Affiliations: [a] Radiant First Research Limited, Hong Kong
| [b] Koi Investment Partners International, Hong Kong
| [c] Institute of Data Science and Department of Mathematics, The University of Hong Kong, Hong Kong
Correspondence:
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Corresponding author: Andy Yip, Radiant First Research Limited, 704 Tai Tung Building, 8 Flemming Road, Hong Kong. E-mail: [email protected].
Abstract: We present an algorithmic trading strategy based upon a graph version of the dynamic mode decomposition (DMD) model. Unlike the traditional DMD model which tries to characterize a stock’s dynamics based on all other stocks in a universe, the proposed model characterizes a stock’s dynamics based only on stocks that are deemed relevant to the stock in question. The relevance between each pair of stocks in a universe is represented as a directed graph and is updated dynamically. The incorporation of a graph model into DMD effects a model reduction that avoids overfitting of data and improves the quality of the trend predictions. We show that, in a practical setting, the precision and recall rate of the proposed model are significantly better than the traditional DMD and the benchmarks. The proposed model yields portfolios that have more stable returns in most of the universes we backtested.
