A derivatives trading recommendation system: The mid‐curve calendar spread case

Adriano S. Koshiyama, Nikan Firoozye and Philip Treleaven

Intelligent Systems in Accounting, Finance and Management, 2019, vol. 26, issue 2, 83-103

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.

Date: 2019
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