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

Koshiyama Adriano S. | Zendy; Firoozye Nikan | Zendy; Treleaven Philip | Zendy

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

Author(s) -

Koshiyama Adriano S.,

Firoozye Nikan,

Treleaven Philip

Publication year - 2019

Publication title -

intelligent systems in accounting, finance and management

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.846

H-Index - 11

eISSN - 2160-0074

pISSN - 1550-1949

DOI - 10.1002/isaf.1445

Subject(s) - interpretability , benchmark (surveying) , lasso (programming language) , computer science , econometrics , derivative (finance) , point (geometry) , machine learning , economics , mathematics , financial economics , geography , cartography , world wide web , geometry

Summary 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.

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