**Dataset**: analyst14
**Region**: USA
**Delay**: 1

# Estimations of Key Fundamentals Feature Engineering Analysis Report

**Dataset**: analyst14
**Category**: Analyst
**Region**: USA
**Analysis Date**: 2024-01-15
**Fields Analyzed**: 857

---

## Executive Summary

**Primary Question Answered by Dataset**: What do professional analysts expect future company fundamentals to be, how confident are they in those expectations, and how do actual reported results compare to those expectations?

**Key Insights from Analysis**:
- The dataset contains dual time dimensions: historical actuals (`actvalue_fy0/fp0`) and forward-looking consensus estimates (`mean/median/stddev` for fy1-fy5, fp1-fp5)
- Analyst disagreement (`stddev`) is as informative as the consensus itself, serving as a proxy for uncertainty and information diffusion
- The gap between reported earnings (`epsrep`) and adjusted earnings (`eps`) captures earnings quality and non-recurring items
- Recommendation counts (buy/sell/hold) provide sentiment overlay independent of quantitative estimates

**Critical Field Relationships Identified**:
- Mean estimates vs. actual values (forecast error analysis)
- Standard deviation vs. number of estimates (uncertainty vs. coverage interaction)
- Short-term (fp1) vs. long-term (fy2+) estimate trajectories (growth curve shape)
- Reported vs. adjusted fundamentals (earnings quality)

**Most Promising Feature Concepts**:
1. **Estimate Revision Momentum** - because analyst updates contain incremental information not yet priced
2. **Coefficient of Variation (Dispersion/Mean)** - because relative disagreement predicts future volatility and earnings surprises
3. **Actual-to-Estimate Growth Gap** - because deviations of actuals from prior expectations drive price rebalancing

---

## Dataset Deep Understanding

### Dataset Description
This dataset provides comprehensive analyst estimates for key financial statement items including revenue, earnings (EPS, net profit), EBITDA, cash flows, and returns metrics (ROE, ROA). It spans both quarterly (fp) and annual (fy) fiscal periods with horizons up to 5 years forward. Each estimate field includes distributional statistics (mean, median, standard deviation, high, low) and coverage metrics (number of estimates). Additionally, the dataset includes actual reported values for recent periods, enabling direct comparison between expectations and realizations.

### Field Inventory
| Field ID | Description | Data Type | Update Frequency | Coverage |
|----------|-------------|-----------|------------------|----------|
| `anl14_actvalue_revenue_fy0` | Revenue Actual - Recent Last Year | Float | Quarterly | 95% |
| `anl14_mean_revenue_fy1` | Revenue Mean Estimate - Upcoming Year | Float | Daily | 90% |
| `anl14_stddev_revenue_fy1` | Revenue Std Dev - Upcoming Year | Float | Daily | 90% |
| `anl14_median_revenue_fy1` | Revenue Median Estimate - Upcoming Year | Float | Daily | 90% |
| `anl14_numofests_revenue_fy1` | Number of Revenue Estimates | Integer | Daily | 90% |
| `anl14_mean_eps_fy1` | EPS Mean Estimate - Upcoming Year | Float | Daily | 92% |
| `anl14_stddev_eps_fy1` | EPS Std Dev - Upcoming Year | Float | Daily | 92% |
| `anl14_actvalue_epsrep_fy0` | Reported EPS Actual - Recent Year | Float | Quarterly | 95% |
| `anl14_mean_ebitda_fy1` | EBITDA Mean Estimate - Upcoming Year | Float | Daily | 85% |
| `anl14_mean_roe_fy1` | ROE Mean Estimate - Upcoming Year | Float | Daily | 80% |
| `anl14_buy` | Number of Buy Recommendations | Integer | Daily | 75% |
| `anl14_sell` | Number of Sell Recommendations | Integer | Daily | 75% |

*(Additional fields follow similar patterns for 20+ fundamentals across 5 forward periods)*

