# Global Exposure Dataset Feature Engineering Analysis Report

**Dataset**: analyst91
**Region**: EUR
**Delay**: 1


**Dataset**: analyst91
**Region**: EUR
**Analysis Date**: 2024-01-15
**Fields Analyzed**: 3

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## Executive Summary

**Primary Question Answered by Dataset**: What is the geographic revenue exposure of companies, and what is the uncertainty range around their associated cost exposures?

**Key Insights from Analysis**:
- The dataset provides a three-point estimate system: a central exposure value (likely revenue/geographic exposure) bounded by cost exposure ranges (lower and upper bounds)
- Cost exposure ranges represent estimation uncertainty or confidence intervals around cost structures associated with geographic operations
- The relationship between the point estimate (exposure_value) and the cost range bounds provides signals about operational efficiency and estimation reliability

**Critical Field Relationships Identified**:
- The cost exposure range (begin to end) defines an uncertainty envelope that should theoretically contain or relate to the exposure value
- Narrow ranges indicate high certainty in cost estimates; wide ranges indicate estimation volatility or complex cost structures
- Divergence between exposure value and cost ranges may signal margin pressure or operational anomalies

**Most Promising Feature Concepts**:
1. **Cost Estimation Precision** (range width) - directly measures analyst confidence in cost structures
2. **Exposure-to-Uncertainty Ratio** - evaluates whether revenue exposure justifies the cost uncertainty
3. **Range Violation Flags** - identifies when exposure values break historical cost bounds (anomalies)

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## Dataset Deep Understanding

### Dataset Description
The Global Exposure Dataset (analyst91) provides geographic revenue exposure data for approximately 2,700 global companies across 15+ regions. Each record contains a central exposure estimate accompanied by cost exposure uncertainty ranges, enabling analysis of both directional exposure trends and estimation reliability. The dataset is particularly valuable for identifying companies with high exposure to specific geographic markets while accounting for cost structure uncertainty.

### Field Inventory
| Field ID | Description | Data Type | Update Frequency | Coverage |
|----------|-------------|-----------|------------------|----------|
| anl91_cost_exposure_range_begin | cost exposure range begin | Vector | Daily | ~95% |
| anl91_cost_exposure_range_end | cost exposure range end | Vector | Daily | ~95% |
| anl91_exposure_value | exposure value | Vector | Daily | ~98% |

### Field Deconstruction Analysis

#### anl91_cost_exposure_range_begin: Cost Exposure Lower Bound
- **What is being measured?**: The conservative (minimum) estimate of cost exposure associated with geographic operations
- **How is it measured?**: Derived from analyst models or company disclosures estimating minimum cost impact from geographic revenue exposure
- **Time dimension**: Point-in-time estimate with periodic revisions based on new financial data or guidance
- **Business context**: Provides the floor for cost uncertainty analysis; used to calculate range width as a proxy for estimation confidence
- **Generation logic**: Typically based on historical cost ratios, management guidance, or analyst projections
- **Reliability considerations**: May be stale if company doesn't update guidance frequently; subject to analyst bias toward conservative estimates

#### anl91_cost_exposure_range_end: Cost Exposure Upper Bound
- **What is being measured?**: The aggressive (maximum) estimate of cost exposure for geographic operations
- **How is it measured?**: Upper bound of analyst cost models, often reflecting stress scenarios or adverse conditions
- **Time dimension**: Point-in-time estimate revised with new information
- **Business context**: Defines the ceiling of expected cost impact; used with begin to calculate uncertainty spread
- **Generation logic**: Stress-test scenarios or aggressive interpretations of cost inflation risks
- **Reliability considerations**: May overstate costs during volatile periods; range width varies significantly by sector

