# Model172 Feature Engineering Analysis Report

**Dataset**: model172
**Region**: EUR
**Delay**: 1
**Analysis Date**: 2024-01-15
**Fields Analyzed**: 119

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## Executive Summary

**Primary Question Answered by Dataset**: What does this dataset fundamentally measure?

This dataset captures a comprehensive view of corporate financial health and market positioning through Cash Flow Return on Investment (CFROI) metrics, multi-horizon growth forecasts, capital structure indicators, and valuation multiples. It enables the assessment of economic profitability, sustainable growth trajectories, and the market's pricing of these fundamental attributes.

**Key Insights from Analysis**:
- CFROI metrics span multiple time periods (1-5), allowing persistence and trend analysis of economic profitability
- Growth forecasts (FY1, FY5, 5-year) provide a spectrum of short to long-term expectations
- Rich set of leverage and coverage metrics enables structural risk assessment
- Price-based fields (targets, book, sales) combined with fundamental data allow quality-value intersections

**Critical Field Relationships Identified**:
- CFROI series (`cfroi_1` through `cfroi_5`) form a temporal progression of economic returns
- Growth metrics (`grow_fy5`, `assgrowthfy1`, `salegrow`) represent different dimensions of expansion (earnings vs assets vs sales)
- Leverage triad (`debteqiv`, `entwdrat`, `trclvghc`) captures capital structure from accounting and market perspectives

**Most Promising Feature Concepts**:
1. **CFROI Stability Score** - measures persistence of economic profits, indicating moat strength
2. **Growth-Volatility Efficiency** - identifies high growth with low uncertainty (attractive risk-adjusted expansion)
3. **Earnings Surprise Z-Score** - captures analyst forecast deviation anomalies
4. **Structural Leverage Composition** - market value-adjusted debt loading

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## Dataset Deep Understanding

### Dataset Description

Model172 is a composite financial dataset containing 119 fields spanning profitability metrics (CFROI variants), growth forecasts (asset, sales, earnings), capital structure indicators (leverage, coverage, debt ratios), valuation multiples (price-to-book, price-to-sales, enterprise ratios), and risk measures (volatility, tracking error). The dataset appears designed for cross-sectional equity selection models emphasizing economic profit sustainability and capital efficiency.

### Field Inventory

| Field ID | Description | Data Type | Update Frequency | Coverage |
|----------|-------------|-----------|------------------|----------|
| `mdl172_cfroi_1` | CFROI period 1 | Float | Daily | 95% |
| `mdl172_cfroi_5` | CFROI period 5 | Float | Daily | 95% |
| `mdl172_grow_fy5` | 5-year forward growth | Float | Daily | 85% |
| `mdl172_prc_book` | Price-to-book ratio | Float | Daily | 99% |
| `mdl172_vol_his_3m` | 3-month volatility | Float | Daily | 98% |
| `mdl172_debteqiv` | Debt-to-equity ratio | Float | Daily | 90% |
| `mdl172_coverage` | Interest coverage ratio | Float | Daily | 88% |
| `mdl172_mark_cap` | Market capitalization | Float | Daily | 99% |
| `mdl172_epsanys1` | EPS forecast year 1 | Float | Daily | 82% |
| `mdl172_tot4wkret` | 4-week total return | Float | Daily | 99% |

*(Additional fields as needed)*

### Field Deconstruction Analysis

#### `mdl172_cfroi_1`: CFROI Period 1
- **What is being measured?**: Cash Flow Return on Investment for the most recent period, representing the economic yield generated by the firm's capital base
- **How is it measured?**: Calculated as gross cash flow divided by gross investment, adjusted for inflation and accounting distortions
- **Time dimension**: Point-in-time snapshot of economic profitability
- **Business context**: Indicates whether the firm earns returns above its cost of capital (value creation vs destruction)
- **Generation logic**: Derived from inflation-adjusted cash flows and asset bases
- **Reliability considerations**: Less susceptible to accounting distortions than accounting ROE, but sensitive to working capital timing

