News and Social Sentiment for Stock Trading

News sentiment systems classify text as positive, negative or neutral, or measure more specific concepts such as uncertainty and relevance. Social-media analytics can also count mentions, identify topics or compare message volume across companies. FINRA’s 2025 report describes industry interest in these tools while discussing investor-protection and market-integrity risks. The data can be noisy: posts may be promotional, duplicated, sarcastic, automated or deliberately misleading, and popular discussion does not necessarily reflect an issuer’s fundamentals. Research findings are conditional. A Federal Reserve study using more than 900,000 news stories found that daily news predicted stock returns only over a short one-to-two-day horizon in that dataset. This is not proof that any sentiment strategy works across markets or time periods. A model can also exploit time leakage if the data source updates old stories, revises timestamps or includes later information. Deduplicate sources, capture publication time, separate company-specific news from broad market topics and test whether sentiment adds information beyond price and volume features. Treat sentiment as one research feature, not a trade instruction. Validate it out of sample with transaction costs, multiple-testing risk and performance by market regime. Check the source’s licensing, personal data and platform rules before collecting social data. If investment advice or performance is communicated to clients, securities regulations may apply. This guide explains a research workflow and does not recommend buying or selling any security.