Estimate Analysis

Estimate Analysis - Professional Analyst Forecast Assessment Skill

This skill framework is designed for professional analysis of analyst estimates and earnings forecast quality. It evaluates consensus expectations, revision patterns, forecast accuracy, and surprises to assess the reliability and predictive power of market consensus.

Analysis Framework

1. Consensus Estimate Analysis 📊

Evaluate the quality and reliability of analyst consensus

• Consensus estimate: Current mean or median estimate for key metrics
• Estimate spread: Range between highest and lowest analyst estimates
• Number of analysts: How many analysts cover this metric
• Coverage breadth: Consistency of coverage over time
• Consensus confidence: How tight are the estimates (tight = high confidence)

Key questions: Is there true consensus or significant disagreement? Are estimates stable or rapidly changing?

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2. Estimate Revision Analysis 📈

Track how analyst expectations have evolved

• Recent revisions (1M/3M): Direction and magnitude of recent estimate changes
• Revision momentum: Are revisions accelerating up or down?
• Revision breadth: What percentage of analysts revising up vs down?
• Revision significance: Are changes large enough to matter (>5% impact)?
• Revision timing: When did major revisions occur relative to guidance?

Key questions: Are analysts becoming more or less bullish? Is momentum accelerating?

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3. Historical Accuracy Analysis 🎯

Evaluate analyst forecast track record

• Accuracy metrics:
  • Mean Absolute Error (MAE): Average magnitude of forecast misses
  • Root Mean Square Error (RMSE): Weighted accuracy measure
  • Bias: Do analysts systematically over/under estimate?
• Hit rate: Percentage of quarters where estimates were within ±5% of actual
• Surprise magnitude: Average magnitude of earnings surprises (actual vs consensus)
• Directional accuracy: Correct direction of surprise (beat or miss)?

Key questions: How accurate have analysts been historically? Any systematic bias?

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4. Beat/Miss Track Record 📋

Analyze earnings surprise patterns

• Beat/Miss frequency: % of quarters beating, in line, or missing
• Beat magnitude: Average positive surprise when beating
• Miss magnitude: Average negative surprise when missing
• Surprise distribution: Normal or fat-tail distribution?
• Seasonal patterns: Do certain quarters have higher surprise rates?
• Magnitude by size: Do large surprises cluster in certain periods?

Key questions: Is this company a chronic beat or miss? What's the surprise magnitude?

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5. Estimate Quality Assessment ✅

Evaluate whether consensus is trustworthy

High-Quality Estimates (Reliable)

• Tight estimate range (low coefficient of variation)
• Stable over time (low revision frequency)
• Consistent with guidance
• Historical hit rate > 80%
• Small average surprise magnitude
• Broad analyst coverage (20+ analysts)

Low-Quality Estimates (Risky)

• Wide estimate range (high disagreement)
• Frequent major revisions
• Inconsistent with management guidance
• Historical hit rate < 50%
• Large average surprise magnitude
• Narrow coverage (< 5 analysts)

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6. Surprise Probability Assessment 🎲

Estimate the likelihood of earnings surprise

Factors Increasing Surprise Probability

• Wide estimate range (analyst disagreement)
• Recent large revisions (changing expectations)
• Divergence from guidance (consensus vs company)
• Business transition or restructuring period
• Historical pattern of surprises
• Controversial or polarizing analyst opinions
• Significant estimate changes post-guidance

Factors Decreasing Surprise Probability

• Narrow, stable estimate range
• Consensus near management guidance
• Stable business environment
• Consistent historical estimates
• Broad consistent analyst coverage

Surprise Probability Score: Low (< 20%), Medium (20-50%), High (> 50%)

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7. Earnings Surprise Decomposition 🔍

Analyze the sources of expected surprises

• Guidance vs consensus: Is management more/less bullish than consensus?
• Estimate uncertainty: Which metrics have widest ranges?
• Key assumption risks: What forecast assumptions are most fragile?
• Trigger events: What company actions could cause large surprises?
• Upside/downside asymmetry: Which direction has more risk (skew)?

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8. Estimate Reliability Index 📉

Factor	Weight	Assessment
Estimate spread (CoV)	20%	Tight/Moderate/Wide
Revision stability	20%	Stable/Moderate/Volatile
Historical accuracy (MAE)	20%	High/Medium/Low
Coverage breadth	15%	Broad/Moderate/Narrow
Consensus-Guidance gap	15%	Small/Moderate/Large
Reliability Score		High/Medium/Low

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Analytical Workflow

Step 1: Consensus Summary (Executive Summary)

Quickly summarize current consensus and major estimate characteristics

Step 2: Detailed Estimate Profile

• Current consensus for key metrics
• Estimate range and distribution
• Analyst count and coverage trend
• Recent revision activity

Step 3: Historical Accuracy Review

• Accuracy metrics over past 4-8 quarters
• Bias analysis (systematic over/under estimation)
• Surprise magnitude and frequency
• Seasonal patterns if any

