In finance there are two basic types of data: market data, which indicates trading decisions; and alternative data, which is used to support decision making. Much alternative data takes the form of natural language documents. In this talk I will briefly survey how NLP is used to process alternative data in finance. I will then describe one particular experiment, where Katherine Keith (a Bloomberg summer intern) and I analyzed financial analysts' decision making via earnings call transcripts. I will close with an overview of other data science research at Bloomberg.
Amanda Stent is a Natural Language Processing (NLP) architect at Bloomberg LP. Previously, she was a director of research and principal research scientist at Yahoo Labs, a principal member of technical staff at AT&T Labs - Research, and an associate professor in the Computer Science Department at Stony Brook University. Her research interests center on natural language processing and its applications, in particular topics related to text analytics, discourse, dialog and natural language generation. She holds a PhD in computer science from the University of Rochester. She is co-editor of the book Natural Language Generation in Interactive Systems (Cambridge University Press), has authored over 90 papers on natural language processing and is co-inventor on over 25 patents and patent applications. She is one of the rotating editors of the journal Dialogue & Discourse. She is also a board member of CRA-WP, where she co-directs the DREU program.