Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Quantitative Economics Année : 2019

Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model

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Roman Matkovskyy
Taoufik Bouraoui
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Résumé

The aim of this paper is to extend the index of financial safety (IFS) approach with improving its predictive performance and to show the applicability of artificial neural networks to economic and financial short time series. To this end, prediction is performed by means of the nonlinear autoregressive with exogenous inputs (NARX) model that represents the neural networks and can emulate any nonlinear dynamic state space model. Thus, a NARX model, trained by means of Levenberg–Marquardt algorithm, was chosen since it gave the best performance. Results reveal that the NARX models are suitable for performing short time series composite indexes prediction.
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Dates et versions

hal-02155402 , version 1 (13-06-2019)

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Citer

Roman Matkovskyy, Taoufik Bouraoui. Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model. Journal of Quantitative Economics, 2019, 17 (2), pp.433-446. ⟨10.1007/s40953-018-0133-8⟩. ⟨hal-02155402⟩
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