Community Detection과 Clustering을 이용한 주식 시장 군집화

Comparisons of Community Detection and Clustering in Stock Market Classifications
  • 박건빈
  • 박성훈
  • 박민우
  • 이정수
  • 윤지환
  • 외 1명

초록

When investing in companies or deciding a portfolio, investors often consider the company’s industry or theme, especially Industry Classification in KOSPI Industry Group Indices. However, the classification is not based on statistical analysis using the stock price of companies. Therefore, the current study aims to suggest a new market classification based on network analysis to diversify portfolios. In the study, we applied a lasso and adaptive lasso in KOSPI data (Jan 2012~Jul 2019) to estimate an undirected graph. Then, We applied community detection and clustering analysis to classify companies in the estimated graph. As a result, most clusters include companies with different sectors in KOSPI Industry Group Indices which are closely related in reality. Moreover, based on silhouettes, new market classification results outperform the existing classification. Thus, new market classification can be a better alternative to the existing classification.

키워드

KOSPI Market ClassificationCommunity DetectionClusteringSilhouettes
제목
Community Detection과 Clustering을 이용한 주식 시장 군집화
제목 (타언어)
Comparisons of Community Detection and Clustering in Stock Market Classifications
저자
박건빈박성훈박민우이정수윤지환한성원
DOI
10.7232/JKIIE.2020.46.6.626
발행일
2020
저널명
대한산업공학회지
46
6
페이지
626 ~ 636