Prompt 1 — Comprehensive 10-Year DCF Model from Historical Financials to Fair Value

Production-ready Prompt Text:

Act as a professional equity analyst. Using the provided historical financials and market inputs, construct a 10-year discounted cash flow (DCF) model. Return output in VALID JSON with keys: assumptions, projections (yearly), wacc (detailed breakdown), terminal_values (Gordon Growth and Exit Multiple), valuation (enterprise value, equity value, per-share fair value), and sensitivity_table. Include calculation formulas and rounding conventions. Avoid extraneous commentary.

Context Variables

• company_name: “Acme Co.”
• fiscal_year_end: “2025-12-31”
• historical_revenue: {2021: 2,100,000; 2022: 2,450,000; 2023: 2,900,000; 2024: 3,300,000} USD
• historical_operating_income, capex_schedule, depreciation_schedule
• working_capital_rules: days receivable, payable; tax_rate (e.g., 21%)
• risk_free_rate (4.10%), market_premium (6.0%), beta (1.05), pre_tax_cost_of_debt (6.5%), target_debt_to_total_capital (25%)
• terminal_Gordon_Growth_rate (2.5%), exit_multiple (e.g., 10.5x NTM EBITDA)
• shares_outstanding (excluding dilutive instruments), net_debt (cash minus debt)

Expected Output Format (JSON Schema)

{
  "assumptions": {
    "discount_rate_description": "WACC calculated using CAPM and cost of debt",
    "tax_rate": 0.21,
    "growth_assumptions": {"year1": 0.08, "year2": 0.06, ...}
  },
  "projections": [
    {"year": 2026, "revenue": 3300000, "unlevered_fcf": 265000, ...},
    ...
  ],
  "wacc": {
    "cost_of_equity": 0.085,
    "cost_of_debt_after_tax": 0.0525,
    "wacc": 0.075
  },
  "terminal_values": {
    "gordon_growth": 123456789,
    "exit_multiple": 130000000
  },
  "valuation": {
    "enterprise_value": 250000000,
    "equity_value": 200000000,
    "per_share": 24.50
  },
  "sensitivity_table": {
    "g_growth": {"1.5%": ..., "2.5%": ...},
    "wacc": {"7.0%": ...}
  }
}

Usage Notes

• Maintain temperature at 0.0 for deterministic, consistent numerical results
• Validate terminal EV/EBITDA multiples—values above 30x merit review
• For complex outputs, paginate requests to stay within token constraints

Example Input Snippet

{
  "company_name": "Acme Co.",
  "fiscal_year_end": "2025-12-31",
  "historical_revenue": {"2021": 2100000, "2022": 2450000, "2023": 2900000, "2024": 3300000},
  "tax_rate": 0.21,
  "shares_outstanding": 8200000,
  "net_debt": 50000000,
  "risk_free_rate": 0.041,
  "market_premium": 0.06,
  "beta": 1.05
}

Example Output (Abridged)

{
  "assumptions": {"tax_rate": 0.21, "terminal_growth": 0.025, "wacc": 0.075},
  "projections": [{"year": 2026, "unlevered_fcf": 265000}, {"year": 2027, "unlevered_fcf": 285500}, ...],
  "wacc": {"cost_of_equity": 0.085, "cost_of_debt_after_tax": 0.0525, "wacc": 0.075},
  "terminal_values": {"gordon_growth": 125000000, "exit_multiple": 138000000},
  "valuation": {"enterprise_value": 260000000, "equity_value": 210000000, "per_share": 25.61}
}

Prompt 2 — Dynamic WACC Calculator with Sensitivity Analysis

Production-ready Prompt Text:

Calculate a detailed weighted average cost of capital (WACC) from inputs provided. Compute cost of equity using CAPM (cost_of_equity = risk_free_rate + beta * market_premium), pre-tax and after-tax cost of debt, and capital structure weights. Generate a sensitivity matrix varying beta (0.8 to 1.6, step 0.1) and market premium (4.0% to 8.0%, step 0.5%). Output a CSV-compatible table with columns: beta, market_premium, wacc.

Context Variables

• risk_free_rate (e.g., 4.10%)
• base_beta (1.05)
• market_premium_base (6.0%)
• pre_tax_cost_of_debt (6.5%)
• tax_rate (21%)
• debt_weight (25%), equity_weight (75%)

Expected Output Snapshot

{
  "base_wacc": 0.075,
  "components": {
    "cost_of_equity": 0.085,
    "cost_of_debt_pre_tax": 0.065,
    "cost_of_debt_after_tax": 0.05135
  },
  "sensitivity_grid": [
    {"beta": 0.8, "market_premium": 0.04, "wacc": 0.0623},
    {"beta": 0.9, "market_premium": 0.045, "wacc": 0.0644},
    ...
  ]
}

Best Practice Tips

• Regularly update market premium and risk-free rate using authoritative sources
• Recalculate WACC when adjusting capital structure or tax assumptions

Prompt 3 — Terminal Value Methodology Comparison and Sensitivity Analysis

Production-ready Prompt Text:

Calculate terminal value using: (a) Gordon Growth Model with specified growth rate and WACC, and (b) Exit Multiple methodology leveraging peer EV/EBITDA multiples. Normalize peer multiples by computing median and quartiles (25th, 75th percentiles). Provide a comparison table with implied per-share values under three valuation scenarios: conservative (25th percentile), base (median), and aggressive (75th percentile). Return JSON including peer multiples used.

Context Variables

• last_projected_ebitda (e.g., 120,000,000 for 2029)
• peer_ev_ebitda_list (e.g., [8.5, 9.8, 11.2, 14.0, 10.3])
• wacc, terminal_growth_rate
• shares_outstanding, net_debt

Expected Output Layout

{
  "peer_stats": {"25th": 9.3, "median": 10.3, "75th": 11.6},
  "terminal_values": {
    "conservative": 950000000,
    "base": 1236000000,
    "aggressive": 1392000000
  },
  "per_share": {
    "conservative": 12.34,
    "base": 16.04,
    "aggressive": 18.08
  }
}

Usage Notes

• Flag terminal multiples that imply unrealistic long-term growth beyond economic fundamentals
• When peer sample size is small (<5), prompt model to augment peer set or warn on confidence

Prompt 4 — Free Cash Flow Bridge and Three-Statement Reconciliation

Production-ready Prompt Text:

Given income statement, balance sheet, and cash flow statement data (annual or quarterly), reconcile net income to unlevered free cash flow (FCF) per period. Provide a stepwise bridge listing net income, depreciation & amortization, capex, changes in working capital, tax effects, and resulting unlevered FCF. Output CSV-compatible table and JSON array with keys: period, net_income, d_and_a, capex, wc_change, unlevered_fcf.

Context Variables

• periods: [“2023-Q4”, “2024-Q1”, “2024-Q2”, …]
• net_income, depreciation_and_amortization, capex, changes_in_working_capital, interest_expense (for tax adjustment)

Expected Output Format

[
  {"period": "2024-Q1", "net_income": 34000000, "d&a": 6500000, "capex": 12000000, "wc_change": -1500000, "unlevered_fcf": 37000000},
  ...
]

Usage Notes

• Explicitly flag missing or negative working capital line items; fail the prompt with error messaging if key data is omitted

Prompt 5 — Batch Scenario and Sensitivity Generator for DCF Models

Production-ready Prompt Text:

Generate three detailed DCF valuation scenarios: Base, Bear, and Bull, based on supplied assumptions. Include for each scenario a 10-year free cash flow projection, WACC, terminal value (Gordon Growth), enterprise and equity valuations, per-share fair value, and a concise 2-3 sentence narrative summarizing key drivers. Output structured JSON keyed by scenario name, including sensitivity matrices for WACC versus terminal growth rate.

Context Variables

• base_assumptions (growth rates, margins)
• downside_adjustments (e.g., margin minus 200bps)
• upside_adjustments (e.g., margin plus 150bps)
• scenario_probabilities (optional; e.g., base 50%, bear 30%, bull 20%)

Expected Output Structure

{
  "Base": {"fcf": [...], "wacc": 0.075, "terminal": 120000000, "equity_value": 200000000, "per_share": 24.4, "narrative": "Base case forecasts margin expansion..."},
  "Bear": {...},
  "Bull": {...}
}

Usage Notes

• Leverage scenario probabilities to compute weighted average equity valuations where applicable
• Ensure scenario narratives clearly link quantitative drivers such as revenue growth, margin shifts, and capex changes without vague language

Prompt 6 — Earnings Call Summary and KPI Extraction

Production-ready Prompt Text:

Extract and summarize the top 8 actionable items from the provided earnings call transcript or bullet points into a JSON structure: {items: [{title, type (guidance/product/operations/finance), quantitative_impact_estimate, confidence_level (low/medium/high), citation (timestamp or paragraph ID)}]}. Also extract numeric updated guidance ranges. Output JSON only.

Context Variables

• transcript_text (UTF-8, abide by token limits)
• recent_guidance (previous guidance figures for comparative analysis)

Expected Output Format

{
  "items": [
    {"title": "Revenue guidance raised", "type": "guidance", "quantitative_impact_estimate": "+3.5% FY revenue", "confidence": "high", "citation": "Q&A 12:34"},
    ...
  ],
  "updated_guidance": {"FY2026_revenue": [320000000, 330000000]}
}

Implementation Notes

• For transcripts exceeding token limits, segment by themes (e.g., prepared remarks, Q&A), summarize each, then synthesize in aggregate
• Attach precise timestamps or paragraph IDs to enable traceability of quantitative figures

Prompt 7 — Reconciliation of Consensus Estimates vs. Reported Earnings

Production-ready Prompt Text:

Compare the quarterly reported financials to provided consensus datasets. Produce a reconciliation table with columns: line_item, consensus_value, reported_value, absolute variance, percentage variance, and explanatory commentary highlighting drivers of variance. Include non-GAAP adjustments for EPS and EBITDA, quantifying their impact on headline metrics. Output JSON and CSV string.

