6:20 PM - 6:40 PM
[1M4-J-13-04] Dynamic clustering of mutual funds based on the return series
Keywords:financial informatics, dynamic clustering, mutual fund analysis
This paper proposes a method to analyze mutual funds using dynamic clustering based on the return series. For the time series data divided into some terms, the proposed method (1) converts the original high-dimensional data to two dimensional data using t-SNE for each term, (2) applies dynamic clustering using $x$-means for each terms, and (3) detects the cluster transitions using FBL-MONIC.
This paper shows the experimental results for 29 Japanese mutual funds that track TOPIX, including four ETFs. The results indicate that there are three clusters at terms 1, 2, 3 and 4, and four clusters at term 5. We consider that the clusters are valid because one of the clusters consists of ETFs for all terms. FBL-MONIC could detect the transitions from a cluster at term 4 to a new cluster at term 5.
This paper shows the experimental results for 29 Japanese mutual funds that track TOPIX, including four ETFs. The results indicate that there are three clusters at terms 1, 2, 3 and 4, and four clusters at term 5. We consider that the clusters are valid because one of the clusters consists of ETFs for all terms. FBL-MONIC could detect the transitions from a cluster at term 4 to a new cluster at term 5.