바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Islamic Banking Ranking Efficiency Based on a Decision Tree in Iran

Asian Journal of Business Environment / Asian Journal of Business Environment, (P)2765-6934; (E)2765-7027
2014, v.4 no.2, pp.5-11
Mahdi Salehi (Ferdowsi University of Mashhad)
Jalil Khaksar (Islamic Azad University)
Elahe Torabi (Islamic Azad University)

Abstract

Purpose – This study attempts to examine Islamic banking practices in Iran based on new scientific methods. Design, methodology, and approach – The study used financial ratios demonstrating healthy or non-healthy banks to assess the financial health of banks listed on the Tehran Stock Exchange. The assessment of these ratios with a decision tree as a non-parametric method for modeling is recommended to present this model. Information about the financial health of banks could affect the decisions of different groups of banks’ financial report users including shareholders, auditors, stock exchanges, central banks, and so on. Results – The results of the study show that a decision tree is a strong approach for classifying Islamic banks in Iran. Conclusions – To date, several studies have been conducted in various countries on the topic of this study. Considering the importance of Islamic banking, this is one of the first studies in Iran the outcomes of the study may prove helpful to the Iranian economy.

keywords
Islamic Banking, Efficiency, Shariah, Iran

Reference

1.

Andrews, J.D., and Morgan, J.M. (1986). Application of the digraph method of fault tree construction to process plant. Reliability Engineering, 14(2), 85-106.

2.

Chen, C., Kang, C., and Sarrafzadeh, M, (2002). ActivitySensitive Clock Tree Construction for Low Power. proceedings of the international symposium on Low power electronics and design (pp.279-282). New York, NY:ACM.

3.

Gehrka, J., Ganti, V., Ramakrishnan, R., and Loh, W. Y. (1999). The Conversion Effects of Islamic Unit to Full Fledged System Islamic Banks in Indonesiamore. BOAT-Optimistic Decision Tree Construction, 28(2), 1-12.

4.

Gherka, J., Ramakrisnan, R., and Ganti, V. (2000). Rainforest-A Frame Work Fast Decision Tree Construction of Large Datasets. Data mining and Knowledge Discovery, 4, 127-162.

5.

Jin, R., and Agrawal, G. (2003). Efficient Decision Tree Construction on Streaming data. Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, New York, NY: ACM.

6.

Lawrence, R, L., and Wright, A, (2001). Rule-Based Classification Systems Using Classification and Regression Tree (CART) Analysis. Photogrammetric Engineering & Remote Sensing, 67(10), 1137-1142.

7.

Lin, C, W., Chen, S, Y., Li, S, F., Chang, Y. W., and Yang, C. L. (2007). Efficient Obstacle-Avoiding Rectilinear Steiner Tree Construction. Proceedings of the international symposium on Physical design (pp.18-21), Austin, Texas:DBLP.

8.

Zhou, Ken., Hou, Q., Wang, R., and Guo, B. (2008). Real-Time KD- Tree Construction on Graphics Hardware. ACM Transactions on Graphics Journal (TOG), 27(5), 1-12.

Asian Journal of Business Environment