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

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