Context Variables

• consensus (line items like revenue, EPS, EBITDA)
• reported_results (matching keys)

Expected Output Example

{
  "comparison": [
    {"line_item": "revenue", "consensus": 320000000, "reported": 327500000, "var_abs": 7500000, "var_pct": 2.34, "commentary": "Stronger pricing power in North America"}
  ],
  "csv": "line_item,consensus,reported,var_abs,var_pct,commentary\n..."
}

Validation Advice

• Flag variances exceeding two standard deviations based on the trailing eight quarters, highlighting potential anomalies

Prompt 8 — Non-GAAP to GAAP Reconciliation and Metric Normalization

Production-ready Prompt Text:

Using reported non-GAAP metrics and disclosed adjustments, generate a reconciliation table for GAAP metrics (EPS, EBITDA). Normalize by removing one-off items. Output JSON with fields: gaap_metric, non_gaap_metric, adjustments (list), and normalized_metric.

Context Variables

• non_gaap_adjustments (named with amounts and detailed descriptions)
• reported_gaap_metrics

Expected Output Sample

{
  "reconciliation": [
    {"metric": "net_income", "gaap": 35000000, "non_gaap": 37000000, "adjustments": [{"name": "restructuring", "amount": 2000000}]}
  ],
  "normalized": {"adjusted_eps": 1.24, "adjusted_ebitda_margin": 0.185}
}

Usage Insights

• Flag adjustments exceeding 1% of revenue or recurring across multiple periods—potential signs of structural shifts

Prompt 9 — Quarter-over-Quarter KPI Trend Analysis with Statistical Signaling

Production-ready Prompt Text:

Analyze quarterly KPIs spanning 12 quarters. For each KPI (e.g., revenue, gross margin, accounts receivable days, churn rate, ARPU), calculate QoQ and YoY percentage changes, 4-quarter rolling averages, and Z-scores relative to 12-quarter means. Output a JSON array with KPI analyses, concise directional signals (improving/declining/stable), and recommended next actions.

Context Variables

• kpi_time_series (e.g., {“revenue”: […], “gross_margin”: […], “churn”: […]})
• reporting_calendar (quarterly timestamp mapping)

Expected Output Example

[
  {"kpi": "revenue", "qoq_change": "1.8%", "yoy_change": "6.5%", "rolling_avg": "3.1%", "z_score": 1.4, "signal": "improving", "action": "Monitor product pricing trends"}
]

Implementation Tips

• Utilize rolling averages to smooth seasonality and prompt model to report statistical sample sizes

Prompt 10 — Professional Earnings Note Draft for Analyst Distribution

Production-ready Prompt Text:

Draft a 300–450 word earnings note for client dissemination, utilizing provided metrics and reconciliations. Organize the note with: key facts (bullet points), financial highlights (tabular bullets), investment implications (three bullets covering catalysts, risks, valuation), and analyst stance (Buy/Hold/Sell with price target and rationale). Include a brief methodology statement referencing model version and data sources. Output JSON keys: headline, key_facts, financial_highlights, implications, stance, methodology.

Context Variables

• headline_metrics (EPS, revenue, guidance updates, percentage variances)
• analyst_prior_rating_and_target

Expected Output Sample

{
  "headline": "Acme Co. beats Q2 estimates; raises guidance",
  "key_facts": ["Revenue $327.5m (+2.3% q/q)", "EPS $0.48 vs $0.42 est"],
  "financial_highlights": [{"line": "Revenue", "value": "327.5"}],
  "implications": [{"catalyst": "New product ramping in Q4", "risk": "Supply chain constraints affecting margins"}],
  "stance": {"rating": "Buy", "new_target": 28.00},
  "methodology": "DCF v3.1; data sourced from company release and consensus via Refinitiv; model date 2026-07-12"
}

Practical Advice

• Specify the tone (neutral, concise, or promotional) and target word count for consistent narrative style

Prompt 11 — Factor Attribution for Portfolio Excess Returns

Production-ready Prompt Text:

Using monthly returns over 60 months for a portfolio and its benchmark, along with factor exposures (market beta, size, value, momentum), perform a factor attribution decomposition. Return JSON with total_return, benchmark_return, active_return, allocation_effect, selection_effect, interaction_effect, plus factor_contributions (factor:contribution list). Summarize top drivers in a brief, three-line professional commentary.

