Koshiyama, AS;
Firoozye, N;
Treleaven, P;
(2019)
A derivatives trading recommendation system: The mid‐curve calendar spread case.
Intelligent Systems in Accounting, Finance and Management
, 26
(2)
pp. 83-103.
10.1002/isaf.1445.
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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 is aimed to develop a trading recommendation system, and to apply this system to the so‐called Mid‐Curve Calendar Spread (MCCS) trade. To suggest that such approach is feasible, we used a list of 35 different types of MCCSs; a total of 11 predictive and 4 benchmark models. Our results suggest that linear regression with l1‐regularisation (Lasso) compared favourably to other approaches from a predictive and interpretability point of views.
| Type: | Article |
|---|---|
| Title: | A derivatives trading recommendation system: The mid‐curve calendar spread case |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/isaf.1445 |
| Publisher version: | http://dx.doi.org/10.1002/isaf.1445 |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | derivatives, machine learning, trading recommendation system |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10091859 |
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