A Machine Learning-based Recommendation System for Swaptions Strategies

TL;DR

This paper presents a machine learning-based recommendation system for swaption strategies, specifically applied to MCCS, which predicts trade returns and ranks trades to assist derivative traders in decision-making.

Contribution

It introduces a novel trading recommendation pipeline for MCCS using multiple predictive models, highlighting the effectiveness of linear regression with lasso regularization.

Findings

Linear regression with lasso outperformed other models in prediction accuracy.

The system successfully ranked trades based on predicted returns.

The approach demonstrated feasibility across 35 MCCS types.

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), 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.