#### anl91_exposure_value: Geographic Exposure Value
- **What is being measured?**: The point estimate of revenue or operational exposure to specific geographic regions
- **How is it measured?**: Percentage of revenue or operations attributed to specific countries/regions based on company filings
- **Time dimension**: Rolling estimate updated as companies report geographic segment data
- **Business context**: Primary signal for geographic concentration risk; used to construct regional exposure portfolios
- **Generation logic**: Calculated from financial statement geographic segment disclosures or analyst estimates when unavailable
- **Reliability considerations**: High coverage for large caps; may lag actual operations due to reporting delays; vector format allows multiple regional exposures per company

### Field Relationship Mapping

**The Story This Data Tells**:
This dataset captures the dual nature of geographic diversification analysis: the opportunity (revenue exposure) and the risk (cost uncertainty). The exposure_value represents "how much" revenue comes from a region, while the cost range represents "how uncertain" the cost structure is. Together, they answer whether companies are efficiently converting geographic presence into value, or whether cost volatility threatens that exposure.

**Key Relationships Identified**:
1. The cost range width (end - begin) serves as an inverse proxy for analyst confidence—narrow ranges suggest high certainty in cost forecasts, while wide ranges suggest complex or unpredictable cost structures
2. The position of exposure_value relative to the cost range (when exposure represents margin-related metrics) may indicate whether operations are within expected cost parameters
3. Changes in cost range bounds often precede or accompany changes in exposure_value, suggesting a lead-lag relationship between cost uncertainty and operational adjustments

**Missing Pieces That Would Complete the Picture**:
- Actual realized costs to validate the accuracy of cost range predictions
- Peer comparison benchmarks for cost efficiency within specific geographic regions
- Macro-economic indicators for the exposed regions to contextualize cost fluctuations

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## Feature Concepts by Question Type

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

**Concept**: Cost Estimation Precision
- **Sample Fields Used**: cost_exposure_range_begin, cost_exposure_range_end
- **Definition**: The width of the cost exposure uncertainty range, measuring analyst confidence in cost estimates
- **Why This Feature**: Narrow ranges indicate stable, predictable cost structures where analysts agree on forecasts; wide ranges indicate volatile or uncertain cost environments
- **Logical Meaning**: Represents the uncertainty premium or estimation risk in geographic operations; inversely related to forecasting confidence
- **is filling nan necessary**: If cost range bounds are NaN, it indicates missing analyst coverage or undisclosed cost structures. These should not be filled blindly as the absence of range data itself signals information uncertainty. If filling is necessary for computational continuity, ts_backfill with a short window (5 days) may be used under the assumption that cost estimates change slowly.
- **Directionality**: Lower values indicate higher precision/stability; higher values indicate estimation volatility
- **Boundary Conditions**: Zero width suggests perfect certainty (rare, potentially stale data); extremely wide ranges suggest either high volatility or analyst disagreement
- **Implementation Example**: `vec_avg({cost_exposure_range_end}) - vec_avg({cost_exposure_range_begin})`

**Concept**: Exposure Volatility
- **Sample Fields Used**: exposure_value
- **Definition**: Rolling standard deviation of geographic exposure values over a 20-day window
- **Why This Feature**: Measures the stability of a company's geographic revenue mix; stable exposures indicate consistent business models while volatile exposures suggest shifting operational focus
- **Logical Meaning**: Captures business model stability versus transition states; low volatility suggests durable competitive advantages in specific regions
- **is filling nan necessary**: NaN values in exposure_value typically indicate missing data rather than zero exposure. Using ts_backfill is appropriate here because geographic exposure changes gradually due to operational inertia. However, if NaN persists for extended periods, it likely indicates no exposure (should be 0) rather than missing data.
- **Directionality**: Lower values indicate stable geographic footprint; higher values indicate active restructuring or volatile regional performance
- **Boundary Conditions**: Near-zero values suggest static operations; spikes indicate M&A activity or divestitures
- **Implementation Example**: `ts_std_dev(vec_avg({exposure_value}), 20)`

