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A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting

A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting
A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting
The yield curve is the centrepiece in bond markets, a massive asset class with an overall size of USD 100 trillion that remains relatively under-investigated using machine learning. This paper is the first comprehensive study using artificial neural networks in the context of yield curve forecasting. Specifically, two models were used for forecasting the European yield curve: multivariate linear regression and multilayer perceptron (MLP), at five forecasting horizons, from next day to 20 days ahead. Five variants of the MLP were analysed with different sets of features: target to predict (univariate); the most relevant features; all generated features; and the former two incorporating synthetic data generated by the linear regression model. Additionally, two different techniques of multitask learning were employed: simultaneous modelling and transformation into multiple single task learning. The results show that considering all forecasting horizons, the MLP using the most relevant features achieved the best results and the addition of synthetic data tends to improve accuracy. Furthermore, different targets and forecasting horizons resulted in different relevant features, reinforcing the importance of custom-built models. In the two multitask learning methodologies no clear differentiation could be demonstrated, and several explaining factors are identified. Overall, the outcome is very encouraging for the development of better forecasting systems for fixed income markets.
machine learning, neural network, multitask learning, yield curve forecasting, yield forecasting, bond market
0957-4174
Nunes, Manuel
c4d739f5-2a58-400e-8578-8acbe383ac64
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
Niranjan, Mahesan
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Nunes, Manuel
c4d739f5-2a58-400e-8578-8acbe383ac64
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Nunes, Manuel, Gerding, Enrico, McGroarty, Frank and Niranjan, Mahesan (2018) A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting. Expert Systems with Applications. (doi:10.1016/j.eswa.2018.11.012).

Record type: Article

Abstract

The yield curve is the centrepiece in bond markets, a massive asset class with an overall size of USD 100 trillion that remains relatively under-investigated using machine learning. This paper is the first comprehensive study using artificial neural networks in the context of yield curve forecasting. Specifically, two models were used for forecasting the European yield curve: multivariate linear regression and multilayer perceptron (MLP), at five forecasting horizons, from next day to 20 days ahead. Five variants of the MLP were analysed with different sets of features: target to predict (univariate); the most relevant features; all generated features; and the former two incorporating synthetic data generated by the linear regression model. Additionally, two different techniques of multitask learning were employed: simultaneous modelling and transformation into multiple single task learning. The results show that considering all forecasting horizons, the MLP using the most relevant features achieved the best results and the addition of synthetic data tends to improve accuracy. Furthermore, different targets and forecasting horizons resulted in different relevant features, reinforcing the importance of custom-built models. In the two multitask learning methodologies no clear differentiation could be demonstrated, and several explaining factors are identified. Overall, the outcome is very encouraging for the development of better forecasting systems for fixed income markets.

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Submitted date: March 2018
Accepted/In Press date: 6 November 2018
e-pub ahead of print date: 6 November 2018
Keywords: machine learning, neural network, multitask learning, yield curve forecasting, yield forecasting, bond market

Identifiers

Local EPrints ID: 426054
URI: http://eprints.soton.ac.uk/id/eprint/426054
ISSN: 0957-4174
PURE UUID: 2a1a2415-81e5-4860-8e0f-4ce2a7910fec
ORCID for Manuel Nunes: ORCID iD orcid.org/0000-0002-7116-5502
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X
ORCID for Frank McGroarty: ORCID iD orcid.org/0000-0003-2962-0927

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Date deposited: 09 Nov 2018 17:30
Last modified: 11 Feb 2020 01:23

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