Asia Awards 2024: Best alternative data provider—RavenPack

Product: RavenPack Factor Library


Discretionary and quantamental investors―those who employ strategies that entail human traders using artificial intelligence (AI) and machine learning to enhance their performance―have historically faced challenges accessing alpha-generating signals used by quantitative traders because they typically lack the data and computing infrastructure needed to exploit very large datasets, and the specialized data science resources to derive signals from this data. As a result, quantitative insights that could enhance decision-making are omitted from many investment workflows.

RavenPack Factors augment the information available to discretionary and quantamental investors with quantitative insights without the need for a dedicated data infrastructure and team. This new offering helps investors streamline their workflows, shorten strategies’ time to market, simplify risk management and capture opportunities to improve their performance 

Peter Hafez, chief data scientist, RavenPack

The solution  

The RavenPack Factor Library removes this hurdle by offering streamlined access to actionable sentiment and market-moving indicators. Derived from unstructured data including news and transcripts, they deliver daily insights ranging from market perception, negative news, and trends for over 100,000 listed companies, to business cycle and macro insights like inflation and growth nowcasts. Even quant traders can benefit from signal components they can quickly test, thanks to this simple extract, transform and load (ETL) tool.

Secret sauce  

The RavenPack Factor Library draws on more than two decades’ expertise in natural language processing (NLP) and quantitative research based on both public and gated sources. It offers clients a collection of company-level and macroeconomic insights that can be integrated into investment decision-making workflows to unlock sources of alpha.

Recent milestones

  • Launch of media attention, news sentiment, and controversy factors for over 100,000 listed companies to gauge market perception, negative news and trends
  • Launch of macro-level indicators and nowcasts that leverage data from the mindset of executives, news, and alternative data to project key daily insights on business cycles, including inflation and growth
  • Launch of earnings intelligence factors, which extract earnings-related insights from news, transcripts, insider transactions, and earnings dates

Future objectives

RavenPack will:

  • Add support for additional asset classes in the Factor Library, starting with FX and fixed income, allowing macro investors to expand their use of quantitative signals.
  • Enable the creation of custom factors, which retain the streamlined approach to signal-building while offering a broader array of construction parameters, with the launch of a Python library.
  • Incorporate machine learning and natural-language processing refinements in upcoming factors for deeper and targeted sentiment analysis.

Why they won

RavenPack, the Marbella, Spain-based data and “insights” specialist follows up last year’s win in the best ESG data provider category by winning the best alternative data provider category in this year’s WatersTechnology Asia Awards on the back of its outstanding RavenPack Factors Library offering. 

The volume and variety of unstructured data flowing through the financial services industry has grown exponentially in recent years, making it nigh on impossible for financial services firms to collect and analyze it themselves with the view to identifying hidden trends and signals within it and by so doing provide greater systematization and repeatability when it comes to making their all-important investment decisions. 

That is precisely what RavenPack has been doing for a number of years now, providing investors with high-quality datasets, research and actionable insights into the vast volumes of unstructured data it collects and interrogates. The quantitative models sitting within RavenPack Factors are available to clients as CSV files, through Snowflake, or as customizable queries using Python libraries.

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