How different types of restaurants behaved differently through the recent recession an analysis of stock market and financial ratios

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Date
2012-01-01
Authors
Wang, Xiaofan
Major Professor
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Tianshu Zheng
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Altmetrics
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Apparel, Events and Hospitality Management

The Department of Apparel, Education Studies, and Hospitality Management provides an interdisciplinary look into areas of aesthetics, leadership, event planning, entrepreneurship, and multi-channel retailing. It consists of four majors: Apparel, Merchandising, and Design; Event Management; Family and Consumer Education and Studies; and Hospitality Management.

History
The Department of Apparel, Education Studies, and Hospitality Management was founded in 2001 from the merging of the Department of Family and Consumer Sciences Education and Studies; the Department of Textiles and Clothing, and the Department of Hotel, Restaurant and Institutional Management.

Dates of Existence
2001 - present

Related Units

  • College of Human Sciences (parent college)
  • Department of Family and Consumer Sciences Education and Studies (predecessor)
  • Department of Hotel, Restaurant, and Institutional Management (predecessor)
  • Department of Textiles and Clothing (predecessor)
  • Trend Magazine (student organization)

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Abstract

This study attempted to identify, quantify, and explain the possible impact the recession had on restaurant stock performance in comparison with the S&P 500 index using ARIMA with Intervention analysis approach and t-tests. Based on the North American Industry Classification System, limited-service (NAICS code 722211) and full-service restaurant segments (NAICS code722110) were examined. The results of this study suggest that the limited-service restaurant segment was recession-proof and outperformed both the full-service restaurant segment and the S&P 500 index. The findings of this study will not only provide the industry with empirical evidence of restaurant performance during and after the recession; but also fill the gap of literature by applying ARIMA with Intervention Analysis in identifying the lag time of impact an event has on time series.

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Sun Jan 01 00:00:00 UTC 2012