A profitable currency portfolio strategy: Learning from connectedness

Wenhao Wang, Feifei Cai, Ziyi Hong, Ruiqi Liu and Qingyi Zhang

Finance Research Letters, 2025, vol. 76, issue C

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.

Keywords: Currency portfolios; Dynamic connectedness; Machine learning; Cosine similarity of currency positions (search for similar items in EconPapers)
JEL-codes: F31 G11 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:76:y:2025:i:c:s1544612325002168

DOI: 10.1016/j.frl.2025.106952

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