No abstract

The failure of companies to remain profit incurred gradually over several years. Market value of a company under financial distress will reduce; as suppliers prefer cash basis payment on the delivery terms and this may cause a cancellation of order from the customer since the anticipated items would not be delivered on time. Several factors can lead to the failure of a company and determinants of financial distress are important to the company, bankers, investors, the asset manager and rating agencies. Early signs of financial distress can help the manager to take preventive actions to save the company from falling prey to distress. Any economic agent that has any interest with the company namely shareholders, managers, employees, bankers and clients will be affected with the company's failure. Therefore, the main objective of this study is to determine financial distress among the companies Practice Note 17 (PN17) listed in Bursa Malaysia by using the Altman Z-Score Model as a proxy to financial distress. Panel data from 18 companies listed in PN17, Bursa Malaysia for a period of eight (8) years, from 2009 to 2016 were analysed using Fixed Effects Model. This research used the Financial Statement from specific variables that are not used in Altman Z-Score model as potential determinants financial distress. The findings indicate that leverage and profitability are significant determinants of financial distress.

This paper provides new empirical evidence of the bank stability in relation to the macroeconomic indicator of Indonesia. The bank stability is first calculated using Z-score, and then regressed using Autoregressive distributive lag (ARDL) model on the macroeconomic variables i.e. Gross Domestic Product (GDP) in US dollar, Interest rates (IR) in percentage and Consumer Price Index (CPI). To analyse further the long run relationship and the impact of bank stability, Cholesky standard deviation shock to the model, ARDL and Impulse Response Function (IRF) are used. These ARDL and IRF are carried out independently and repeated over data for three different models: (i) the commercial banks model, (ii) Islamic banks model, and (iii) the overall banking industry model. The empirical findings suggest long run relationship between the stability of commercial banks and macroeconomic factors. The findings also suggest the long run relationship between the stability of overall banking industry and macroeconomic factors. However, there is no evidence of long run relationship between the stability of Islamic banks and macroeconomics factors. Nevertheless, this finding is subject to the limitation of data, on the number of Islamic banks included in the test. The sample of Islamic banks was 5 banks from a total of 10 Islamic banks, due to insufficient data, as compared to the larger number of commercial banks taken into, as the sample.

The present study provides new empirical evidence of bank stability measures for 50 banks in Malaysia, for a period from 1999 to 2015. There are two methods of measuring bank stability that is using Z-score and CAMELS variables. After calculating, these variables are ranked, with the highest average is ranked as one, and the lowest average is ranked last, or fifty. This is following the method by (Roman and Procedia Economics and Finance, 6(13), 703-712 Roman and Şargu 2013) and (Procedia-Social and Behavioral Sciences, 24, 1530-1545Dincer et al. 2011) and extending it by introducing the average of total ranking for all variables. The empirical findings suggest that both local Islamic and conventional banks are ranked favourable in overall average bank stability score, sensitivity to market risk, asset quality, earning and profitability, but local conventional banks are recorded favourable ranking in liquidity. Comparing the two types of local banks, conventional banks are ranked better in liquidity, sensitivity to market risk and earning and profitability.