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


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.


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




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google