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
Link To Document