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Forecasting Cash Money Withdrawals Using Wavelet Analysis and Wavelet Neural Networks

Zapranis, Achilleas, Alexandridis, Antonis (2009) Forecasting Cash Money Withdrawals Using Wavelet Analysis and Wavelet Neural Networks. International Journal of Financial Economics and Econometrics, . ISSN 0975-2072. (KAR id:29260)

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Abstract

The increasing demand for easily accessible cash drives banks to expand their Automatic

while the operating costs rise significantly. Cash demand needs to be forecasted accurately so

paper is motivated by the Neural Network Association and the NN5 competition. The

money withdrawals in different ATMs. More precisely, the data consists of 2 years of daily

England. The only available information is the total cash withdrawals in each ATM at the end

use wavelet analysis to extract the dynamics of the underlying process of each ATM. Next

and to forecast the cash money demand up to 56 day ahead. The performance of the proposed

technique is evaluated using various error and fitting criteria.

Item Type: Article
Uncontrolled keywords: Cash Money Withdrawals, Modeling, Pricing, Forecasting, Wavelet Networks
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Social Sciences > Kent Business School > Accounting and Finance
Depositing User: Antonis Alexandridis
Date Deposited: 04 Apr 2012 12:07 UTC
Last Modified: 29 May 2019 08:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/29260 (The current URI for this page, for reference purposes)
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