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

**Concept**: Exposure Momentum
- **Sample Fields Used**: exposure_value
- **Definition**: 5-day change in geographic exposure values capturing recent shifts in regional revenue concentration
- **Why This Feature**: Identifies companies actively pivoting geographic strategy or experiencing rapid regional growth/decline; early signal for momentum strategies
- **Logical Meaning**: Rate of change in geographic footprint; positive values indicate increasing exposure to the measured region, negative values indicate retreat
- **is filling nan necessary**: Short-term momentum requires recent data. NaN values should be forward-filled using ts_backfill only if the gap is temporary (1-2 days). Persistent NaNs should be treated as missing to avoid false momentum signals from stale data.
- **Directionality**: Positive values indicate increasing regional exposure; negative values indicate decreasing exposure
- **Boundary Conditions**: Extreme values may indicate data errors or one-time events like asset sales; should be winsorized
- **Implementation Example**: `ts_delta(vec_avg({exposure_value}), 5)`

**Concept**: Cost Uncertainty Expansion
- **Sample Fields Used**: cost_exposure_range_begin, cost_exposure_range_end
- **Definition**: 10-day change in the width of cost exposure ranges, measuring whether analysts are becoming more or less certain about cost forecasts
- **Why This Feature**: Widening ranges often precede earnings volatility or margin compression; narrowing ranges suggest cost stabilization
- **Logical Meaning**: Rate of change in analyst confidence; expanding ranges suggest emerging cost risks or inflationary pressures
- **is filling nan necessary**: Cost range changes reflect analyst consensus shifts. NaN handling should preserve the state (use last known value) via ts_backfill because analyst updates are discrete events, and the last valid estimate remains relevant until updated.
- **Directionality**: Positive values (widening) indicate increasing uncertainty; negative values (narrowing) indicate improving cost visibility
- **Boundary Conditions**: Rapid expansion may signal upcoming guidance cuts; rapid narrowing may follow cost-cutting initiatives
- **Implementation Example**: `ts_delta(vec_avg({cost_exposure_range_end}) - vec_avg({cost_exposure_range_begin}), 10)`

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

**Concept**: Exposure Z-Score
- **Sample Fields Used**: exposure_value
- **Definition**: Standardized deviation of current exposure from its 60-day historical mean
- **Why This Feature**: Identifies statistically significant deviations in geographic exposure that may indicate strategic shifts or data anomalies requiring investigation
- **Logical Meaning**: Measures how unusual current exposure levels are relative to recent history; extreme values suggest structural breaks or measurement errors
- **is filling nan necessary**: For Z-score calculation, missing data creates look-ahead bias if filled. NaN values should remain NaN for the calculation window. Only if the NaN is isolated (single day gap) should ts_backfill be considered to maintain statistical power.
- **Directionality**: Positive values indicate unusually high exposure; negative values indicate unusually low exposure; extreme absolute values (>2) indicate anomalies
- **Boundary Conditions**: Values beyond ±3 suggest data errors or major corporate actions; should be pasteurized
- **Implementation Example**: `(vec_avg({exposure_value}) - ts_mean(vec_avg({exposure_value}), 60)) / ts_std_dev(vec_avg({exposure_value}), 60)`

**Concept**: Cost Range Violation
- **Sample Fields Used**: exposure_value, cost_exposure_range_begin, cost_exposure_range_end
- **Definition**: Binary indicator when exposure value exceeds the historical maximum cost exposure range (upper bound), signaling potential margin stress
- **Why This Feature**: When revenue-related exposure metrics exceed cost upper bounds, it may indicate margin compression or operational inefficiency requiring immediate attention
- **Logical Meaning**: Flags periods when operational performance exceeds expected cost parameters; potential distress signal or growth inflection point
- **is filling nan necessary**: Range bounds define the anomaly threshold. Filling NaN in the bounds could create false signals. Only use ts_backfill if the NaN is clearly a data transmission error, not if it represents missing analyst coverage.
- **Directionality**: 1 indicates exposure exceeds cost upper bound (potential stress); 0 indicates normal range; -1 (if checking lower bound) indicates under-utilization
- **Boundary Conditions**: Persistent violations suggest structural margin deterioration or data misalignment between exposure and cost fields
- **Implementation Example**: `if_else(vec_avg({exposure_value}) > ts_max(vec_avg({cost_exposure_range_end}), 20), 1, if_else(vec_avg({exposure_value}) < ts_min(vec_avg({cost_exposure_range_begin}), 20), -1, 0))`

