Voya

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.