Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/14530
Title: Predicting shifts in the mean of a multivariate time series process: An application in predicting business failures
Authors: Theodossiou, Panayiotis 
metadata.dc.contributor.other: Θεοδοσίου, Παναγιώτης
Major Field of Science: Social Sciences
Field Category: Economics and Business
Keywords: Bankruptcy prediction;Business failure prediction models;Discriminant analysis;Multivariate cumulative sum procedure;Serial correlation;Sequential procedure
Issue Date: Jun-1993
Source: Journal of the American Statistical Association, 1993, vol. 88, no. 422, pp. 441-449
Volume: 88
Issue: 422
Start page: 441
End page: 449
Journal: Journal of the American Statistical Association 
Abstract: A firm in the early stages of financial distress exhibits characteristics different from those of healthy firms. As the economic condition of a firm worsens, its financial characteristics shift toward those of failed firms. Practitioners in the financial sector have long been interested in the early detection of a firm’s slide toward insolvency. Several models have been developed with this purpose in mind, but these older models are static in nature. Therefore, a need exists for the development of business failure prediction models that assess the financial condition of firms sequentially over time. This article addresses this need by presenting a sequential business failure prediction model.
URI: https://hdl.handle.net/20.500.14279/14530
ISSN: 01621459
DOI: 10.1080/01621459.1993.10476294
Rights: © Taylor & Francis
Type: Article
Affiliation : Rutgers University-Camden campus 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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