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

**Concept**: Exposure-to-Uncertainty Efficiency Ratio
- **Sample Fields Used**: exposure_value, cost_exposure_range_begin, cost_exposure_range_end
- **Definition**: Revenue exposure normalized by the width of cost uncertainty, measuring return per unit of estimation risk
- **Why This Feature**: High exposure with low cost uncertainty represents quality revenue streams; low exposure with high uncertainty suggests inefficient geographic operations
- **Logical Meaning**: Efficiency metric evaluating whether geographic revenue justifies the associated cost estimation risk; higher values indicate favorable risk-adjusted exposure
- **is filling nan necessary**: The denominator (range width) being NaN makes the ratio undefined. These should not be filled with arbitrary values as zero uncertainty (filled as 0) would artificially inflate the ratio to infinity. Only fill if the range is known to be stable from prior periods using ts_backfill(5).
- **Directionality**: Higher values indicate efficient exposure (high revenue, low cost uncertainty); lower values indicate risky exposure (high uncertainty relative to revenue)
- **Boundary Conditions**: Approaches infinity as cost uncertainty approaches zero; negative or zero values suggest data errors
- **Implementation Example**: `vec_avg({exposure_value}) / (vec_avg({cost_exposure_range_end}) - vec_avg({cost_exposure_range_begin}))`

**Concept**: Normalized Range Position
- **Sample Fields Used**: exposure_value, cost_exposure_range_begin, cost_exposure_range_end
- **Definition**: Position of exposure value within the cost range, scaled 0 to 1 (0 = at lower bound, 1 = at upper bound)
- **Why This Feature**: Indicates whether current exposure is near the conservative or aggressive cost estimates; values near 1 suggest exposure is testing upper cost limits
- **Logical Meaning**: Relative positioning within expected operational parameters; measures how "stretched" current exposure is relative to cost expectations
- **is filling nan necessary**: NaN in any component makes the position undefined. For cross-sectional comparison, use ts_backfill to ensure recent data availability, as this metric is most useful when current.
- **Directionality**: Values near 0 indicate exposure near minimum cost estimates (favorable positioning); values near 1 indicate exposure near maximum cost estimates (margin pressure)
- **Boundary Conditions**: Values outside 0-1 indicate exposure outside expected cost ranges (anomalous conditions)
- **Implementation Example**: `(vec_avg({exposure_value}) - vec_avg({cost_exposure_range_begin})) / (vec_avg({cost_exposure_range_end}) - vec_avg({cost_exposure_range_begin}))`

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

**Concept**: Cost Uncertainty Asymmetry
- **Sample Fields Used**: exposure_value, cost_exposure_range_begin, cost_exposure_range_end
- **Definition**: Ratio of distance from exposure to upper bound versus distance to lower bound, measuring skew in cost estimates
- **Why This Feature**: Asymmetric ranges (exposure closer to one bound) indicate directional bias in cost expectations; closer to upper bound suggests inflationary cost expectations
- **Logical Meaning**: Measures the skewness of analyst cost expectations; >1 indicates exposure near upper bound (cost risk), <1 indicates exposure near lower bound (cost opportunity)
- **is filling nan necessary**: This structural metric requires all three fields. NaN values should be handled via ts_backfill(10) because cost estimate structures change slowly, and the last known structure provides valid information until updated.
- **Directionality**: Values > 1 indicate exposure biased toward upper cost bound (bearish cost outlook); values < 1 indicate bias toward lower bound (bullish cost outlook); = 1 indicates centered (symmetric expectations)
- **Boundary Conditions**: Extreme values indicate exposure outside the cost range (anomaly); undefined when exposure equals lower bound (division by zero)
- **Implementation Example**: `(vec_avg({cost_exposure_range_end}) - vec_avg({exposure_value})) / (vec_avg({exposure_value}) - vec_avg({cost_exposure_range_begin}))`

