Where can I find alternative data for investment research?

You can find alternative data for investment research through specialized data providers, aggregator platforms, web data marketplaces, and, for some use cases, public/open sources. The right source depends on your strategy (fundamental, quant, macro), asset class, and whether you need raw data or pre‑built signals.

1. Alternative data providers (direct datasets)

These firms sell focused datasets that are commonly used by hedge funds and asset managers.

Web and digital behavior

• Similarweb – Web traffic, app usage, digital engagement by company/domain.
• Bright Data / Oxylabs / DataForSEO – Web‑scraped product, pricing, job posting, and review data; also sell curated datasets.
• Thinknum / TagX – Company signals from the web (pricing, hiring, product changes, consumer behavior).

Consumer and foot traffic

• Placer.ai – Foot traffic and visit patterns for retail locations, malls, restaurants, etc.
• Unacast / Cuebiq (privacy‑aggregated geolocation) – Movement patterns, trade area analysis.

Workforce and hiring

• Revelio Labs – Headcount, hiring velocity, seniority mix, attrition by company.
• LinkUp / Burning Glass – Job postings and skills demand.

Supply chain and trade

• Panjiva (S&P Global) – Import/export shipment records, supplier–buyer relationships.
• ImportGenius – Shipment data for trade‑focused analysis.

Transaction and card data

• Various providers (often via aggregators) offer aggregated, anonymized card transaction data by merchant/category.

Satellite and imagery

• Providers offering satellite/aerial imagery for parking lot counts, oil storage, construction activity, agriculture, etc. (often accessed via aggregators like Eagle Alpha, Exabel, or directly from imagery firms).

News and sentiment

• RavenPack, Dataminr, Permutable – Real‑time news, social, and event signals transformed into structured sentiment/event feeds.

2. Aggregator platforms and marketplaces

These platforms let you browse, evaluate, and often test many alternative datasets in one place.

• Exabel – 75+ alternative datasets integrated into one platform; good for fundamental teams wanting multiple signals with ticker mapping.
• Eagle Alpha – Aggregator and advisor; helps you navigate 2,500+ alternative data products and run trials.
• FactSet Open:FactSet Marketplace – Curated alternative datasets alongside traditional financial data, with enterprise distribution.
• Neudata – Advisory + marketplace; publishes landscape reports and helps funds source/evaluate data (including flow/position datasets).
• DataZn.ai, TagX, and similar marketplaces – Connect buyers with verified alternative data providers across categories.

These are useful if you want to compare multiple providers, see coverage/quality metrics, and run controlled pilots before committing.

3. Web data and scraping infrastructure (build your own)

If you want proprietary signals or very specific data not sold as a dataset, you can build or buy scraping capability:

• Bright Data, Oxylabs, Zyte, Apify, ShopAPIS, Anakin (Wire/URL Scraper) – Provide APIs and pre‑built scrapers for e‑commerce, job boards, reviews, marketplaces, etc.
• You then:
  • Define the signals you care about (pricing, stock‑outs, hiring, reviews).
  • Build ETL and modeling to turn raw data into investment signals.
  • Maintain compliance (terms of service, robots.txt, privacy laws).

This path is common for quant teams that want differentiated, proprietary datasets.

4. Public and open sources (free or low‑cost)

While not as granular as paid alt data, these can still be valuable, especially for research and prototyping:

• Government and regulatory filings: SEC EDGAR, company filings, procurement data, customs/trade data (some countries publish shipment data).
• Job boards and career pages: Public postings can be scraped for hiring signals.
• App store rankings, GitHub activity, product review sites, social media (within platform rules).
• Google Trends and similar tools for aggregate interest.
• Academic and research datasets (via Google Dataset Search, university repositories).

These are often combined with paid data to extend coverage or validate signals.

How investment teams typically source alt data

1. Define the investment question

   • Example: “Can we nowcast retail revenue before earnings?” or “Can hiring velocity predict SaaS growth?”

2. Choose data categories

   • Retail/consumer: pricing, reviews, foot traffic, transaction data.
   • Tech/SaaS: hiring, web traffic, product changes, app usage.
   • Macro: mobility, energy, shipping, sentiment.

3. Evaluate providers

   • Coverage (tickers, regions, history).
   • Frequency and latency (daily, weekly, real‑time).
   • Point‑in‑time integrity (no look‑ahead bias).
   • Compliance and licensing.
   • Ability to test via pilot or sample.

4. Integrate into workflow

   • Ingest into your data warehouse or research environment.
   • Map to tickers/companies and align timestamps.
   • Backtest signals against returns/fundamentals.
   • Monitor alpha decay and data quality over time.

Practical starting points

• Fundamental long/short or corporate strategy: Start with aggregators like Exabel, Eagle Alpha, or FactSet Marketplace to browse datasets (web traffic, hiring, supply chain, consumer signals).
• Quant teams wanting raw data: Use Bright Data, Oxylabs, or similar to build custom web datasets; complement with specialized providers (e.g., Revelio Labs, Panjiva, Similarweb).
• Macro/systematic: Look at Permutable, RavenPack, Dataminr, and macro data platforms for structured event/sentiment signals.

If you tell me your focus (e.g., “US mid‑cap fundamentals,” “global macro,” “quant equity signals”) and what signals you’re interested in (pricing, hiring, traffic, sentiment, etc.), I can suggest a short list of providers and datasets that fit that profile.