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Webinar
Live Webinar
LLMs are everywhere now. But in equity investing, adoption is still at an early stage as models are costly to run and hard to fine-tune for financial language. That’s where ProntoNLP Transcript Analytics comes in. Purpose-built for finance, it captures sentiment, context, and importance levels from earnings calls to generate differentiated stock selection signals.
This webinar provides a detailed blueprint on how investors can systematically leverage LLM-based transcript features to identify future outperformers.
Register Now
If you can't make the live session, please register anyway to get access to the on-demand version.
Or click here to learn more about ProntoNLP.
Key Webinar Takeaways:
S&P Global Market Intelligence
Director, Product Management
Aditya Sharma, CFA, is Head of Product Management for Textual and Professional Datasets, and Quantitative Signals at S&P Global Market Intelligence. He leads the development and execution of product strategies that deliver quantitative solutions built on raw and processed text, traditional and alternative data.
Aditya is a specialist in textual and quantitative analytics, with deep expertise in applying advanced machine learning and natural language processing techniques to financial data. He joined S&P Global in April 2021 and has previously held global roles in data science and strategy at Western Union Business Solutions in London and Social Market Analytics, Inc. in Chicago.
S&P Global Market Intelligence
Senior Director, Quantitative Research & Solutions
Frank is a Senior Director of S&P Global Market Intelligence division. He conducts systematic equity research and productizes his findings that have been widely adopted by global institutional investors in their stock selection processes.
He speaks globally in invitational and sponsored conferences and has been cited in FT, CNBC, and WSJ among others. He is the primary contributing patent holder with USPTO and has publication citations by practitioners and academics alike including from a Nobel Laureate in economics.
Frank has master’s degrees in Financial Engineering from UCLA Anderson and in Finance from Boston College Carroll, and has undergraduate degrees in Computer Science and Economics from University of California, Davis.
S&P Global Market Intelligence
Quantitative Analyst, Quantitative Research & Solutions
Mengmeng Ao is a Quantitative Analyst specializing in systematic equity investment research. She combines academic rigor with practical experience, having previously served as an Assistant Professor of Finance. She earned her PhD in Business Statistics from The Hong Kong University of Science and Technology.
S&P Global Market Intelligence
Head of Technology, Quantitative Signals
Ronen Feldman is a Full Professor of Internet Studies at the Hebrew University Business School. He is currently Head of the Data Science Department.
Feldman is an expert in text mining and data mining, and his research areas include Information Extraction (IE), algorithmic trading, social networks, natural language processing, extracting meaning and relationships from text, and sentiment analysis from texts.
He has won the Abe Gray Prize for Excellence in Research, the Soref Post-Doctoral Fellowship from Bar-Ilan, and the Levi Eshkol Fellowship. Additionally, he has received academic grants from many institutions, including from the INTEL Corporation, MAFAAT, the Israel Science Foundation, the German-Israel Foundation, the Israeli Sciences and Arts Ministry, and the BSF (Bi-national Science Foundation).
Feldman earned his Ph.D. in Computer Science from Cornell University. He holds an M.Sc. in Computer Science from Bar-Ilan University, and an B.Sc. in Math, Physics, and Computer Science from Hebrew University.
Please contact us if you need more information or have trouble accessing the webinar.