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A profitable currency portfolio strategy: Learning from connectedness

Author

Listed:
  • Wang, Wenhao
  • Cai, Feifei
  • Hong, Ziyi
  • Liu, Ruiqi
  • Zhang, Qingyi

Abstract

This study proposes a profitable currency portfolio strategy integrating dynamic connectedness into machine learning (ML) predictions. The portfolio is constructed using consensus predictions of return levels from LSTM and MLP and return directions from SVM and RF. Our findings reveal that connectedness slightly enhances returns but significantly reduces return volatility, implying its role in risk management. Compared to eight classical currency trading strategies, ML-based portfolios outperform in returns and mitigating extreme losses. Notably, portfolios incorporating RF predictions achieve the highest average return and Sharpe ratio among all strategies. Additionally, ML-based portfolios exhibit significant differences from classical strategies in determining currency positions.

Suggested Citation

  • Wang, Wenhao & Cai, Feifei & Hong, Ziyi & Liu, Ruiqi & Zhang, Qingyi, 2025. "A profitable currency portfolio strategy: Learning from connectedness," Finance Research Letters, Elsevier, vol. 76(C).
    • Handle: RePEc:eee:finlet:v:76:y:2025:i:c:s1544612325002168
    DOI: 10.1016/j.frl.2025.106952
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    More about this item

    Keywords

    Currency portfolios; Dynamic connectedness; Machine learning; Cosine similarity of currency positions;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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