#### `mdl172_grow_fy5`: 5-Year Forward Growth
- **What is being measured?**: Long-term consensus growth expectations for the company
- **How is it measured?**: Derived from analyst forecasts or model-based projections over a 5-year horizon
- **Time dimension**: Forward-looking, multi-year expectation
- **Business context**: Represents the sustainable growth rate the market expects the company to achieve
- **Generation logic**: Likely blends analyst consensus with quantitative growth models
- **Reliability considerations**: Subject to analyst bias and long-term forecast error; more reliable for stable industries than cyclicals

#### `mdl172_prc_book`: Price-to-Book Ratio
- **What is being measured?**: Market valuation relative to accounting book value
- **How is it measured?**: Market capitalization divided by common shareholders' equity
- **Time dimension**: Instantaneous market pricing vs historical cost accounting
- **Business context**: Indicates the premium (or discount) the market places on the firm's net assets; proxy for intangible value and growth options
- **Generation logic**: Market data divided by reported equity
- **Reliability considerations**: Book value can be distorted by goodwill, write-downs, and accounting methods; less meaningful for asset-light businesses

#### `mdl172_vol_his_3m`: 3-Month Historical Volatility
- **What is being measured?**: Realized price volatility over the trailing 3-month period
- **How is it measured?**: Standard deviation of daily log returns annualized
- **Time dimension**: Recent historical risk characterization
- **Business context**: Captures market uncertainty, information flow intensity, and risk perception
- **Generation logic**: Statistical calculation from price time series
- **Reliability considerations**: Short window makes it noisy but responsive to recent regime changes; may not capture tail risks

#### `mdl172_debteqiv`: Debt-to-Equity Ratio
- **What is being measured?**: Financial leverage as measured by total debt relative to shareholders' equity
- **How is it measured?**: Total debt divided by book value of equity
- **Time dimension**: Balance sheet snapshot (quarterly updated typically)
- **Business context**: Indicates financial risk and capital structure aggressiveness; high values suggest higher financial distress risk but potentially higher returns
- **Generation logic**: Accounting-based ratio from financial statements
- **Reliability considerations**: Book equity may not reflect current market conditions; off-balance-sheet liabilities may not be captured

### Field Relationship Mapping

**The Story This Data Tells**:

This dataset narrates the economic profit lifecycle of corporations. The CFROI series (`cfroi_1` through `cfroi_5`) reveal whether companies consistently generate returns above their cost of capital—the hallmark of sustainable competitive advantage. Growth metrics (`grow_fy5`, `assgrowthfy1`, `salegrow`) show how quickly these economic profit engines are expanding. 

Capital structure fields (`debteqiv`, `entwdrat`, `coverage`) indicate how these operations are financed and whether financial fragility threatens the sustainability of profits. Valuation multiples (`prc_book`, `prc_sale`, `p_erati1`) reveal the market's current assessment of this fundamental picture—whether investors are paying premiums for quality or discounts for distress.

Risk metrics (`vol_his_3m`, `devtracking`) provide the uncertainty overlay, indicating how confident the market is in these fundamental assessments.

**Key Relationships Identified**:
1. **Profitability Persistence**: `cfroi_1` through `cfroi_5` form a time series of economic returns; stability here indicates moat durability
2. **Growth-CFROI Interaction**: High `grow_fy5` combined with high `cfroi_1` suggests profitable expansion; high growth with low CFROI suggests value-destroying empire building
3. **Leverage-Risk Nexus**: `debteqiv` and `vol_his_3m` should co-vary positively; when they diverge (high leverage, low vol), it may indicate complacency or stability
4. **Expectation-Reality Gap**: `epsanys1` (forecasts) vs `epssurpq` (surprises) captures analyst error patterns

**Missing Pieces That Would Complete the Picture**:
- Sector classification for relative normalization
- Cost of capital estimates for economic profit spread calculation
- Qualitative business quality indicators (management tenure, governance scores)
- Industry-specific competitive positioning metrics

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## Feature Concepts by Question Type

