RavenPack: news and event analytics (licensed)

Licensed: not exercised here

RavenPack is a paid subscription, so it carries no provenance badge: the access path below was not run in this session (no RavenPack credentials were available). The page documents the access route and the gotchas; treat it as unverified until someone exercises it through a licensed account. This is the honest grade under the institute’s Verified discipline, which forbids a badge for an unrun claim.

RavenPack converts unstructured news and other text into structured, machine-readable entity-event records: each detected company, person, or place in a story gets a timestamped row carrying a sentiment score, a relevance score, a novelty score, and a topic taxonomy. It is a commercial feed for news-based signals in finance. The Kwan, Liu & Matthies attention paper uses RavenPack 1.0 to classify the topic, subject, and sentiment of the news that institutional investors read, and to map stories to stock tickers.

• Cost: licensed, subscription. No free tier.
• Vendor: RavenPack (news and event analytics; the analytics product has versioned editions, e.g. a legacy 1.0 and later editions, with different fields and taxonomies; confirm the edition against current vendor documentation).
• Coverage: global news (newswires such as Dow Jones, plus web and press-release sources); millions of entity-event records per day.

Access (when licensed)

• Direct from the vendor. Delivered as historical flat-file dumps plus an ongoing feed (SFTP / API / cloud share). You pull dated files and the point-in-time analytics for each story.
• Via WRDS. Subscribing institutions can reach RavenPack News Analytics through WRDS, which is the easiest academic route if your university licenses it. Query it like any other WRDS library; filter on date and relevance before pulling.
• Either way, credentials are required. Keep them in .env, never hard-coded.

Gotchas (the ones that bite pipelines)

These are the failure modes to expect; they are documented, not verified here.

• Filter on relevance first. Each record has a RELEVANCE score (0 to 100). A story that merely mentions a company in passing scores low; keep RELEVANCE = 100 (or a high threshold) when you want stories that are about the entity, or your signal is mostly noise.
• Deduplicate with the novelty score. Newswires re-run and syndicate the same story. The Event Novelty Score (ENS) flags the first report (ENS = 100) versus echoes; without it you double-count one event many times.
• Timestamps are UTC; align to the trading calendar. The production timestamp is when the record was created, in UTC. Convert to the market timezone and decide deliberately how to treat after-close and weekend news before forming a daily signal.
• Use the point-in-time timestamp, not the story date, to avoid look-ahead. Signals must be built from when the analytics were available, not when the event nominally happened.
• Sentiment scales differ by version. The Event Sentiment Score and Composite Sentiment Score conventions changed between RavenPack 1.0 and later editions (e.g. a 0 to 100 scale with 50 neutral). Confirm which edition and scale you are on before thresholding.
• Entity mapping is its own step. Records key on RP_ENTITY_ID, not a ticker or PERMNO. Use RavenPack’s mapping files to join to security identifiers, and remember entities include private firms, people, and places, not just listed equities.
• Volume. The raw feed is large; filter on entity, relevance, and date at read time rather than loading everything.

Citation

Cite the product and edition, e.g.: RavenPack News Analytics (RavenPack 1.0), RavenPack; data licensed and accessed YYYY-MM-DD. State the relevance and novelty filters used, since they materially define the sample.