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The memory advantage of long short-term memory networks for bond yield forecasting

The memory advantage of long short-term memory networks for bond yield forecasting
The memory advantage of long short-term memory networks for bond yield forecasting
The importance of bond markets in the financial industry stems from its dimension, its direct relevance for other asset classes and for the overall economy. In this paper, we conduct the first study of bond yield forecasting using deep learning long short-term memory (LSTM) networks, validating the potential of LSTMs networks for that purpose, and identifying the LSTM's memory advantage over standard feedforward neural networks, in particular, the multilayer perceptron (MLP). Specifically, we model the 10-year Euro government bond yield using univariate LSTMs with different input sequences (6, 21 and 61 time steps), considering five forecasting horizons, from next day to 20 days ahead. We compare those LSTM models with MLPs, both univariate as well as using the most relevant features for each forecasting horizon. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using information from markets and the economy. Moreover, the direct comparison of models in identical conditions, i.e. small input sequence of 5 time steps, leads to results with LSTMs that are similar or better with lower standard deviations. Furthermore, with the LSTMs, shorter forecasting horizons require smaller input sequences and vice-versa. In summary, the results are encouraging for its use incorporating macroeconomic and market information, adjusting the input sequence length to the forecasting horizon considered.
Nunes, Manuel Clemente Mendonca
c4d739f5-2a58-400e-8578-8acbe383ac64
Gerding, Enrico
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McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
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 (2019) The memory advantage of long short-term memory networks for bond yield forecasting. International Conference on Forecasting Financial Markets, Venice, Italy, Ca' Foscari University of Venice, Venice, Italy. 19 - 21 Jun 2019. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

The importance of bond markets in the financial industry stems from its dimension, its direct relevance for other asset classes and for the overall economy. In this paper, we conduct the first study of bond yield forecasting using deep learning long short-term memory (LSTM) networks, validating the potential of LSTMs networks for that purpose, and identifying the LSTM's memory advantage over standard feedforward neural networks, in particular, the multilayer perceptron (MLP). Specifically, we model the 10-year Euro government bond yield using univariate LSTMs with different input sequences (6, 21 and 61 time steps), considering five forecasting horizons, from next day to 20 days ahead. We compare those LSTM models with MLPs, both univariate as well as using the most relevant features for each forecasting horizon. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using information from markets and the economy. Moreover, the direct comparison of models in identical conditions, i.e. small input sequence of 5 time steps, leads to results with LSTMs that are similar or better with lower standard deviations. Furthermore, with the LSTMs, shorter forecasting horizons require smaller input sequences and vice-versa. In summary, the results are encouraging for its use incorporating macroeconomic and market information, adjusting the input sequence length to the forecasting horizon considered.

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More information

Accepted/In Press date: 20 June 2019
Venue - Dates: International Conference on Forecasting Financial Markets, Venice, Italy, Ca' Foscari University of Venice, Venice, Italy, 2019-06-19 - 2019-06-21

Identifiers

Local EPrints ID: 444710
URI: http://eprints.soton.ac.uk/id/eprint/444710
PURE UUID: 0a443eb6-c63b-4fb8-93fd-e8d493439ba9
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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