Boost event-driven credit strategies with research-based signals from hard and soft corporate catalysts detected in top reputable sources.
RavenPack’s Credit Research demonstrates how news-driven signals improve portfolio performance, particularly through the timely tracking of information on public and private bond issuers at scale.
The RavenPack Credit News Factors provide signals around 120 credit catalysts, offering customizable options to harness the complexity of the RavenPack technology.
Two factor types are provided:
Credit traders can trade these signals to enhance credit event-driven strategies and alpha capture, and mitigate credit risk:
Track bond issuers in real-time and at a large scale, monitoring particularly unscheduled events, across listed and private companies.
Capture mid-frequency alpha across 120 types of soft and hard catalysts from both company disclosures and financial analysts.
The performance of these factors has been backtested against a point-in-time archive of news from September 2015 until February 2024. Long-short credit portfolios for a diverse array of trading scenarios were used. The results highlight the robustness and versatility of the signals across targeted universes. Factors use a mapping of identifiers maintained by RavenPack daily for a comprehensive and up-to-date referencing of available bonds.
The Factors are the outcome of a multi-step process including:
RavenPack analyzes textual content from high-reputation sources over 40,000 curated ones to identify credit-related events in news for over 7 millions of companies, including both public-listed and private companies.
For each corporate catalyst identified, RavenPack applies sentiment analysis to compute credit sentiment scores by matching stories usually categorized by credit analysts as having a positive or negative impact on credit spreads. We retain stories for bond issuers and events co-detected in the headlines of novel content in top reputable sources.
Documents considered include full articles, news flashes, press releases, and tabular material exclusively from top reputable sources.
The factors are aggregated until 60 minutes before the credit markets close, and at the event type level of business events from the RavenPack Event Taxonomy. This high level of granularity ensures to isolate individually around 120 types of hard and soft catalysts with specific repricing effects. After aggregation, the credit sentiment scores are exponentially smoothed to control the factor turnover. Our flexible approach involves constructing these daily factors with multiple cutoff times, facilitating the timely capture of alpha across different corporate bond universes throughout each trading day.
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