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What’s In the News? Using Textual Data To Forecast Financial Returns
What’s In the News? Using Textual Data To Forecast Financial Returns
From SQA—Prof. Mamaysky will discuss two new papers which apply natural language processing (NLP) techniques to textual data to forecast financial returns. Specifically, he and his co-authors developed a classification methodology for the context and content of textual data. In a paper with Charles Calomiris, he combined unusualness (entropy) of work flow with sentiment and frequency in central bank communications to forecast currency returns. In a brand new paper with Paul Glasserman, he applies NLP techniques to news articles to forecast stock returns in the US.