### Field Deconstruction Analysis

#### `anl14_mean_revenue_fy1`: Consensus Revenue Expectation
- **What is being measured?**: The arithmetic mean of all active analyst forecasts for the upcoming fiscal year's revenue
- **How is it measured?**: Aggregation of individual analyst models, typically sum of segment forecasts or top-down growth assumptions
- **Time dimension**: Forward-looking point estimate (next 12 months), updated continuously as analysts revise
- **Business context**: Represents the market's collective expectation of top-line growth; deviations drive significant price reactions
- **Generation logic**: Calculated by data aggregators (e.g., FactSet, Bloomberg) from submitted analyst reports
- **Reliability considerations**: Heavily influenced by recency bias; large dispersion indicates model uncertainty or incomplete information diffusion

#### `anl14_stddev_revenue_fy1`: Analyst Disagreement
- **What is being measured?**: Dispersion of individual analyst estimates around the consensus mean
- **How is it measured?**: Standard deviation of the cross-sectional distribution of estimates at a point in time
- **Time dimension**: Instantaneous snapshot of disagreement, though temporal patterns reveal information convergence/divergence
- **Business context**: Proxy for uncertainty, information asymmetry, and fundamental volatility; high dispersion often precedes high realized volatility
- **Generation logic**: Statistical dispersion of submitted estimates; not time-series volatility
- **Reliability considerations**: Sensitive to outlier analysts; low coverage (numofests < 3) makes this unreliable

#### `anl14_actvalue_eps_fy0`: Realized Reported Earnings
- **What is being measured?**: The actual reported earnings per share from the company's most recent completed fiscal year
- **How is it measured?**: Company audited financial statements (10-K), GAAP or IFRS reported figures
- **Time dimension**: Historical realized value, updated quarterly with fiscal year results
- **Business context**: Ground truth against which forward estimates are judged; surprise relative to prior expectations drives returns
- **Generation logic**: Direct from company filings, unadjusted
- **Reliability considerations**: Subject to accounting discretion and non-recurring items; may differ from adjusted/operating earnings

### Field Relationship Mapping

**The Story This Data Tells**:
Analysts form expectations about future fundamentals based on management guidance, macro trends, and industry analysis. As information arrives, these estimates are revised, creating a time-series of consensus updates. The dispersion of estimates reveals how much disagreement exists about the company's trajectory. When actual results are reported, they either confirm or refute the consensus, generating earnings surprises. The interplay between estimate levels, estimate changes, estimate uncertainty, and actual realizations captures the full information generation process about a company's fundamentals.

**Key Relationships Identified**:
1. **Mean vs. Median**: Skewness in analyst expectations (mean ≠ median indicates bullish/bearish tail risk)
2. **Estimates vs. Actuals**: Forecast error dynamics and analyst bias (systematic over-optimism)
3. **Dispersion vs. Coverage**: High dispersion with high coverage suggests genuine uncertainty; high dispersion with low coverage suggests information gaps
4. **Short-term vs. Long-term**: Growth trajectory curvature (accelerating vs. decelerating growth expectations)

**Missing Pieces That Would Complete the Picture**:
- Historical estimate data for the same fiscal period (what did fy1 estimates look like 6 months ago?)
- Analyst-specific identifiers (to track individual analyst accuracy)
- Management guidance figures (to separate guidance-based vs. independent estimates)
- Price target dispersion relative to estimate dispersion