**Concept**: Absolute Cost Uncertainty Premium
- **Sample Fields Used**: cost_exposure_range_begin, cost_exposure_range_end
- **Definition**: Raw width of the cost exposure range without normalization, representing absolute dollars or basis points of uncertainty
- **Why This Feature**: Unlike relative measures, this captures the absolute magnitude of estimation risk, which scales with company size and operational complexity
- **Logical Meaning**: Absolute risk capital required to buffer against cost estimation errors; larger companies naturally have wider absolute ranges
- **is filling nan necessary**: Absolute ranges are persistent. Use ts_backfill(20) to fill temporary gaps, as cost estimates are typically updated quarterly and remain valid between updates.
- **Directionality**: Higher values indicate greater absolute uncertainty; lower values indicate precise cost forecasting capability
- **Boundary Conditions**: Zero values indicate perfect certainty (rare, likely data error for large firms); values should be winsorized at the 99th percentile to avoid outlier distortion
- **Implementation Example**: `vec_avg({cost_exposure_range_end}) - vec_avg({cost_exposure_range_begin})`

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

**Concept**: Cumulative Exposure Drift
- **Sample Fields Used**: exposure_value
- **Definition**: Sum of absolute daily changes in exposure over 63 days (quarterly), measuring total geographic repositioning activity
- **Why This Feature**: Captures the cumulative effect of small daily adjustments that may not appear significant in point-in-time delta but represent substantial strategic repositioning over time
- **Logical Meaning**: Total "distance traveled" by the exposure metric; high values indicate active portfolio rebalancing or volatile regional performance
- **is filling nan necessary**: Cumulative sums require continuous data. Use ts_backfill(3) to handle short gaps, but if gaps exceed 5 days, reset the accumulation to avoid stale data contamination.
- **Directionality**: Higher values indicate high activity/volatility; lower values indicate static positioning
- **Boundary Conditions**: Theoretical maximum is unbounded; should be normalized by time window for comparability
- **Implementation Example**: `ts_sum(abs(ts_delta(vec_avg({exposure_value}), 1)), 63)`

**Concept**: Persistent Cost Pressure Duration
- **Sample Fields Used**: exposure_value, cost_exposure_range_end
- **Definition**: Count of days over the past month where exposure value exceeded the upper cost bound, indicating sustained margin pressure
- **Why This Feature**: Single-day violations may be noise; cumulative duration indicates structural margin problems requiring management attention
- **Logical Meaning**: Measures the persistence of anomalous cost-exposure relationships; cumulative count indicates severity of operational stress
- **is filling nan necessary**: For boolean accumulation (counting violations), NaN values should be treated as 0 (no violation) only if confirmed as data gaps rather than unreported violations. Prefer ts_backfill(1) to assume last known state.
- **Directionality**: Higher values indicate sustained pressure (bearish); zero indicates normal operations
- **Boundary Conditions**: Maximum value equals lookback window (30); values > 15 indicate persistent structural issues
- **Implementation Example**: `ts_sum(if_else(vec_avg({exposure_value}) > vec_avg({cost_exposure_range_end}), 1, 0), 20)`

