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A novel approach to exploring company’s financial soundness: Investor’s perspective
Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-2185-8973
2013 (English)In: Expert systems with applications, ISSN 0957-4174, Vol. 40, no 13, 5085-5092 p.Article in journal (Refereed) Published
Abstract [en]

Prediction of company's life cycle stage change; creation of an ordered 2D map allowing to explore company's financial soundness from a rating agency perspective; and prediction of trends of main valuation attributes usually used by investors are the main objectives of this article. The developed algorithms are based on a random forest (RF) and a nonlinear data mapping technique ''t-distributed stochastic neighbor embedding''. Information from five different perspectives, namely balance, income, cash flow, stock price, and risk indicators was aggregated via proximity matrices of RF to enable exploration of company's financial soundness from a rating agency perspective. The proposed use of information not only from companies' financial statements but also from the stock price and risk indicators perspectives has proved useful in creating ordered 2D maps of rated companies. The companies were well ordered according to the credit risk rating assigned by the Moody's rating agency. Results of experimental investigations substantiate that the developed models are capable of predicting short term trends of the main valuation attributes, providing valuable information for investors, with low error. The models reflect financial soundness of actions taken by company's management team. It was also found that company's life cycle stage change can be determined with the average accuracy of 72.7%. Bearing in mind fuzziness of the transition moment, the obtained prediction accuracy is rather encouraging. © 2013 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Oxford: Pergamon Press, 2013. Vol. 40, no 13, 5085-5092 p.
Keyword [en]
Committee, Random forest, Data proximity, Classification, Variable selection, Financial soundness
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-22972DOI: 10.1016/j.eswa.2013.03.031ISI: 000320210900005Scopus ID: 2-s2.0-84878343900OAI: oai:DiVA.org:hh-22972DiVA: diva2:630745
Available from: 2013-06-19 Created: 2013-06-19 Last updated: 2014-11-19Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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