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Carry Trade Returns with Support Vector Machines
Author(s) -
Colombo Emilio,
Forte Gianfranco,
Rossignoli Roberto
Publication year - 2019
Publication title -
international review of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 18
eISSN - 1468-2443
pISSN - 1369-412X
DOI - 10.1111/irfi.12186
Subject(s) - support vector machine , carry (investment) , financial distress , market liquidity , volatility (finance) , computer science , imperfect , machine learning , financial market , artificial intelligence , econometrics , economics , finance , financial system , linguistics , philosophy
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.