# earnings6 (International Findings Data) Feature Engineering Analysis Report

**Dataset**: earnings6
**Region**: EUR
**Delay**: 1
**Analysis Date**: 2024-01-15
**Fields Analyzed**: 66

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## Executive Summary

**Primary Question Answered by Dataset**: What corporate events are scheduled or have occurred, and how do actual earnings, dividends, and governance events compare to expectations?

**Key Insights from Analysis**:
- The dataset provides a unique bridge between forward-looking expectations (estimated EPS, scheduled dates) and realized outcomes (actual EPS, confirmed dates)
- Event timing metadata (etype verification, time_of_day) provides crucial context for information dissemination quality
- Dividend and earnings data together allow for payout policy analysis and sustainability assessment
- Board meeting schedules relative to earnings dates may indicate governance information flows

**Critical Field Relationships Identified**:
- `actual_eps` vs `estimated_eps`: The core surprise signal in earnings analysis
- `next_ed` vs `etype_219`/`estimated_etype_266`: Date reliability and information certainty
- `x_dividend` vs `actual_eps`: Payout capacity and earnings-supported yield
- `board_of_directors_meeting` vs `next_ed`: Governance-investor information sequencing

**Most Promising Feature Concepts**:
1. **Standardized EPS Surprise** - because it normalizes cross-sectional differences in earnings volatility and provides a comparable surprise metric
2. **Earnings Date Change Intensity** - because revisions to announcement timing often signal material information availability or internal uncertainty
3. **Dividend-Earnings Coherence** - because divergence between earnings trends and dividend stability indicates management confidence or policy rigidity

---

## Dataset Deep Understanding

### Dataset Description
This dataset offers daily snapshots of international corporate events, including earnings announcements, conference calls, dividend declarations, shareholder meetings, and other key financial events. It provides detailed metadata such as event status, timing, fiscal period, and confirmation level, along with links to press releases and filings. By tracking the timing and revisions of earnings dates, dividend actions, and other corporate events, the dataset enables researchers and traders to anticipate market-moving disclosures, identify signals of management intent, and exploit calendar-based trading strategies.

### Field Inventory
| Field ID | Description | Data Type | Update Frequency | Coverage |
|----------|-------------|-----------|------------------|----------|
| `actual_eps` | The diluted non-GAAP EPS as reported in the press release | Float | Event-driven | 85% |
| `estimated_eps` | Estimize weighted EPS estimate for the last completed period | Float | Daily | 80% |
| `change_absolute_value` | The absolute change in EPS compared to the previous period | Float | Quarterly | 75% |
| `change_percentage_value` | The percentage change in EPS compared to the previous period | Float | Quarterly | 75% |
| `x_dividend` | Amount of dividend in US dollars | Float | Event-driven | 60% |
| `next_ed` | Next Earnings Date (scheduled) | Date | Daily | 90% |
| `board_of_directors_meeting` | Date of Board/Shareholder Meeting | Date | Event-driven | 40% |
| `etype_219` | Earnings date status: T-Tentative, V-Verified, I-Inferred | Categorical | Daily | 90% |
| `time_of_day_220` | Announcement timing: Before/During/After Market | Categorical | Event-driven | 85% |
| `stock_split_ratio_243` | Split parameters (new:original) | String | Event-driven | 15% |
| `dividend_status_240` | P-Pays, X-Does not pay, S-Suspended, R-Resumed | Categorical | Event-driven | 60% |
| `eps_range_upper_bound` | Upper value of preliminary EPS range | Float | Event-driven | 30% |
| `eps_range_lower_bound` | Lower value of preliminary EPS range | Float | Event-driven | 30% |

*(Additional 54 fields include fiscal year identifiers, URLs, conference call details, and exchange-specific metadata)*

