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Summary
As large language models move from experimentation toward adoption, investors face a critical question: when does a new technique meaningfully improve on established signals? This webinar examines how earnings call sentiment analysis has evolved from traditional lexicon-based approaches to context-aware LLM models—and what that evolution means for equity investors.
Using a long history of U.S. earnings calls, we compare lexicon-based and LLM-derived sentiment signals, focusing on financial performance, event importance, and recent market conditions.
What You’ll Learn
- How lexicon-based sentiment models work—and where they fall short
- How LLMs extract sentiment, context, and importance
- Where LLMs add incremental value over legacy approaches
- How to incorporate transcript signals into systematic frameworks
Who Should Attend
- Equity investors
- Quantitative researchers
- NLP and data-driven analysts
Speakers
S&P Global Market Intelligence
Frank Zhao
Senior Director
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
Mengmeng Ao
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
Aditya Sharma
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.
Questions?
Please contact us if you need more information or have trouble accessing the webinar.