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Dynamic Portfolio Strategy Using Clustering Approach.

Fei Ren1,2, Ya-Nan Lu1, Sai-Ping Li3

  • 1School of Business, East China University of Science and Technology, Shanghai 200237, China.

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Summary
This summary is machine-generated.

This study introduces a dynamic portfolio strategy for Chinese stock markets using Minimum Spanning Tree (MST) networks. Central portfolios perform better in rising markets, while peripheral ones excel in stable-to-drawdown transitions.

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Area of Science:

  • Quantitative Finance
  • Financial Markets
  • Network Theory

Background:

  • Portfolio optimization is crucial in asset management.
  • Chinese stock markets exhibit dynamic, time-varying structures.
  • Existing strategies may not fully capture market condition nuances.

Purpose of the Study:

  • Propose a novel dynamic portfolio strategy for Chinese stock markets.
  • Incorporate time-varying network structures and market conditions.
  • Enhance investment decision-making through optimal portfolio selection.

Main Methods:

  • Utilized Minimum Spanning Tree (MST) networks to analyze stock market structures.
  • Employed five topological parameters (degree, betweenness centrality, etc.) for stock selection.
  • Defined market conditions based on index movement ratios and amplitude sums.
  • Compared central and peripheral portfolios under various market scenarios.

Main Results:

  • Central portfolios outperformed peripheral ones in drawup or stable-to-drawup market conditions.
  • Peripheral portfolios showed higher gains during stable-to-drawdown market transitions.
  • Empirical tests indicated significant outperformance over random strategies in Shanghai and Shenzhen A-Share markets (65% and 70% respectively).

Conclusions:

  • The proposed dynamic strategy effectively leverages MST network topology and market conditions.
  • Optimal portfolio selection varies based on market state dynamics.
  • The strategy demonstrates robust performance, outperforming random approaches in key Chinese stock markets.