The objective of this study is to examine the determinants of Enterprise Risk Management (ERM) adoption in Malaysian Public Listed Companies (PLCs). The study focuses on ten industries from five hundred and seventy four Public Listed Companies in Malaysia for the period 2007. These ten industries include industrial products, trading/services, consumer products, properties, constructions, plantations, infrastructure projects, technology, hotels and mining. Logit regression approach will be employed, and a dummy variable equals one if companies adopt ERM and zero otherwise, is used as the dependent variable. Seven independent variables used are Size, Leverage, Profitability, International Diversification, Ownership, Chief Risk Officer and Turnover. The main results of this research is that companies with high turnover, hiring Chief Risk Officer and companies that are not diversified internationally are more likely to adopt ERM. Interestingly, Size, Leverage, Profitability, and Ownership are not significant determinants of ERM practices.

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 performance of Real Estate Investment Trusts (REITs) or listed property trusts in Malaysia using three standard performance measurement methods (Sharpe Index, Treynor Index and Jensen Index) for 1995 to 2005. In addition, it investigates the degree of systematic risks of REITs and to determine whether REITs give higher returns than the market portfolio. The results indicate that the risk-adjusted performance of REITs vary over time. REITs in general outperformed the market portfolio during the 1997-1998 financial crisis but underperformed in the pre-crisis (1995)(1996)(1997) and post-crisis period (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005). This study also found that the average systematic risks of REITs were slightly higher than the market portfolio during the pre-crisis and crisis period but were significantly lower in the post-crisis period.

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