterprise_value"]/1e9, net_debt=financials["net_debt"]/1e9, shares=financials["shares_outstanding"]/1e6, ev_rev=financials["ev_revenue_multiple"], scenario_rows="\n".join([ f"| {row['Scenario']} | {row['EV_Revenue_Multiple']}x | ${row['Implied_EV_B']:.2f}B | ${row['Implied_Share_Price']:.2f} | +{row['Takeout_Premium_Pct']:.0f}% |" for _, row in valuation_df.iterrows() ]), conservative_price=valuation_df[valuation_df["Scenario"]=="Conservative"]["Implied_Share_Price"].values[0], conservative_prem=valuation_df[valuation_df["Scenario"]=="Conservative"]["Takeout_Premium_Pct"].values[0], base_price=valuation_df[valuation_df["Scenario"]=="Base Case"]["Implied_Share_Price"].values[0], base_prem=valuation_df[valuation_df["Scenario"]=="Base Case"]["Takeout_Premium_Pct"].values[0], optimistic_price=valuation_df[valuation_df["Scenario"]=="Optimistic"]["Implied_Share_Price"].values[0], optimistic_prem=valuation_df[valuation_df["Scenario"]=="Optimistic"]["Takeout_Premium_Pct"].values[0], conservative_ev=valuation_df[valuation_df["Scenario"]=="Conservative"]["Implied_EV_B"].values[0], optimistic_ev=valuation_df[valuation_df["Scenario"]=="Optimistic"]["Implied_EV_B"].values[0], ev_upside_low=((valuation_df[valuation_df["Scenario"]=="Conservative"]["Implied_EV_B"].values[0]*1e9) / financials["enterprise_value"] - 1) * 100, ev_upside_high=((valuation_df[valuation_df["Scenario"]=="Optimistic"]["Implied_EV_B"].values[0]*1e9) / financials["enterprise_value"] - 1) * 100, growth=financials["revenue_growth"]*100 ) # Read existing content and append with open(summary_path, 'r') as f: existing_content = f.read() with open(summary_path, 'w') as f: f.write(existing_content + valuation_section) print(f" Updated: {summary_path}") print(" Added section: 'Step 3: SentinelOne Valuation Analysis'") return summary_path def main(): """Execute the complete valuation analysis workflow.""" print("\n" + "=" * 60) print("STARTING STEP 3: SENTINELONE VALUATION ANALYSIS") print("=" * 60) # Step 1: Load data financials = load_sentinelone_data() # Step 2: Derive balance sheet proxies financials = derive_balance_sheet_proxies(financials) # Step 3: Calculate implied valuations valuation_df = calculate_implied_valuations(financials) # Step 4: Generate football field chart create_football_field_chart(valuation_df, financials) # Step 5: Save valuation metrics to CSV save_valuation_metrics(valuation_df, financials) # Step 6: Update screening summary update_screening_summary(valuation_df, financials) # Final summary print("\n" + "=" * 60) print("STEP 3 COMPLETE") print("=" * 60) print("\nKey Results:") for _, row in valuation_df.iterrows(): print(f" {row['Scenario']}: ${row['Implied_Share_Price']:.2f} (+{row['Takeout_Premium_Pct']:.0f}% premium)") print("\nOutputs:") print(f" - {RESULTS_DIR}/sentinelone_valuation.csv") print(f" - {FIGURES_DIR}/valuation_football_field.png") print(f" - {RESULTS_DIR}/screening_summary.md (updated)") return valuation_df, financials if __name__ == "__main__": valuation_df, financials = main()