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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF MARYLAND, COLLEGE PARK
Doing Business As Name:University of Maryland College Park
PD/PI:
  • Kathleen W Hanley
  • (610) 758-3432
  • kwh315@lehigh.edu
Co-PD(s)/co-PI(s):
  • Gerard Hoberg
Award Date:08/29/2014
Estimated Total Award Amount: $ 299,683
Funds Obligated to Date: $ 164,469
  • FY 2014=$164,469
Start Date:09/01/2014
End Date:05/31/2016
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:EAGER: III: CIFRAM: Dynamic Identification and Interpretation of Emerging Systemic Risks Using Textual Analysis
Federal Award ID Number:1449578
DUNS ID:790934285
Parent DUNS ID:003256088
Program:Info Integration & Informatics
Program Officer:
  • Maria Zemankova
  • (703) 292-7348
  • mzemanko@nsf.gov

Awardee Location

Street:3112 LEE BLDG 7809 Regents Drive
City:COLLEGE PARK
State:MD
ZIP:20742-5141
County:College Park
Country:US
Awardee Cong. District:05

Primary Place of Performance

Organization Name:University of Maryland College Park
Street:4471 Van Munching Hall
City:College Park, MD
State:MD
ZIP:20742-5141
County:College Park
Country:US
Cong. District:05

Abstract at Time of Award

This project will employ linguistic tools to determine how and why financial crises, such as the 2008 crisis following the Lehman Brothers bankruptcy, form and grow in magnitude. By examining textual information gleaned by processing large volumes of verbal data from 10-K filings to the Securities and Exchange Commission, the principal investigators will use techniques developed by computer scientists to assess verbal themes and link them to market data to assess whether future crises are forming. The techniques employed enable the identification of actual risks allowing regulators and market participants the ability to respond appropriately in advance of a major development. The free provision of data and computer code on the internet will lower the cost for future researchers to also examine these issues. The principal investigators will also present the research at conferences, submit the work for publication, and will work with and train graduate students. The material will be taught to future business leaders in the classroom, where MBA students and undergraduate students can openly discuss the results and their implications. The work will also be submitted to conferences attended by regulators to share insights on how they can be used to manage potential crises before they can cause extensive damage. The principal investigators will use methods from computational linguistics, including Latent Dirichlet Allocation (LDA) and document similarity analysis, to identify a set of verbal topics that are common among financial firms, non-financial firms with exposure to the finance industry, and then all firms in the economy. The investigators will then use clustering and network methods to assess and categorize the business links among firms in the economy and to examine how they evolve over time. The resulting firm-relatedness network will then be compared to market data during various time intervals to understand how and why stock prices comove differently in neighboring periods, especially periods leading up to major crises. The verbal factors will be interpretable, and hence this technique will provide a fully automated description of why firms comove in different ways in different time periods. This method will be replicable and not subjected to researcher prejudice, allowing the data to inform researchers regarding the most salient issues affecting markets, even if the researcher is ex-ante unfamiliar with the true drivers of a specific systemic risk event. Once the textual drivers of comovement are understood, these factors can be used to back-test how the dynamic topic structure evolves during other systemic events. If successful, this research could create an early warning system for potential future crises and serve as a risk management tool by addressing the drivers of crisis before they occur, thereby reducing the cost of resolution. For further information see the project web site: http://scholar.rhsmith.umd.edu/khanley/nsf-grant?destination=node/1084

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