| *商品について |
|
表紙はついていません（本文のみ中綴じ製本です）。
号単位でも購入できます。
すべてモノクロ印刷です。
Extended Summaryはついていません。
|
 |
| ・会員価格 ¥550 |
| ・一般価格 ¥770 |
|
|
| こちらはBookPark「電気学会 電子図書館（IEEJ Electronic Library）」による文献紹介ページです。 |
|
 |
 |
電気学会会員の方はこちらから一旦ログインのうえ、マイページからお入りください。
会員価格で購入することができます。
|
|
| 非会員の方はログインの必要はありません。このまま お進みください。 |
|
|
| ■論文No. |
|
| ■ページ数 |
6ページ |
■発行日
|
2017/10/01 |
| ■タイトル |
Building Classification Trees on Japanese Stock Groups Partitioned by Network Clustering |
| ■タイトル(英語) |
Building Classification Trees on Japanese Stock Groups Partitioned by Network Clustering |
| ■著者名 |
Takashi Isogai (Bank of Japan (BOJ)), Hieu Chi Dam (Japan Advanced Institute of Science and Technology (JAIST)) |
| ■著者名(英語) |
Takashi Isogai (Bank of Japan (BOJ)), Hieu Chi Dam (Japan Advanced Institute of Science and Technology (JAIST)) |
| ■価格 |
会員 ¥550 一般 ¥770 |
| ■書籍種類 |
論文誌(論文単位) |
| ■グループ名 |
【C】電子・情報・システム部門 |
| ■本誌 |
電気学会論文誌C（電子・情報・システム部門誌） Vol.137 No.10 （2017）
|
| ■本誌掲載ページ |
1387-1392ページ |
| ■原稿種別 |
論文／英語 |
| ■電子版へのリンク |
https://www.jstage.jst.go.jp/article/ieejeiss/137/10/137_1387/_article/-char/ja/
|
| ■キーワード |
stock return，correlation，hierarchical clustering，classification tree，machine learning，data mining |
| ■要約(日本語) |
|
| ■要約(英語) |
We built classification trees that provide sorting rules to reproduce the stock groups identified by hierarchical network clustering of Japanese stocks listed on the Tokyo Stock Exchange. The clustering method was based on a modularity maximization algorithm, which is frequently used for community detection in a network. We applied the clustering method to a correlation network in our previous research work. When building the classification trees, we tested various types of non-price data and selected effective variables with relative importance scores. The selection of variables proved to be consistent with a standard stock price model. Specifically, market capitalization and price book-value ratio were included as significantly important variables. Some other factors were also included as variables that clarify the properties of the Japanese stock market. The classification tree method can be applied to categorize stocks without a full set of continuous time series data into some groups, when our network clustering is difficult to be implemented. It can eventually contribute to improving risk measurement and management of stock portfolios. |
| ■版 型 |
A4 |
|
|
|
