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Dissertation/Thesis Abstract

Forecasting recessions: The convergence of information and predictive analytics
by Naidoo, Jefrey Subramoney, Ph.D., The University of Alabama, 2010, 164; 3439830
Abstract (Summary)

The purpose of this study is to augment the predictive power of conventional recession-forecasting models by examining the interrelationships among macroeconomic indicators, government information sources and performance data of public companies. The latter two information sources are collectively referred to as institutional artifacts in this study. Evidence was sought of a predictive relationship between institutional artifacts and macroeconomic vulnerability, and the ensuing associations were modeled to provide long-range predictive insights that will serve as a forewarning of impending recessions.

The inclusion of public policy dialogue and corporate performance data as predictor variables in recession forecasting models not only extends the information paradigm associated with recession forecasting, but it also designates the unique contribution that this study makes to this area of research. To obtain a valid estimation of the predictive power of institutional artifacts, and to avoid falsely inflating their significance, the new variables were not modeled in isolation. Macroeconomic indicators published by government agencies and private institutions were retained as variables in the respective regression models used in this study.

The study found that the current ratio and total debt to assets ratio of Fortune 500 companies, and congressional hearings on economic matters significantly predicted the movement of the yield spread twelve months ahead. The study also found that the odds of a recession increase by 1.06 times, or 6%, for every one unit of increase in the number of congressional hearings held, holding other variables constant.

Indexing (document details)
Advisor: Wallace, Danny P.
Commitee: Allaway, Arthur, Aversa, Elizabeth S., Black, Jason E., Borrelli, Stephen A.
School: The University of Alabama
Department: Communication & Information Sciences
School Location: United States -- Alabama
Source: DAI-A 72/04, Dissertation Abstracts International
Subjects: Management, Commerce-Business, Information science
Keywords: Business intelligence, Corporate institutional artifact, Forecasting recession, Government institutional artifacts, Information, Predictive analytics, Predictive model
Publication Number: 3439830
ISBN: 978-1-124-45637-9
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