Research Article: 2018 Vol: 17 Issue: 2

**Muhammad Akbar, Universitas Padjadjaran**

**Dian Masyita, Universitas Padjadjaran **

**Erie Febrian, Universitas Padjadjaran**

**Herry Achmad Buchory, Universitas Padjadjaran**

Macroeconomics Factor, Capital Structure, Liquidity, Performance of Foreign Banks, CAR, ROA, ROE, NIM.

**Research Background**

Foreign banks group in Indonesia were under pressure throughout 2015 as their larger loan portion was distributed to corporations rather than to the retail segment. In fact, corporations are less expansive throughout the year due to the economic slowdown and the weakening of commodity prices. The bank's net profit slumped for the first time since 2012 which continued to record positive growth. Based on statistics from the Indonesian Financial Services Authority, throughout the eleven months of 2015, the net profit of foreign bank group in Indonesia slumped by 30.16% compared to the same period in 2014.

The business model of the branches of foreign bank group in principle consists of two major parts of the investment banking business and the conventional banking business. Investment banking business such as JP Morgan Chase Bank. While conventional banking business such as Citibank NA, Bank of Tokyo Mitsubishi UFJ Ltd., etc. Bank of Tokyo Mitsubishi UFJ Ltd. posted the highest profit growth of 262.39% to IDR 395 billion as of February 2017 and the largest loss was recorded by JP Morgan Chase Bank with a net loss of IDR 2.7 billion. Based on the intermediary function, Bank of Tokyo Mitsubishi UFJ Ltd. became the largest credit provider, amounting to IDR 90.98 trillion, followed by HSBC for IDR 46.5 trillion and Citibank NA IDR 38.14 trillion. Based on monthly financial report data of February 2017, total foreign banks posted net profit of IDR 1.51 trillion, up 1.95% from the same period in 2016. However, net interest income fell 0.32% to IDR 2.96 trillion.

Viewed from capital structure, foreign banks generally have strong capital structure which is well above the national banking average of 22.91% per position in December 2016, only Standard Chartered Bank has a minimal CAR compared to other Foreign Banks. The low value of the company is allegedly due to the company's less financial performance in the last five years. This is indicated by the low financial performance measured by one of the financial ratios of Return on Assets (ROA). There are foreign banks whose performance tends to decline and even lose. But in general the financial performance of the company tends to be stable. Foreign banks tend to be conservative in conducted the improvement of strategies.

The condition above allegedly caused due to the aspect of liquidity. Commercial banks are one financial institution that has a vital role in the nation’s economy, especially for countries which its economy is still very dependent on the presence of banks as a source of financing of its economic activities. In the macroeconomic order, the bank is a transmission belt that transmits monetary policy, while in micro-economic order, banks are a source of financing for both business and individual (Koch & Mac Donald, 2000). So that the role of banks in the fulfilment of liquidity for business and individuals is vital as well make banks very vulnerable to liquidity risk.

Refer to Diamond & Dybvig (1983); Rauch et al. (2008), one of the main reasons why banks are particularly vulnerable to liquidity risk is their role in transforming maturities and providing guarantees in order to meet the liquidity needs of their depositors. This resulted in bank liquidity being suddenly depleted and the difficulties of liquidity in a bank may spread to other banks, resulting a systemic risk as described above and there are only a few studies devoted to analysing one of the major factors to make bank as a secure and trustworthy institution when there is an economic shock.

Based on this background, it is interesting to examine the effect of macroeconomics factor, capital structure and liquidity on the performance of foreign banks in Indonesia.

**Research Objective**

The objective of this study is to examine the effect of macroeconomics factor, capital structure and liquidity on the performance of foreign bank in Indonesia.

**Liquidity**

Liquidity can be defined as the ability of financial institutions to fulfil all their obligations related to the demand for funds (Yeager & Seitz, 1989; Gitman, 2009). This opinion is also in line with the definition of liquidity proposed by Sauer (2007); Williamson (2008); Bank for International Settlements (2008); Moore (2009), namely the ability of banks to fund the increase in assets and meet the obligations that have matured without experiencing an unacceptable loss. For that bank needs to keep the liquid assets to meet the obligations of its customers or tend to be precautionary (precautionary). If the bank does not have sources of funds in meeting its customers' demand, the bank must borrow to the interbank money market or central bank.

Refer to Farag, Harland & Nixon (2013), the source of bank liquidity consists of cash or assets that can be converted into cash within a short time at a reasonable cost. A slightly different opinion is expressed by Myers & Rajan (1998) where liquidity is described as the ease of converting assets into other assets through trade. So that liquidity can also be interpreted as a convenience in converting assets into money used in the trading process.

