Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning

Predicting the earning manipulation is an inseparable part of financial-economic analysis, helping shareholders, investors, creditors and outsiders acquire high quality of firm�s financial information. Thus, the aim of the paper is to compare the earnings manipulation prediction models developed b...

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Main Authors: Rahman, R.A., Masrom, S., Zakaria, N.B., Nurdin, E., Abd Rahman, A.S.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29444 /
Published: IJETAE Publication House 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113970398&doi=10.46338%2fIJETAE0821_13&partnerID=40&md5=f9af28cad1877442642a708e62d3aa35
http://eprints.utp.edu.my/29444/
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spelling utp-eprints.294442022-03-25T02:06:55Z Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning Rahman, R.A. Masrom, S. Zakaria, N.B. Nurdin, E. Abd Rahman, A.S. Predicting the earning manipulation is an inseparable part of financial-economic analysis, helping shareholders, investors, creditors and outsiders acquire high quality of firm�s financial information. Thus, the aim of the paper is to compare the earnings manipulation prediction models developed by using two types of machine learning algorithms; linear and tree categories. The linear based machine learning are Logistic Regression and Generalized Linear Model while the tree based are Decision Tree and Random Forest. All of the algorithms were tested on dataset of earnings manipulation among 1874 firm-year observations of firms listed on Bursa Malaysia . The results indicate that the performances of the two kinds of machine learning is not extremely different except with the Decision Tree. Furthermore, the most outperformed algorithm has been presented by the linear based machine learning, which produced the best accuracy in the shortest total time completion. All the models present better ability in detecting the false cases of earnings manipulation rather than the true cases mainly from the tree based machine learning. © 2021 Sociedad Argentina de Genetica. All rights reserved. IJETAE Publication House 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113970398&doi=10.46338%2fIJETAE0821_13&partnerID=40&md5=f9af28cad1877442642a708e62d3aa35 Rahman, R.A. and Masrom, S. and Zakaria, N.B. and Nurdin, E. and Abd Rahman, A.S. (2021) Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning. International Journal of Emerging Technology and Advanced Engineering, 11 (8). pp. 111-120. http://eprints.utp.edu.my/29444/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Predicting the earning manipulation is an inseparable part of financial-economic analysis, helping shareholders, investors, creditors and outsiders acquire high quality of firm�s financial information. Thus, the aim of the paper is to compare the earnings manipulation prediction models developed by using two types of machine learning algorithms; linear and tree categories. The linear based machine learning are Logistic Regression and Generalized Linear Model while the tree based are Decision Tree and Random Forest. All of the algorithms were tested on dataset of earnings manipulation among 1874 firm-year observations of firms listed on Bursa Malaysia . The results indicate that the performances of the two kinds of machine learning is not extremely different except with the Decision Tree. Furthermore, the most outperformed algorithm has been presented by the linear based machine learning, which produced the best accuracy in the shortest total time completion. All the models present better ability in detecting the false cases of earnings manipulation rather than the true cases mainly from the tree based machine learning. © 2021 Sociedad Argentina de Genetica. All rights reserved.
format Article
author Rahman, R.A.
Masrom, S.
Zakaria, N.B.
Nurdin, E.
Abd Rahman, A.S.
spellingShingle Rahman, R.A.
Masrom, S.
Zakaria, N.B.
Nurdin, E.
Abd Rahman, A.S.
Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning
author_sort Rahman, R.A.
title Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning
title_short Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning
title_full Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning
title_fullStr Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning
title_full_unstemmed Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning
title_sort prediction of earnings manipulation on malaysian listed firms: a comparison between linear and tree-based machine learning
publisher IJETAE Publication House
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113970398&doi=10.46338%2fIJETAE0821_13&partnerID=40&md5=f9af28cad1877442642a708e62d3aa35
http://eprints.utp.edu.my/29444/
_version_ 1741197242591608832
score 11.62408

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