Dynamic Portfolio Strategy Using Clustering Approach

• Published in: PloS one Vol. 12; no. 1; p. e0169299
• Main Authors: Ren, Fei, Lu, Ya-Nan, Li, Sai-Ping, Jiang, Xiong-Fei, Zhong, Li-Xin, Qiu, Tian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.01.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Amplitudes; Asset management; China; Cluster Analysis; Clustering; Computer and Information Sciences; Criteria; Drawdown; Economic conditions; Financial Statements - economics; Graphs; Humans; Investment; Investments - economics; Investments - statistics & numerical data; Markets; Methods; Models, Economic; Multivariate analysis; Neural networks; Optimization; Parameter identification; Physical Sciences; Portfolio management; Portfolios; Research and Analysis Methods; Skewness; Social Sciences; Strategy; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0169299

The problem of portfolio optimization is one of the most important issues in asset management. We here propose a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for investment. A portfolio strategy comprises two stages: First, select the portfolios by choosing central and peripheral stocks in the selection horizon using five topological parameters, namely degree, betweenness centrality, distance on degree criterion, distance on correlation criterion and distance on distance criterion. Second, use the portfolios for investment in the investment horizon. The optimal portfolio is chosen by comparing central and peripheral portfolios under different combinations of market conditions in the selection and investment horizons. Market conditions in our paper are identified by the ratios of the number of trading days with rising index to the total number of trading days, or the sum of the amplitudes of the trading days with rising index to the sum of the amplitudes of the total trading days. We find that central portfolios outperform peripheral portfolios when the market is under a drawup condition, or when the market is stable or drawup in the selection horizon and is under a stable condition in the investment horizon. We also find that peripheral portfolios gain more than central portfolios when the market is stable in the selection horizon and is drawdown in the investment horizon. Empirical tests are carried out based on the optimal portfolio strategy. Among all possible optimal portfolio strategies based on different parameters to select portfolios and different criteria to identify market conditions, 65% of our optimal portfolio strategies outperform the random strategy for the Shanghai A-Share market while the proportion is 70% for the Shenzhen A-Share market.