Step 4: Revision Trend Analysis

• Recent revision direction and momentum
• Breadth of revisions (% of analysts moving)
• Timing of revisions relative to guidance
• Implication for next quarter expectations

Step 5: Surprise Probability Assessment

• Factors supporting upside surprise
• Factors supporting downside surprise
• Overall surprise probability score
• Direction bias if asymmetric

Step 6: Key Estimate Risks

• Which metrics have highest uncertainty
• What assumptions are most fragile
• Triggers that could cause estimate changes
• Recommendation on estimate reliability

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Output Guidelines

✅ Do:

• Present consensus with estimate range clearly
• Show revision trends with magnitude and timing
• Quantify historical accuracy with specific metrics
• Identify highest-risk estimate assumptions
• Provide clear surprise probability assessment

❌ Don't:

• Omit estimate range (consensus alone is incomplete)
• Ignore historical track record
• Treat all analyst estimates equally regardless of coverage quality
• Assume past accuracy guarantees future accuracy
• Mix aggregate statistics with outlier effects

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Analysis Template

When users provide estimate data, output following this structure:

# [Company Name] Estimate Analysis - Q[X]

## 📌 Consensus Summary
[Current consensus, spread, and key characteristics]

## 📊 Consensus Estimate Profile
[Key metric estimates with ranges]

## 📈 Estimate Revision Trends
[Recent revision activity and momentum]

## 🎯 Historical Accuracy Review
[Accuracy metrics and beat/miss patterns]

## 🎲 Earnings Surprise Assessment
[Probability and potential magnitude]

## ⚠️ Key Estimate Risks
[Most uncertain metrics and fragile assumptions]

## 📋 Estimate Reliability Score
[Overall assessment: High/Medium/Low confidence]

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Key Metrics & Formulas

Estimate Dispersion Measures

• Coefficient of Variation (CoV) = Std Dev of estimates / Mean estimate
  • CoV < 5% = Tight consensus (high confidence)
  • CoV 5-15% = Moderate spread
  • CoV > 15% = Wide spread (low confidence)

Accuracy Metrics

• Mean Absolute Error (MAE) = Average |Actual - Estimate|
• Mean Absolute Percentage Error (MAPE) = Average |Actual - Estimate| / Actual
• Forecast Bias = Average (Actual - Estimate)
  • Positive bias = analysts systematically underestimate
  • Negative bias = analysts systematically overestimate

Surprise Metrics

• Earnings Surprise % = (Actual - Consensus) / Consensus × 100%
• Surprise Magnitude = Average absolute value of surprise %
• Beat Frequency = # quarters with positive surprise / Total quarters

Estimate Reliability Score

Score based on: Spread (20%) + Stability (20%) + Accuracy (20%) + Coverage (15%) + Guidance gap (15%)

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Skill Triggers

This skill is used when users provide scenarios like:

• "What's the analyst consensus for this company?"
• "How reliable are these earnings estimates?"
• "What's the probability of an earnings surprise?"
• "Compare estimate spread across metrics"
• "Analyze the revision trend in analyst estimates"
• "Is consensus closer to bull or bear case?"
• "How have analyst estimates trended over recent quarters?"

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Data Requirements

Essential

• Current analyst consensus (latest estimates)
• Consensus range (high/low estimates)
• Number of analysts covering each metric
• Most recent earnings surprise data

Important

• Recent estimate revisions (1-month and 3-month changes)
• Historical earnings surprises (last 4-8 quarters)
• Management guidance vs consensus comparison
• Historical estimate accuracy metrics

Useful

• Detailed analyst breakdown (individual estimates)
• Estimate revision history with dates
• Historical Beat/Miss frequency by quarter
• Prior quarters' analyst expectation ranges

Reference

• Industry average estimate accuracy
• Peer company estimate patterns
• Macroeconomic forecast consensus
• Historical volatility of surprises

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Common Estimate Pitfalls

Pitfall	How to Identify	Impact
Consensus anchoring	Estimates don't change despite new information	Surprise risk increases
Herding behavior	Analysts cluster around one estimate	False high confidence
Estimate bias	Systematic over/under estimation	Recurring surprises
Late revision	Estimates change right before earnings	Surprise already priced in
Coverage gap	Few analysts following certain metrics	Unreliable consensus
Stale estimates	Lack of recent revisions	May not reflect current reality

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Analysis Quality Indicators

A good Estimate Analysis should:

✅ Provide Complete Consensus Picture

• Show current estimates with ranges
• Display number of analysts
• Include revision history

✅ Assess Estimate Quality

• Evaluate confidence level
• Compare to historical accuracy
• Identify potential biases

✅ Quantify Surprise Risk

• Probability assessment with rationale
• Potential surprise magnitude
• Direction of highest risk

✅ Be Actionable

• Clear reliability verdict on consensus
• Specific assumptions at risk
• Metrics requiring closest monitoring