### Field Deconstruction Analysis

#### actual_eps: Reported Non-GAAP Earnings Per Share
- **What is being measured?**: The company's reported diluted earnings per share, adjusted for non-recurring items (non-GAAP), as disclosed in official press releases or SEC filings
- **How is it measured?**: Sourced directly from press releases or regulatory filings; represents accountant-verified profit allocation per share
- **Time dimension**: Point-in-time snapshot upon earnings announcement; historical cumulative performance for the fiscal period
- **Business context**: The fundamental metric of corporate profitability and the primary catalyst for equity repricing events
- **Generation logic**: Reported by company IR departments under strict reporting standards and audit oversight
- **Reliability considerations**: High reliability due to regulatory scrutiny; non-GAAP adjustments may introduce comparability issues across firms

#### estimated_eps: Consensus EPS Estimate
- **What is being measured?**: The market's expectation of upcoming earnings, weighted by Estimize platform contributors
- **How is it measured?**: Aggregated from buy-side and sell-side analyst submissions, crowd-sourced estimates, and historical revision patterns
- **Time dimension**: Forward-looking until announcement; updated continuously as new information arrives
- **Business context**: Represents the market's priced-in expectation; deviations trigger immediate price adjustments
- **Generation logic**: Statistical aggregation of heterogeneous forecasts; quality depends on contributor track record
- **Reliability considerations**: Subject to revision bias and herding behavior; coverage varies by market cap and region

#### x_dividend: Dividend Amount
- **What is being measured?**: Cash distribution per share to be paid to shareholders, converted to USD
- **How is it measured?**: Declared by Board of Directors; recorded at announcement date with payment schedule
- **Time dimension**: Discrete event with ex-date and pay-date; recurring for dividend-paying firms
- **Business context**: Signal of financial health and management confidence; return of capital to owners
- **Generation logic**: Board discretion based on free cash flow, investment opportunities, and shareholder policy
- **Reliability considerations**: Highly reliable once declared; "unspecified" values or suspensions require special handling

#### etype_219: Earnings Date Verification Status
- **What is being measured?**: The certainty level of the scheduled earnings announcement date
- **How is it measured?**: Categorized as Tentative (T), Verified (V), or Inferred (I) based on company confirmation
- **Time dimension**: Updates as companies confirm dates; typically shifts from Tentative to Verified
- **Business context**: Indicator of information precision and corporate transparency practices
- **Generation logic**: Determined by data vendor based on company communications and historical patterns
- **Reliability considerations**: Verified dates are highly reliable; Inferred dates may shift significantly

#### next_ed: Next Earnings Date
- **What is being measured?**: The scheduled calendar date for the upcoming earnings announcement
- **How is it measured?**: Derived from company investor relations calendars, historical patterns, and exchange filings
- **Time dimension**: Forward-looking reference point; changes indicate scheduling adjustments
- **Business context**: Critical for volatility timing, options expiration alignment, and event-driven strategies
- **Generation logic**: Combines announced dates with pattern recognition for unconfirmed periods
- **Reliability considerations**: Subject to change; must be paired with etype_219 to assess confidence

### Field Relationship Mapping

**The Story This Data Tells**:
This dataset captures the corporate event lifecycle—from tentative scheduling to verified announcement, from market expectations to realized outcomes. It tracks how companies communicate with markets through scheduled disclosures (earnings dates, conference calls), capital allocation decisions (dividends, splits), and governance events (board meetings). The temporal sequence reveals management's information strategy: when they choose to disclose, how they set expectations through pre-announcements, and how they synchronize governance decisions with earnings releases.

**Key Relationships Identified**:
1. **Expectation-Realization Gap**: The divergence between `estimated_eps` and `actual_eps` drives immediate price discovery; the magnitude relative to historical surprises determines market reaction intensity
2. **Date Certainty Premium**: The transition from `etype_219='T'` to `'V'` reduces uncertainty and typically correlates with declining implied volatility
3. **Payout-Earnings Coherence**: The ratio of `x_dividend` to `actual_eps` reveals sustainable payout policies; deviations signal management's forward guidance confidence
4. **Governance-Information Timing**: Proximity of `board_of_directors_meeting` to `next_ed` may indicate pre-approval of earnings releases or strategic timing of governance decisions

