CaseMark Analyzing Alternative Data Signals
When To Use
- Evaluating a new alternative data vendor or dataset for potential alpha generation
- Assessing signal strength, decay, and capacity of non-traditional data sources (satellite imagery, credit card transactions, web traffic, app usage, geolocation, NLP sentiment, job postings, shipping/logistics)
- Integrating an alt data signal into an existing systematic or factor-based strategy
- Performing due diligence on data coverage, history length, survivorship bias, and licensing terms before committing to a feed
- Comparing multiple alt data sources for the same investment thesis
Inputs To Gather
- Dataset specification: Vendor name, data type (satellite, NLP, transactional, geolocation, web-scraped), delivery format (API, flat file, streaming), update frequency (real-time, daily, weekly)
- Coverage and history: Universe of securities/entities covered, geographic scope, historical backfill depth, and any known gaps or survivorship issues
- Target hypothesis: The specific alpha thesis the signal is meant to capture (e.g., "satellite car-count data predicts same-store-sales surprises for big-box retailers")
- Benchmark and universe: The investment universe and benchmark against which signal performance will be measured
- Existing signals: Current factor exposures or signals in the portfolio, to assess incremental value and correlation structure
- Constraints: Licensing restrictions, PII/compliance concerns, cost, exclusivity terms, and redistribution limitations [VERIFY regulatory requirements per jurisdiction — GDPR, CCPA, and securities regulations may restrict certain data types]
Workflow
Classify the data source
- Categorize by type: imagery/geospatial, transactional/consumer, web/social, sensor/IoT, workforce/HR, government/regulatory filings
- Identify the economic mechanism linking the data to asset returns (revenue nowcasting, demand estimation, supply-chain tracking, sentiment shift detection)
- Flag whether the data is exhaust data (generated as byproduct) vs. purposefully collected — this affects persistence and competitive dynamics
Assess data quality and coverage
- Check history length vs. minimum required for statistically meaningful backtest (typically 5+ years for equity signals, 2+ for higher-frequency)
- Evaluate coverage breadth: what percentage of the target universe has usable observations, and is coverage biased (e.g., urban-only geolocation, large-cap-only web traffic)
- Test for stale/missing data patterns, time-zone alignment issues, and retroactive revisions
- Confirm point-in-time availability — verify no lookahead bias in timestamps
Construct and normalize the signal
- Define the raw metric extraction (e.g., pixel intensity → car counts, article text → sentiment score)
- Apply cross-sectional normalization (z-score, percentile rank) to control for sector, market-cap, or geographic effects
- Set signal update lag realistically — account for data delivery delay, processing time, and any embargo periods
- Determine appropriate signal transformation: level, change, surprise vs. consensus, acceleration
Backtest for alpha content
- Run univariate long/short quintile or decile sorts; report annualized spread return, Sharpe, hit rate, and turnover
- Measure signal decay: IC (information coefficient) at multiple horizons (1-day, 5-day, 21-day, 63-day)
- Test robustness across sub-periods, sectors, and market regimes (risk-on/risk-off, high/low volatility)
- Control for known factors (market, size, value, momentum, quality) — report incremental IC after factor-neutralization
- Assess capacity: estimate the dollar AUM at which market impact erodes >50% of gross alpha
Evaluate operational and compliance risk
- Review vendor contract for exclusivity window, data clawback provisions, and termination terms
- Confirm compliance with web-scraping terms of service, data privacy regulations, and material non-public information (MNPI) boundaries [VERIFY with compliance counsel — MNPI classification varies by data type and jurisdiction]
- Assess vendor concentration risk: single-source dependency, vendor financial stability, alternative suppliers
- Document data lineage and transformation pipeline for audit trail
Determine integration path
- Quantify marginal Sharpe improvement when combined with existing signal library (correlation analysis, mean-variance optimization)
- Define signal weighting scheme: equal weight, IC-weighted, or optimized
- Specify rebalance frequency and portfolio construction rules for the combined signal
- Set monitoring triggers: minimum IC threshold, coverage deterioration alert, regime-break detector
Output
Produce an Alternative Data Signal Assessment Report containing:
- Executive summary: Data type, vendor, target thesis, and go/no-go recommendation with confidence level
- Signal profile table: IC mean/median, IC-IR, quintile spread return, Sharpe, turnover, max drawdown, decay half-life
- Factor exposure analysis: Correlation with standard factors and existing proprietary signals
- Capacity estimate: Estimated max AUM with acceptable slippage
- Coverage and quality scorecard: History depth, universe coverage %, missing-data rate, point-in-time verification status
- Risk and compliance flags: MNPI concerns, licensing restrictions, vendor dependency, privacy regulation exposure
- Integration recommendation: Suggested weight, rebalance cadence, and monitoring framework
Quality Checks
- Confirm no lookahead bias — all signal values must be constructed from data available at the point-in-time of each observation
- Verify that backtest returns are reported net of realistic transaction costs and market impact estimates
- Ensure factor-neutralized results are reported alongside raw results to isolate true incremental alpha
- Check that out-of-sample or walk-forward validation is included (not just full-sample backtest)
- Validate that coverage statistics exclude stale or interpolated observations
- Flag any data source where legal/compliance review has not been completed as [VERIFY]
- Confirm signal decay analysis covers horizons relevant to the target strategy's holding period