June 23, 2022
Meet RavenPack at the Neudata London Data Insights and Scouting Summer Summit to discuss how our newly-released ESG Controversy Framework can empower your fundamental and systematic traders.
Addressing the data challenge has become a foundational step in any long-term ESG strategy, as investors increasingly rely on data-driven processes to achieve scalability and efficiency across ESG applications.
This Spring, RavenPack is unveiling a new ESG Controversy Framework that empowers fundamental and systematic traders. Join our speakers and representatives at Neudata’s Data Insights & Scouting Summit to explore sustainability themes and enjoy an engaging AltDating networking program (both online and in-person).
RavenPack’s ESG Controversy Scoring framework is based on real-time detections of events across 40,000+ news sources in 13 different languages. It follows an event-based approach that can help assess the extent of a firm’s ESG controversy exposure through time by monitoring real-time news coverage.
See full agenda
: 23rd June 2022
: 22 Bishopsgate, London, EC2N 4AJ UK / Online
Chief Data Scientist
Tracking Real-time ESG Controversy Using Natural Language Processing
For ESG investors, determining the impact of issues remains a difficulty. News analytics, on the other hand, can provide crucial information on a company's performance and governance decisions. Peter will illustrate how RavenPack data may be used to track the amount of a company's ESG scandal exposure over time by tracking real-time press coverage during the session. This enables a better understanding of the influence of disputes on a company's stock price, as well as better risk management and systematic and transparent communication to management and clients.
Peter is a pioneer in the field of applied news analytics, bringing alternative data to banks and hedge funds. He has more than 15 years of experience in quantitative finance with companies such as Standard & Poor's, Credit Suisse First Boston, and Saxo Bank. He holds a Master's degree in Quantitative Finance from Sir John Cass Business School along with an undergraduate degree in Economics from Copenhagen University. Peter is a recognized speaker at quant finance conferences on alternative data and AI, and has given lectures at some of the world’s top academic institutions including London Business School, Courant Institute of Mathematics at NYU, and Imperial College London.
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