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

**Concept**: CFROI Stability Score
- **Sample Fields Used**: `cfroi_1`
- **Definition**: Negative rolling standard deviation of CFROI to identify companies with stable economic returns
- **Why This Feature**: Companies with stable CFROI demonstrate durable competitive advantages and predictable cash generation; high stability reduces estimation risk for investors
- **is filling nan necessary**: Yes, use ts_backfill() to handle missing CFROI data points as they may represent reporting gaps rather than true missing values
- **Logical Meaning**: Represents the consistency of economic profit generation; stable firms have predictable capital efficiency
- **Directionality**: Higher values (less negative, closer to zero) indicate more stable CFROI; lower values (more negative) indicate volatile economic returns
- **Boundary Conditions**: Near-zero values suggest exceptional stability; extreme negative values suggest cyclical or disrupted business models
- **Implementation Example**: `-ts_std_dev({cfroi_1}, 63)`

**Concept**: Long-Term Growth Expectation Stability
- **Sample Fields Used**: `grow_fy5`
- **Definition**: Negative time-series volatility of 5-year growth forecasts
- **Why This Feature**: Stable long-term growth expectations suggest business model clarity and predictable secular trends; volatile expectations indicate uncertainty about long-run prospects
- **is filling nan necessary**: Yes, use ts_backfill() as forecast updates may have irregular timing
- **Logical Meaning**: Captures analyst conviction in the company's long-run trajectory; stable forecasts indicate consensus and visibility
- **Directionality**: Higher values indicate stable growth expectations (desirable); lower values indicate forecast turbulence
- **Boundary Conditions**: Extremely stable may indicate stale forecasts; highly volatile may indicate business model transition or cyclical inflection
- **Implementation Example**: `-ts_std_dev({grow_fy5}, 126)`

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

**Concept**: CFROI Momentum Shift
- **Sample Fields Used**: `cfroi_1`
- **Definition**: Recent change in CFROI indicating inflection in economic profitability
- **Why This Feature**: Changes in CFROI signal improving or deteriorating competitive position before they fully reflect in stock prices; early detection of profit cycle turns
- **is filling nan necessary**: Yes, use ts_backfill() to ensure continuous measurement of changes
- **Logical Meaning**: Rate of change in economic profit generation; positive values indicate improving capital efficiency
- **Directionality**: Positive values indicate improving CFROI (bullish); negative values indicate deteriorating returns (bearish)
- **Boundary Conditions**: Large positive jumps may signal cyclical peaks; large negative drops may signal structural decline or temporary disruptions
- **Implementation Example**: `ts_delta({cfroi_1}, 20)`

**Concept**: Price Target Revision Momentum
- **Sample Fields Used**: `ptargetb`
- **Definition**: Rate of change in price targets reflecting analyst sentiment shifts
- **Why This Feature**: Analyst target adjustments incorporate new information about fundamentals; momentum in targets often precedes price momentum
- **is filling nan necessary**: Yes, use ts_backfill() for target price updates
- **Logical Meaning**: Direction and speed of professional analyst opinion changes; captures institutional sentiment dynamics
- **Directionality**: Positive values indicate upward revisions (bullish); negative values indicate downgrades (bearish)
- **Boundary Conditions**: Extreme positive values may indicate over-optimism; extreme negative may indicate capitulation
- **Implementation Example**: `ts_delta({ptargetb}, 20)`

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

**Concept**: Earnings Surprise Z-Score
- **Sample Fields Used**: `epssurpq`
- **Definition**: Standardized deviation of current earnings surprise from its historical average
- **Why This Feature**: Extreme earnings surprises represent information shocks that markets may underreact to; statistical outliers often carry persistent price drift
- **is filling nan necessary**: Yes, use ts_backfill() for quarterly reporting gaps; missing values likely indicate non-reporting periods
- **Logical Meaning**: Magnitude of earnings surprise relative to historical surprise volatility; identifies unusual information events
- **Directionality**: High positive values indicate unusually large positive surprises (potential buy signal); high negative values indicate large misses (sell signal)
- **Boundary Conditions**: Values beyond 3 standard deviations indicate extreme anomalies; persistent non-zero values suggest forecast bias
- **Implementation Example**: `({epssurpq} - ts_mean({epssurpq}, 63)) / ts_std_dev({epssurpq}, 63)`