Based on those definition, the liquidity used in this study is in accordance with the definition from Bank for International Settlements (BIS), namely as the ability of banks to fund the increase in assets and meet its obligations without causing harm. Because the definition proposed by BIS has become the reference of the banking in the world and also very comprehensive and includes various definitions that have been put forward by previous researchers. In this research, liquidity is measured by the dimension of loan to deposit ratio.

**Foreign Bank Performance**

According to Owolabi, Obiakor & Okwu (2011); Vodova (2011), the bank's performance is associated with profitability as measured by the amount of revenue generated by a firm that exceeds the relevant costs associated with generating that income. Lartey, Antwi & Boadi (2013) define profitability as the ability of banks in generating revenue far greater than the cost required.

There are some proxies that used by the previous researcher, Anbar & Alper (2011) measuring profitability using Return on Assets (ROA) and Return on Equity (ROE) as a function of the determinant factors of specific variables of banks and macroeconomics. Saleem & Rehman (2011) use ROA, ROE and Return on Investment (ROI) as proxy of profitability, where liquidity gives significant impact to ROA but not significant to ROE and ROI. Alshatti (2015) also uses the same proxy of ROE and ROA as proxy of profitability, where its research finds that there is the influence of liquidity to bank profitability indicated by ROE and bank ROA.

Hahn & Powers (2010) examined the performance of banks by using Return on Assets (ROA) because ROA is a primary measure of the performance of banking industry (FDIC, 1995). ROA is one form of ROI, where the use of this measure is consistent with Porter's suggestion (1980 & 1985) where ROI is an appropriate performance measure. Based on previous research, ROA is defined as the net income divided by total assets (Lenz, 1980; Robinson & Pearce, 1988; Bernstein, 1993). On the other hand Al-Tamimi & Jabnoun (2010) measure the performance of banks with ROA and ROE.

Based on the description above, the performance of foreign banks in this study is measured by dimensions of:

1. CAR (Capital Adequacy Ratio)

2. ROA (Return on Asset)

3. ROE (Return on Equity)

4. NIM (Net Interest Margin)

**Hypotheses**

Based on the description above, the hypothesis is proposed as follow:

*H: Macroeconomic Factor, Capital Structure and Liquidity effect on Performance (CAR, ROA, ROE, NIM) either simultaneously or partially.*

This study uses a quantitative method approach to achieve the purpose and to answer the question of the research as well as to examine the hypothesis. This study also uses a dynamic panel data analysis based on the frame of model data panel.

The type of data used is secondary data, i.e., data/information of foreign banks listed on Financial Service Authority period 2007-2016, sourced from OJK and BI. Meanwhile, the data collected is bank liquidity and performance.

The unit of analysis is restricted to foreign Bank who listed on OJK. The population in this study is foreign banks listed on Financial Service Authority period 2007-2016, as many as 10 banks (cross-section), where the periodization of financial statements is determined for 10 years i.e., 2007-2016 (time series). So the data obtained is a combination of cross section data and time series called as panel data. The panel data structure is expected to provide more information. The periodization of data is determined for 10 years (2007-2016), among others, to meet the requirements of data analysis and to represent the population taken.

The design of the analysis to be used in this study is the regression for panel data. Panel data regression is a regression analysis that combines time series data with a cross section, where the same cross section unit is measured at different times.

In this section will be described the results of hypothesis testing on the effect of Macroeconomic, Capital Structure and Liquidity to the Performance of Foreign Banks (**Table 1**). The performance of Foreign Banks is measured by CAR, ROA, ROE and NIM.

Table 1: Recapitulation Of The Effect Of Macroeconomic, Capital Structure, Liquidity On Foreign Bank Performance |
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Variable | Indicator | Foreign Bank Performance | |||
---|---|---|---|---|---|

CAR | ROA | ROE | NIM | ||

MacroEconomicFactor |
BI RATE | 1491.904* | 167.870* | 787.974* | 280.525* |

INFLATION | 0.045 | 0.06 | 0.106 | 0.031 | |

EXCHANGE RATE | 0 | -0.001* | -0.003* | 0.000* | |

INTERBANK OVERNIGHT (O/N) RATE | -0.458* | -0.167 | -17.350* | -0.432* | |

Capital Structure |
DTA | -116.119* | -1.346 | 11.364 | 6.152* |

DTE | -0.018* | -0.001* | -0.008* | 0 | |

DPKTE | 0.025 | 0.003* | 0.013* | 0 | |

Method |
Random Effect | Random Effect | Random Effect | Random Effect | |

F Test |
10.832 | 16.866 | 17.917 | 20.342 | |

(p-value=0.00) | (p-value=0.00) | (p-value=0.00) | (p-value=0.00) | ||

R^{2} |
3.15625 | 3.921527778 | 4.024305556 | 4.236805556 |

**Macroeconomic, Capital Structure & Liquidity to Car**

**Model of Common (Pool) Effect or Fixed Effect**

The test is done by Chow-Test with hypothesis:

*H _{0}: Model uses common effect model.*

*H _{1}: Model uses fixed effect model.*

The calculation results Prob < α (0.05), so that can be concluded that H_{1} is accepted, so the model used in this study is fixed effect model (**Table 2**).