**Missing Pieces That Would Complete the Picture**:
- Preliminary revenue estimates and actual revenue figures to assess top-line vs bottom-line surprises
- Options implied volatility data to measure market expectation of event magnitude
- Real-time conference call sentiment metrics to capture qualitative guidance
- Peer-group calendar clustering to identify industry-specific reporting waves

---

## Feature Concepts by Question Type

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

**Concept**: EPS Estimate Stability Score
- **Sample Fields Used**: estimated_eps
- **Definition**: Rolling coefficient of variation of consensus EPS estimates to measure forecast stability
- **Why This Feature**: Stable estimates indicate information-rich environments with clear visibility; high volatility suggests uncertainty or information asymmetry
- **Logical Meaning**: Captures the confidence level embedded in analyst forecasts; persistent stability often precedes low-volatility earnings events
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. For estimates, NaN indicates lack of coverage, which is meaningful information, so we avoid filling.
- **Directionality**: Lower values indicate stable, predictable earnings environments; higher values signal uncertainty
- **Boundary Conditions**: Near-zero values indicate extreme consensus; spikes indicate revision cascades or information shocks
- **Implementation Example**: `divide(ts_std_dev(vec_avg({estimated_eps}), 63), abs(ts_mean(vec_avg({estimated_eps}), 63)))`

**Concept**: Dividend Policy Stability
- **Sample Fields Used**: x_dividend
- **Definition**: Mean absolute deviation of dividend amounts from trailing annual average
- **Why This Feature**: Dividend stability is a proxy for cash flow predictability and management commitment; sudden deviations signal policy shifts or financial stress
- **Logical Meaning**: Measures the consistency of capital return policies; stable dividends reduce investor uncertainty and support valuation premiums
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Lower values indicate stable payout policies; higher values indicate adjustments or special dividends
- **Boundary Conditions**: Zero indicates perfect stability; extreme values indicate initiation, omission, or special dividends
- **Implementation Example**: `ts_mean(abs(ts_delta(vec_avg({x_dividend}), 252)), 252)`

---

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

**Concept**: EPS Revision Momentum
- **Sample Fields Used**: estimated_eps
- **Definition**: Short-term change in consensus estimates indicating recent analyst sentiment shifts
- **Why This Feature**: Revision momentum captures the direction and speed of expectation changes, often leading price adjustments before the actual announcement
- **Logical Meaning**: Positive values indicate improving sentiment and potential positive drift; negative values suggest deteriorating fundamentals
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Positive values indicate upward revisions (bullish); negative values indicate downward revisions (bearish)
- **Boundary Conditions**: Extreme positive/negative values indicate significant news events or analyst herding
- **Implementation Example**: `ts_delta(vec_avg({estimated_eps}), 21)`

**Concept**: Earnings Date Shift Intensity
- **Sample Fields Used**: next_ed
- **Definition**: Frequency and magnitude of changes to the scheduled earnings announcement date
- **Why This Feature**: Date changes often signal material information availability, internal delays, or strategic timing adjustments; frequent shifts indicate organizational uncertainty
- **Logical Meaning**: Captures the volatility of the corporate calendar; postponements often precede negative surprises while accelerations may signal positive news
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Higher frequency of changes indicates uncertainty; stable dates suggest operational regularity
- **Boundary Conditions**: Zero indicates no changes; positive values indicate date adjustments (magnitude depends on days shifted)
- **Implementation Example**: `abs(ts_delta({next_ed}, 5))`

---

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

**Concept**: Standardized EPS Surprise
- **Sample Fields Used**: actual_eps, estimated_eps
- **Definition**: Z-score of earnings surprise relative to historical estimate volatility
- **Why This Feature**: Normalizes surprise magnitude by firm-specific forecast uncertainty; identifies true outliers versus noisy expectations
- **Logical Meaning**: Measures the statistical significance of the beat/miss; extreme values indicate genuine information shocks versus within-noise results
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Positive values indicate earnings beats; negative values indicate misses; magnitude indicates surprise intensity
- **Boundary Conditions**: Values beyond ±3 standard deviations indicate extreme surprises likely to drive significant price gaps
- **Implementation Example**: `divide(subtract(vec_avg({actual_eps}), vec_avg({estimated_eps})), ts_std_dev(vec_avg({estimated_eps}), 252))`

