Carry Trade Returns with Support Vector Machines

Emilio Colombo, Gianfranco Forte and Roberto Rossignoli

International Review of Finance, 2019, vol. 19, issue 3, 483-504

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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1111/irfi.12186

Related works:
Working Paper: Carry trade returns with Support Vector Machines (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:irvfin:v:19:y:2019:i:3:p:483-504

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1369-412X

Access Statistics for this article

International Review of Finance is currently edited by Bruce D. Grundy, Naifu Chen, Ming Huang, Takao Kobayashi and Sheridan Titman

More articles in International Review of Finance from International Review of Finance Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().