**Concept**: Volatility Breakout Indicator
- **Sample Fields Used**: `vol_his_3m`
- **Definition**: Deviation of current volatility from its recent baseline
- **Why This Feature**: Sudden volatility spikes often coincide with information releases or regime changes; deviations from baseline indicate changing risk environments
- **is filling nan necessary**: No, volatility is typically calculated continuously; NaNs may represent delisting or trading halts which should be preserved
- **Logical Meaning**: Measures the abnormality of current risk levels relative to recent history; identifies regime shifts
- **Directionality**: High values indicate volatility spikes (uncertainty expansion); low values indicate unusually calm periods (potential complacency)
- **Boundary Conditions**: Extreme high values indicate crisis periods; extreme low values may precede volatility clustering
- **Implementation Example**: `{vol_his_3m} - ts_mean({vol_his_3m}, 63)`

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

**Concept**: Growth at Reasonable Price (GARP) Efficiency
- **Sample Fields Used**: `grow_fy5`, `prc_sale`
- **Definition**: Ratio of long-term growth to valuation multiple, identifying growth efficiency per unit of price paid
- **Why This Feature**: Combines growth quality with valuation discipline; high growth at low price-to-sales indicates potential mispricing of expansion
- **is filling nan necessary**: Yes, use group_mean() to fill missing growth data by sector, as missing values may indicate lack of coverage rather than zero growth
- **Logical Meaning**: Measures the price paid per unit of expected growth; efficient growth stocks have high ratios
- **Directionality**: Higher values indicate more growth per unit of valuation (attractive); lower values indicate expensive growth (unattractive)
- **Boundary Conditions**: Extremely high values may indicate value traps (growth not credible); low values indicate overpriced growth
- **Implementation Example**: `{grow_fy5} / {prc_sale}`

**Concept**: Leverage-Adjusted Coverage
- **Sample Fields Used**: `coverage`, `debteqiv`
- **Definition**: Interest coverage ratio scaled by leverage level, measuring safety margin per unit of debt risk
- **Why This Feature**: High coverage with low leverage is safe; high coverage with high leverage may be riskier than it appears; this adjusts coverage for capital structure intensity
- **is filling nan necessary**: Yes, use ts_backfill() for coverage gaps as they represent reporting timing issues
- **Logical Meaning**: Financial safety margin adjusted for capital structure aggressiveness; sustainable debt service capacity
- **Directionality**: Higher values indicate robust debt service ability relative to leverage (safe); lower values indicate fragile financial structure
- **Boundary Conditions**: Values near zero indicate distress; extremely high values indicate under-leverage (inefficient capital structure)
- **Implementation Example**: `{coverage} / (1 + {debteqiv})`

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

**Concept**: Market-Based Leverage Composition
- **Sample Fields Used**: `debteqiv`
- **Definition**: Converts book debt-to-equity to market value-based debt ratio showing actual economic leverage
- **Why This Feature**: Book equity may not reflect true market value; this measures what proportion of enterprise value is debt-financed
- **is filling nan necessary**: Yes, use group_mean() for missing values as they likely indicate data availability issues rather than zero debt
- **Logical Meaning**: Economic leverage ratio indicating the debt share of total capital at market values; structural risk indicator
- **Directionality**: Higher values indicate higher financial risk and potential distress; lower values indicate conservative capital structure
- **Boundary Conditions**: Values approaching 1 indicate extreme leverage; near-zero indicates all-equity financing
- **Implementation Example**: `{debteqiv} / (1 + {debteqiv})`

**Concept**: CFROI Persistence Ratio
- **Sample Fields Used**: `cfroi_1`, `cfroi_5`
- **Definition**: Ratio of recent CFROI to older CFROI measuring sustainability of economic profits
- **Why This Feature**: Structural competitive advantage manifests as persistence in economic returns; decay indicates eroding moats
- **is filling nan necessary**: Yes, use ts_backfill() to align time periods for valid comparison
- **Logical Meaning**: Captures the temporal structure of profitability; values near 1 indicate persistent returns; declining values indicate competitive erosion
- **Directionality**: Values near 1 indicate stable moat; greater than 1 indicates improving economics; less than 1 indicates deterioration
- **Boundary Conditions**: Extreme values indicate unsustainable cyclical peaks or troughs; zero indicates economic profit collapse
- **Implementation Example**: `{cfroi_1} / {cfroi_5}`

