Forecasting recessions: convergence of information and predictive analytics

dc.contributorAversa, Elizabeth Smith
dc.contributorBorrelli, Stephen
dc.contributorBlack, Jason Edward
dc.contributorAllaway, Arthur Warren
dc.contributor.advisorWallace, Danny P.
dc.contributor.authorNaidoo, Jefrey Subramoney
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractThe 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.en_US
dc.format.extent164 p.
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectInformation science
dc.subjectEconomics, Commerce-Business
dc.titleForecasting recessions: convergence of information and predictive analyticsen_US
dc.typetext of Alabama. College of Communication and Information Sciences & Information Sciences University of Alabama
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