Alternative Data and NLP in Quant Strategies › What is Alternative Data

What makes alternative data distinctive is not its format or origin, but rather its novelty relative to what most market participants analyze. When only a handful of funds monitored satellite imagery in 2010, it qualified as alternative data. As satellite-derived insights become standard inputs to earnings models, that informational edge diminishes. The data becomes "traditional" through widespread adoption.

Alternative data sources generally fall into several broad categories.

• Transactional data: Credit card transactions, point-of-sale records, and electronic payment flows reveal real-time consumer behavior. Aggregated and anonymized spending data can signal retail performance weeks before official reports.

• Geospatial and sensor data: Satellite imagery tracks physical activity, including cars in parking lots, ships in ports, and oil in storage tanks. IoT sensors monitor industrial production, agricultural conditions, and infrastructure utilization.

• Web and social data: Online reviews, social media posts, job listings, app downloads, and web traffic patterns capture consumer sentiment and corporate activity.

• Text and document data: News articles, regulatory filings, earnings call transcripts, patent applications, and legal documents contain information that machines can now parse at scale.

• Specialist datasets: Industry-specific data from healthcare claims, flight tracking, shipping manifests, or energy grid monitoring provides granular views of particular sectors.

The value proposition of alternative data rests on timeliness, granularity, and differentiation. Traditional data arrives quarterly or monthly, while alternative data often flows daily or in real-time. Traditional metrics aggregate to national or industry levels, while alternative data can track individual stores or products. Traditional sources are universal, while alternative datasets may be proprietary or require specialized acquisition.