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LSTM-LagLasso for bond yield forecasting: Peeping into the long short-term memory networks' black box

LSTM-LagLasso for bond yield forecasting: Peeping into the long short-term memory networks' black box
LSTM-LagLasso for bond yield forecasting: Peeping into the long short-term memory networks' black box
Modern decision-making in fixed income asset management benefits from intelligent systems, which involve the use of state-of-the-art machine learning models and appropriate methodologies. We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks, validating its potential and identifying its memory advantage. Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons. We compare those with multilayer perceptrons (MLP), univariate and with the most relevant features. To demystify the notion of black box associated with LSTMs, we conduct the first internal study of the model. To this end, we calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures, uni and multivariate. We then proceed to explain the states’ signals using exogenous information, for what we develop the LSTM-LagLasso methodology. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using macroeconomic and market information. Furthermore, shorter forecasting horizons require smaller input sequences and vice-versa. The most remarkable property found consistently in the LSTM signals, is the activation/deactivation of units through time, and the specialisation of units by yield range or feature. Those signals are complex but can be explained by exogenous variables. Additionally, some of the relevant features identified via LSTM-LagLasso are not commonly used in forecasting models. In conclusion, our work validates the potential of LSTMs and methodologies for bonds, providing additional tools for financial practitioners.
Nunes, Manuel Clemente Mendonca
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Gerding, Enrico
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McGroarty, Frank
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Niranjan, Mahesan
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Nunes, Manuel Clemente Mendonca
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 Clemente Mendonca, Gerding, Enrico, McGroarty, Frank and Niranjan, Mahesan (2020) LSTM-LagLasso for bond yield forecasting: Peeping into the long short-term memory networks' black box. Workshop on Advancing Machine Learning in Finance, Insurance and Economics, Cass Business School and University of Glasgow, London, United Kingdom. 17 Jan 2020. (doi:10.13140/RG.2.2.10212.53129).

Record type: Conference or Workshop Item (Paper)

Abstract

Modern decision-making in fixed income asset management benefits from intelligent systems, which involve the use of state-of-the-art machine learning models and appropriate methodologies. We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks, validating its potential and identifying its memory advantage. Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons. We compare those with multilayer perceptrons (MLP), univariate and with the most relevant features. To demystify the notion of black box associated with LSTMs, we conduct the first internal study of the model. To this end, we calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures, uni and multivariate. We then proceed to explain the states’ signals using exogenous information, for what we develop the LSTM-LagLasso methodology. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using macroeconomic and market information. Furthermore, shorter forecasting horizons require smaller input sequences and vice-versa. The most remarkable property found consistently in the LSTM signals, is the activation/deactivation of units through time, and the specialisation of units by yield range or feature. Those signals are complex but can be explained by exogenous variables. Additionally, some of the relevant features identified via LSTM-LagLasso are not commonly used in forecasting models. In conclusion, our work validates the potential of LSTMs and methodologies for bonds, providing additional tools for financial practitioners.

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More information

e-pub ahead of print date: 17 January 2020
Venue - Dates: Workshop on Advancing Machine Learning in Finance, Insurance and Economics, Cass Business School and University of Glasgow, London, United Kingdom, 2020-01-17 - 2020-01-17

Identifiers

Local EPrints ID: 444707
URI: http://eprints.soton.ac.uk/id/eprint/444707
PURE UUID: bc5a6aff-c10f-40fb-9f4b-4d428eeef3c5
ORCID for Manuel Clemente Mendonca 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: 30 Oct 2020 17:31
Last modified: 30 Oct 2020 17:31

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Contributors

Author: Manuel Clemente Mendonca Nunes ORCID iD
Author: Enrico Gerding ORCID iD
Author: Frank McGroarty ORCID iD
Author: Mahesan Niranjan

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