---

## Feature Concepts by Question Type

### Q1: "What is stable?" (Invariance Features)

**Concept**: Estimate Volatility Ratio
- **Sample Fields Used**: `revenue_fy1`, `stddev_revenue_fy1`
- **Definition**: Rolling time-series volatility of the consensus mean normalized by the absolute level of the mean
- **Why This Feature**: Identifies stocks where analyst opinions are converging (decreasing volatility) vs. diverging (increasing volatility) relative to the magnitude of expectations
- **Logical Meaning**: Measures the stability of the consensus itself; low values indicate stable, agreed-upon expectations while high values indicate ongoing information discovery or fundamental instability
- **is filling nan necessary**: Yes, use ts_backfill with limited lookback (5 days) for occasional missing estimates, but preserve NaNs for periods with no analyst coverage as zero coverage is meaningful
- **Directionality**: Lower values (stable consensus) suggest lower future realized volatility; higher values suggest uncertainty and potential for large price moves on new information
- **Boundary Conditions**: Approaches zero for perfectly stable expectations; spikes during earnings announcements or guidance changes
- **Implementation Example**: ts_std_dev({revenue_fy1}, 20) / abs({revenue_fy1})

**Concept**: Consensus Persistence
- **Sample Fields Used**: `ebitda_fy1`
- **Definition**: Correlation between current consensus estimates and their lagged values over a rolling window
- **Why This Feature**: Captures the stickiness vs. volatility of analyst beliefs; persistent estimates suggest strong conviction or slow information diffusion
- **Logical Meaning**: High persistence indicates either stable underlying fundamentals or analyst herding/anchoring; low persistence indicates rapid information updating or noisy estimation
- **is filling nan necessary**: Yes, forward-fill using ts_backfill for up to 3 days to handle weekend gaps, as estimate staleness is minimal over short periods
- **Directionality**: High values suggest trend-following in estimates (momentum), low values suggest mean-reversion or high uncertainty
- **Boundary Conditions**: Ranges from -1 (complete reversal) to 1 (perfect persistence); values near 0 indicate random walk in estimates
- **Implementation Example**: ts_corr({ebitda_fy1}, ts_delay({ebitda_fy1}, 5), 20)

---

### Q2: "What is changing?" (Dynamics Features)

**Concept**: Estimate Revision Momentum
- **Sample Fields Used**: `eps_fy1`
- **Definition**: Percentage change in forward earnings estimates over a short lookback period
- **Why This Feature**: Analysts update estimates incrementally as new information arrives; recent revisions predict future revisions and earnings surprises
- **Logical Meaning**: Captures the direction and magnitude of changing analyst sentiment; positive values indicate upgrades, negative indicate downgrades
- **is filling nan necessary**: No, NaNs indicate missing data periods and should be preserved to avoid false signals from stale backfilled data
- **Directionality**: Positive revisions associated with future outperformance (analysts underreact initially); extreme revisions may indicate overreaction
- **Boundary Conditions**: Unbounded on both sides; extreme values (>20% in 5 days) typically follow earnings surprises or guidance changes
- **Implementation Example**: ts_delta({eps_fy1}, 5) / abs(ts_delay({eps_fy1}, 5))

**Concept**: Uncertainty Expansion/Contraction
- **Sample Fields Used**: `stddev_revenue_fy1`
- **Definition**: Rate of change in analyst dispersion (standard deviation) over time
- **Why This Feature**: Changes in disagreement often precede price volatility; expanding uncertainty suggests information shocks or divergent interpretations
- **Logical Meaning**: Positive values indicate analysts are disagreeing more (divergent views), negative values indicate convergence toward consensus
- **is filling nan necessary**: Yes, use group_mean() cross-sectionally to fill occasional NaNs in stddev for thinly covered stocks, as zero dispersion is different from missing dispersion
- **Directionality**: Rapid expansion predicts future volatility and potential large earnings surprises; contraction suggests information resolution
- **Boundary Conditions**: Zero indicates stable disagreement; large positive spikes occur during crisis periods or earnings misses
- **Implementation Example**: ts_delta({stddev_revenue_fy1}, 10) / ts_delay({stddev_revenue_fy1}, 10)

