sentiment-analysis

To generate trading signals from financial news, classify text sentiment using NLP models like FinBERT, then map sentiment to tickers and aggregate scores with timestamps for actionable trading decisions.

FinBERT sentiment analysis is an NLP technique that classifies financial text into positive, negative, or neutral sentiment. It works by leveraging language models fine-tuned on financial corpora to produce per-ticker sentiment scores suitable for trading signal generation.

Yes, you can analyze earnings call transcripts by ingesting the text, extracting entity-specific sentiment, and outputting per-ticker scores with timestamps for short-to-medium holding horizons.

Aggregate social media sentiment by ticker by ingesting text, mapping sentiment to entities, and producing timestamped per-ticker scores structured for backtest-ready analysis with quality controls.

Sentiment analysis works on both equities and macro markets, processing financial text across news, social media, and earnings calls to generate per-ticker sentiment signals for tradable assets in either category.

NLP sentiment signals for trading are limited by text quality, model accuracy, and holding horizon suitability. Signals require quality controls during aggregation and are designed for short-to-medium horizons, not long-term fundamental analysis.