Sentiment-augmented earnings, revenues, and dividends insights delivered daily from 40,000 curated news sources.
Earnings seasons are critical for investors, but assessing market sentiment across thousands of articles published in just a few days can be daunting. The RavenPack EI News factors streamline this analysis to help you make informed and timely investment decisions with:
Discretionary and quantitative investors can leverage these factors to:
Identify earnings surprises that could increase volatility, and review sector concentration to reduce exposure through diversification or rotation.
Capture earnings momentum with timely portfolio rebalances that incorporate guidance and market consensus into your strategies.
RavenPack Earnings Intelligence News factors leverage the differentiated detection of over 400 earnings-related event categories, including a complete separation of factual reporting from company guidance. Recent research has identified that factual and guidance earnings signals are only 11% correlated for US Mid/Large-Caps, and 8% for Small-Caps. Tracking their sentiment separately increases annualized revenue up to a 20-day trading horizon.
The model that computes the Factors analyzes all events in the Revenue, Earnings, and Dividends groups. A partial development of the Earnings group illustrates the breadth and specificity of coverage underpinning the indicators:
The performance of the factor is documented below for each trading universe and for the various decay durations available. Performance is consistent over time across all holding periods and indicates a substantial market impact from News for short holding periods, with news exhibiting the highest performance among all individual factors, gradually tapering with the effective holding period.
The Factors are the outcome of a multi-step process including:
RavenPack analyzes textual content from over 40,000 high-quality sources to identify earnings-related events. Documents considered include articles and press releases in English.
Additional filters ensure the data remains focused on contemporary observations, relevant and non-neutral events.
For each event identified, RavenPack uses sentiment analysis to compute multiple scores by matching stories usually categorized by financial experts as having a positive or negative financial or economic impact.
Sentiment scores are aggregated and smoothed over time with various decays, then presented as daily factors.
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