No abstract

Data Envelopment Analysis (DEA) Approach is used to estimate the overall, pure technical and scale efficiencies for Malaysian commercial banks during the period 2000-2006. The results suggest that domestic banks were relatively more efficient than foreign banks. Our results also suggest that domestic banks' inefficiency were attributed to pure technical inefficiency rather than scale inefficiency. In contrast, foreign banks inefficiency is attributed to scale inefficiency rather than pure technical inefficiency. The study further examines whether the domestic and foreign banks are drawn from the same environment by performing a series of parametric and non-parametric tests. The results from the parametric and non-parametric tests suggest that for the years 2000-2004, both domestic and foreign banks possessed the same technology whereas results for 2005 and 2006 suggest otherwise. This implies that banks in recent years have had access to different and more efficient technology.

Globalization and technological advancement has created a highly competitive market in the banking and finance industry. Performance of the industry depends heavily on the accuracy of the decisions made at managerial level. This study uses multiple linear regression technique and feed forward artificial neural network in predicting bank performance. The study aims to predict bank performance using multiple linear regression and neural network. The study then evaluates the performance of the two techniques with a goal to find a powerful tool in predicting the bank performance. Data of thirteen banks for the period 2001-2006 was used in the study. ROA was used as a measure of bank performance, and hence is a dependent variable for the multiple linear regressions. Seven variables including liquidity, credit risk, cost to income ratio, size, concentration ratio, inflation and GDP were used as independent variables. Under supervised learning, the dependent variable, ROA was used as the target output for the artificial neural network. Seven inputs corresponding to seven predictor variables were used for pattern recognition at the training phase. Experimental results from the multiple linear regression show that two variables: credit risk and cost to income ratio are significant in determining the bank performance.  Two variables were found to explain about 60.9 percent of the total variation in the data with a mean square error (MSE) of 0.330. The artificial neural network was found to give optimal results by using thirteen hidden neurons. Testing results show that the seven inputs explain about 66.9 percent of the total variation in the data with a very low MSE of 0.00687. Performance of both methods is measured by mean square prediction error (MSPR) at the validation stage. The MSPR value for neural network is lower than the MPSR value for multiple linear regression (0.0061 against 0.6190). The study concludes that artificial neural network is the more powerful tool in predicting bank performance.

This study examines the relative efficiency levels of domestic and foreign commercial banks in Malaysia between 2000 and 2006, using accounting-based ratio, stochastic cost and profit frontier approach. Using accounting-based ratio, the results suggest that interest margin and operating cost are slightly higher for domestic banks than for foreign banks. Further, the results also suggest that profit ratios are slightly higher for foreign banks relative to domestic banks. Using the stochastic frontier approach, the results indicate that domestic banks are found to be more cost-efficient but less profit -efficient relative to foreign banks.

The partition of premium life tables of the monthly payment defines the ratio of the riders. The overall monthly fee for the Integration model is RM50 and the overall total is divided into different portions; savings, pension, death coverage, death benefit, hospital bills, loss of ability to work or critical illnesses. This new plan offers complete riders for two people in one product plan; participant and a child. The overall total of the portions in riders are 54. Therefore, each partition out of RM5 is RM0.0926. The model offers buying multiple units for the product business. If the participant buys more than 1 unit, the value of premium, riders, surrender value and maturity value will be multiplied by the numbers of units bought by the participant.