Table 2: Result Of Chow Test Of Hypotesis 1a |
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Hypothesis | F count | Prob | Conclusion |
---|---|---|---|

Hypotesis 1a | 6.761311 | 0.000 | H0 rejected; Fixed Effect |

The next process is selecting best panel model that still need to continue with Hausman Test to find out whether the model of panel data follows fixed effect model or random effect model.

**Model of Fixed Effect or Random Effect**

The test is done by Hausman test with hypothesis:

*H _{0}: Model uses random effect model.*

*H _{1}: Model uses fixed effect model.*

Based on the above **Table 3** it is known that p value>α (0.05), so that H_{0} is accepted, then it can be concluded that the data more precisely to use random effect model.

Table 3: Result Of Hausman Test Of Hypotesis 1a |
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Hypothesis | Statistics Test Χ^{2} |
Prob | Conclusion |
---|---|---|---|

Hypothesis 1a | 0.0000 | 1.0000 | H_{0} acceptedRandom Effect |

**Model of Common Effect or Random Effect**

The test done by Hausman test with hypothesis:

*H _{0}: Model uses common effect model.*

*H _{1}: Model uses random effect model.*

Based on the above **Table 4 **it is known that p value>α (0.05), so that *H _{0}* is rejected, then it can be concluded that the data more precisely to use random effect model.

Table 4: Result Of Lagrange Multiplier (Lm) Test Of Hypothesis 1a |
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Hypothesis | Statistics Lagrange Multiplier (LM) | Prob. | Conclusion |
---|---|---|---|

Hypothesis 1a | 30.87070 | 0.0000 | H_{0} rejectedRandom Effect |

The test results in **Table 5 **of Econometric Model are:

CAR_{it}=41.96339+968.9789BIRATE_{it}+0.657810INFL_{it}-0.000332EXCH_{it}+1.318105ONINT_{it}-87.57441DTA_{it}-0.020530DTE_{it}+0.028914DPKTE_{it}+70.96147LP_{it}-35.74618LI_{it}+e10_{it}

Table 5: Result Of Random Effect Estimation Of Hypothesis 1a |
||||

Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|

C | 41.96339 | 39.32665 | 1.067047 | 0.2888 |

BIRATE | 968.9789 | 354.9687 | 2.729759 | 0.0076 |

INFLATION | 0.657810 | 0.970286 | 0.677956 | 0.4996 |

EXCHANGE RATE | -0.000332 | 0.001343 | -0.247051 | 0.8054 |

INTERBANK OVERNIGHT (O/N) RATE | 1.318105 | 0.507298 | 2.598285 | 0.0109 |

DTA | -87.57441 | 34.12119 | -2.566569 | 0.0119 |

DTE | -0.020530 | 0.006439 | -3.188469 | 0.0020 |

DPKTE | 0.028914 | 0.012298 | 2.351205 | 0.0209 |

LP | 70.96147 | 25.13792 | 2.822885 | 0.0059 |

LI | -35.74618 | 34.39478 | -1.039291 | 0.3015 |

Effects Specification | S.D. | Rho | ||

Cross-section random | 8.726276 | 0.1550 | ||

Idiosyncratic random | 20.37397 | 0.8450 | ||

Weighted Statistics | ||||

R-squared | 0.478403 | Mean dependent var | 29.34253 | |

Adjusted R-squared | 0.425658 | S.D. dependent var | 29.43847 | |

S.E. of regression | 22.31518 | Sum squared resid | 44319.10 | |

F-statistic | 9.069989 | Durbin-Watson stat | 0.891789 | |

Prob(F-statistic) | 0.000000 |

The regression equation above is in line with the hypothesis proposed that the increasing of macroeconomics factors and capital structure as well as liquidity will improve CAR (Performance).

**Simultaneous Hypothesis (1)**

H_{0}: β_{31}=β_{32}=β_{33}...β_{37}=0; there is no effect of macroeconomics factor and capital structure as well as liquidity on CAR.

H_{1}: At least there is β_{ij} ? 0; there is the effect of macroeconomics factor and capital structure as well as liquidity on CAR.

The result of testing in **Table 6 **shows that there is the simultaneous effect of macroeconomics factor and capital structure as well as liquidity on CAR, with the value of R^{2} resulted from the model is 47.84%.

