skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Modeling and predicting earnings per share via regression tree approaches in banking sector: Middle East and North African countries case

Investment management & financial innovations, 2020-05, Vol.17 (2), p.51-68 [Peer Reviewed Journal]

Jul 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1810-4967 ;EISSN: 1812-9358 ;DOI: 10.21511/imfi.17(2).2020.05

Full text available

Citations Cited by
  • Title:
    Modeling and predicting earnings per share via regression tree approaches in banking sector: Middle East and North African countries case
  • Author: Elamir, Elsayed A H
  • Subjects: Book value ; Earnings per share ; earnings performance ; forecasting ; investment decision ; machine learning ; predictive model ; risk management
  • Is Part Of: Investment management & financial innovations, 2020-05, Vol.17 (2), p.51-68
  • Description: The regression tree approach is an effective and easy to interpret technique where it utilizes a recursive binary partitioning algorithm that divides the sample into partitioning variables with the strongest correlation to the response variable. Earnings per share can be considered as one of the main factors in making the investment decision. This study aims to build a predictive model for earnings per share in the context of the Middle East and North African countries (MENA) . The sample of the study consists of sixty-three banks, which were chosen from eight countries, with a total of six-hundred thirty observations. The simple regression, regression tree, and its pruned regression tree, conditional inference tree, and cubist regression are used to build the predictive model for earnings per share that depends on total assets, total liability, bank book value, stock volatility, age of the bank, and net cash. The results show that the cubist regression is outperforming other approaches where it improves root mean square error for the predictive model by approximately double in comparison with other methods. More interesting results are obtained from the important scores, where it shows that the total assets of the bank, bank book value, and total liability have the biggest impact on the prediction of earnings per share. Also, the cubist regression gives an improvement in R-squared over other methods by at least 30% and 23% using training and testing data, respectively.
  • Publisher: Sumy: Business Perspectives Ltd
  • Language: English
  • Identifier: ISSN: 1810-4967
    EISSN: 1812-9358
    DOI: 10.21511/imfi.17(2).2020.05
  • Source: DOAJ Directory of Open Access Journals
    ProQuest Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait