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Carry Trade Returns with Support Vector Machines

Author

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  • Emilio Colombo
  • Gianfranco Forte
  • Roberto Rossignoli

Abstract

This paper proposes a novel approach to directional forecasts for carry trade strategies based on support vector machines (SVMs), a learning algorithm that delivers extremely promising results. Building on recent findings in the literature on carry trade, we condition the SVM on indicators of uncertainty and risk. We show that this provides a dramatic performance improvement in strategy, particularly during periods of financial distress such as the recent financial crises. Disentangling the measures of risk, we show that conditioning the SVM on measures of liquidity risk rather than on market volatility yields the best performance.

Suggested Citation

  • Emilio Colombo & Gianfranco Forte & Roberto Rossignoli, 2019. "Carry Trade Returns with Support Vector Machines," International Review of Finance, International Review of Finance Ltd., vol. 19(3), pages 483-504, September.
    • Handle: RePEc:bla:irvfin:v:19:y:2019:i:3:p:483-504
    DOI: 10.1111/irfi.12186
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    Cited by:

    1. Colombo, Emilio & Pelagatti, Matteo, 2020. "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1260-1289.
    2. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    3. Tzu-Pu Chang & Yu-Cheng Chang & Po-Ching Chou, 2022. "The Trend is Your Friend: A Note on An Ensemble Learning Approach to Finding It," Bulletin of Applied Economics, Risk Market Journals, vol. 9(1), pages 19-25.
    4. Yang ZHANG & Ziang QIU Ziang & Donghyun PARK & Shu TIAN, 2026. "Role of Artificial Intelligence in Finance: Selective Literature Review and Implications for Asia's Financial Stability," Working Papers wp61, South East Asian Central Banks (SEACEN) Research and Training Centre, revised Feb 2026.

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