Table 6: Simultaneous Testing Of Hypothesis 1a |
|||

Hypothesis |
F-statistic |
Prob(F-statistic) |
Description |

Hypothesis 1a | 9.069989 | 0.000* | H0 rejected |

*Significant at a=0.05

**Partial Hypothesis**

Partially only BIRATE, Interbank Overnight (O/N) Rate, DTA, DTE, DPKTE and LP which have a significant effect on CAR (**Table 7**).

Table 7: Partial Testing Of Hypothesis 1a |
||||

Hypothesis | ?_{ij} |
t-Statistic | Prob | Description |
---|---|---|---|---|

BIRATE | 968.9789 | 2.729759 | 0.0076 | Significant |

INFLATION | 0.657810 | 0.677956 | 0.4996 | Not Significant |

EXCHANGE RATE | -0.000332 | -0.247051 | 0.8054 | Not Significant |

INTERBANK OVERNIGHT (O/N) RATE | 1.318105 | 2.598285 | 0.0109 | Significant |

DTA | -87.57441 | -2.566569 | 0.0119 | Significant |

DTE | -0.020530 | -3.188469 | 0.0020 | Significant |

DPKTE | 0.028914 | 2.351205 | 0.0209 | Significant |

LP | 70.96147 | 2.822885 | 0.0059 | Significant |

LI | -35.74618 | -1.039291 | 0.3015 | Not Significant |

**Macroeconomic, Capital Structure & Liquidity to ROA**

**Model of Common (Pool) Effect or Fixed Effect**

The test is done by Chow-Test with hypothesis:

H_{0}: Model uses common effect model.

H_{1}: Model uses fixed effect model.

The calculation results Prob < α (0.05), so that can be concluded that H_{1} is accepted, so the model used in this study is fixed effect model (**Table 8**).

Table 8: Result Of Chow Test Of Hypothesis 1b |
|||

Hypothesis | F count | Prob | Conclusion |
---|---|---|---|

Hypothesis 1b | 9.239678 | 0.0000 | H0 rejected; Fixed Effect |

**Model of Fixed Effect or Random Effect**

The test is done by Hausman test with hypothesis:

Table 9: Result Of Hausman Test Of Hypotesis 1b |
|||

Hypothesis | Statistic Uji Χ^{2} |
Prob | Conclusion |
---|---|---|---|

Hypothesis 1b | 0.0000 | 1.0000 | H_{0} acceptedRandom Effect |

H_{0}: Model uses random effect model.

H_{1}: Model uses fixed effect model.

Table 10: Result Of Lagrange Multiplier (Lm) Test Of Hypothesis 1b |
|||

Hypothesis | Statistic Lagrange Multiplier (LM) | Prob | Conclusion |
---|---|---|---|

Hypothesis 1b | 72.69979 | 0.0000 | H_{0} rejectedRandom Effect |

Based on the above **Table 10**, it is known that p value<α (0.05) so that H_{0} is rejected, it can be concluded that the data more precisely to use random effect model.

The test results in **Table 11 **of Econometric Model are:

Table 11: Result Of Random Effect Estimation Of Hypothesis 1b |
|||||

Variable | Coefficient | Std. Error | t-Statistic | Prob. | |
---|---|---|---|---|---|

C | -2.755719 | 3.163573 | -0.871078 | 0.3861 | |

BIRATE | 182.1704 | 30.01244 | 6.069828 | 0.0000 | |

INFLATION | 0.023615 | 0.082482 | 0.286307 | 0.7753 | |

EXCHANGE RATE | -0.000486 | 0.000114 | -4.248783 | 0.0001 | |

INTERBANK OVERNIGHT (O/N) RATE | -0.233849 | 0.212903 | -1.098386 | 0.2750 | |

DTA | 0.323598 | 2.666179 | 0.121371 | 0.9037 | |

DTE | -0.001567 | 0.000545 | -2.876799 | 0.0050 | |

DPKTE | 0.002760 | 0.001042 | 2.648363 | 0.0096 | |

LP | -3.620585 | 1.548350 | 2.338351 | 0.0215 | |

LI | -1.248571 | 2.766482 | -0.451321 | 0.6529 | |

Effects Specification | S.D. | Rho | |||

Cross-section random | 0.576867 | 0.0994 | |||

Idiosyncratic random | 1.736276 | 0.9006 | |||

Weighted Statistics | |||||

R-squared | 0.520387 | Mean dependent var | 2.491038 | ||

Adjusted R-squared | 0.471887 | S.D. dependent var | 2.802090 | ||

S.E. of regression | 2.039267 | Sum squared resid | 370.1161 | ||

F-statistic | 10.72959 | Durbin-Watson stat | 0.940878 | ||

Prob(F-statistic) | 0.000000 |

ROAit=-2.755719+182.1704BIRATEit+0.023615INFLit-0.000486EXCHit-0.233849ONINTit +0.323598DTAit-0.001567DTEit+0.002760DPKTEit-3.620585LPit-1.248571LIit+e13it

The regression equation above is in line with the hypothesis proposed that the increasing of macroeconomics factors and capital structure as well as liquidity will improve ROA (performance).