---

### Q3: "What is anomalous?" (Deviation Features)

**Concept**: Actual-to-Consensus Growth Gap
- **Sample Fields Used**: `actvalue_revenue_fy0`, `revenue_fy1`, `stddev_revenue_fy1`
- **Definition**: Standardized deviation of the most recent actual reported value from the forward consensus estimate, normalized by estimate dispersion
- **Why This Feature**: Large gaps between what was achieved (actual) and what is expected (consensus) indicate potential mean-reversion or trend continuation opportunities
- **Logical Meaning**: Measures how many standard deviations the actual realization is from the forward-looking expectation; captures the "expectations gap"
- **is filling nan necessary**: No, NaNs in actual values indicate recent IPOs or data lags and should not be filled; NaNs in estimates indicate no coverage
- **Directionality**: Large positive gaps (actual >> consensus) may indicate over-optimism in forecasts or negative momentum; large negative gaps may indicate overly pessimistic forecasts
- **Boundary Conditions**: Extreme values (>3 or <-3) indicate significant analyst forecast errors likely to drive estimate revisions
- **Implementation Example**: ({actvalue_revenue_fy0} - {revenue_fy1}) / {stddev_revenue_fy1}

**Concept**: Relative Uncertainty Spike
- **Sample Fields Used**: `stddev_eps_fy1`, `eps_fy1`
- **Definition**: Coefficient of variation (dispersion normalized by mean) compared to its recent historical average
- **Why This Feature**: Identifies when analyst disagreement is unusually high relative to the typical disagreement for that stock's earnings level
- **Logical Meaning**: High values indicate that uncertainty is spiking disproportionately to the earnings level, often preceding significant corporate events or fundamental breaks
- **is filling nan necessary**: Yes, use ts_backfill for up to 5 days for the denominator (eps_fy1) to avoid division by near-zero values, but cap the backfill to prevent stale data bias
- **Directionality**: High values predict earnings surprises and volatility; extremely low values may indicate complacency or stale estimates
- **Boundary Conditions**: Values near zero indicate perfect consensus; values >1 indicate high relative uncertainty (stddev > mean)
- **Implementation Example**: {stddev_eps_fy1} / abs({eps_fy1})

---

### Q4: "What is combined?" (Interaction Features)

**Concept**: Growth-Adjusted Value Proxy
- **Sample Fields Used**: `eps_fy1`, `eps_fy2`, `bvps_fy1`
- **Definition**: Long-term earnings growth (change from fy1 to fy2) scaled by book value per share
- **Why This Feature**: Combines profitability growth expectations with asset base to identify efficient growth vs. asset-heavy growth
- **Logical Meaning**: Measures expected earnings growth per unit of book value; higher values indicate capital-efficient growth expectations
- **is filling nan necessary**: Yes, use group_mean() cross-sectional fill for missing fy2 estimates in the numerator, as growth patterns are comparable within sectors
- **Directionality**: Higher values suggest quality growth; negative values suggest expected earnings declines
- **Boundary Conditions**: Extreme positive values may indicate unsustainable growth expectations; negative values clearly signal expected contraction
- **Implementation Example**: ({eps_fy2} - {eps_fy1}) / {bvps_fy1}

**Concept**: Analyst Confidence-Uncertainty Tradeoff
- **Sample Fields Used**: `numofests_revenue_fy1`, `stddev_revenue_fy1`
- **Definition**: Number of covering analysts divided by the dispersion of their estimates
- **Why This Feature**: High coverage with low dispersion indicates strong confident consensus; high coverage with high dispersion indicates fundamental controversy
- **Logical Meaning**: Proxy for information precision; high values indicate many analysts agreeing (high confidence), low values indicate either sparse coverage or high disagreement
- **is filling nan necessary**: Yes, fill numofests with 0 when NaN (indicating no coverage) and add small constant (0.01) to denominator to avoid division by zero
- **Directionality**: Higher values predict lower future volatility and more accurate forecasts; very low values predict high uncertainty and potential surprises
- **Boundary Conditions**: Approaches infinity as dispersion approaches zero; approaches zero as coverage drops or dispersion explodes
- **Implementation Example**: {numofests_revenue_fy1} / ({stddev_revenue_fy1} + 0.01)

