Mutual Fund & ETF Factsheet Analysis Assistant
A comprehensive analytical prompt for reviewing mutual fund and ETF monthly factsheets, assessing risk-adjusted returns, evaluating portfolio concentration, and generating clear investor summaries.
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Please conduct a comprehensive, objective analytical evaluation of this mutual fund / ETF factsheet. ### Fund Overview - **Fund Name**: Vanguard 500 Index Fund / Mirae Asset Large Cap Fund - **Fund Category & Asset Class**: Large Cap Equity (Active) - **Benchmark Index**: Nifty 50 TRI / S&P 500 TR Index - **Expense Ratio & Exit Load**: 0.85% (Direct Plan) / 1.75% (Regular Plan); 1% exit load if redeemed within 365 days - **Historical Returns**: - 1-Year: 18.4% (Fund) vs 16.2% (Benchmark) - 3-Year CAGR: 14.8% (Fund) vs 13.9% (Benchmark) - 5-Year CAGR: 16.2% (Fund) vs 15.1% (Benchmark) - **Top Portfolio Holdings**: 1. HDFC Bank (8.8%) 2. Reliance Industries (7.9%) 3. ICICI Bank (6.5%) 4. Infosys (5.2%) 5. Tata Consultancy Services (4.1%) Top 5 Holdings constitute 32.5% of total portfolio. Sector exposure: Financials (34%), IT (16%), Energy (11%). ### Evaluation Framework 1. **Performance vs. Benchmark**: - Compare the fund's 1-year, 3-year, and 5-year annualized returns against **Nifty 50 TRI / S&P 500 TR Index**. - Calculate or estimate the alpha generated relative to category peers. 2. **Cost Drag & Expense Evaluation**: - Evaluate whether the **0.85% (Direct Plan) / 1.75% (Regular Plan); 1% exit load if redeemed within 365 days** is justified by historical alpha, especially compared to passive index alternatives. 3. **Concentration & Sector Risk**: - Analyze the top 10 holdings. Is the portfolio excessively concentrated in a single sector or top 3 stocks? 4. **Investor Profile Fit**: - What type of investor is this fund suited for (e.g. long-term SIP, risk-averse wealth preservation, aggressive tactical growth)? 5. **Executive Summary & Actionable Verdict**: - Conclude with a clear summary: [Strengths, Primary Risks, Bottom-Line Recommendation].
How to Use & Customize This Prompt
Fill in your target framework, environment rules, or source metrics in the interactive builder above. The assembled template updates instantly.
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Refer to the Model Guidance below to choose the highest-performing frontier model or local runtime for this specific task.
Paste into your AI coding tool, chat interface, or local Ollama runtime. Verify syntax, types, and schema compliance against expected output.
Workflow Guidance
- Fill in the fund name, benchmark index, expense ratio, and trailing returns from the monthly factsheet PDF.
- Provide the top holdings and sector breakdown from the factsheet table.
- Submit the prompt to your AI assistant to generate a balanced investment evaluation report.
Representative AI Output
Sample output produced when using frontier AI models with the default parameters:
### Executive Factsheet Evaluation - **Benchmark Alpha**: The fund has delivered a modest positive alpha of +0.9% to +1.1% CAGR over 3-year and 5-year periods. - **Cost Drag Analysis**: In the Regular plan (1.75%), the 0.9% alpha is completely wiped out by distributor commissions. The Direct plan (0.85%) marginally outperforms, but a low-cost Nifty 50 Index Fund (0.10% expense ratio) delivers virtually identical net returns with zero manager risk. - **Portfolio Concentration**: 32.5% in top 5 holdings and 34% in financials is standard for large-cap mandates, but presents single-sector vulnerability. **Bottom-Line Verdict**: Suitable for core equity allocation via Direct SIP, but cost-conscious investors may prefer a passive index ETF for identical large-cap beta at a fraction of the cost.
Pro Optimization & Edge-Case Tips
- Always compare active large-cap funds against low-cost direct index ETFs; over 10-year horizons, the majority of active large-cap funds underperform their benchmark net of fees.
- Check portfolio turnover ratio in the factsheet; a turnover above 80% indicates aggressive short-term trading which may increase trading transaction costs.
- Review whether the fund manager has personal skin-in-the-game invested in the fund.
Recommended AI Models for Financial Extraction & Analysis
Rigorous models with structured JSON guarantees, numerical precision, and zero-hallucination compliance.
OpenAI GPT-4o (Structured Outputs)
Parsing complex balance sheets, cash flow statements, and income statements into validated JSON.
- Guaranteed JSON schema output via constrained decoding grammar
- Flawless extraction of nested sub-line items and parenthetical footnotes
- High precision with multi-currency and negative balance formatting
Claude 3.5 Sonnet
Cross-validating mathematical checksums, auditing disclosures, and detecting hidden risk factors.
- Methodical balance checking (Assets = Liabilities + Shareholder Equity)
- Detailed explanation of non-GAAP reconciliation line items
- Extracts subtle risk disclosures from 10-K management discussions
Google Gemini 1.5 Flash
Bulk processing of mutual fund PDFs, expense ratio comparisons, and portfolio holdings breakdown.
- Ultra-low inference cost for bulk financial document pipelines
- Native multimodal processing of tabular PDF screenshots and charts
- Fast extraction of fund AUM, Sharpe ratios, and beta benchmarks
Copy your assembled prompt and test immediately with approved frontier models or your own API key.
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