Analysis of Financial Stability Determinants in Indonesia

ABSTRACT

Financial Stress Index (FSI) is one of the indices to measure financial stress which can lead to a financial crisis. Quantitative analysis was conducted to some banking sector performance indicator which impacts financial stability with FSI as a proxy. Data population was taken from banking company listed in Indonesian Stock Exchange, sampling using purposive sampling of 38 banks. Using pooled data regression analysis was founded that NPL, CAR, and ROA positively significant to financial stability, while NIM negative but not significant to financial stability. The research found that NPL and NIM are not in line with the hypothesis. NPL is an indicator for bad debt, which means that increase in NPL will make financial stability vulnerable, but the research shows that an increase in NPL causes financial stability incline to increased, this could have happened if any other factors maintain financial stability tend to increase. On the other hand, NIM is decreasing which means the productivity of banks decreased but financial stability tends to increase because other factors that maintain financial stability tend to increase.

INTRODUCTION
Financial Stability is very important for all countries because it is related to the effectiveness of the market economy function. A stable condition in the financial system becomes mandatory for rational decision making to allocate resources and improve the investment climate for any country (Crockett, 1997).
Andrew D. Crockett (Crockett, 1997) proposed financial stability refers to the smooth functioning of the markets that create the financial system. McFarlane (1999) describes financial stability as avoidance of financial crisis, which financial crisis is a more modern term to describes banking panics, bank runs, and banking collapse. Schinasi (2010) defining financial stability as the ability to facilitate and improve economic processes, risk risks and absorb shocks, financial stability is considered a continuum that may change over time and are consistent with some combination of the constituent elements of finance.
Financial stability is a condition that should be maintained to [1] creates a trustworthy and supportive environment for customers and investors to invest in financial institutions; [2] encouraging efficient financial intermediation; [3] encourage market operations and improve resource allocation in the economy. ISSN : P 2720-9938 E 2721-5202 Macfarlane (MacFarlane, 1999) and Anwar Nasution (2003)  Safe and reliable payment system. Financial Crisis will occur because it is triggered by various risks originating from these elements.
Since the 1970s Indonesia has already experienced a lot of financial crises (Hadi Soesastro, 2001;Pangestu & Habir, 2003), but the most impactful crisis are the Asian Financial Crisis of 1997 until 1998 and the Global Financial Crisis within the year 2008 to 2009 (Basri & Rahardja, 2011;Zhuang & Dowling, 2010).
For Indonesia, the financial crisis causes decreasing economic growth, a rise in fiscal cost, and rising unemployment and poverty rates, and significant social costs. The most severe impact was social and political chaotics which was happened within the year 1998 (L. Smith, 2003).
Following the crisis and the contagion evident around the region, The Asian Development Bank (ADB) uses their knowledge to help monitor the financial recovery and report objectively on potential vulnerabilities and policy solutions. Financial Stress Index (FSI) is being used by ADB to monitor the recovery by measuring the degree of financial stress in four financial markets within the Asia Region. Figure 1 shows the Financial Stress Index (FSI) for The Asia Region. Figure 1. Asia Financial Stress Index (Source: Asian Development Bank, 2020) Some policymakers and academic researchers have been focusing on some quantitative measures to measure financial stability. Financial Stress Index (FSI) which was developed by The Asian Development Bank (ADB) measures the degree of financial stress in four financial markets-banks, foreign exchange, equity, bonds. The methodology for computation was developed by Park and Mecardo (2013) which computed using measures for 4 major financial sectors with the equation presented as follows: Where, β is a measure of banking stress which measures the ratio of bank share prices to total share prices given by: where r is the returns to the banking sector stock price index and m is the overall stock price index. If β is larger than 1, then the banking sector is relatively risky because the volatility of returns on bank shares is greater than the volatility of returns for the overall market. The higher β, the greater the banking sector's stress.
StockReturns is a measure of Equity Market Returns given by: Where y t is the current period's equity return and y (t-1) is the previous period's equity returns.

