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

Roman Matkovskyy; Taoufik Bouraoui

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

Roman Matkovskyy ¹ Taoufik Bouraoui ¹

Abstract : 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.

Keywords : Index of financial safety (IFS) Forecasting Nonlinear autoregressive with exogenous input (NARX) model Neural networks

Document type :

Journal articles

Domain :

Quantitative Finance [q-fin]

Complete list of metadatas

https://hal-rennes-sb.archives-ouvertes.fr/hal-02155402
Contributor : Isabelle Robert <>
Submitted on : Thursday, June 13, 2019 - 3:24:14 PM
Last modification on : Thursday, June 13, 2019 - 3:24:16 PM

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

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

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