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Mid-Curve Recommendation System: A Stacking Approach Through Neural Networks

Koshiyama, A; Firoozye, N; Treleaven, P; (2018) Mid-Curve Recommendation System: A Stacking Approach Through Neural Networks. In: Vellasco, Marley and Estevez, Pablo, (eds.) Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). IEEE: Piscataway, NJ, USA. Green open access

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Abstract

Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily-basis; a concrete case is the so-called Mid-Curve Calendar Spread (MCCS). The actual procedure in place is full of pitfalls and a more systematic approach where more information at hand is crossed and aggregated to find good trading picks can be highly useful and undoubtedly increase the trader's productivity. Therefore, in this work we propose an MCCS Recommendation System based on a stacking approach through Neural Networks. In order to suggest that such approach is methodologically and computationally feasible, we used a list of 15 different types of US Dollar MCCSs regarding expiration, forward and swap tenure. For each MCCS, we used 10 years of historical data ranging weekly from Sep/06 to Sep/16. Then, we started the modelling stage by: (i) fitting the base learners using as the input sensitivity metrics linked with the MCCS at time t, and its subsequent annualized returns as the output; (ii) feeding the prediction from each base model to a particular stacker; and (iii) making predictions and comparing different modelling methodologies by a set of performance metrics and benchmarks. After establishing a backtesting engine and setting performance metrics, our results suggest that our proposed Neural Network stacker compared favourably to other combination procedures.

Type: Proceedings paper
Title: Mid-Curve Recommendation System: A Stacking Approach Through Neural Networks
Event: 2018 International Joint Conference on Neural Networks (IJCNN), 8-13 July 2018, Rio de Janeiro, Brazil
ISBN-13: 978150906015-3
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/IJCNN.2018.8489229
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Predictive models, Neural networks, Stacking, Computational modeling, Sensitivity, Instruments
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10075177
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