A Derivatives Trading Recommendation System: the Mid-Curve Calendar Spread Case

43 Pages Posted: 11 Nov 2018

See all articles by Adriano Koshiyama

Adriano Koshiyama

University College London, Financial Computing and Analytics Group, Department of Computer Science, Students

Nick Firoozye

UCL - Computer Science

Philip Treleaven

University College London

Date Written: October 18, 2018

Abstract

Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their job. Hence, this work aims to develop a trading recommendation system, and apply this system to the so-called Mid-Curve Calendar Spread (MCCS). To suggest that such approach is feasible, we used a list of 35 different types of MCCS; a total of 11 predictive models; and 4 benchmark models. Our results suggest that linear regression with lasso regularisation compared favourably to other approaches from a predictive and interpretability perspective.

Keywords: Trading Recommendation System; Machine Learning; Derivatives, Swaptions

JEL Classification: C53, G11

Suggested Citation

Soares Koshiyama, Adriano and Firoozye, Nick and Treleaven, Philip, A Derivatives Trading Recommendation System: the Mid-Curve Calendar Spread Case (October 18, 2018). Available at SSRN: https://ssrn.com/abstract=3269496 or http://dx.doi.org/10.2139/ssrn.3269496

Adriano Soares Koshiyama (Contact Author)

University College London, Financial Computing and Analytics Group, Department of Computer Science, Students ( email )

Gower Street
London, London WC1E 6BT
United Kingdom

Nick Firoozye

UCL - Computer Science ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Philip Treleaven

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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