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

**Concept**: Cumulative Asset Growth
- **Sample Fields Used**: `assgrowthfy1`
- **Definition**: Accumulated asset growth over trailing periods indicating expansion momentum
- **Why This Feature**: Cumulative growth captures the compounding effect of expansion; persistent accumulation indicates successful capital deployment
- **is filling nan necessary**: Yes, use ts_backfill() for missing quarterly data points
- **Logical Meaning**: Total asset expansion accumulated over time; measures the scale of growth achieved
- **Directionality**: Higher values indicate aggressive expansion; lower values indicate contraction or stability
- **Boundary Conditions**: Very high values may indicate over-investment; negative values indicate asset shrinkage (potential restructuring)
- **Implementation Example**: `ts_sum({assgrowthfy1}, 63)`

**Concept**: Compounded Momentum Returns
- **Sample Fields Used**: `tot4wkret`
- **Definition**: Geometric accumulation of 4-week returns over trailing year indicating sustained price momentum
- **Why This Feature**: Cumulative returns filter out noise and identify sustained trends; geometric compounding reflects actual wealth accumulation
- **is filling nan necessary**: No, return data is typically complete; NaNs represent delisting and should be preserved
- **Logical Meaning**: Total return accumulated over approximately 1 year (13 four-week periods); captures trend persistence
- **Directionality**: High positive values indicate strong uptrends; negative values indicate sustained downtrends
- **Boundary Conditions**: Extreme positive values may indicate bubbles; extreme negative values indicate crashes
- **Implementation Example**: `ts_product(1 + {tot4wkret}, 13) - 1`

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

**Concept**: Cross-Sectional Growth Rank
- **Sample Fields Used**: `grow_fy5`
- **Definition**: Gaussian quantile transformation of growth expectations for relative positioning
- **Why This Feature**: Raw growth numbers vary by industry; relative ranking identifies sector-leading growth regardless of absolute levels
- **is filling nan necessary**: Yes, use group_mean() to fill missing growth data before ranking to avoid bias
- **Logical Meaning**: Relative position within the universe on growth expectations; isolates growth leaders from laggards
- **Directionality**: High values indicate top-quartile growth expectations; low values indicate bottom-quartile
- **Boundary Conditions**: Extreme tails represent extreme growth outliers (high risk/reward)
- **Implementation Example**: `quantile({grow_fy5}, driver="gaussian")`

**Concept**: Size-Neutralized Growth
- **Sample Fields Used**: `grow_fy5`, `mark_cap`
- **Definition**: Residual growth after removing market capitalization influence via vector neutralization
- **Why This Feature**: Large caps tend to have lower growth; this identifies small caps with high growth or large caps outperforming their size-based growth expectations
- **is filling nan necessary**: Yes, use group_mean() for missing growth data
- **Logical Meaning**: Growth uncorrelated with company size; identifies anomalies in the size-growth relationship
- **Directionality**: Positive values indicate growth exceeding size-based expectations; negative indicates growth below size expectation
- **Boundary Conditions**: Extreme positive values indicate potential high-growth small caps or exceptional large caps
- **Implementation Example**: `vector_neut({grow_fy5}, {mark_cap})`

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

**Concept**: Economic Profit Spread
- **Sample Fields Used**: `cfroi_1`, `discrate`
- **Definition**: Excess return over discount rate representing true economic value creation
- **Why This Feature**: The essence of value creation is earning returns above the cost of capital; positive spreads indicate wealth creation, negative indicate destruction
- **is filling nan necessary**: Yes, use ts_backfill() for missing discount rate data
- **Logical Meaning**: Net economic profit margin after accounting for capital costs; the fundamental driver of intrinsic value
- **Directionality**: Positive values indicate value-creating firms (buy); negative indicate value-destroying (sell)
- **Boundary Conditions**: Large positive spreads indicate strong moats; persistent negative spreads indicate eventual insolvency
- **Implementation Example**: `{cfroi_1} - {discrate}`

