Natural Language Processing: Feature Engineering in the Context of Stock Investing
Natural Language Processing: Feature Engineering in the Context of Stock Investing

Abstract: 

Unstructured data is largely underexplored in equity investing due to its higher costs. As a result, the information content remains largely untapped and offers an investment edge for investors. Discover an application of Natural Language Processing (NLP) in the context of systematic equity investing by introducing new stock selection ideas in the areas of I) Topic Identification II) Call Transparency III) Call Sentiment using more intricate yet intuitive NLP techniques and features.

Bio: 

Frank is a Senior Director and a key member of S&P Global Market Intelligence’s Quantamental Research group. His primary focus is to conduct systematic alpha research on global equities with publications on natural language processing, newly discovered stock selection anomalies, event-driven strategies and industry-specific signals. 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.

Open Data Science

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