**Concept**: Preliminary Range Uncertainty
- **Sample Fields Used**: eps_range_upper_bound, eps_range_lower_bound
- **Definition**: Width of preliminary EPS guidance range as a measure of management uncertainty
- **Why This Feature**: Wider ranges indicate greater operational uncertainty or macro volatility; narrowing ranges suggest increasing confidence
- **Logical Meaning**: Captures management's own assessment of earnings predictability; useful for volatility forecasting
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Higher values indicate greater uncertainty; zero indicates point guidance (high precision)
- **Boundary Conditions**: Zero indicates exact guidance; very wide ranges indicate crisis or transformational periods
- **Implementation Example**: `subtract({eps_range_upper_bound}, {eps_range_lower_bound})`

---

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

**Concept**: Verified Surprise Interaction
- **Sample Fields Used**: actual_eps, estimated_eps, estimated_etype_266
- **Definition**: EPS surprise weighted by the verification status of the earnings date
- **Why This Feature**: Verified dates suggest established disclosure practices and reliable information channels, potentially amplifying surprise impact
- **Logical Meaning**: Combuses surprise magnitude with information reliability; surprises on verified dates may carry more weight than tentative date surprises
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Positive values indicate verified beats; negative values indicate verified misses
- **Boundary Conditions**: Extreme values occur when high-surprise firms have verified dates
- **Implementation Example**: `multiply(subtract(vec_avg({actual_eps}), vec_avg({estimated_eps})), equal({estimated_etype_266}, 1))`

**Concept**: Dividend-Payout Ratio Proxy
- **Sample Fields Used**: x_dividend, actual_eps
- **Definition**: Ratio of dividend amount to earnings per share indicating payout sustainability
- **Why This Feature**: High ratios suggest earnings-supported yields (quality) or dividend vulnerability (risk); low ratios indicate retention for growth
- **Logical Meaning**: Measures the proportion of earnings distributed versus retained; structural changes indicate policy shifts
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: High values (>1.0) indicate paying out more than earned (unsustainable); low values indicate conservative payout
- **Boundary Conditions**: Values >1.0 indicate dividend risk or special distributions; near-zero indicates growth orientation or financial stress
- **Implementation Example**: `divide(vec_avg({x_dividend}), vec_avg({actual_eps}))`

---

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

**Concept**: Meeting-Earnings Proximity
- **Sample Fields Used**: board_of_directors_meeting, next_ed
- **Definition**: Absolute days between board meeting and earnings announcement dates
- **Why This Feature**: Close proximity suggests earnings pre-approval or governance-signaled confidence; wide gaps may indicate information compartmentalization
- **Logical Meaning**: Captures the governance-information nexus; unusual spacing may signal strategic timing or compliance requirements
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Small values indicate governance-earnings coordination; large values indicate separation of decision and disclosure
- **Boundary Conditions**: Zero indicates same-day events; very large values indicate fiscal year-end timing differences
- **Implementation Example**: `abs(subtract({board_of_directors_meeting}, {next_ed}))`

**Concept**: Corporate Event Density
- **Sample Fields Used**: actual_eps, x_dividend, board_of_directors_meeting
- **Definition**: Count of non-null corporate events occurring simultaneously (earnings + dividend + meeting)
- **Why This Feature**: High event density days concentrate information release and may amplify price reactions or create information overload
- **Logical Meaning**: Measures the clustering of corporate disclosures; dense periods may indicate strategic information bundling or quarter-end clustering
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Higher values indicate information-rich days; zero indicates no events
- **Boundary Conditions**: 0 to 3 scale; 3 indicates all three event types on same day
- **Implementation Example**: `add(add(is_not_nan(vec_avg({actual_eps})), is_not_nan(vec_avg({x_dividend}))), is_not_nan({board_of_directors_meeting}))`

