Measuring market sentiment around Trump 2.0 tariffs with scalable retrieval

Bigdata.com’s API allows financial analysts and strategists to move beyond limits imposed by conventional search tools and LLM pipelines.

While typical systems may return a few hundred documents, Bigdata lets you process 1 million documents across 1,000 companies in minutes, enabling comprehensive market-wide sentiment analysis and faster decision-making.

Use case example: Sector-wide sentiment impact of Trump 2.0 tariffs

Objective

Identify which sectors of the Russell 1000 are most negatively impacted by the potential return of Trump-era tariffs — and to what degree.

Why it matters

Policy changes like tariff reinstatements can have immediate, sector-specific implications. With Bigdata, you can:

• Gauge sentiment at the company and sector level
• Detect market-wide exposure to trade-related risk
• Spot emerging signals from hundreds of thousands of filings, news articles, and reports

The solution with Bigdata.com

1. Run 1,000 parallel search queries Each company in the Russell 1000 is queried using a combination of entity recognition and a custom similarity query: e.g., “Trump 2.0 tariffs impact”
2. Retrieve 1 million documents In just under 4 minutes, Bigdata retrieves up to 1,000 relevant documents per company, covering:
   • Filings
   • Earnings call transcripts
   • Analyst notes
   • Real-time news reports
3. Compute sentiment at scale Using Bigdata’s chunk-level sentiment and relevance scores, a weighted sentiment score is calculated for every company and mapped across sectors.
4. Visualize the results A custom bar chart highlights the sectors most negatively impacted by the proposed tariffs. For example:
   • Telecom
   • Consumer goods
   • Tech
5. These sectors had the highest share of companies with strongly negative sentiment (defined as below –0.45).

Technical stack

• Bigdata API for parallel search + document processing
• Chunk-level sentiment analysis using built-in scoring
• Pandas + Matplotlib for sector aggregation and visualization
• Runtime: 3 minutes 44 seconds for 1M documents

Ideal for

• Macro analysts identifying large-scale risk exposure
• Fund managers monitoring sector performance drivers
• Strategy teams evaluating policy scenario impact
• Data scientists building financial forecasting models

Practical applications

• Comprehensive market sentiment Track tone and polarity across the entire Russell 1000 — daily.
• Supply chain risk detection Identify weak links or escalating disruption in upstream narratives.
• Competitive intelligence at scale Monitor industry peers, disruptors, and adjacent verticals in parallel.
• Policy impact forecasting Evaluate macro and regulatory events by sector, geography, or revenue exposure.

Ready to search across millions of documents? Follow our step-by-step guide to start.