Simultaneous Hypothesis (2)

H_{0}: β_{61}=β_{62}=β_{63}...β_{67}=0; there is no effect of macroeconomics factor and capital structure as well as liquidity on ROA.

H_{1}: At least there is β_{ij}≠ there is the effect of macroeconomics factor and capital structure as well as liquidity on ROA.

The result in **Table 12 **of testing shows that simultaneously there is the effect of macroeconomic factor and capital structure as well as liquidity on ROA, with the value of R^{2} resulted from the model is 52.04%.

Table 12: Simultaneous Testing Of Hypothesis 1b |
|||

Hypothesis | F-statistic | Prob.(F-statistic) | Description |
---|---|---|---|

Hypothesis 1b | 10.72959 | 0.0000* | H0 rejected |

*Significant at a=0.05

Partial Hypothesis

Partially only BIRATE, Exchange Rate, DTE, DPKTE and LP which have a significant effect on ROA (**Table 13**).

Table 13: Partial Testing Of Hypothesis 1b |
||||

Hypothesis | ?_{ij} |
t-Statistic | Prob | Description |
---|---|---|---|---|

BIRATE | 182.1704 | 6.069828 | 0.0000 | Significant |

INFLATION | 0.023615 | 0.286307 | 0.7753 | Not Significant |

EXCHANGE RATE | -0.000486 | -4.248783 | 0.0001 | Significant |

INTERBANK OVERNIGHT (O/N) RATE | -0.233849 | -1.098386 | 0.2750 | Not Significant |

DTA | 0.323598 | 0.121371 | 0.9037 | Not Significant |

DTE | -0.001567 | -2.876799 | 0.0050 | Significant |

DPKTE | 0.002760 | 2.648363 | 0.0096 | Significant |

LP | -3.620585 | 2.338351 | 0.0215 | Significant |

LI | -1.248571 | -0.451321 | 0.6529 | Not significant |

**Macroeconomic, Capital Structure & Liquidity to ROE**

**Model of Common (Pool) Effect or Fixed Effect**

The test is done by Chow-Test with hypothesis:

*H _{0}: Model uses common effect model.*

*H _{1}: Model uses fixed effect model.*

The calculation results Prob < α (0.05), so that can be concluded that *H _{1}* is accepted, so the model used in this study is fixed effect model (

Table 14: Result Of Chow Test Of Hypothesis 1c |
|||

Hypothesis |
F count |
Prob |
Conclusion |

Hypothesis 1c | 12.258481 | 0.0000 | H0 rejected; Fixed Effect |

The next process is selecting best panel model that still need to continue with Hausman Test to find out whether the model of panel data follows fixed effect model or random effect model.

**Model of Fixed Effect or Random Effect**

The test is done by Hausman test with hypothesis:

*H _{0}: Model uses random effect model.*

*H _{1}: Model uses fixed effect model.*

Based on the above **Table 15 **it is known that p value>α (0.05), so that H_{0} is accepted, then it can be concluded that the data more precisely to use random effect model.

Table 15: Result Of Hausman Test Of Hypotesis 1c |
|||

Hypothesis |
Statistic Uji χ^{2} |
Prob |
Conclusion |

Hypothesis 1c | 0.0000 | 1.0000 | H_{0} acceptedRandom Effect |

**Model of Common Effect or Random Effect**

The test done by Hausman test with hypothesis:

*H _{0}*: Model uses common effect model.

*H _{1}*: Model uses Random effect model.

Based on the above **Table 16 **it is known that p value<α (0.05) so that H_{0} is rejected, it can be concluded that the data more precisely to use random effect model.

Table 16: Result Of Lagrange Multiplier (Lm) Test Of Hypothesis 1c |
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Hypothesis | Statistic Lagrange Multiplier (LM) | Prob. | Conclusion |
---|---|---|---|

Hypothesis 1c | 96.91325 | 0.0000 | H0 rejected Random Effect |

The test results in **Table 17 **of Econometric Model are:

Table 17: Result Of Random Effect Estimation Of Hypothesis 1c |
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Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|

