標題: Affinity Propagation Clustering for Intelligent Portfolio Diversification and Investment Risk Reduction
作者: Chang, Chu-Chun
Koc, Wai-Wan
Chou, Chin
Lin, Zhi-Ting
Huang, Szu-Hao
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: portfolio selection;artificial intelligence;clustering;affinity propagation;diversified investment;machine learning
公開日期: 1-Jan-2016
摘要: In this paper, an intelligent portfolio selection method based on Affinity Propagation clustering algorithm is proposed to solve the stable investment problem. The goal of this work is to minimize the volatility of the selected portfolio from the component stocks of S&P 500 index. Each independent stock can be viewed as a node in graph, and the similarity measurements of stock price variations between companies are calculated as the edge weights. Affinity Propagation clustering algorithm solve the graph theory problem by repeatedly update responsibility and availability message passing matrices. This research tried to find most representative and discriminant features to model the stock similarity. The testing features are divided into two major categories, including time-series covariance, and technical indicators. The historical price and trading volume data is used to simulate the portfolio selection and volatility measurement. After grouping these investment targets into a small set of clusters, the selection process will choose fixed number of stocks from different clusters to form the portfolio. The experimental results show that the proposed system can effectively generate more stable portfolio by Affinity Propagation clustering algorithm with proper similarity features than average cases with similar settings.
URI: http://dx.doi.org/10.1109/CCBD.2016.25
http://hdl.handle.net/11536/146144
ISSN: 2378-3680
DOI: 10.1109/CCBD.2016.25
期刊: 2016 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA (CCBD)
起始頁: 145
結束頁: 150
Appears in Collections:Conferences Paper