BridgeWise launches podcast sentiment feed for U.S. stocks

Context Analytics, a BridgeWise company, launched the Podcast Sentiment Analysis Feed to produce ticker-level sentiment scores for U.S. equities from transcribed financial, business and news podcasts. The product is intended for quantitative traders and systematic investors and is available for trial access covering U.S. stocks.

The feed ingests audio from a continuously expanding set of more than 3 million podcasts. Audio is converted to text with automatic speech recognition and then processed through Context Analytics’ natural language processing pipeline. Company names and tickers are identified at the entity level to produce structured, time-stamped outputs.

The system produces two main outputs. The first is quantitative sentiment scores and related metrics formatted for algorithmic trading and research workflows. The second is AI-generated summaries that capture individual company mentions and podcast context for qualitative review. Metadata accompanies each mention to support integration into trading systems.

Context Analytics reported preliminary backtesting that sorted stocks into podcast-sentiment quintiles and found roughly a 10% cumulative return spread between the most positively and most negatively discussed companies over an approximately 10-year period.

Data delivery options include JSON, API and FTP to allow direct integration with existing data systems. Trial access is offered now for U.S. equities.

More than 4.5 million podcasts exist worldwide and nearly 600 million people listen globally; podcasting has grown as an information channel for business audiences and investment commentary. Context Analytics positions the new feed as a complement to its SentimentWise product, which already produces sentiment signals from written news, corporate filings and social media.

Joe Gits, Managing Director for North America at Context Analytics, described podcast audio as containing market signals that had not previously been measurable and framed the feed as a way to convert that audio into structured data for quantitative use.