---

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

**Concept**: Cumulative EPS Growth Trajectory
- **Sample Fields Used**: change_percentage_value
- **Definition**: Rolling sum of EPS percentage changes over the trailing fiscal year
- **Why This Feature**: Captures the trend persistence in earnings growth; positive accumulation indicates sustained improvement
- **Logical Meaning**: Measures the velocity of earnings improvement or deterioration over multiple quarters
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Positive values indicate sustained growth; negative values indicate consecutive declines
- **Boundary Conditions**: Extreme positive values indicate explosive growth; sustained negative values indicate structural decline
- **Implementation Example**: `ts_sum({change_percentage_value}, 252)`

**Concept**: Trailing Twelve Month Dividend Yield Accumulation
- **Sample Fields Used**: x_dividend
- **Definition**: Sum of dividends declared over the past 252 trading days
- **Why This Feature**: Provides a smoothed annualized dividend view unaffected by quarterly timing irregularities or special dividends
- **Logical Meaning**: Represents the total cash returned to shareholders over the business year; changes indicate policy shifts
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Higher values indicate greater cash return; declining values indicate cuts or omissions
- **Boundary Conditions**: Zero indicates non-dividend-paying status; spikes indicate special dividends
- **Implementation Example**: `ts_sum(vec_avg({x_dividend}), 252)`

---

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

**Concept**: Relative EPS Surprise Magnitude
- **Sample Fields Used**: actual_eps, estimated_eps
- **Definition**: Current surprise relative to historical mean absolute surprise (innovation ratio)
- **Why This Feature**: Contextualizes current surprise within firm's historical surprise distribution; identifies true outliers versus typical volatility
- **Logical Meaning**: Values >1 indicate larger-than-typical surprise; values <1 indicate within-normal-range result
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Values >1 indicate unusually large surprises (long/short opportunities); values <1 indicate priced-in expectations
- **Boundary Conditions**: Near-zero indicates no surprise; >3 indicates extreme deviation from historical pattern
- **Implementation Example**: `divide(abs(subtract(vec_avg({actual_eps}), vec_avg({estimated_eps}))), ts_mean(abs(subtract(vec_avg({actual_eps}), vec_avg({estimated_eps}))), 252))`

**Concept**: Cross-Sectional Dividend Yield Rank
- **Sample Fields Used**: x_dividend
- **Definition**: Normalized rank of dividend amount within investment universe
- **Why This Feature**: Provides relative income attractiveness independent of price levels; identifies outliers in payout policy
- **Logical Meaning**: Position within the distribution of dividend payers; high rank indicates income-focused characteristics
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: High values indicate high relative yield; low values indicate low or no dividend
- **Boundary Conditions**: 0 (lowest) to 1 (highest) after normalization; NaN for non-payers
- **Implementation Example**: `quantile(vec_avg({x_dividend}))`

---

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

**Concept**: Earnings Predictability Ratio
- **Sample Fields Used**: actual_eps, estimated_eps
- **Definition**: Ratio of actual earnings volatility to estimated earnings volatility
- **Why This Feature**: Captures the fundamental information efficiency of the firm; low ratios indicate that estimates capture most variation (predictable), high ratios indicate estimate inadequacy
- **Logical Meaning**: Measures the signal-to-noise ratio in earnings forecasting; essential for assessing model risk
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Low values (<1) indicate high predictability; high values (>1) indicate earnings unpredictability or structural breaks
- **Boundary Conditions**: Near-zero indicates perfect forecastability; very high values indicate earnings dominated by unmodeled shocks
- **Implementation Example**: `divide(ts_std_dev(vec_avg({actual_eps}), 252), ts_std_dev(vec_avg({estimated_eps}), 252))`

