Clustering analysis as one of the key components of data mining has been widely applied. This paper aimed to apply a clustering algorithm to classify and evaluate securities investment funds. It established a fund evaluation index system by researching the indexes that are influenced by the performance of funds. It drew upon domestic and foreign mature funds evaluation theory and used the data mining function of Excel to establish a clustering analysis model. Finally, this paper used 40 equity funds as sample data to conduct an empirical research. The cluster results would be beneficial in evaluating funds’ performance and guiding the decision making on rational investment.
TreynorJ., How to Rate Management of Investment Funds, Harvard Business Review, 1995, pp. 63–75.
2.
SharpeW.F., Mutual fund performance, Journal of Businesspart II (1996), 119–138.
3.
JensenM.C., The performance of mutual funds in the period 1945–1964, Journal of Finance26 (1968), 389–416.
4.
LeeC.-F., PatroD.K. and LiuB., Functional Forms for Performance Evaluation: Evidence from Closed-End Country Funds, Springer, pp. 2010, 1523–1553.
5.
PendarakiK. and ZopounidisC., Evaluation of equity mutual funds’ performance using a multi-criteria methodology, Operational Research3(1) (2003), 69–90.
6.
WongF.S., WangP.Z. and TehH.H., A stock selection strategy using fuzzy neural networks, Computer Science in Economics and Management4(2) (1991), 77–89.
7.
KoharaK., FukuharaY. and NakamuraY., Selective presentation learning for neural network forecasting of stock markets, Neural Computing & Applications4(3) (1996), 143–148.
8.
HuangC.-J., ChenP.-W. and PanW.-T., Using multi-stage data mining technique to build forecast model for Taiwan stocks, Neural Computing and Applications21(8) (2012), 2057–2063.
9.
BoillatP.-Y. and de SkowronskiN. and TuchschmidN, Cluster analysis: Application to sector indices and empirical validation, Financial Markets and Portfolio Management16(4) (2002), 467–486.
10.
DragutA.B., Stock data clustering and multiscale trend detection, Methodology and Computing in Applied Probability14(1) (2012), 87–105.
11.
WuC.M., ChouS.C. and LiawH.T., A trend based investment decision approach using clustering and heuristic algorithm, Science China Information Sciences57(9) (2014), 1–14.
12.
CzekalaM. and KuziakK., Clustering of Stocks in Risk Context, Dresden, Proceedings of the 22nd Annual GFKl Conference, 1998, pp. 447–452.
13.
ShiY. and ShiZ., Clustering Based Stocks Recognition, Third International Conference, FSKD 2006, Xi’an, China, vol. 4223, 2006, pp. 1121–1129.
14.
MoldovanD. and SilaghiG.C., Gene Trajectory, Clustering for Learning the Stock Market Sector, 9th International Conference, ICANNGA 2009, Kuopio, Finland, vol. 5495, 2009, pp. 559–568.
15.
HanJ., KamberM. and PeiJ., Data mining concepts and techniques, Beijing: China Machine Press, 2012.
16.
ZhouX., LiK. and ZhouY., et al., Adaptive processing for distributed skyline queries over uncertain data, IEEE Transactions on Knowledge and Data Engineering, 2015, pp. 1–1.
17.
GuB., ShengV.S., TayK.Y., et al., Incremental support vector learning for ordinal regression, IEEE Transactions on Neural Networks & Learning Systems26 (2015), 1403–1416.
18.
TsaparasP., MannilaH. and GionisA., Clustering aggregation, Acm Transactions on Knowledge Discovery from Data1(1) (2007), 341–352.
19.
ZhaoY., ZhangH. and KongG., Image segmentation by generalized hierarchical fuzzy C-means algorithm, Journal of Intelligent & Fuzzy Systems28(2) (2015), 4024–4028.
20.
LeeJ. and LeeD., An improved cluster labeling method for support vector clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence27 (2005), 461–464.
21.
TsengV.S. and KaoC.-P., Efficiently mining gene expression data via a novel parameterless clustering method, IEEE/ACM Transactions on Computational Biology and Bioinformatics2 (2005), 355–365.
22.
FuX.J. and WangL.P., Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance, IEEE Trans, System, Man, Cybern, Part B-Cybernetics33(3) (2003), 399–409.
23.
KamelN. and BouchetaR., A New Clustering Algorithm Based on Chameleon Army Strategy, Proceedings of the 10th International Conference on Computing and Information Technology (IC2IT2014), vol. 265, 2014, pp. 23–32.
24.
HeH., ChenJ., JinH. and ChenS.-H., Trading strategies based on K-means clustering and regression models, Computational Intelligence in Economics and Finance2 (2007), 123–134.
25.
HuangJ., Study on Financial Data Hybrid Clustering Based on Stock Trading Rule, Proceedings of the 2012 International Conference on Cybernetics and Informatics, Kuopio, Finland, vol. 163, 2014, pp. 2351–2357.
