DocumentCode :
2207879
Title :
Two-level self-organizing maps for analysis of financial statements
Author :
Kiviluoto, Kimmo ; Bergius, Pentti
Author_Institution :
Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland
Volume :
1
fYear :
1998
fDate :
4-8 May 1998
Firstpage :
189
Abstract :
We propose a novel tool based on a hierarchy of two self-organizing maps (SOMs) for analyzing financial statements. The inputs to the first-level SOM are financial indicators derived from a company´s annual financial statements; these determine the company´s position on the first-level SOM each year. The inputs to the second-level SOM are the coordinates of the company on the first-level SOM during two or more consecutive years. The second-level SOM turns out to give a more accurate description of the state of the company than the first-level SOM. Moreover, it is easy to interpret, as each point on the second-level SOM corresponds to a trajectory on the first-level SOM. With our method, several different patterns of corporate behavior can be recognized
Keywords :
business data processing; financial data processing; pattern classification; self-organising feature maps; company financial data; corporate rating; financial statements; neural nets; pattern classification; self-organizing maps; Application software; Companies; Concatenated codes; Data visualization; Displays; Helium; Laboratories; Pattern recognition; Self organizing feature maps; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
ISSN :
1098-7576
Print_ISBN :
0-7803-4859-1
Type :
conf
DOI :
10.1109/IJCNN.1998.682260
Filename :
682260
Link To Document :
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