Stockvolatility is a measure of Equity Market
Volatility which follow GARCH (1,1) process and given by: Where  2 refers to the variance, and ε the error term in the regression is given by: Where y t is the current period's equity return and y (t−1) is the previous period's equity returns.
EMPI represents a currency crisis which is defined as periods of significant devaluations, losses in foreign exchange reserves, and/or defensive interest rate hikes. The EMPI captures the depreciation of the local currency against US dollar and the reduction in foreign exchange reserves. It is given by: where ∆e and ∆RES denote month-on-month percent changes in the foreign exchange rate of local currency per US dollar and foreign exchange reserves. While σ and μ are standard deviation and mean, respectively. Non-Performing Loan (NPL) is a measure of the ability of a bank to resist the risk of credit default by debtors (Gunadi, Taruna, & Harun, 2013). From the debtor's point of view, Mudrajad Kuncoro & Suhardjono (2002) mentioned that NPL is a condition of debtors are unable to pay part or all of their obligations to the bank as committed in the contract. Bank of Indonesia defined NPL as follows: Capital Adequacy Ratio (CAR) is a Capital ratio to measure the health of a bank which indicates the adequacy of capital owned by the bank (Gunadi et al., 2013). With the increase in its capital, the health of a bank related to the capital ratio is increasing. This indicator reflects the level of bank resilience from the internal side (pressure) as it relates to bank liquidity. CAR is calculated based on the ratio between the capital owned by the bank and the number of Risk-Weighted Assets (ATMR), where ATMR is the total value of each bank's asset after being multiplied by the respective risk weightings for these assets. Assets that are least risky are assigned a weight of 0% and most risky assets are assigned a weight of 100%.
Steps to calculate Capital Adequacy Ratio (CAR) is described as follows (Dendawijaya, 2009): a. ATMR of balance sheet assets are calculated by multiplying the nominal value of the respective assets with the risk weight of each item on the balance sheet assets; b. ATMR of administrative assets are calculated by multiplying the nominal value of the administrative account concerned with the risk weight of each account item; c. Total ATMR = (ATMR of Balance sheet assets) + (ATMR of administrative assets d. The bank capital ratio is calculated by comparing the bank capital (core capital + supplementary capital) and the total ATMR. The ratio can be formulated as follows Return on Asset (ROA) is a measure of the effectiveness of banking in generating profits by utilizing its assets. The greater the ROA, the better the banking performance (Gunadi et al., 2013). The higher the ROA, the more stable the condition of the banking financial system. ROA is given by: Net Interest Margin (NIM) is a ratio between net interest income to average earning assets, it measures a bank's ability to earn net interest income compared to the amount of credit delivered.
The ratio illustrates the level of the amount of net interest income earned by using the productive assets owned by the bank (Achmad & Kusumo, 2003). Taswan (2009)

METODE
This research used a quantitative research approach, where the theoretical framework, ideas from experts, and understanding from the researcher are developed based on previous research. Analysis of data using pooled data analysis. Secondary data was taken from The Asian Development Bank and published financial report derived from the Indonesian Stock Exchange from the second semester of 2015 until the second semester of 2019 (semi-annual) of 38 listed banks in the Indonesian Stock Exchange. The framework for this research is shown in Figure 2. Where α is a constant, t is period, i is the entity of independent variable, and e is variable from outside of the model.

RESULTS AND DISCUSSION
Pooled data regression estimation in this researched aimed to predict regression model parameters including constant (α) and regression coefficient (β). Widarjono (2007) explained that a pooled data estimation model performed using the approach of three models, i.e. Where α is a constant, β is the regressor, and e is the error. The index i and t are company index and period, respectively.  The form of panel data regression equation is given by: Where w it = u i + e it , u and e are individual error and combination error between time series and crosssection respectively. H 1 : there is a random effect, choose Random Effect Model (p<0.05) Error! Reference source not found. shows the result of the LM Test where the probability for both of cross-section and period is 0.0000 which is less than 0.05 (<0.05) which means that H 0 is rejected, thus The Random Effect Model is selected as the best model.

