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| 1 | +# Sample Investment Hypotheses |
| 2 | + |
| 3 | +This directory contains sample investment hypotheses for fictitious companies that can be used to test and demonstrate the investment analysis capabilities of the application. |
| 4 | + |
| 5 | +## Available Sample Companies |
| 6 | + |
| 7 | +### 1. TechCorp Industries (TECH) |
| 8 | +**File:** `analysis-hypothesis-techcorp.txt` |
| 9 | +**Sector:** Technology - Cloud Infrastructure & SaaS |
| 10 | +**Investment Type:** Growth Stock |
| 11 | +**Risk Level:** Medium |
| 12 | +**Key Theme:** AI-powered analytics platform with strong growth trajectory |
| 13 | + |
| 14 | +**Use Case:** Demonstrates analysis of high-growth technology companies with strong fundamentals but premium valuations. |
| 15 | + |
| 16 | +### 2. GreenPower Energy Solutions (GPWR) |
| 17 | +**File:** `analysis-hypothesis-greenpower.txt` |
| 18 | +**Sector:** Renewable Energy - Solar & Battery Storage |
| 19 | +**Investment Type:** Growth with ESG Focus |
| 20 | +**Risk Level:** Medium |
| 21 | +**Key Theme:** Renewable energy benefiting from regulatory tailwinds |
| 22 | + |
| 23 | +**Use Case:** Showcases ESG-focused investment analysis with policy risk considerations and margin expansion story. |
| 24 | + |
| 25 | +### 3. HealthPlus Therapeutics (HLTH) |
| 26 | +**File:** `analysis-hypothesis-healthplus.txt` |
| 27 | +**Sector:** Biotechnology - Oncology |
| 28 | +**Investment Type:** Speculative/Binary Event |
| 29 | +**Risk Level:** High |
| 30 | +**Key Theme:** Clinical-stage biotech with Phase 3 catalyst |
| 31 | + |
| 32 | +**Use Case:** Illustrates high-risk, high-reward binary event analysis with probability-weighted scenarios and risk management strategies. |
| 33 | + |
| 34 | +### 4. UrbanMobility Transit (UMOB) |
| 35 | +**File:** `analysis-hypothesis-urbanmobility.txt` |
| 36 | +**Sector:** Consumer Discretionary - Micro-Mobility |
| 37 | +**Investment Type:** Turnaround/Inflection |
| 38 | +**Risk Level:** Medium-High |
| 39 | +**Key Theme:** Profitability inflection in emerging mobility sector |
| 40 | + |
| 41 | +**Use Case:** Demonstrates turnaround analysis with unit economics improvement and path to profitability. |
| 42 | + |
| 43 | +## How to Use These Samples |
| 44 | + |
| 45 | +### Testing the Analysis Workflow |
| 46 | +1. Create a new opportunity of the company to analyse. |
| 47 | +2. Use the hypothesis to run a new analysis |
| 48 | +- Request analysis on specific aspects (valuation, risks, catalysts) |
| 49 | +3. Compare AI-generated insights |
| 50 | +4. Test scenario analysis and what-if questions |
| 51 | + |
| 52 | +### Example Queries to Test |
| 53 | + |
| 54 | +**For TechCorp (Growth Stock):** |
| 55 | +- "What are the key risks to TechCorp's premium valuation?" |
| 56 | +- "Analyze TechCorp's competitive moat and sustainability" |
| 57 | +- "What happens to the stock if growth slows to 20%?" |
| 58 | + |
| 59 | +**For GreenPower (ESG/Policy-Driven):** |
| 60 | +- "How would changes to tax incentives impact GreenPower?" |
| 61 | +- "Evaluate the ESG profile and impact metrics" |
| 62 | +- "What's the sensitivity to interest rate changes?" |
| 63 | + |
| 64 | +**For HealthPlus (Binary Event):** |
| 65 | +- "What's the risk-adjusted expected return for HealthPlus?" |
| 66 | +- "Analyze the probability-weighted scenarios" |
| 67 | +- "How should I size this position given the binary risk?" |
| 68 | + |
| 69 | +**For UrbanMobility (Turnaround):** |
| 70 | +- "What evidence supports the profitability inflection thesis?" |
| 71 | +- "Evaluate the execution risk of the turnaround plan" |
| 72 | +- "What are the key metrics to monitor for this investment?" |
| 73 | + |
| 74 | +### Multi-Company Analysis |
| 75 | +Test portfolio construction and comparison: |
| 76 | +- "Compare the risk/reward profiles of these four companies" |
| 77 | +- "Build a diversified portfolio using these opportunities" |
| 78 | +- "Which investment has the best risk-adjusted return?" |
| 79 | +- "Identify correlation and diversification benefits" |
| 80 | + |
| 81 | +## Data Characteristics |
| 82 | + |
| 83 | +### Diversity |
| 84 | +- Different sectors and industries |
| 85 | +- Various investment styles (growth, value, speculative, turnaround) |
| 86 | +- Range of risk levels |
| 87 | +- Different catalysts and time horizons |
| 88 | +- Mix of B2C and B2B business models |
| 89 | + |
| 90 | +### Complexity |
| 91 | +- Multiple valuation methodologies |
| 92 | +- Scenario analysis with probabilities |
| 93 | +- Detailed risk assessment |
| 94 | +- Strategic considerations |
| 95 | +- ESG factors |
| 96 | + |
| 97 | +## Testing Scenarios |
| 98 | + |
| 99 | +### Basic Analysis |
| 100 | +- Extract key investment thesis |
| 101 | +- Identify top 3 risks and opportunities |
| 102 | +- Summarize valuation approach |
| 103 | +- List upcoming catalysts |
| 104 | + |
| 105 | +### Advanced Analysis |
| 106 | +- Build DCF model from provided assumptions |
| 107 | +- Perform sensitivity analysis on key variables |
| 108 | +- Compare across different investment styles |
| 109 | +- Generate portfolio allocation recommendations |
| 110 | +- Stress test scenarios (recession, rate hikes, etc.) |
| 111 | + |
| 112 | +### What-If Analysis |
| 113 | +- "What if TechCorp's growth slows by 10%?" |
| 114 | +- "What if GreenPower loses its tax credits?" |
| 115 | +- "What if HealthPlus trial fails?" |
| 116 | +- "What if UrbanMobility doesn't reach profitability on time?" |
| 117 | + |
| 118 | +## Notes |
| 119 | + |
| 120 | +- All companies, data, and scenarios are **completely fictitious** |
| 121 | +- Financial projections are illustrative examples only |
| 122 | +- These are designed to test analytical capabilities, not provide real investment advice |
| 123 | +- Use these to validate the AI's ability to: |
| 124 | + - Extract and synthesize complex information |
| 125 | + - Identify key risks and opportunities |
| 126 | + - Perform quantitative analysis |
| 127 | + - Provide balanced, nuanced perspectives |
| 128 | + - Handle different investment styles and risk profiles |
| 129 | + |
| 130 | +--- |
| 131 | + |
| 132 | +*Last Updated: November 19, 2025* |
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