C | -5.372930 | 18.59716 | -0.288911 | 0.7733 |

BIRATE | 905.5274 | 140.4776 | 6.446061 | 0.0000 |

INFLATION | -0.151358 | 0.376486 | -0.402027 | 0.6886 |

EXCHANGE RATE | -0.002490 | 0.000520 | -4.791602 | 0.0000 |

INTERBANK OVERNIGHT (O/N) RATE | -2.401873 | 0.978840 | -2.453796 | 0.0161 |

DTA | 0.662717 | 17.30920 | 0.038287 | 0.9695 |

DTE | -0.006850 | 0.002530 | -2.706904 | 0.0081 |

DPKTE | 0.011674 | 0.004813 | 2.425675 | 0.0173 |

LP | -26.07934 | 10.88442 | -2.396025 | 0.0187 |

LI | -20.50477 | 15.52283 | -1.320943 | 0.1899 |

Effects Specification | S.D. | Rho | ||

Cross-section random | 7.910843 | 0.5042 | ||

Idiosyncratic random | 7.844646 | 0.4958 | ||

Weighted Statistics | ||||

R-squared | 0.599487 | Mean dependent var | 4.100622 | |

Adjusted R-squared | 0.558985 | S.D. dependent var | 12.03884 | |

S.E. of regression | 7.997622 | Sum squared resid | 5692.615 | |

F-statistic | 14.80165 | Durbin-Watson stat | 1.175829 | |

Prob(F-statistic) | 0.000000 |

*ROE _{it}=-5.372930+905.5274BIRATE_{it}-0.151358INFL_{it}-0.002490EXCH_{it}-0.401873ONINT_{it} +0.662717DTA_{it}-0.006850DTE_{it}+0.011674DPKTE_{it}-26.07934LP_{it}-20.50477LI_{it}+e14_{it}*

The regression equation above is in line with the hypothesis proposed that the increasing of macroeconomics factors and capital structure as well as liquidity will improve ROE (Performance).

Simultaneous Hypothesis (3)

*H _{0}: β_{71}=β_{72}=β_{73}...β_{77}=0; there is no effect of macroeconomics factor and capital structure as well as liquidity on ROE.*

H_{1}: At least there is β_{ij} ≠ 0; there is the effect of macroeconomics factor and capital structure as well as liquidity on ROE.

The result in **Table 18 **of testing shows that simultaneously there is the effect of macroeconomic factor and capital structure as well as liquidity on ROE, with the value of R^{2} resulted from the model is amounted to 59.95%.

Table 18: P Simultaneous Testing Of Hypothesis 1c |
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Hypothesis | F-statistic | Prob(F-statistic) | Description |
---|---|---|---|

Hypothesis 1c | 14.80165 | 0.0000* | H0 rejectes |

*Significant at a=0.05

Partially only BIRATE, Exchange Rate, Interbank Overnight (O/N) Rate, DTE, DPKTE and LP which have a significant effect on ROE (**Table 19**).

Table 19: Partial Testing Of Hypothesis 1c |
||||

Hypothesis | ?ij | t-Statistic | Prob | Description |
---|---|---|---|---|

BIRATE | 905.5274 | 6.446061 | 0.0000 | Significant |

INFLATION | -0.151358 | -0.402027 | 0.6886 | Not Significant |

EXCHANGE RATE | -0.002490 | -4.791602 | 0.0000 | Significant |

INTERBANK OVERNIGHT (O/N) RATE | -2.401873 | -2.453796 | 0.0161 | Significant |

DTA | 0.662717 | 0.038287 | 0.9695 | Not Significant |

DTE | -0.006850 | -2.706904 | 0.0081 | Significant |

DPKTE | 0.011674 | 2.425675 | 0.0173 | Significant |

LP | -26.07934 | -2.396025 | 0.0187 | Significant |

LI | -20.50477 | -1.320943 | 0.1899 | Not Significant |

**Macroeconomic, Capital Structure & Liquidity to NIM**

**Model of Common (Pool) Effect or Fixed Effect**

The test is done by Chow-Test with hypothesis:

*H _{0}*: Model uses common effect model.

H_{1}: Model uses fixed effect model.

The calculation results Prob < α (0.05) so that can be concluded that H_{1} is accepted, so the model used in this study is fixed effect model (**Table 20**).

Table 20: Result Of Chow Test Of Hypothesis 1d |
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Hypothesis | F hitung | Prob | Conclusion |
---|---|---|---|

Hypothesis 1d | 6.991251 | 0.0000 | H0 rejected; Fixed Effect |

The next process is selecting best panel model that still need to continue with Hausman Test to find out whether the model of panel data follows fixed effect model or random effect model.

**Model of Fixed Effect or Random Effect**

The test is done by Hausman test with hypothesis:

*H _{0}*: Model uses random effect model.

H_{1}:Model uses fixed effect model.

Based on the above **Table 21 **it is known that p value>α (0.05) so that *H _{0}* is accepted, then it can be concluded that the data more precisely to use random effect model.

Table 21: Result Of Hausman Test Of Hypotesis 1d |
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Hypothesis | Statistik Uji χ^{2} |
Prob | Conclusion |
---|---|---|---|

Hypothesis 1d | 0.0000 | 1.0000 | H0 accepted Random Effect |

**Model of Common Effect or Random Effect**

The test done by Hausman test with hypothesis:

*H _{0}: Model uses common effect model.*

*H _{1}: Model uses random effect model.*

Based on the above **Table 22 **it is known that p value<α (0.05) so that *H _{0}* is rejected, then it can be concluded that the data more precisely to use random effect model.

Table 22: Result Of Lagrange Multiplier (Lm) Test Of Hypothesis 1d |
|||

Hypothesis |
Statistic Lagrange Multiplier (LM) |
Prob |
Conclusion |
---|---|---|---|

Hypothesis 1d | 29.62981 | 0.0000 | H_{0} rejectedRandom Effect |

*NIM _{it}=-13.48899+272.9219BIRATE_{it}+0.062627INFL_{it}-0.000346EXCH_{it}-0.382705ONINT_{it} +4.187919DTA_{it}-0.000113DTE_{it}+0.000276DPKTE_{it}-2.731567LP_{it}+2.071330LI_{it}+e15_{it}*

The regression equation above is in line with the hypothesis proposed that the increasing of macroeconomics factors and capital structure as well as liquidity will improve NIM (Kinerja)**Table 23**.

Table 23: Result Of Random Effect Estimation Of Hypothesis 1d |
||||

Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|

C | -13.48899 | 4.160675 | -3.242019 | 0.0017 |

BIRATE | 272.9219 | 36.63059 | 7.450656 | 0.0000 |

INFLATION | 0.062627 | 0.099858 | 0.627159 | 0.5322 |

EXCHANGE RATE | -0.000346 | 0.000138 | -2.505321 | 0.0141 |

INTERBANK OVERNIGHT (O/N) RATE | -0.382705 | 0.158209 | 2.418984 | 0.0175 |

DTA | 4.187919 | 1.656723 | 2.527833 | 0.0132 |

DTE | -0.000113 | 0.000664 | -0.169540 | 0.8658 |

DPKTE | 0.000276 | 0.001267 | 0.217478 | 0.8283 |

LP | 2.731567 | 1.333774 | 2.047998 | 0.0434 |

LI | 2.071330 | 3.630298 | 0.570568 | 0.5697 |

Effects Specification | S.D. | Rho | ||

Cross-section random | 1.006976 | 0.1878 | ||

Idiosyncratic random | 2.094304 | 0.8122 | ||

Weighted Statistics | ||||

R-squared | 0.580744 | Mean dependent var | 2.597837 | |

Adjusted R-squared | 0.538348 | S.D. dependent var | 3.372903 | |

S.E. of regression | 2.290624 | Sum squared resid | 466.9795 | |

F-statistic | 13.69788 | Durbin-Watson stat | 1.064136 | |

Prob(F-statistic) | 0.000000 |

Simultaneous Hypothesis (4)

*H _{0}: β_{81}=β_{82}=β_{83}...β_{87}=0; there is no effect of macroeconomics factor and capital structure as well as liquidity on NIM.*

*H _{1}: At least there is β_{ij} ≠ 0 there is the effect of macroeconomics factor and capital structure as well as liquidity on NIM.*

The result of testing shows that there is the simultaneous effect of macroeconomics factor and capital structure as well as liquidity on NIM, with the value of R^{2} resulted from the model is 58% (**Table 24**).

Table 24: Simultaneous Testing Of Hypothesis 1d |
|||

Hypothesis | F-statistic | Prob(F-statistic) | Description |
---|---|---|---|

Hypothesis 1d | 13.69788 | 0.0000* | H_{0} rejected |

*Significant at a=0.05.

Partial Hypothesis

Partially only BIRATE, Exchange Rate, Interbank Overnight (O/N) Rate, DTA and LP which have a significant effect on ROE (**Table 25**).

Table 25: Partial Testing Of Hypotesis 1d |
||||

Hypothesis | ?_{ij} |
t-Statistic | Prob | Description |
---|---|---|---|---|

BIRATE | 272.9219 | 7.450656 | 0.0000 | Significant |

INFLATION | 0.062627 | 0.627159 | 0.5322 | Not Significant |

EXCHANGE RATE | -0.000346 | -2.505321 | 0.0141 | Significant |

INTERBANK OVERNIGHT (O/N) RATE | -0.382705 | 2.418984 | 0.0175 | Significant |

DTA | 4.187919 | 2.527833 | 0.0132 | Significant |

DTE | -0.000113 | -0.169540 | 0.8658 | Not Significant |

DPKTE | 0.000276 | 0.217478 | 0.8283 | Not Significant |

LP | 2.731567 | 2.047998 | 0.0434 | significant |

LI | 2.071330 | 0.570568 | 0.5697 | Not Significant |

**Conclusion**

Macroeconomic factor, Capital Structure and liquidity simultaneously effect on the performance of foreign bank in Indonesia. Partially:

1. BIRATE, Interbank Overnight (O/N) Rate, DTA, DTE, DPKTE and Precautionary liquidity which have a significant effect on CAR.

