LLM-Powered Market Sentiment That Moves at the Speed of Social
The Client:
An institutional investment firm seeking to incorporate real-time social sentiment signals into its trading analysis workflow without building a large data engineering team.
The Engagement:
SSI built an end-to-end sentiment analysis platform that ingests social content at scale, scores stock-related sentiment using LLMs, and delivers daily signals and earnings intelligence directly to trading desks.
The Challenge:
Social platforms like Twitter and Reddit generate enormous volumes of stock-relevant content daily. Manually monitoring this at scale was impossible, and existing rule-based sentiment tools were too coarse to extract actionable signals.
The firm also needed earnings intelligence: the ability to compare quarterly reports across periods and extract changes in narrative tone and financial signals not just raw numbers.
Signals needed to be delivered in a format trading desks could act on dashboards and structured data feeds, not raw text outputs.
The Solution:
SSI built an ingestion pipeline that collects posts from Twitter and Reddit continuously, filters for stock-relevant content, and passes it through LLM-based sentiment scoring models.
Each stock’s sentiment is tracked over a rolling 24-hour window, giving analysts a view of how market perception is shifting intraday. Scores are calibrated against historical price movement to surface the signals with the highest predictive value.
A document intelligence layer compares quarterly earnings reports across periods identifying narrative shifts, changes in management tone, and emerging financial signals. All outputs are delivered via dashboards and structured data feeds for direct integration with trading systems.
The Impact:
The platform gave the investment team access to a social sentiment signal layer they previously had no systematic way to monitor. The earnings comparison engine reduced analyst time on report review and surfaced narrative changes that pure quantitative analysis would miss.
