Dynamic Portfolio Strategy Using Clustering Approach

Abstract

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.

Investigating the Evolution of Linkage Dynamics among Equity Markets Using Network Models and Measures: The Case of Asian Equity Market Integration

“…Our analysis has also shown that the stock markets of Hong Kong have had the highest influence over the Indian capital market, followed by Singapore, Indonesia and South Korea. In general, in an integrated financial market, the central markets (as reflected by rankings in centrality measures) take part in vital functionalities and inflict a robust impact on other markets, while the correlations amongst the peripheral markets are feeble in nature and possess relatively higher levels of noise than central markets [79].…”

“…The selection of a regionally-diversified cross-market portfolio can be attempted based on topological parameters such as centrality measures. The study by Ren et al [79] has successfully demonstrated the use of network metrics for portfolio selection of stocks. An analogous approach can be employed for the selection of asset classes for regional or international diversified portfolios.…”

Risk-aware multi-armed bandit problem with application to portfolio selection

“…Onnela et al [ 36 ] shows that the MST of assets shrinks during a crisis, which supports the above arguments on the compactness of the eigenvalues of correlation matrix. More importantly, Onnela et al [ 36 ], Pozzi et al [ 37 ] and Ren et al [ 38 ] suggest that investing in the assets located on the peripheral parts of the MST can facilitate diversification and reduce the exposure to systemic risk during a crisis.…”

Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio

“…Complex network theory is a tool and a methodology, with broad applications ranging from the controllability of transport and engineered systems, characterization of protein behaviors in biological systems, description of dynamic processes in social systems, to risk evaluation and portfolio optimization in complex financial systems [12], [13]. Recently, the study of portfolio optimization, based on the complex network theory, mainly concentrates on selecting a basket of assets using the indices of the constructed financial network topological structure like degree, diameter, betweenness centrality, eccentricity, closeness, and eigenvector centrality [11], [14].…”