In this real-world case study, we show how RavenPack’s Artificial Intelligence (AI) platform including its proprietary Natural Language Processing (NLP) engine can transform your own internal digital content (emails, messages, files) into investment insights and trading signals.
Asset management firms store massive amounts of digital content, which largely remains under analysed and untapped. By systematically structuring and enriching this content in real time, we demonstrate that there is incremental alpha to be captured internally, beyondusing public sources like news and social media.
In particular, we found that:
- Given their portfolio, approximately 80% of identified stock-related events were detected in the firm’s internal content, while 20% originated from public news and social media
- Positive sentiment signals derived from internal content provide strong long-only signals for up to several weeks, while value from public news decays faster
- Factor risk analysis of sentiment portfolios shows stable P&L coming from idiosyncratic stock price moves, demonstrating persistent alpha generation from a traditional factor model perspective (as shown in the plot below)
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About RavenPack Text Analytics
The RavenPack Text Analytics Engine provides real-time structured sentiment, relevance and novelty data for entities and events detected in unstructured text from any internal sources including emails, instant messages, documents, contact databases, and more into a single, enriched format tailored specifically for financial applications. RavenPack generates analytics data on over 52,000 public and private companies, top products and services, all major currencies and commodities, financially relevant places and organizations, and key business and political figures. Request a trial here.Request White Paper