A Machine Learning-based Recommendation System for Swaptions Strategies
This work addresses a specific need for derivative traders by providing a recommendation system for exotic swaption strategies, though it appears incremental as it applies existing methods to a new financial domain.
The paper tackled the problem of derivative traders needing to scan numerous trades daily by developing a machine learning-based recommendation system for Mid-Curve Calendar Spread (MCCS) swaption strategies, finding that linear regression with lasso regularization performed best among 11 predictive models and 4 benchmarks.
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), an exotic swaption-based derivatives package. In summary, our trading recommendation system follows this pipeline: (i) on a certain trade date, we compute metrics and sensitivities related to an MCCS; (ii) these metrics are feed in a model that can predict its expected return for a given holding period; and after repeating (i) and (ii) for all trades we (iii) rank the trades using some dominance criteria. 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 in general linear regression with lasso regularisation compared favourably to other approaches from a predictive and interpretability perspective.