A derivatives trading recommendation system: The mid‐curve calendar spread case. Issue 2 (12th August 2019)

• Record Type: Journal Article
• Authors: Koshiyama, Adriano S.; Firoozye, Nikan; Treleaven, Philip
• Is Part Of: Intelligent systems in accounting, finance and management. Volume 26:Issue 2(2019)
• Page Start: 83
• Page End: 103
• Publication Date: 2019-08-12
• Subjects: derivatives -- machine learning -- trading recommendation system; Accounting -- Data processing -- Periodicals; Business -- Data processing -- Periodicals; Expert systems (Computer science) -- Periodicals; Artificial intelligence -- Periodicals; 657.028563
• DOI: 10.1002/isaf.1445
• Languages: English
• ISSNs: 1055-615X

Abstract

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.