AlphaSense Expands Wall Street Insights with Credit Suisse Research
Knowledge professionals can now leverage Credit Suisse research to inform critical decision making in a rapidly evolving market. AlphaSense, the...2 min read
Technology
Tanvi Sahay
|
June 5, 2020
4 min read
Every earnings season, AlphaSense users–some of the smartest and busiest analysts and strategists in the world–are expected to have granular and current knowledge about dozens of companies. Historically, this has meant many tedious hours spent identifying the nuances revealed in the latest Earnings calls and determining how they are going to impact that company’s strategy.
At AlphaSense, we understand that our clients are looking to get ahead of the market, and that therefore, time is of the essence. Being able to extract the most important topics from transcripts, in the context of key metrics like quarter-over-quarter increase in mentions, positive/negative sentiment, or overall mentions, is a powerful way to highlight the ‘aboutness’ of a transcript without having to read it line by line. The value is that it contains the possibility of massive time savings for users while also identifying the most relevant information at scale across every single competitor’s transcript.
Though topic extraction is not new, its value-add in the context of transcripts requires a nuanced understanding of financial language. Since language used in earnings calls can often be convoluted and contain boilerplate language that provides no information of import, generic open source models won’t contain relevant results. Other challenges with open source models include the fact that different domains often have their own specialized jargon, a generic open source model won’t get the best results. Many open source models limit their output to 50 – 100 topics, which considering the length of some of our transcripts (100+ pages) is not sufficient to capture all relevant information..
Luckily, at AlphaSense, we have already spent years investing in the world’s highest accuracy sentiment analysis model for financial language. Building upon our existing body of AI expertise, we’ve developed a proprietary tool called Document Themes to help our clients quickly excavate signals buried in documents like transcripts.
Document Themes is the culmination of many high-impact AI initiatives that AlphaSense has spearheaded:
To get started with Document Themes,
Tanvi Sahay is an Artificial Intelligence Researcher at AlphaSense Inc. A graduate of UMass Amherst, she focuses on creating interpretable, customer focused solutions to NLU and Information Extraction problems while listening to anime soundtracks.
2 min read