Carry Trade Returns with Support Vector Machines

Emilio Colombo, Gianfranco Forte, Roberto Rossignoli

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

2 Citazioni (Scopus)


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.
Lingua originaleEnglish
pagine (da-a)1-22
Numero di pagine22
RivistaInternational Review of Finance
Stato di pubblicazionePubblicato - 2018


  • Support vector machine


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