The Effect of Camel Ratio in Predicting Financial Distress Conditions in Banking Companies Registered in Indonesia Stock Exchange (BEI)

This research was conducted to analyze the effect of Capital Adequacy Ratio (CAR), Non Performing Loans (NPL), Net Interest Margin (NIM), Operational Costs Operating Income (BOPO), and Loan to Deposit Ratio (LDR) on Financial Distress. The banking sector listed on the Indonesia Stock Exchange (IDX) for the 2014-2017 period was chosen as the population used in this study. The sampling technique used is probability sampling. The number of samples used were 22 banks listed on the Indonesia Stock Exchange (IDX). The analysis technique used is panel data regression.The results of this study indicate that the Capital Adequacy Ratio (CAR), Non Performing Loans (NPL), Net Interest Margin (NIM), Operational Costs Operating Income (BOPO), and Loan to Deposit Ratio (LDR) simultaneously have a significant effect on Financial Distress . Based on partial testing, Capital Adequacy Ratio (CAR), Net Interest Margin (NIM), Loan to Deposit Ratio (LDR) and Operational Costs Operating Income (BOPO) have an effect on Financial Distress while Non Performing Loans (NPL) have no effect on Financial Distress . Keywords : Financial Distress, CAR, NPL, NIM, BOPO, and LDR . DOI: 10.7176/EJBM/12-18-10 Publication date: June 30th 2020

financial distress, the company has bad news that shows a negative signal for investors so that this can affect the disclosure of management in making disclosure, whereas if the company is financially healthy it means the company has good news for investors so this will affect the management in providing company information, management is interested in conveying information that can increase the company's credibility and success even though the information is not required.
Signaling theory in this study emphasizes the importance of information released by the company on financial distress. If the financial distress condition is known in advance, the company is expected to take action to improve the situation so that the company will not experience difficulties or liquidity.
Financial distress is a condition where the company is facing financial difficulties, namely the company's operating cash flow is unable to pay off current obligations (for example interest expense) and the company is forced to make corrective actions to avoid the threat of bankruptcy / liquidation.
According to Wongsosudono and Chrissa (2013) "Financial difficulties can be interpreted as the inability of companies to pay their financial obligations when due which cause bankruptcy of the company".
According to Fahmi (2012), "the cause of financial distress is starting from the company's inability to fulfill its obligations, especially short-term obligations including liquidity obligations and also included obligations in the solvency category". Indicators in Predicting Financial Distress Companies that experience financial difficulties through several stages, there are always indications that can be used as initial predictions. According to Munawir (2015) there are several indicators or sources of information about possible financial difficulties, namely: 1. Analysis of cash flow statements for the present and future periods. 2. Analysis of corporate strategy by considering the potential of the competitor companies or institutions concerned with the relative cost structure, expansion or expansion in the industry, management's ability to control costs and quality of management. 3. Analysis of the company's financial statements with a comparison technique with several companies that can be focused on a single financial variable (univariate analysis) or with various combinations of financial variables (multivariate analysis).

RESEARCH METHODS.
Operationalization of Variables.

Independent Variable
The independent variable used in this study is the CAMEL ratiowhich is included in the CAMEL ratio in this study are as follows: Capital Adequacy Ratio (CAR) It is a ratio that shows how much the total assets of banks that contain risks (credit, investments, securities, bills) are financed from their own capital in addition to obtaining funds from sources outside the bank (Almilia and Herdiningtyas, 2005