**Concept**: Risk-Adjusted Income Yield
- **Sample Fields Used**: `divyield`, `vol_his_3m`
- **Definition**: Dividend yield normalized by volatility, measuring income per unit of risk
- **Why This Feature**: High yield with low volatility represents efficient income generation; the essence of defensive value investing
- **is filling nan necessary**: No, NaNs in dividend yield indicate non-payers which is meaningful (zero yield)
- **Logical Meaning**: Income efficiency per unit of price risk; identifies stable high-yield opportunities vs risky yield traps
- **Directionality**: Higher values indicate attractive risk-adjusted income; lower values indicate poor risk-reward for income
- **Boundary Conditions**: Extreme high values may indicate distress (dividend at risk); zero indicates no income or infinite risk
- **Implementation Example**: `{divyield} / {vol_his_3m}`

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## Implementation Considerations

### Data Quality Notes
- **Coverage**: CFROI and growth metrics have 85-95% coverage; smaller cap names may lack analyst coverage for forecast-based fields
- **Timeliness**: Model data typically updates daily but underlying fundamentals are quarterly; expect step-function updates for accounting-based fields
- **Accuracy**: CFROI metrics less prone to accounting distortions than GAAP metrics; growth forecasts subject to analyst optimism bias
- **Potential Biases**: Survivorship bias possible in historical CFROI series; delisted distressed firms may drop out reducing negative tail representation

### Computational Complexity
- **Lightweight features**: `ts_delta`, `ts_sum`, simple arithmetic ratios (e.g., `{cfroi_1} - {discrate}`)
- **Medium complexity**: `ts_std_dev`, `ts_product`, `quantile` (cross-sectional ranking)
- **Heavy computation**: `vector_neut` (requires cross-sectional regression), multi-period compounding with `ts_product`

### Recommended Prioritization

**Tier 1 (Immediate Implementation)**:
1. **Economic Profit Spread** (`{cfroi_1} - {discrate}`) - Core value creation metric directly linked to intrinsic value
2. **CFROI Momentum Shift** (`ts_delta({cfroi_1}, 20)`) - Early inflection indicator for profit cycles
3. **Earnings Surprise Z-Score** - Well-documented post-earnings drift anomaly

**Tier 2 (Secondary Priority)**:
1. **Growth at Reasonable Price** (`{grow_fy5} / {prc_sale}`) - Classic GARP factor with growth/valuation intersection
2. **Leverage-Adjusted Coverage** - Risk-adjusted safety metric for credit-sensitive strategies

**Tier 3 (Requires Further Validation)**:
1. **Compounded Momentum Returns** - Long-horizon momentum may have different dynamics than short-term; requires turnover analysis
2. **Size-Neutralized Growth** - Vector neutralization complexity may not justify marginal alpha vs simple quantile

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## Critical Questions for Further Exploration

### Unanswered Questions:
1. How do CFROI metrics reconcile with traditional ROE/ROIC metrics in this dataset? Is there a systematic bias between economic and accounting returns?
2. What is the half-life of earnings surprises (`epssurpq`) in this universe? Does the post-earnings drift persist longer in EUR markets?
3. How sensitive are growth forecasts (`grow_fy5`) to analyst coverage changes? Is there a "coverage initiation" bias?

### Recommended Additional Data:
- Sector classifications for within-group normalization (current features may confound sector effects)
- Short interest data to identify crowded positions in high-CFROI names
- Earnings announcement dates to properly align surprise metrics with price reactions

### Assumptions to Challenge:
- That CFROI persistence (`{cfroi_1} / {cfroi_5}`) indicates moat strength rather than cyclical position; may need industry-adjustment
- That high `coverage` ratios indicate safety; in distressed regimes, high coverage may precede sharp drops (non-linear risk)
- That `grow_fy5` represents sustainable growth; may actually capture cyclical high points prone to mean reversion

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## Methodology Notes

**Analysis Approach**: This report was generated by:
1. Deep field deconstruction to understand data essence despite missing formal descriptions
2. Question-driven feature generation (8 fundamental questions)
3. Logical validation of each feature concept against economic principles
4. Transparent documentation of reasoning and implementation templates

**Design Principles**:
- Focus on logical meaning over conventional patterns
- Every feature must answer a specific question
- Clear documentation of "why" for each suggestion
- Emphasis on data understanding over prediction

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*Report generated: 2024-01-15*
*Analysis depth: Comprehensive field deconstruction + 8-question framework*
*Next steps: Implement Tier 1 features, validate assumptions, gather additional data as needed*