The San Francisco Innovation Lab at Thomson Reuters creates quantitative models for investors. Following the financial crisis, it developed the first commercially available credit risk model to measure corporate financial health by systematically evaluating the language used in news, conference call transcripts, financial filings, and analyst research. Six years later, the company is updating it to take advantage of deep learning.
While deep learning promises breakthroughs in the ability to model complex data and power new products, when does it make sense to migrate existing analytics to use deep learning, and what advantages does it offer in practice? Furthermore, what are the trade-offs?
Ryan Roser answers these questions and describes the process of updating an existing quantitative model to use deep learning.
Ryan Roser is the director of data science and text analytics within the San Francisco Innovation Lab at Refinitiv, where he develops quantitative models and predictive analytics for investors and works with unstructured text to identify new trends and insights. Previously, Ryan was a principal quantitative research analyst at StarMine, where he developed a first-of-its-kind text-based corporate credit risk model. Ryan lives in Portland, Oregon. He enjoys gardening and raising chickens.
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