A novel approach to designing an adaptive committee applied to predicting company's future performance

Authors:
Zivile Kalsyte;Antanas Verikas;Marija Bacauskiene;Adas Gelzinis
Affiliations:
Department of Electrical & Control Equipment, Kaunas University of Technology Studentu 50, LT-51368 Kaunas, Lithuania;Department of Electrical & Control Equipment, Kaunas University of Technology Studentu 50, LT-51368 Kaunas, Lithuania and CAISR, Halmstad University, Box 823, S 301 18 Halmstad, Sweden;Department of Electrical & Control Equipment, Kaunas University of Technology Studentu 50, LT-51368 Kaunas, Lithuania;Department of Electrical & Control Equipment, Kaunas University of Technology Studentu 50, LT-51368 Kaunas, Lithuania
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

This article presents an approach to designing an adaptive, data dependent, committee of models applied to prediction of several financial attributes for assessing company's future performance. Current liabilities/Current assets, Total liabilities/Total assets, Net income/Total assets, and Operating Income/Total liabilities are the attributes used in this paper. A self-organizing map (SOM) used for data mapping and analysis enables building committees, which are specific (committee size and aggregation weights) for each SOM node. The number of basic models aggregated into a committee and the aggregation weights depend on accuracy of basic models and their ability to generalize in the vicinity of the SOM node. A random forest is used a basic model in this study. The developed technique was tested on data concerning companies from ten sectors of the healthcare industry of the United States and compared with results obtained from averaging and weighted averaging committees. The proposed adaptivity of a committee size and aggregation weights led to a statistically significant increase in prediction accuracy if compared to other types of committees.