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The Simulation of US Consumer Credit Fluctuation Using Artificial Neural Networks

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Abstract.
The present paper shows the discussion and results of the research that simulated the fluctuation of
the US Consumer Credit (CONS) using Artificial Neural Network (ANN). The research had several
objectives, like: building, training and using an ANN as a possible tool for decision making, through
the simulation of the US Consumer Credit. The condition for a successful training of the ANN was
established as a smaller difference than 1.5% between the real data and the simulated data. A feed
forward artificial neural network and a back propagation algorithm were used for the training and
preparation of future use of the ANN. For the training result, two testing sessions were used. For the
use of ANN in CONS forecasting, the research was extended with the simulation of CONS trend using
trained ANN and a new set of consecutive values for each of the input data. Also, the new
simulations determined a hierarchy of the inputs that were considered for the simulations of the
CONS. In the conclusion, the researchers consider the ANN training and testing a success due to the
values obtained: a difference of [-0.69; 0.32] % between the real and simulated CONS values. The
trend simulation also shows the training success with accuracy smaller than 1.5%. The authors
consider that the research can be extended to other countries or by adding others indicators.

Keywords: Consumer credit outstanding, Artificial Neural Network (ANN), simulation, trend.

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Ketersediaan

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Informasi Detail

Judul Seri

Communications of the IBIMA

No. Panggil

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Penerbit

: IBIMA Publishing., 2012

Deskripsi Fisik

Communications of the IBIMA. Vol. 2012 (2012), Article ID 525995, 20 pages

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

Vol. 2012 (2012)

Subjek

EKONOMI KEUANGAN

Info Detail Spesifik

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Pernyataan Tanggungjawab

agus

Versi lain/terkait

Lampiran Berkas

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