Evaluates alternative data sources including satellite, NLP sentiment, web scraping, and geolocation for alpha signal generation. Use when analyzing alt data, evaluating new data sources, or integrating non-traditional signals.
Evaluates alternative data sources including satellite, NLP sentiment, web scraping, and geolocation for alpha signal generation. Use when analyzing alt data, evaluating new data sources, or integrating non-traditional signals.
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]