Golshan, Nargess

Research Overview
We apply new information technologies, such as machine learning, natural language processing, and voice feature extractions, to the text, language and voice in corporate disclosures and conference call audio files to extract meaning and provide new insights into corporate disclosure.

Project: Voice Feature Extraction from Earnings Conference Calls
In this project, we are trying to extract features from the voice of the executives and financial analysts and examine outcomes for financial markets and corporate decisions.

Students:
Mark Cheng, (Sung-Yuan Cheng) Ph.D. student in Accountancy, Added 04/15/2021

Collaborators:

Computational methods:
Voice feature extraction

Software:
Python, SAS

Grants:

Publications:

Research Activities:

In my research, I explore how firms change their financial reporting policies and how do firm disclosures affect information environments

Project:

Local peers and financial reporting policies.

Personnel:

Nargess Golshan, PI (University of Kentucky)

Collaborators:

Inder Khurana (University of Missouri)

Jere Francis (Maastricht University)

Computational methods:

The Quadratic Assignment Procedure

Software:

SAS, STATA (if available), and R

Grants:

Publications: Pending

Statement: The project is to address a referee comment for second-round submission of a paper to The Accounting Review journal. The paper is co-authored by Nargess Golshan (University of Kentucky), Inder Khurana (University of Missouri), Jere Francis (Maastricht University).