Random Effect (RE)
Here is the Random Effect modeling: Y = α + β1 + β2 + β3 + 4 +β5 + ω Where : ω_it + ε_it + u_it ω_it is a combined error term consisting of two components: ε_it, which is a cross-section or individual-specific error component, and u_it, which is a combined time-series and cross section error component.   ISSN 2222-1905(Paper) ISSN 2222-2839(Online) Vol.12, No.18, 2020 Based on the results of testing the model using the Random Effect model that has been done, an F-statistic value of 18.95752 was obtained with an F-statistic probability of 0.000000 which is smaller than the significance level of 5%. Because the probability value is smaller than 0.05, H₀ is rejected, and the independent variables in this study, namely CAR, NPL, NIM, BOPO and LDR together have a significant effect on the Financial Distress of banking companies listed on the Indonesia Stock Exchange (BEI) for the 2014 period -2017. T Test Statistics and Discussion. a. Effect of CAR on Financial Distress. The coefficient value of the CAR variable that is equal to 0.021084 moves positively, the t-statistic value is 4.484761 with a probability of 0.0000 <0.05, then Ha is accepted. There is a significant influence between CAR variables on the Financial Distress of banking companies listed on the Indonesia Stock Exchange (IDX) for the 2014-2017 period. This means that the higher the CAR, the better the bank's ability to bear the risk of any credit / earning assets that are at risk and avoid the conditions of Financial Distress. The results of this study are supported by Pratama research (2015) which states that CAR has a significant positive effect on the condition of Financial Distress and also research Almilia and Herdiningtyas (2005) which states that CAR has a significant effect on the condition of Financial Distress. b. Effect of NPLs on Financial Distress. The coefficient value of the NPL variable that is 0.006693 moves positively, the t-statistic value is 0.518730 with a probability of 0.6053> 0.05, then H₀ is accepted. There is no influence between NPL variables on the Financial Distress of banking companies listed on the Indonesia Stock Exchange (IDX) for the 2014-2017 period. NPLs reflect credit risk. This means that the lower the NPL ratio, the lower the credit risk borne by the bank. The results of this study are supported by the research of Almilia and Herdiningtyas (2005) which states that NPL has no significant positive effect on problematic conditions. Aji Nugroho's research (2011) also states that NPL has no significant positive effect on the condition of Financial Distress. c. Effect of NPLs on Financial Distress. The coefficient value of the NPL variable that is 0.006693 moves positively, the t-statistic value is 0.518730 with a probability of 0.6053> 0.05, then H₀ is accepted. There is no influence between NPL variables on the Financial Distress of banking companies listed on the Indonesia Stock Exchange (IDX) for the 2014-2017 period. NPLs reflect credit risk. This means that the lower the NPL ratio, the lower the credit risk borne by the bank. The results of this study are supported by the research of Almilia and Herdiningtyas (2005) which states that NPL has no significant positive effect on problematic conditions. Aji Nugroho's research (2011) also states that NPL has no significant positive effect on the condition of Financial Distress. d. Effect of NIMs on Financial Distress. The coefficient value of the NIM variable is 0.06693, it moves positively, the t-statistic value is 3.336502 with a probability of 0.0013 <0.05, then Ha is accepted. There is a significant influence between the NIM variables on the Financial Distress of banking companies listed on the Indonesia Stock Exchange (IDX) for the 2014-2017 period. That is, the greater the NIM, the interest income on earning assets managed by the bank increases so that the possibility of a bank in problematic conditions is getting smaller. The results of this study are supported by research by Dea Septian (2013) which states that NIM has a significant positive effect on the level of bank health.

e. Effect of BOPO on Financial Distress
The coefficient value of the BOPO variable is -0.016401, it moves negative, the t-statistic value is 0-5.762654 with a probability of 0.0000 <0.05, then Ha is accepted. There is a significant influence between BOPO variables on the Financial Distress of banking companies listed on the Indonesia Stock Exchange (IDX) for the 2014-2017 period. That is, the greater the value of the BOPO, it indicates that the bank's operational burden is greater than bank's income. Bank revenue that is far less than operating expenses will certainly provide a smaller profit. This shows that the increased value of BOPO will cause banks to be closer to the risk of Financial Distress. The results of this study are supported by research Hutasoit and Mulyo (2016) which states that the BOPO has a negative effect on the risk of bankruptcy.

f. Effect of LDR on Financial Distress
The coefficient value of the LDR variable is 0.008637, it moves positively, the t-statistic value is 3.169758 with a probability of 0.0021 <0.05, then Ha is accepted. There is a significant influence between the LDR variables on the Financial Distress of banking companies listed on the Indonesia Stock Exchange (IDX) for the 2014-2017 period. LDR is used to measure the amount of funds placed in the form of credit originating from funds collected by banks. This means that the amount of the LDR will affect the level of profitability of banks in getting interest from loans so that the greater the credit extended will increase bank income. The higher the level of the LDR ratio, the higher the potential for the bank to experience Financial Distress. The results of this study are supported by research by Christiana Kurniasari (2013) which states that LDR has a significant effect on Financial Distress.