• DocumentCode
    175619
  • Title

    Comparing corporate financial performance and qualitative information from annual reports using self-organizing maps

  • Author

    Hajek, Petr ; Olej, Vladimir

  • Author_Institution
    Inst. of Syst. Eng. & Inf., Univ. of Pardubice, Pardubice, Czech Republic
  • fYear
    2014
  • fDate
    19-21 Aug. 2014
  • Firstpage
    93
  • Lastpage
    98
  • Abstract
    This paper develops a methodology to extract concepts containing qualitative information from corporate annual reports. The concepts are extracted from the corpus of U.S. corporate annual reports using WordNet ontology and singular value decomposition, and further visualized using self-organizing maps. The methodology makes it possible to easily compare the concepts with future financial performance. The results suggest that annual reports differ in terms of the concepts emphasized reflecting future financial performance.
  • Keywords
    financial data processing; ontologies (artificial intelligence); self-organising feature maps; U.S. corporate annual reports; WordNet ontology; annual reports; comparing corporate financial performance; qualitative information; self-organizing maps; singular value decomposition; Companies; Data mining; Eigenvalues and eigenfunctions; Neurons; Profitability; annual report; financial performance; self-organizing map; singular value decomposition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2014 10th International Conference on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-1-4799-5150-5
  • Type

    conf

  • DOI
    10.1109/ICNC.2014.6975816
  • Filename
    6975816