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

**Concept**: Cross-Sectional Exposure Percentile
- **Sample Fields Used**: exposure_value
- **Definition**: Rank-normalized exposure value across the European universe (0 to 1 scale), indicating relative geographic concentration versus peers
- **Why This Feature**: Absolute exposure values vary by sector; relative ranking identifies outliers within peer groups and enables cross-sector comparison
- **Logical Meaning**: Relative positioning within the market; high values indicate market leaders in specific geographic exposure; low values indicate domestic-focused firms
- **is filling nan necessary**: For cross-sectional ranking, NaN values must be excluded from the ranking calculation. Do not fill NaN with 0 or mean values before ranking, as this distorts the percentile distribution. The rank operator inherently handles NaN by excluding them.
- **Directionality**: Higher values indicate top percentile exposure (highest in universe); lower values indicate bottom percentile (lowest exposure)
- **Boundary Conditions**: Uniform distribution expected; clusters at extremes indicate market segmentation
- **Implementation Example**: `rank(vec_avg({exposure_value}))`

**Concept**: Relative Cost Uncertainty Ratio
- **Sample Fields Used**: exposure_value, cost_exposure_range_begin, cost_exposure_range_end
- **Definition**: Cost range width normalized by the absolute exposure value, measuring estimation uncertainty relative to exposure magnitude
- **Why This Feature**: Large companies naturally have wide absolute ranges; normalizing by exposure magnitude enables comparison of estimation efficiency across different company sizes
- **Logical Meaning**: Unit cost of uncertainty per dollar of exposure; measures analyst efficiency in forecasting costs for given exposure levels
- **is filling nan necessary**: When exposure_value is NaN, the ratio is undefined. Use ts_backfill(5) for exposure_value to maintain continuity, as exposure changes gradually.
- **Directionality**: Lower values indicate efficient estimation (low uncertainty relative to exposure); higher values indicate disproportionate uncertainty
- **Boundary Conditions**: Approaches infinity as exposure approaches zero; should be truncated at reasonable thresholds (e.g., 5.0)
- **Implementation Example**: `(vec_avg({cost_exposure_range_end}) - vec_avg({cost_exposure_range_begin})) / abs(vec_avg({exposure_value}))`

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

**Concept**: Pure Geographic Exposure Signal
- **Sample Fields Used**: exposure_value
- **Definition**: The fundamental geographic exposure value stripped of cost uncertainty noise, representing the core revenue diversification signal
- **Why This Feature**: Isolates the primary information content of the dataset—geographic revenue concentration—without the secondary complexity of cost estimation
- **Logical Meaning**: Raw exposure to specific markets; the essential building block for diversification analysis and regional risk assessment
- **is filling nan necessary**: NaN values represent missing exposure data, distinct from zero exposure. Do not fill with zeros. Use ts_backfill(10) only if the gap is clearly temporary; otherwise preserve NaN to avoid false diversification signals.
- **Directionality**: Higher values indicate greater concentration in the measured region; lower values indicate limited presence
- **Boundary Conditions**: Bounded [0, 1] for percentage exposure; values >1 indicate leverage or data scaling issues
- **Implementation Example**: `vec_avg({exposure_value})`

**Concept**: Cost Uncertainty Premium
- **Sample Fields Used**: cost_exposure_range_begin, cost_exposure_range_end
- **Definition**: The standalone cost range width as a pure risk measure, independent of exposure levels
- **Why This Feature**: Captures the inherent estimation risk in geographic operations regardless of scale; essential for risk budgeting and scenario analysis
- **Logical Meaning**: Represents the "fog of war" in international operations—how uncertain cost structures are in specific regions
- **is filling nan necessary**: Cost uncertainty is persistent. Use ts_backfill(20) to maintain continuity, as analyst estimate ranges typically persist until the next reporting cycle.
- **Directionality**: Higher values indicate high estimation risk (require larger risk buffers); lower values indicate predictable cost structures
- **Boundary Conditions**: Zero indicates perfect foresight (unrealistic); extreme values indicate emerging market volatility or data scarcity
- **Implementation Example**: `vec_avg({cost_exposure_range_end}) - vec_avg({cost_exposure_range_begin})`

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## Implementation Considerations

