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Statistical models for rating financial performance and health of companies
University of Borås, Faculty of Textiles, Engineering and Business. (Textile Management)ORCID iD: 0000-0003-2015-6275
TriloByte Statistical Software Ltd., Czech Republic.
College of Engineering & Technology, East Carolina State University, USA.
2015 (English)In: / [ed] College of Textiles, North Carolina State University, 2015Conference paper, Oral presentation with published abstract (Other academic)
Sustainable development
The content falls within the scope of Sustainable Development
Abstract [en]

Authors analyze financial data from a set of almost 200 major US companies from both manufacturing and service. The selection of the firms considered the spectrum of sectors of Dow Jones Industrial composite including manufacturing companies in aerospace & defence, automotive, beverages, footware & apparel, health technology, oil & gas and service-oriented companies like consumer services, discount stores, telecom services, insurance. Short overview of recent development in business health modelling is given. Based on the data and known expert ratings, the recently published Stagewise regression algorithm was employed to identify the most relevant predictors out of all possible financial ratios as based on the data. Support Vector Machine was used subsequently to construct a quantitative probabilistic prediction model for business investment risk evaluation. With this approach, it is possible to build less rigid, more specific models suitable for smaller sectors an shorter periods, thus allowing investors and management to better react to dynamic changes in business environment. Quantitative prediction based on Support Vector Machine (SVM) models provides more information and better decission support than traditional binary prediction (good/bad). The paper provides directly applicable parametric decission models predicting both numerical rating and good/bad classification probability  for manufacturing and service sectors. Short-term and more specific models can be used to characterize not only the business subject themselves, but also to characterize, parametrize and compare business environments.

Place, publisher, year, edition, pages
2015.
National Category
Probability Theory and Statistics Economics and Business
Research subject
Textiles and Fashion (General)
Identifiers
URN: urn:nbn:se:hb:diva-528OAI: oai:DiVA.org:hb-528DiVA, id: diva2:841967
Conference
32nd Quality & Productivity Research (QPRC)
Available from: 2015-07-15 Created: 2015-07-15 Last updated: 2018-04-28Bibliographically approved

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Pal, Rudrajeet

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf