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Classification of capital expenditures and revenue expenditures: An analysis of correlation and neural networks


Siraj, Fadzilah and Abu Bakar, Nurazzah and Abolgasim, Adnan (2009) Classification of capital expenditures and revenue expenditures: An analysis of correlation and neural networks. In: International Conference on Computing and Informatics 2009 (ICOCI09), 24-25 June 2009, Legend Hotel, Kuala Lumpur.

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Abstract

The classification between the capital expenditures and revenue expenditures is one of the common problems in the accounting literature since it has a significant impact on financial statements.This study aims to analyze the correlation of classification model such as Neural Networks (NN) in order to develop a model that can be trained to recognize hidden patterns of the borderline between the two expenditures types, viz: the capital and revenue expenditure.Twelve criterions were identified in order to classify between the two expenditures types and a Backpropagation Learning was utilized in this study.The highest classification accuracy obtained by NN is 94.20%. Correlation analysis reveals a significant correlation between some identified criterions with the model’s target.Strong correlation between target and criterion LASMFY (0.532) indicates that any expenditure lasts for more than a fiscal year will be more probable to be classified into a capital expenditure.Also, criterion RESALE proves its strong influence, with correlation of (-0.874) which implies more probability of classification into revenue expenditure if any expenditure was spent for intent for resale. Medium correlation shown by criterion REGULR (-0.251) indicates a moderate probability of classification into revenue expenditure if expenditure was spent in a regular basis.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN 978-983--44150-2-0 Organized by: UUM College of Arts and Sciences, Universiti Utara Malaysia.
Uncontrolled Keywords: Capital Expenditure, Revenue Expenditure, Classification, Regression, Neural Network
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Prof Madya Fadzilah Siraj
Date Deposited: 07 Apr 2015 02:07
Last Modified: 07 Apr 2015 02:07
URI: http://repo.uum.edu.my/id/eprint/13573

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