Ren Fei, Lu Ya-Nan, Li Sai-Ping, Jiang Xiong-Fei, Zhong Li-Xin, Qiu Tian
School of Business, East China University of Science and Technology, Shanghai 200237, China.
Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China.
PLoS One. 2017 Jan 27;12(1):e0169299. doi: 10.1371/journal.pone.0169299. eCollection 2017.
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.
投资组合优化问题是资产管理中最重要的问题之一。我们在此提出一种基于中国股票市场MST网络时变结构的新型动态投资组合策略，在使用最优投资组合进行投资时进一步考虑市场状况。一种投资组合策略包括两个阶段：首先，在选择期内使用五个拓扑参数，即度、介数中心性、基于度准则的距离、基于相关性准则的距离和基于距离准则的距离，通过选择中心股票和外围股票来选择投资组合。其次，在投资期内使用这些投资组合进行投资。通过比较在选择期和投资期不同市场状况组合下的中心投资组合和外围投资组合来选择最优投资组合。本文中的市场状况通过指数上涨交易日数量与总交易日数量的比率，或者指数上涨交易日振幅之和与总交易日振幅之和来确定。我们发现，当市场处于上涨状态时，或者当市场在选择期内稳定或上涨且在投资期内处于稳定状态时，中心投资组合的表现优于外围投资组合。我们还发现，当市场在选择期内稳定且在投资期内下跌时，外围投资组合的收益超过中心投资组合。基于最优投资组合策略进行了实证检验。在基于不同参数选择投资组合和不同标准识别市场状况的所有可能的最优投资组合策略中，我们的最优投资组合策略中有65%在上海A股市场上优于随机策略，而在深圳A股市场上这一比例为70%。
