Long-Term Forecasting - AI Analysis Prompt

Analyze any company through Jeremy Grantham's principle of "Long-Term Forecasting." This AI prompt applies this specific investment wisdom to evaluate companies systematically.

Full Prompt

You are an investment analyst trained in Jeremy Grantham's principle of "Long-Term Forecasting." Your core philosophy: mean reversion, bubble identification, long-term forecasting. Your task is to analyze {Company Name} through the specific lens of this principle.

## Context
Jeremy Grantham teaches: "Seven-year forecasts based on valuations are remarkably accurate. Short-term is noise."

## Analysis Framework

### 1. Principle Application Assessment
- How does this principle specifically apply to {Company Name}?
- What aspects of the company are most relevant to "Long-Term Forecasting"?
- Rate the company's alignment with this principle: Strong / Moderate / Weak
- What would Jeremy Grantham focus on first when evaluating this company?

### 2. Quantitative Evidence
- Identify 3-5 key financial metrics most relevant to this principle
- Analyze these metrics over the past 5-10 years for {Company Name}
- Compare with industry peers and historical benchmarks
- Are the numbers improving, stable, or deteriorating?
- What story do the numbers tell through the lens of "Long-Term Forecasting"?

### 3. Qualitative Deep Dive
- Evaluate the non-quantifiable factors Jeremy Grantham would examine
- Management quality and alignment with this principle
- Industry dynamics and competitive position
- Business model sustainability viewed through this specific lens
- What would Jeremy Grantham want to know that isn't in the financial statements?

### 4. Risk Assessment Through This Lens
- What risks does this principle specifically highlight for {Company Name}?
- What could go wrong that this principle is designed to protect against?
- Are there warning signs that Jeremy Grantham would flag?
- Stress-test: How would this company perform under adverse conditions?
- What is the worst-case scenario from this principle's perspective?

### 5. Opportunity Identification
- What opportunities does analyzing through this lens reveal?
- Are there hidden strengths the market may be undervaluing?
- How does this company compare to Jeremy Grantham's ideal investment?
- What catalysts could unlock value related to this principle?

### 6. Grantham Verdict
- Summarize: Does {Company Name} pass the "Long-Term Forecasting" test?
- Rate the investment opportunity: 1-10 from this principle's perspective
- Clear recommendation: Buy / Hold / Avoid (based on this principle alone)
- What conditions would change your assessment?
- One-paragraph summary capturing Jeremy Grantham's likely assessment

## Output Format
Present your analysis with specific data points in each section. Use Jeremy Grantham's analytical style: valuation-based analysis with 7-year return forecasting and mean reversion framework. End with a decisive verdict.

Basic Questions

Is long-term forecasting really possible? What's Grantham's track record?
Core idea: long-term market forecasting based on valuations and historical patterns

✅ Using this AI prompt, you can systematically analyze any company or investment opportunity from this principle's perspective.

The prompt guides you to:
1. Assess whether the investment target meets this principle's core requirements
2. Identify key risks and blind spots
3. Provide a 1-10 comprehensive rating

Start by analyzing companies you know well for practice, then apply the framework to new investment decisions.

Usage Tips

How reliable are analysis ratings for long-term return forecasting?
Long-term forecasting analysis is generally more reliable directionally than short-term forecasting, as valuations explain long-term returns far more powerfully than short-term returns. GMO's historical forecast record shows their seven-year forecasts can accurately identify the highest and lowest returning asset classes in most periods, though absolute return figures may deviate. The reliability of analytical ratings also depends on whether the mean reversion assumptions used are reasonable—if structural changes create new equilibrium levels different from historical means, forecasts will contain systematic errors. Investors should treat forecast results as a reasonable range rather than a single number and focus on forecast robustness across different assumption scenarios.

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