The problem: “alternative data” is treated as one category
In practice, web-derived signals fall into two broad buckets: directional signals that shift conviction and risk posture, and predictive signals that can be turned into repeatable features with measurable lift. Many research failures happen when teams model the first category as if it were the second.
Definitions that map to research decisions
Directional web data
Data that indicates pressure, momentum, or regime context, but is not reliably calibrated to forecast a specific magnitude or timing of an outcome.
- Best use: overlays, confirmation, risk posture, sizing
- Common failure mode: overfit narrative signals in backtests
- Typical shape: noisy, asymmetric, context-dependent
- Regime awareness
- Conviction support
- Event confirmation
Predictive web data
Data that exhibits stable lead–lag relationships and can be operationalized into repeatable, monitored model features with measurable out-of-sample performance.
- Best use: model inputs, systematic forecasting, screening
- Common failure mode: pipeline drift breaks comparability
- Typical shape: structured, consistent cadence, definitional stability
- Backtest-ready
- Feature engineering
- Production monitoring
Why “directional” signals often look good in a notebook
Directional web signals (news velocity, social chatter, review sentiment, forum activity) are often highly reactive to the same information that moves markets. That makes them easy to fit in-sample—and fragile out-of-sample.
- Reflexivity: the signal responds to price, and price responds to the signal.
- Timing ambiguity: timestamps are messy; aggregation windows can create artificial lead/lag.
- Selection bias: the “loudest” entities dominate coverage; quiet names disappear.
- Regime dependence: the relationship flips in stress vs calm markets.
A simple test: what decision does the data support?
One way to classify a web dataset is to ask what it can safely drive in a research workflow. The same source can sit in different buckets depending on how it’s collected and normalized.
| Question | If “Yes” → Likely | Implication |
|---|---|---|
| Does the signal have consistent timestamps and cadence? | Predictive candidate | Feature engineering and lag selection become meaningful. |
| Do definitions stay stable across months/years? | Predictive candidate | Backtests are more likely to survive production. |
| Is the signal primarily narrative / interpretation-heavy? | Directional | Use as overlay, confirmation, or regime context. |
| Can you reconstruct history with continuity (not just snapshots)? | Predictive candidate | Supports robust validation across regimes. |
| Does it break frequently when sites change? | Directional (unless engineered) | Invest in monitoring, parsers, and versioning. |
Examples: what tends to be directional vs predictive
Below are common web-sourced datasets used in hedge fund research and where they typically fall—assuming a baseline (non-bespoke) collection approach.
Usually directional. Best for regime context, event confirmation, and risk overlays.
Often directional. Can become predictive in narrow domains with strict normalization.
Frequently predictive when collected with high coverage and stable product mapping.
Often predictive, especially when measured consistently and tied to a defined universe.
Mixed. Can be directional at low resolution; more predictive with role taxonomy + deduping.
Directional by default; becomes predictive when change events are quantified and historically reconstructed.
How bespoke crawling turns directional into predictive
In many cases, the web already contains the ingredients for a predictive signal—but the default data collection approach destroys them. Predictiveness improves when you collect for continuity, not convenience.
Define the universe and entity mapping
Lock down tickers, brands, SKUs, locations, and identifiers so your coverage doesn’t drift over time.
Choose cadence based on the economic mechanism
Collect at the frequency the business changes (hourly pricing vs weekly hiring), not what’s easiest to scrape.
Normalize to stable schemas + version everything
Stability beats cleverness. Schema enforcement and versioning preserve comparability across site changes.
Store raw snapshots + derived tables
Keep “ground truth” snapshots so you can rebuild features as definitions evolve—without losing history.
Instrument monitoring (breakage + drift)
Production signals need health checks: missingness, sudden distribution shifts, and extraction failures.
A practical output: classify signals before you model them
This distinction is less philosophical than operational. If you label a dataset as predictive, you implicitly commit to requirements: cadence, completeness, stable definitions, and monitoring. If it’s directional, you should evaluate it like an overlay: does it improve decision quality, drawdowns, or timing?
- Directional evaluation: does it improve hit rate, timing, sizing, or risk control?
- Predictive evaluation: does it add incremental lift out-of-sample and survive production constraints?
- Pipeline evaluation: can you keep it stable for quarters/years without silent breaks?
Questions About Directional vs Predictive Web Data
These are common questions hedge funds ask when deciding whether a web dataset should be treated as an overlay, a model feature, or research scaffolding.
What does “directional” mean in alternative data? +
Directional web data supports conviction and context—it suggests whether pressure is building, sentiment is shifting, or a regime is changing. It is often valuable for overlays and confirmation, but not reliably calibrated to forecast a specific magnitude or timing of an outcome.
What makes web data “predictive” rather than just correlated? +
Predictive web data tends to have stable collection cadence, consistent entity mapping, and definitions that hold across time. The signal should survive out-of-sample testing and production constraints (missingness, site changes, drift).
- Stable timestamps and coverage
- Consistent normalization and schemas
- Historical depth for regime testing
- Monitoring for breakage and drift
Why do directional signals often “fail” in live trading? +
Directional signals are frequently reactive to the same information that moves markets. In-sample, they can look predictive, but out-of-sample they are sensitive to regime shifts, timing choices (aggregation windows), and reflexivity.
Can the same data source be directional for one fund and predictive for another? +
Yes. The difference is usually collection design and signal engineering: universe definition, cadence, normalization, historical reconstruction, and monitoring. Two teams can scrape “the same site” and end up with radically different signal quality.
How does Potent Pages help increase signal predictiveness? +
We build durable collection systems designed for continuity: stable schemas, change detection, monitoring, and structured delivery. The goal is to preserve comparability over time so research teams can validate signals without pipeline uncertainty.