2. BIRATE, Exchange Rate, DTE, DPKTE and LP which have a significant effect on ROA.

3. BIRATE, Exchange Rate, Interbank Overnight (O/N) Rate, DTE, DPKTE and LP which have a significant effect on ROE.

4. BIRATE, Exchange Rate, INTERBANK OVERNIGHT (O/N) RATE, DTA and LP which have a significant effect on ROE.

The result of this study is expected to be a recommendation for the management of foreign banks in increasing their performance especially ROE and NIM through the increase of liquidity. This finding is resulted from the unit of analysis of foreign bank listed in Financial Service Authority, so the next research can be study by taking the unit of analysis of national banking.

Al-Tamimi, H. & Hassan, A. (2010). *Factors influencing performance of the UAE Islamic and conventional national banks*. Department of Accounting, Finance and Economics, College of Business Administration, University of Sharjah.

Alshatti, A.S. (2015). The effect of the liquidity management on profitability in the Jordanian commercial banks. *International Journal of Business and Management*, *10*(1), 62-71.

Anbar, A. & Alper, D. (2011). Bank specific and macroeconomic determinants of commercial bank profitability: Empirical evidence from Turkey. *Business and Economics Research Journal*, *2*(2), 139-152.

Bank for International Settlements. (2008). *Principles for sound liquidity risk management and bank for international settlements*. Diambil kembali dari.

Bernstein, L.A. (1993). *Financial statement analysis: Theory, application and interpretation (Fifth Edition)*. Boston, MA: Irwin.

Diamond, D. & Dybvig, P. (1983). Bank runs, deposit insurance and liquidity. *Journal of Political Economy*, *105*(91), 401-419.

Farag, M., Harland, D. & Nixon, D. (2013). *Bank capital and liquidity*.

FDIC. (1995). *The FDIC quarterly banking profile*. Washington, DC: The Federal Deposit Insurance Corporation.

Gitman, L. (2009). *Principles of managerial finance (Twelth Edition)*. The Addison.

Hahn, W. & Powers, T.L. (2010). Strategic plan quality, implementation capability and firm performance. *Academy of Strategic Management Journal*, *9*(1), 63-81.

Koch, T. & Mac Donald, S. (2000). *Bank management*. Orlando: The Dryden Press, Harcourt Brace College Publishers.

Lartey, V.C., Antwi, S. & Boadi, E. (2013). The relationship between liquidity and profitability of listed banks in Ghana. *International Journal of Business and Social Science*, *4*(3), 48-56.

Lenz, R.T. (1980). Environment, strategy, organization structure and performance: Patterns in one industry. *Strategic Management Journal*, *1*, 227-248.

Moore, W. (2009). *How do financial crises affect commercial bank liquidity? *Evidence from Latin America and the Caribbean.

Myers, S. & Rajan, R. (1998). The paradox of liquidity. *Quarterly Journal of Economics*, *113*, 733-771.

Owolabi, S., Obiakor, R. & Okwu, A. (2011). Investigating liquidity-profitability relationship in business organizations: A study of selected quoted companies in Nigeria. *British Journal of Economics, Finance and Management Sciences*, *1*(2), 11-29.

Porter, M.E. (1980). *Competitive strategy: Techniques for analysing industries and competitors*. The Free Press.

Porter, M.E. (1985). *Competitive advantage: Creating and sustaining superior performance: With a new introduction*. The Free Press: New York, USA.

Rauch, C., Steffen, S., Hackethal, A. & Tyrell, M. (2008). *Determinants of bank liquidity creation -evidence from savings banks*. Working Paper, Germen.

Robinson, R.B. & Pearce, J.A. (1983). The impact of formalized strategic planning on financial performance in small firms. *Strategic Management Journal*, *4*, 197-207.

Saleem, Q. & Rehman, R.U. (2011). Impacts of liquidity ratios on profitability. *Interdisciplinary Journal of Research in Business*, *1*(7), 95-98.

Sauer, S. (2007). *Liquidity risk and monetary policy*. Munich: Department of Economics, University of Munich.

Vodova, P. (2011). Liquidity of Czech commercial banks and its determinants. *International Journal of Mathematical Models and Methods in Applied Sciences*, *5*(6), 1060-1067.

Williamson, S. (2008). *Liquidity constraints in the new pargrave dictionary of economics (Second Edition)*. Steven Durlauf & lawrence Blume.

Yeager, F. & Seitz, N. (1989). *Financial institution management: Text and cases (Third Edition)*. New Jersey: Prentice Hall: Englewood Cliffs.