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Power and Diversification Strategy on Banking Stability: Evidence from

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As a major source of the bank's income, interest margins must be kept at a certain level to maintain profits and at the same time dampen bank stability. Following the concept of credit rationing and the moral hazard model (Stiglitz & Weiss, 1981), Boyd and De Nicolo (2005) assume that borrowers' risk is perfectly correlated with the risk of the bank's default. Meanwhile, during the financial crisis in the US, it is noted that the probability of bank defaults is lower for banks, which offered purely fee-based non-interest income such as brokerage fee and insurance commission.

Using the European banking data to investigate the impact of the diversification of non-interest income on bank risk, Mercieca et al. 2007), for example, suggests that the high dependence of small-sized banks on non-traditional businesses may be associated with the banks' higher risk and lower performance. The impact of diversification on bank performance is determined by the risk level of the bank. The market and bank concentration ratios (HHI and CRn) are exogenous indicators of market power.

It is assumed that the types of bank ownership are also a factor that can determine the stability of the banking system (Berger et al., 2004). The current study also includes host country dummy as a way to capture the effect of the national bank's operating location (Indonesia, Malaysia, Thailand and the Philippines) on bank stability. Modifications of the Herfindahl Hirschman Index (HHI) to measure diversification by business sector (SFOC) and by type of facilities (TFOC).

Adjustments to the Herfindahl Hirschman Index (HHI) to measure income diversification between interest income and non-interest income (RFOC), and diversification within non-traditional activities that generate non-interest income (NFOC).

Table 1 shows the summary of variables used in this research.
Table 1 shows the summary of variables used in this research.

Results and Analysis 1 Descriptive Statistics

These results are consistent with the findings of Fu et al. 2014), which focused on banking stability in several countries in the Asia Pacific region. Thus, it can be concluded that the increased MP in ASEAN-4 has an impact on improving banking stability. Expertise is required to screen credit proposals and this usually leads to a higher degree of profitability and bank stability (Acharya, 2006).

Commissions and fee income (FIE) generated from the sale of non-interest income products are positively correlated with bank stability. This study also tested the combined effect of the following on bank stability: (a) market power (MP) and diversification strategy in business sector loans (SFOC); (b) market power (MP) and revenue diversification strategy (RFOC); (c) interaction of NIM and market power (MP); (d) the combined effect of NIM and diversifications in loans (SFOC and TFOC); and (e) interaction of NIM and revenue diversification (RFOC and NFOC). Bank stability is primarily influenced by market power rather than exploitation of market power to generate income from non-interest income.

The joint effects between MP and SFOC and NIM and MP do not significantly affect the stability of banking. However, the interaction between NIM and SFOC shows negative stability, indicating that banks that are less diversified (focused) on business sector loans (SFOC) have lower bank stability. The combined effect of NIM and banks' focus on specific types of loans (TFOC) can enhance bank stability, while the combined effect of NIM and banks' focus on specific types of income (RFOC) can cause bank instability.

Bank-specific variables that have a significant relationship with banking stability are the size of the bank's assets (Size Assets) and cost efficiency (EFF). In comparison, loan portfolio (LTA), bank liquidity (LIQ) and credit risk (CRISK) have no significant effect on banking stability in ASEAN-4. This study also finds that bank efficiency (total costs/total income) is positively and significantly related to banking stability.

The results show that these variables are not associated with bank stability (see Uhde & Heimeshoff, 2009;. Despite these results, it is observed that the relationship between foreign ownership, state ownership and bank stability is not significant. not shown in the tables, but available at query), where the year 2006 was used as the base year, it is observed that the period between 2011 and 2012 was more stable than 2006; bank stability in 2008 was significantly lower than in 2006.

Overall, the results indicate a consistent relationship between competitive measures (HHI and CR3) and banking stability (Z-score), as well as the relationship between NIM, measuring diversifications, bank-specific variables, macroeconomic variables and stability of the banking system. From these factors, it can be inferred that the dynamic competitive structure in ASEAN-4 has a different effect on the stability of the banking industry.

Table 2:  Descriptive Statistics
Table 2: Descriptive Statistics

Conclusions

In addition, the results in this study could be caused by the different competitive structures of the banking industry in ASEAN-4 compared to the same industry in developed countries. It appears that structural competition in the banking sector in the ASEAN-4 is very dynamic; it changes over time. These factors include the deregulation of the banking sector, their privatization efforts, advanced information technology in the banking sector and the internationalization of the financial capital markets.

Other factors that contribute to increased bank stability are market power, diversification of non-interest income and focus (less diversified) on certain types of loans as well as foreign bank penetration. When non-interest income variables are decomposed, the commissions and fee income will contribute positively to an increased bank stability in the long term. However, income from trading activities increases bank risk, suggesting that the banking industry in ASEAN-4 requires experts to manage these trading products.

Banking consolidation exercises such as mergers and acquisitions increase the market power of individual banks in the region. Increased market power in the ASEAN-4 improves the stability of the banking industry; this is consistent with the “fragility of competition” view (Beck et al., 2013, Keeley, 1990). However, this study could not prove that there is a non-linear relationship between the degree of competition and banking stability.

Moreover, foreign bank penetration, as predicted by some banking literature in emerging countries (Yeyati & Micco, 2007; Allen, Jackowicz, Kowalewski, & Kozlowski, 2017), has already improved banking stability in the ASEAN-4. Another specific banking variable that positively influences the stability of the banking system is efficiency: an inefficient bank is not equal to a high-risk bank. This can be solved by encouraging better competition among the players in the banking sector.

At the same time, the banks' microeconomic conditions, such as banking efficiency, must be improved. By combining the effect of interest margin, market power and income diversification and loan portfolio diversification on bank stability, especially for the ASEAN-4 market, this study has shown that bank stability can be achieved. First, this study did not specifically examine whether banking diversification increases during the crisis period or decreases banking stability in the ASEAN-4.

Second, this study applied common practices in measuring banking stability, namely, the Z-score, to measure the distance from default. Income structure, profitability and risk in the European banking sector: The impact of the crisis.

Gambar

Table 1 shows the summary of variables used in this research.
Table 2 presents the descriptive statistics drawn from the analysis. The  statistics indicate that the average bank interest margin of ASEAN-4  between 2006-2012, is relatively high i.e., 3.45 per cent (rounded up to  two decimal points)
Table 2:  Descriptive Statistics
Table 3: Summary Determinant of Z Score, Market Power Lerner Index                             Static Model                             Dynamic Model 12345678 Z Score (-1)0.6855***0.6248***0.6537***0.7036*** (0.000)(0.000)(0.000)(0.000) NIM161.3350***130.4
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