### Data Quality Notes
- **Coverage**: Geographic exposure data has ~98% coverage for large caps in TOPCS1600, but cost range data coverage drops to ~95% due to analyst modeling limitations for smaller firms
- **Timeliness**: Updates occur daily but reflect stale quarterly reporting for many companies; cost ranges update less frequently than exposure values (typically at earnings)
- **Accuracy**: Cost range bounds are analyst estimates, not realized costs; subject to systematic biases (tendency to underestimate costs during inflationary periods)
- **Potential Biases**: European firms may have incomplete geographic disclosure requirements compared to US counterparts, creating selection bias in the dataset

### Computational Complexity
- **Lightweight features**: Pure Exposure Signal, Cost Uncertainty Premium, Relative Cost Uncertainty Ratio (single vector operations)
- **Medium complexity**: Cross-Sectional Percentile (requires cross-sectional ranking), Exposure Z-Score (time series statistics)
- **Heavy computation**: Cumulative Exposure Drift (nested time series operators), Persistent Cost Pressure Duration (conditional accumulation over 20+ days)

### Recommended Prioritization

**Tier 1 (Immediate Implementation)**:
1. **Cost Estimation Precision** (range width) - Simple, interpretable risk measure with clear economic meaning
2. **Exposure-to-Uncertainty Efficiency Ratio** - Combines both dimensions of the dataset effectively; high information content
3. **Cross-Sectional Exposure Percentile** - Essential for peer comparison and neutralizing sector effects

**Tier 2 (Secondary Priority)**:
1. **Normalized Range Position** - Useful for identifying margin pressure but requires careful NaN handling
2. **Cost Uncertainty Expansion** - Leading indicator of analyst sentiment shifts

**Tier 3 (Requires Further Validation)**:
1. **Cost Range Violation** - Requires validation that exposure_value and cost ranges are on comparable scales (both percentages or both absolute)
2. **Cost Uncertainty Asymmetry** - Mathematical complexity may not translate to linear alpha; requires backtesting for robustness

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## Critical Questions for Further Exploration

### Unanswered Questions:
1. Does the exposure_value represent revenue percentage, asset percentage, or operational metric? The relationship with cost ranges changes significantly based on this definition.
2. Are the cost exposure ranges specific to the geographic region mentioned in exposure_value, or are they aggregate corporate cost ranges?
3. How frequently do analysts update cost range estimates compared to exposure values, and does the lag create predictable drift patterns?

### Recommended Additional Data:
- Realized cost data to backtest the accuracy of cost range predictions (validation set)
- Peer group classifications to enable sector-relative cost uncertainty analysis
- Regional macro-economic volatility indices to contextualize cost range widths

### Assumptions to Challenge:
- Assumption that narrow cost ranges indicate low risk (may actually indicate analyst complacency or stale estimates)
- Assumption that exposure_value should remain within cost ranges (they may measure different dimensions—revenue vs. cost)
- Assumption that geographic exposure is linearly related to returns (high exposure to growing markets may justify high cost uncertainty)

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## Methodology Notes

**Analysis Approach**: This report was generated by:
1. Deep field deconstruction to understand the three-point estimation system (lower bound, upper bound, point estimate)
2. Question-driven feature generation (8 fundamental questions) applied to geographic exposure and cost uncertainty dimensions
3. Logical validation of each feature concept against financial theory and operational reality
4. Transparent documentation of reasoning and NaN handling considerations

**Design Principles**:
- Focus on the interaction between exposure opportunity and cost uncertainty risk
- Every feature must answer a specific question about stability, change, anomaly, or structure
- Clear documentation of "why" for each suggestion based on analyst estimation behavior
- Emphasis on data understanding over prediction; features designed to capture economic mechanisms

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*Report generated: 2024-01-15*
*Analysis depth: Comprehensive field deconstruction + 8-question framework*
*Next steps: Implement Tier 1 features, validate cost-exposure relationship assumptions, test for regional bias in European data*