The existence of Islamic banks that grow rapidly in every corner of the world has caused strong competition among them and other conventional banks in Malaysia. This requires the banks management to choose wisely on the determinants that make them to remain strong and relevant in the Islamic banking sector. The main purpose of this study is to determine internal and external significant determinants for Islamic banks in Malaysia using Principal Component Analysis (PCA). The Kaiser-Meyer-Olkin (KMO) was applied in this study to measure the data sampling is acceptable and adequate. While, and Bartlett’s Test is used to validate the internal and external component. The significant banking determinants selected for the study include deposit ratio, operating efficiency and market concentration. The result shows that the internal and external significant determinants are Bank Size and Gross Domestic Product. The study utilized secondary data from year 2010 until year 2016 for 13 Islamic banks in Malaysia.
Abidin, S. N. Z., & Jaffar, M. M. (2014). Forecasting share prices of small size companies in bursa Malaysia using geometric Brownian motion. Applied Mathematics & Information Sciences, 8(1), 107.
Assare-Kumi, A. A., Darkwah, K. A., Nortey, E. N. N., & Chapman-Wardy, C. (2016). Variable Reduction and Determinants of Working Capital Management on Profits for Ghanaian Banks. European Scientific Journal, 12(7), 316-330.
Blyth, K. & Kaka, A. (2006). A novel multiple linear regression model for forecasting S-curves. Engineering, Construction and Architectural Management, 13(1), 82-95.
Ichsani, S., & Suhardi, A. R. (2015). The Effect of Return on Equity (ROE) and Return on Investment (ROI) on Trading Volume. Procedia-Social and Behavioral Sciences, 211, 896-902.
Ioni??, I., & ?chiopu, D. (2010). Using Principal Component Analysis in Loan Granting. The Bulletin of Petroleum-Gas University of Ploiesti, 62(1/2010), 88–96.
Jolliffe, I. (2002). Principal component analysis. John Wiley & Sons, Ltd.
Karamizadeh, S., Abdullah, S. M., Manaf, A. A., Zamani, M., & Hooman, A. (2013). An overview of principal component analysis. Journal of Signal and Information Processing, 4(03), 173.
Mohd Alias, L. (2011). Introductory Business Forecasting: A Practical Approach 3rd Edition (3rd ed.). Shah Alam, Malaysia: Universiti Teknologi MARA Press.
Pallant, J. (2011). SPSS Survival Manual: A step by step guide to data analysis using SPSS (4th ed.). Crows Nest, New South Wales: Allen & Unwin.
Robu, I. B., & Istrate, C. (2015). The analysis of the principal components of the financial reporting in the case of Romanian listed companies. Procedia Economics and Finance, 20, 553–561.
Wong, J. M. W., Chan, A. P. C., & Chiang, Y. H. (2011). Construction manpower demand forecasting: A comparative study of univariate time series, multiple regression and econometric modelling techniques. Engineering, Construction and Architectural Management, 18(1), 7-29.
Wuensch, K. L. (2012). Principal Components Analysis - SPSS. Retrieved from http://core.ecu.edu/psyc/wuenschk/MV/FA/PCA-SPSS.docx
In-Text Citation: (Ismail, Shahri, & Basir, 2018)
To Cite this Article: Ismail, R., Shahri, N. H. N. M., & Basir, N. L. (2018). Selecting The Most Significant Determinants that contributes to Islamic banks profitability using Principal Component Analysis. International Journal of Academic Research in Business and Social Sciences, 8(12), 923–931.
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