---

### Q5: "What is structural?" (Composition Features)

**Concept**: Long-term Growth Trajectory
- **Sample Fields Used**: `revenue_fy1`, `revenue_fy2`
- **Definition**: Year-over-year growth rate between the upcoming fiscal year and the following year
- **Why This Feature**: Captures the shape of the growth curve (accelerating vs. decelerating) which predicts multiple expansion/contraction
- **Logical Meaning**: Positive values indicate accelerating growth expectations; negative values indicate expected growth deceleration or decline
- **is filling nan necessary**: Yes, use ts_backfill for fy2 estimates up to 10 days as long-term estimates update less frequently
- **Directionality**: Accelerating growth (positive values) associated with multiple expansion; deceleration with multiple compression
- **Boundary Conditions**: Extreme values (>50% or <-50%) indicate binary outcomes (breakthrough products vs. product cycle maturity)
- **Implementation Example**: ({revenue_fy2} / {revenue_fy1}) - 1

**Concept**: Estimate Coverage Intensity
- **Sample Fields Used**: `numofests_revenue_fy1`, `actvalue_revenue_fy0`
- **Definition**: Logarithm of analyst coverage count adjusted for firm size (proxied by actual revenue)
- **Why This Feature**: Identifies stocks with unusually high or low analyst attention relative to their economic footprint
- **Logical Meaning**: High values indicate intense institutional scrutiny; low values indicate neglected or underfollowed stocks
- **is filling nan necessary**: Yes, use log(abs({actvalue_revenue_fy0}) + 1) to handle negative or zero revenues, and fill missing numofests with 0
- **Directionality**: High intensity may lead to more efficient pricing; low intensity may create informational inefficiencies and alpha opportunities
- **Boundary Conditions**: Negative values indicate tiny firms with coverage; zero indicates average coverage intensity
- **Implementation Example**: {numofests_revenue_fy1} - log(abs({actvalue_revenue_fy0}) + 1)

---

### Q6: "What is cumulative?" (Accumulation Features)

**Concept**: Cumulative Estimate Drift
- **Sample Fields Used**: `eps_fy1`
- **Definition**: Sum of estimate changes over a rolling window to capture persistent revision trends
- **Why This Feature**: Single-period revisions may be noise; cumulative drift indicates sustained information flow in one direction
- **Logical Meaning**: Accumulated analyst sentiment shift; positive values indicate persistent upgrades, negative indicate persistent downgrades
- **is filling nan necessary**: No, preserve NaNs as they break the accumulation chain and filling would create false drift signals
- **Directionality**: Large cumulative positive drift predicts future outperformance; negative drift predicts underperformance
- **Boundary Conditions**: Bounded by the lookback period length; extreme values indicate sustained earnings momentum or trend
- **Implementation Example**: ts_sum(ts_delta({eps_fy1}, 5), 20)

**Concept**: Multi-Period Consensus Average
- **Sample Fields Used**: `ebitda_fy1`, `ebitda_fy2`
- **Definition**: Simple average of upcoming year and year-after estimates to smooth through single-year anomalies
- **Why This Feature**: Reduces noise from one-time items in single-year estimates and captures sustainable fundamental levels
- **Logical Meaning**: Longer-run fundamental expectation less sensitive to fiscal year timing effects or one-off gains/losses
- **is filling nan necessary**: Yes, if fy2 is missing, fall back to fy1 using if_else logic or group_mean for missing fy2 within sector
- **Directionality**: Higher values indicate stronger sustainable fundamentals; declining values across years indicate expected deterioration
- **Boundary Conditions**: Smooths out year-specific spikes; most useful for cyclical industries with volatile single-year estimates
- **Implementation Example**: ({ebitda_fy1} + {ebitda_fy2}) / 2

---

### Q7: "What is relative?" (Comparison Features)

**Concept**: Consensus Deviation from Median
- **Sample Fields Used**: `revenue_fy1`, `median_revenue_fy1`
- **Definition**: Difference between mean and median estimates, normalized by the mean
- **Why This Feature**: Detects skewness in the distribution of analyst estimates; mean > median indicates bullish outliers, mean < median indicates bearish outliers
- **Logical Meaning**: Measures the influence of outlier analysts; positive values suggest some analysts have very high estimates pulling the mean up
- **is filling nan necessary**: No, mean and median typically have identical coverage patterns; preserve NaNs when both are missing
- **Directionality**: Positive skewness (mean > median) may indicate optionality or hidden upside; negative skewness indicates tail risk concerns
- **Boundary Conditions**: Zero indicates symmetric distribution; extreme values (>10%) indicate significant disagreement with directional bias
- **Implementation Example**: ({revenue_fy1} - {median_revenue_fy1}) / abs({revenue_fy1})

**Concept**: Relative ROE Uncertainty
- **Sample Fields Used**: `stddev_roe_fy1`, `roe_fy1`
- **Definition**: Dispersion of return on equity estimates relative to the consensus ROE level
- **Why This Feature**: Normalizes uncertainty by profitability level; 5% dispersion is meaningful for 10% ROE but not for 50% ROE
- **Logical Meaning**: Captures uncertainty per unit of expected return; high values indicate analysts disagree significantly on capital efficiency
- **is filling nan necessary**: Yes, fill ROE with 0.01 (1%) minimum to avoid division by zero for near-zero profitability firms, using if_else
- **Directionality**: High relative uncertainty predicts future profitability volatility and potential rating changes
- **Boundary Conditions**: Approaches infinity as ROE approaches zero; stable for profitable firms with positive ROE
- **Implementation Example**: {stddev_roe_fy1} / abs({roe_fy1})

---

### Q8: "What is essential?" (Essence Features)

**Concept**: Earnings Quality Spread
- **Sample Fields Used**: `epsrep_fy1`, `eps_fy1`
- **Definition**: Difference between reported EPS estimates and adjusted/operating EPS estimates
- **Why This Feature**: Captures the market's expectation of non-recurring items and accounting adjustments; large spreads indicate low-quality earnings expectations
- **Logical Meaning**: Positive values indicate expected write-downs or non-recurring charges (reported < operating); negative values indicate expected one-time gains
- **is filling nan necessary**: Yes, use ts_backfill for occasional missing epsrep data, as the relationship between reported and adjusted is typically stable short-term
- **Directionality**: Large positive spreads (reported << operating) predict future negative surprises; persistent large spreads indicate business model volatility
- **Boundary Conditions**: Zero indicates clean earnings with no expected adjustments; extreme values indicate major restructuring or asset sales expected
- **Implementation Example**: {epsrep_fy1} - {eps_fy1}

**Concept**: Fundamental Acceleration
- **Sample Fields Used**: `ebitda_fy1`
- **Definition**: Second derivative of estimates (change in the change) to detect inflection points in growth expectations
- **Why This Feature**: First derivative (momentum) captures direction; second derivative captures inflection points which are critical for valuation multiple changes
- **Logical Meaning**: Positive values indicate accelerating growth expectations (concave up), negative values indicate deceleration (concave down)
- **is filling nan necessary**: No, second derivatives amplify noise from filled data; preserve NaNs to ensure signal quality
- **Directionality**: Positive acceleration associated with multiple expansion even if levels are moderate; deceleration often precedes derating
- **Boundary Conditions**: Zero indicates stable growth rate; spikes indicate inflection points in business cycles or product adoption
- **Implementation Example**: ts_delta({ebitda_fy1}, 5) - ts_delta(ts_delay({ebitda_fy1}, 5), 5)

---

## Implementation Considerations

### Data Quality Notes
- **Coverage**: Coverage varies significantly by metric (EPS > Revenue > Cash Flow > ROE); smaller caps have sparse data for fy2+
- **Timeliness**: Estimates update continuously but with clustering around earnings dates; stale estimates (>30 days) may not reflect current information
- **Accuracy**: Analyst estimates exhibit systematic optimism bias (especially for fy2+); actuals tend to fall below consensus in recessions
- **Potential Biases**: Survivorship bias in long-term estimates (analysts drop coverage of troubled firms, leaving only optimistic estimates)

### Computational Complexity
- **Lightweight features**: Estimate Revision Momentum, Long-term Growth Trajectory, Earnings Quality Spread (single arithmetic operations)
- **Medium complexity**: Estimate Volatility Ratio, Consensus Persistence, Cumulative Estimate Drift (require ts operators with 20-day lookbacks)
- **Heavy computation**: Features requiring cross-sectional operations on the full universe with nested time-series (none in Tier 1)

### Recommended Prioritization

**Tier 1 (Immediate Implementation)**:
1. **Estimate Revision Momentum** - Strongest empirical alpha in analyst data; captures information diffusion
2. **Coefficient of Variation (Relative Uncertainty)** - Powerful predictor of earnings surprises and future volatility
3. **Actual-to-Consensus Growth Gap** - Mean-reversion signal based on analyst forecast errors

**Tier 2 (Secondary Priority)**:
1. **Cumulative Estimate Drift** - Captures persistent trends missed by single-period changes
2. **Earnings Quality Spread** - Differentiates between operating and reported earnings expectations
3. **Analyst Confidence-Uncertainty Tradeoff** - Coverage-weighted uncertainty measure

**Tier 3 (Requires Further Validation)**:
1. **Fundamental Acceleration** - Second derivative features are noisy and may require smoothing
2. **Estimate Coverage Intensity** - Neglected stock premium requires careful sector adjustment

---

## Critical Questions for Further Exploration

### Unanswered Questions:
1. How does the interaction between estimate revisions and price momentum (earnings drift) vary by estimate horizon (fy1 vs. fy2)?
2. Do analyst recommendation changes (buy/sell) lead estimate revisions or lag them, and what is the optimal combination?
3. How persistent is the predictive power of high dispersion (uncertainty) across different market regimes (bull vs. bear)?

### Recommended Additional Data:
- Historical snapshots of estimates (time-series of fy1 estimates as of specific past dates) to measure revision path, not just current vs. lagged
- Management guidance data to distinguish between guidance-driven and independent analyst revisions
- Short interest data to test if high dispersion predicts short squeeze potential or informed short selling

### Assumptions to Challenge:
- That mean estimates are more accurate than median (mean may be skewed by outlier analysts)
- That more analyst coverage always improves forecast accuracy (herding behavior may reduce information diversity)
- That forward estimates (fy1) are unbiased; they likely embed risk premiums and strategic conservatism

---

## Methodology Notes

**Analysis Approach**: This report was generated by:
1. Deep field deconstruction to understand the dual actual/estimate structure and the information content of dispersion vs. level
2. Question-driven feature generation (8 fundamental questions) applied to analyst expectations, actual realizations, and their interactions
3. Logical validation of each feature concept against behavioral finance theories of analyst behavior and market efficiency
4. Transparent documentation of reasoning regarding data quality and implementation constraints

**Design Principles**:
- Focus on the information generation process (estimates forming and updating) rather than just levels
- Emphasis on relative measures (dispersion/mean, actual/consensus) which are more stationary than absolute levels
- Careful attention to the difference between reported and adjusted fundamentals as a measure of earnings quality
- Recognition that analyst data is inherently forward-looking and noisy, requiring robust normalization

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*Report generated: 2024-01-15*
*Analysis depth: Comprehensive field deconstruction + 8-question framework*
*Next steps: Implement Tier 1 features, validate against historical earnings surprise data*