Title: Hybrid clustering technique of PCA-SM-GHSOM for abnormal and normal classification with quarterly financial ratios of listed TCM company sector
Authors: Ruicheng Yang
Addresses: School of Finance, Inner Mongolia University of Finance and Economics, Hohhot 010070, China
Abstract: This paper provides a hybrid technique PCA-SM-GHSOM for clustering the quarterly financial data into normal and abnormal groups. For evaluating the performance of this hybrid method, we give some empirical analysis for the listed traditional Chinese medicine (TCM) companies. Three stages are proposed for the clustering experiment. Firstly, we use the PCA method to reduce the high dimensions of financial ratios into low dimensions. Secondly, we adopt cosine similarity method to select three best matching companies in the same TCM sector, and further get the deviation dataset of the considered company. Finally, we put the deviation dataset into the GHSOM system and get the clustering results for training dataset and testing dataset respectively. Furthermore, we give some comparisons with other different techniques of single GHSOM, PCA-GHSOM and SM-GHSOM, and find that the proposed hybrid technique can improve significantly the accuracy for clustering the financial data into normal and abnormal groups.
Keywords: PCA-SM-GHSOM; cosine similarity; quarterly financial ratios; traditional Chinese medicine; TCM firms; hybrid clustering; classification; growing hierarchical SOM; GHSOM; self-organising maps; principal component analysis; PCA; similarity matching; financial data clustering.
International Journal of Simulation and Process Modelling, 2016 Vol.11 No.3/4, pp.220 - 228
Available online: 19 Aug 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article