Multicollinearity Test
A multicollinearity test is performed to examines any correlation between the independent variables because in a good panel regression model there should be no correlation between the independent variables (Ghozali, 2006).  Table 6 and Table 7 show the result of The multicollinearity test. Table 6 shows that the maximum value of the correlation matrix is 0.636115 which is less than 10 indicate that there is no significant multicollinearity within the independent variables. While in Table 7 shows that Centered VIF values are less than 10, which means that there is no multicollinearity within the independent variables. Coefficient of determination (R 2 ) Variance in the dependent variable caused by the independent variable must be analyzed. This paper is using Adjusted R-square for analysis because more than one independent variable is involved. Table 8 shows the Coefficient of determination (R 2 ) of the model, which is 0.020382 or 2.0382% which means that independent variables explain the dependent variable about 2.0382%, while the rest is caused by another variable outside the model.

F-Test
F-Test performs to evaluate the significance of all independent altogether of the regression model. The hypothesis of this test as follows: H 0 : β 1 = β 2 = β 3 = β 4 = 0, independent variables together have no effect on the dependent variable

t-Test
A t-test was conducted to examine the significance of the independent variables toward the dependent variable individually. Table 9 shows that the independent variable NPL, CAR, and ROA have probability 0.0157, 0.0105, 0.0279 respectively which are less than 0.05 (>0.05), which means that NPL, CAR, and ROA affect FSI significantly. On the other hand, NIM has a probability of 0.1804 which is more than 0.05 (>0.05) which means that NIM not affecting the dependent variable.

Analysis
The analysis in this paper gives some result as follows: a. Non-Performing Loan (NPL) is significantly positive affecting the Financial Stress Index (FSI), which means that any increases in NPL will reduce financial stability. b. Capital Adequacy Ratio (CAR) is significantly positive affecting the Financial Stress Index (FSI), which means that any increases in CAR will reduce financial stability. c. Return on Asset (ROA) is significantly positive affecting the Financial Stress Index (FSI), which means that any increases in CAR will also increase financial stability. d. Net Interest Margin (NIM) does not significantly affect the Financial Stress Index (FSI) which means that any changes within NIM will not affect financial stability significantly.

CONCLUSION
Financial Stress Index (FSI) is an index that has been used by The Asian Development Bank (ADB) to measures the degree of financial stress in four financial markets-banks, foreign exchange, equity, bonds all Asian countries. The index is representing the financial stability condition of all countries within the Asian region. In the banking market, performance indicators i.e. Non-Performing Loan (NPL), Capital Adequacy Ratio (CAR), Return on Asset (ROA), and Net Interest Margin (NIM) examined in this paper for their relation with the Financial Stress Index (FSI) as a proxy of financial stability.
Non-Performing Loan (NPL), Capital Adequacy Ratio (CAR), Return on Asset (ROA), and Net Interest Margin (NIM) are altogether affecting Financial Stress Index (FSI) which represents financial stability. The analysis result shows that Non-Performing Loan (NPL), Capital Adequacy Ratio (CAR), Return on Asset (ROA) are significantly affecting Financial Stress Index (FSI) which means that any changes in NPL, CAR, and ROA will be affecting financial stability, on the other hand, the Net Interest Margin (NIM) not affecting financial stability. NPL, CAR, and ROA are significantly positive affecting financial stability while NIM is negatively affecting financial stability.
This paper concluded that NPL, CAR, and ROA can be used as variables that affect financial stability, which implies that the indicators must be controlled and managed by financial institutions to prevent financial instability.
The research implies that some banking performance indicators can be used as an early warning indicator for financial stability from the banking sector. All countries can use their banking sector performance indicators to detect financial stress which may cause vulnerability in financial stability.
This paper has limited independent variables involved for analysis which was derived from the banking sector. In the context of financial stability, there are lots of performance indicators of the banking sector that can be involved which may cause financial stability vulnerability. Further research can be conducted using another banking performance indicator.
Financial Stress Index (FSI) is an index developed by The Asian Development Bank (ADB). Since 1997, financial stability had become a concern for many countries in the world, thus many indicators or indices were developed, therefore those indicators or indices can be compared to each other to examine the best indicator or index that could be used for a country.