Context Variables

• portfolio_returns (60-element monthly array)
• benchmark_returns (60-element monthly array)
• factor_returns (matrix: factors × months)
• current_factor_exposures (vector)

Expected Output Sample

{
  "total_return": 0.182,
  "benchmark_return": 0.145,
  "active_return": 0.037,
  "factor_contributions": [{"market": 0.025}, {"size": 0.004}, {"value": -0.002}]
}

Best Practice Tips

• Specify attribution methodology used (e.g., Brinson-Fachler) in output
• Validate total factor contributions sum to active return within a small rounding tolerance (e.g., 1e-4)

Prompt 12 — Portfolio VaR & CVaR Estimation Using Historical and Parametric Methods

Production-ready Prompt Text:

Estimate 1-day and 10-day Value at Risk (VaR) and Conditional VaR (CVaR) for the portfolio using: (1) historical simulation over 500 trading days and (2) parametric variance-covariance approach assuming normal and t-distributions (df=5). Output JSON with VaR and CVaR at 95% and 99% confidence levels, assumptions, and include a concise Python snippet illustrating calculations.

Context Variables

• positions: list of {ticker, weight, price_series}
• return_series: daily returns aligned by ticker
• holding_period_days: 1 or 10

Output Example

{
  "historical_VaR": {"1d": {"95%": 0.024, "99%": 0.042}, "10d": {...}},
  "parametric_VaR_normal": {...},
  "parametric_VaR_tdist": {...},
  "python_snippet": "import numpy as np\n..."
}

Usage Recommendations

• Check for return series stationarity and flag structural breaks (e.g., 2020 pandemic) that may bias historical simulation outputs

Prompt 13 — Custom Stress Test Scenario Generation for Portfolios

Production-ready Prompt Text:

Generate five bespoke stress-test scenarios tailored to the client portfolio considering sector weights, geographic exposures, leverage, and liquidity constraints. For each scenario provide: scenario_name, macroeconomic shock description (including GDP, interest rates, FX, commodities), expected P&L impact (% portfolio NAV), rationale, and recommended hedging strategies. Output JSON reflecting historical extremes and near-term credible risks as of July 2026.

Context Variables

• sector_weights, geographic_exposure, leverage
• holding_period_days, liquidity_constraints

Expected Output Sample

{
  "scenarios": [
    {
      "name": "Rapid Rate Spike",
      "macro_shock": "10-year rates +150 bps",
      "pnl_impact": "-4.2%",
      "hedge": "Increase duration hedges via interest rate swaps"
    }
  ]
}

Best Practice Guidelines

• Include actionability scores (1-5) and estimated hedging costs per scenario

Prompt 14 — Portfolio Optimization Using Mean-Variance with Constraints

Production-ready Prompt Text:

Optimize portfolio allocations using expected returns, covariance matrix, and
  
  
  

  


    

    

      
      
      
      
      

      Please leave this field empty



Infrastructure & EngineeringInsight & AnalyticsGovernance, Privacy & EthicsProduct & StrategyAdministration & ManagementFinance & AccountingSales & MarketingOperations & ProductionHuman ResourcesInformation TechnologyKnowledge Seekers & CreatorsEnthusiasts & Specialized UsersStudentHousehold ManagerInfluencerResearcher


      

        
Thank you! Please check your inbox (and spam folder) for a confirmation email. Click the link to get instant access to our 40,000+ ChatGPT Prompt Library.Check your inbox or spam folder to confirm your subscription.
        
        
        
      
    

      

    
  
  

  


    

    

      
      
      
      
      

      Please leave this field empty



Infrastructure & EngineeringInsight & AnalyticsGovernance, Privacy & EthicsProduct & StrategyAdministration & ManagementFinance & AccountingSales & MarketingOperations & ProductionHuman ResourcesInformation TechnologyKnowledge Seekers & CreatorsEnthusiasts & Specialized UsersStudentHousehold ManagerInfluencerResearcher


      

        
Thank you! Please check your inbox (and spam folder) for a confirmation email. Click the link to get instant access to our 40,000+ ChatGPT Prompt Library.Check your inbox or spam folder to confirm your subscription.
        
        
        
      
    

          
      

    
  
  

  


    

    

      
      
      
      
      

      Please leave this field empty



Infrastructure & EngineeringInsight & AnalyticsGovernance, Privacy & EthicsProduct & StrategyAdministration & ManagementFinance & AccountingSales & MarketingOperations & ProductionHuman ResourcesInformation TechnologyKnowledge Seekers & CreatorsEnthusiasts & Specialized UsersStudentHousehold ManagerInfluencerResearcher


      

        
Thank you! Please check your inbox (and spam folder) for a confirmation email. Click the link to get instant access to our 40,000+ ChatGPT Prompt Library.Check your inbox or spam folder to confirm your subscription.