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Trading strategy based on dynamic mode decomposition: Tested in Chinese stock market

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  • Cui, Ling-xiao
  • Long, Wen

Abstract

Dynamic mode decomposition (DMD) is an effective method to capture the intrinsic dynamical modes of complex system. In this work, we adopt DMD method to discover the evolutionary patterns in stock market and apply it to Chinese A-share stock market. We design two strategies based on DMD algorithm. The strategy which considers only timing problem can make reliable profits in a choppy market with no prominent trend while fails to beat the benchmark moving-average strategy in bull market. After considering the spatial information from spatial–temporal coherent structure of DMD modes, we improved the trading strategy remarkably. Then the DMD strategies profitability is quantitatively evaluated by performing SPA test to correct the data-snooping effect. The results further prove that DMD algorithm can model the market patterns well in sideways market.

Suggested Citation

  • Cui, Ling-xiao & Long, Wen, 2016. "Trading strategy based on dynamic mode decomposition: Tested in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 498-508.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:498-508
    DOI: 10.1016/j.physa.2016.06.046
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    Cited by:

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    2. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    3. Elmore, Clay T. & Dowling, Alexander W., 2021. "Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition," Energy, Elsevier, vol. 232(C).

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