To make advanced insights more accessible,
RavenPack has analyzed terabytes of news articles, filings, and transcripts, to produce actionable sentiment and market-moving
factors that work for every investor — from fundamental to discretionary trading. Leverage these factors to:
Daily factors aggregate information relevant to your portfolio that you can easily import into spreadsheets and dashboards. Skip the data wrangling and focus on your trading strategies.
All factors are driven by proprietary models proven to capture alpha —from job and news for companies, to macro nowcasts.
You can even fine-tune signals to your universe, focus, and holding period.
Monitor market-moving signals from the systematic analysis of thousands of news and social media sources with easy-to-consume factors that can flag emerging risks.
leverage advanced analytics from the systematic processing of unstructured data to capture actionable insights for equities, bonds, and macro markets.
Macroeconomic factor to identify business cycles for the U.S. economy. Compiled from the insights of business leaders on the US economy during earnings calls.
To anticipate the most important inflection points in US economic activity and provide a more accurate assessment of the state of the economy.
The outcome of a Bayesian neural network model, this factor combines RavenPack news analysis and macroeconomic variables to predict monthly inflation rates (Core and Headline CPI) on a daily basis.
To effectively hedge against the risk of inflation with asset allocation strategies.
The outcome of a nowcasting model for the US economy using sentiment analysis.
To improve the performance of traditional economic nowcasting models both in terms of timeliness and accuracy.
The outcome of a nowcasting model for the Chinese economy using alternative data, macroeconomic variables, and sentiment analysis.
Leverage real-time news sentiment, alternative data, and macroeconomic variables to track China’s economic activity daily.
Company-level factors of emerging ESG controversies identified from the systematic analysis of 40,000 news and social media sources.
To identify stocks and bonds with low controversy profile to capture more alpha and lower risk.
News sentiment and media attention insights on market-moving events at a document and event levels, 6 times daily.
Quantify the impact of media coverage for inclusion into strategies and dashboards.
Sentiment-augmented earnings, revenues, and dividends insights delivered daily from 40,000 curated news sources.
Leverage earnings facts and guidance insights to mitigate risks and capture momentum.
Investment decision-making processes are often sophisticated, but integrating additional insights should not be.
RavenPack Factors make the output of advanced quantitative models accessible to all investors with several convenient
delivery mechanisms including CSV files, as data warehouses on Snowflake, or
customizable queries using Python libraries.
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