Date of Award

Spring 4-2016

Embargo Period

3-21-2019

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Tepper School of Business

Advisor(s)

Bryan Routledge

Abstract

In the world of the financial economics, we have abundant text data. Articles in the Wall Street Journal and on Bloomberg Terminals, corporate SEC filings, earnings-call transcripts, social media messages, etc. all contain ample information about financial markets and investor behaviors. Extracting meaningful signals from unstructured and high dimensional text data is not an easy task. However, with the development of machine learning and computational linguistic techniques, processing and statistically analyzing textual documents tasks can be accomplished, and many applications of statistical text analysis in social sciences have proven to be successful.

Available for download on Thursday, March 21, 2019

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