Wimboh Santoso

Productivity changes and risk management in Indonesian banking: a Malmquist analysis

In this study, we utilize a nonparametric efficiency measurement approach which combines the Semi-Oriented Radial Measure–Data Envelopment Analysis (SORM–DEA) approach for dealing with negative data (Emrouznejad et al., 2010) with the Slacks-Based Efficiency Measures (SBM) of Tone (2001, 2002) to analyse productivity changes for Indonesian banks over the period Q1 2003 to Q2 2007. The first part of the analysis showed that average productivity changes for the Indonesian banking industry tended to be driven by technological progress rather than by frontier shift, although a relatively stable pattern was exhibited for most of the period. With respect to the risk management analysis, most of the balance sheet variables were shown to have had the expected impact on Risk Management Efficiency (RME), with the state-owned grouping exhibiting the highest degree of RME and the listed and Islamic banks outperforming their nonlisted and conventional bank counterparts, respectively. A strategy based on the gradual adoption of newer technology, with a particular focus on internal risk management enhancement, seems to offer the highest potential for boosting the productivity of the financial intermediary operations of Indonesian banks.

Coordinated failure? a cross-country bank failure prediction model

This paper empirically investigates the causes of bank failures in Japan and Indonesia. Using logistic regression analysis of financial ratios, we explore the usefulness of domestic bank failure prediction models with a cross-country model that allows for cross-correlation of the error terms. Our results suggest that loans, both as a ratio to total assets, deposits and in some cases the ratio of non-performing loans, are the most significant predictors of bank failure in both Japan and Indonesia. Regulatory capital ratios, on the contrary, do not seem to be significant indicators of failure. In addition to the domestic models, we explore the usefulness of a cross-country model of bank failure prediction and find that this model outperforms the domestic models on several diagnostic tests.