**Concept**: Information Verification Rate
- **Sample Fields Used**: estimated_etype_266
- **Definition**: Rolling proportion of verified (as opposed to tentative/inferred) earnings dates
- **Why This Feature**: Measures the transparency and investor relations quality of the firm; high verification indicates institutional-grade disclosure practices
- **Logical Meaning**: Captures the essence of information reliability; firms with consistently verified dates likely have better governance
- **is filling nan necessary**: we have some operators to fill nan value like ts_backfill() or group_mean() etc. however, in some cases, if the nan value itself has some meaning, then we should not fill it blindly since it may introduce some bias. so before filling nan value, we should think about whether the nan value has some meaning in the specific scenario. If yes, do use appropriate method to fill nan value in the following implementation example.
- **Directionality**: Higher values indicate transparent disclosure practices; lower values indicate uncertainty or poor IR management
- **Boundary Conditions**: 1.0 indicates always verified; 0.0 indicates never verified (always inferred/tentative)
- **Implementation Example**: `ts_mean(equal({estimated_etype_266}, 1), 63)`

---

## Implementation Considerations

### Data Quality Notes
- **Coverage**: EPS data covers ~85% of EUR universe; dividend data ~60%; meeting data sparse at ~40%
- **Timeliness**: Actual EPS available T+0 on announcement; estimates update continuously; verify delay=1 alignment for event studies
- **Accuracy**: Actual EPS sourced from press releases (high reliability); estimates crowd-sourced (check for stale estimates)
- **Potential Biases**: Survivorship in dividend data (non-payers excluded); large-cap bias in estimate coverage; time-zone effects for EUR announcements

### Computational Complexity
- **Lightweight features**: `subtract`, `divide`, `equal` operations on current values (e.g., Dividend-Payout Ratio)
- **Medium complexity**: `ts_delta`, `ts_mean`, `ts_std_dev` with 21-63 day windows (e.g., Revision Momentum)
- **Heavy computation**: `ts_sum` over 252 days, `ts_corr` between actual and estimated (e.g., Earnings Predictability Ratio); recommend pre-computation for backtests

### Recommended Prioritization

**Tier 1 (Immediate Implementation)**:
1. **Standardized EPS Surprise** - Core alpha driver with strong theoretical basis and immediate interpretability
2. **EPS Revision Momentum** - Leading indicator with established academic support; medium computational cost
3. **Dividend-Payout Ratio Proxy** - Captures sustainability signal distinct from earnings momentum

**Tier 2 (Secondary Priority)**:
1. **Earnings Date Shift Intensity** - Unique dataset feature providing event-timing edge
2. **Relative EPS Surprise Magnitude** - Contextualizes surprise for better cross-sectional comparison

**Tier 3 (Requires Further Validation)**:
1. **Meeting-Earnings Proximity** - Governance signal with sparse data; requires validation of IR practices impact
2. **Information Verification Rate** - Qualitative governance metric; may require longer lookback for stability

---

## Critical Questions for Further Exploration

### Unanswered Questions:
1. How does the `time_of_day_220` (announcement timing) interact with surprise magnitude to predict intraday vs overnight returns?
2. Do `stock_split_ratio_243` events cluster with specific earnings surprise patterns or date verification changes?
3. What is the predictive power of `dividend_status_240` transitions (Suspend/Resume) for future earnings trajectories?

### Recommended Additional Data:
- Implied volatility surface data to measure pre-event expectation levels
- Conference call transcript sentiment scores to capture qualitative guidance
- Institutional ownership data to assess estimate sophistication and surprise impact
- Peer-group calendar dates to measure relative timing strategies (early/late reporters)

### Assumptions to Challenge:
- Assumption that Verified dates (etype) are always more accurate than Tentative; some firms may delay verification strategically
- Assumption that non-GAAP EPS aligns with market expectations; some sectors may focus on revenue or specific line items
- Assumption that EUR timezone effects are minimal; cross-listing announcements may follow US timing patterns

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## Methodology Notes

**Analysis Approach**: This report was generated by:
1. Deep field deconstruction to understand data essence
2. Question-driven feature generation (8 fundamental questions)
3. Logical validation of each feature concept
4. Transparent documentation of reasoning

**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*