Macro and micro determinants of the non-performing finance: The case of Indonesian Islamic bank

Rahmah, Anggia Zainur; Armina, Sheema Haseena

doi:10.20885/jeki.vol6.iss1.art4

Cited by 17 publications

(33 citation statements)

References 13 publications

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“…As an indicator showing losses due to financing risk, this is reflected in the NPF. Non-Performing Financing (NPF) is the ratio between problematic financing and total financing disbursed by Islamic Commercial Banks (Rahmah & Armina, 2020). Based on the criteria set by Central Bank of Indonesia, the categories included in the NPF are substandard, doubtful, and non-performing financing (Rahman & Fatmawati, 2020;Permataningayu, & Mahdaria, 2020).…”

Section: Non-performing Financing (Npf)mentioning

confidence: 99%

“…Therefore, this research analyses the factors that affect the financing of Islamic Commercial Banks for the agricultural sector in Indonesia from January 2015 to April 2019. The related previous studies include Beik and Aprianti (2013), Mughits and Wulandari (2016), Septindo et al (2016), Maulana and Iskandar (2018), Kusumawati (2013), Purwanto (2018), Ashari (2009), Ashari and Saptana (2016), Saragih (2017), Sudarsono (2017), Rahmah and Armina (2020), Majid and Ulina (2020), Jaya et al (2015), Veratama (2013), andNasution (2016).…”

Section: Introductionmentioning

confidence: 98%

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Analysis of factor affecting Islamic commercial bank financing for the agricultural sector in Indonesia

Wicaksono

Fitriyani

2020

AJIM

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This study aims to analyse the factors that influence Islamic commercial bank financing for the agricultural sector in Indonesia from January 2015 to April 2019. Methodology:Variables used in this study are Agricultural Financing, DPK, NPF, Inflation, Exchange Rates, SBIS, and IPI. The method used in this research is the VAR/VECM method with Impulse Response Function (IRF) analysis and Forecast Error Variance Decomposition (FEVD). Findings:The results show that the variables that significantly influence agricultural financing in the long-term are DPK, Exchange Rate, and IPI. Taking a closer detail, IPI variable has a positive effect, while the DPK and Exchange Rate have a negative effect on agricultural financing. Then NPF, inflation, and SBIS have no effect on agricultural financing in the short and long-term. Based on the results of FEVD, the variable that has the greatest contribution to agricultural financing is DPK compared to Exchange Rate and IPI.Practical applications: Islamic bank directors prioritize efforts to increase funding in order to encourage an increase in the portion of financing in the agricultural sector.Originality: This study examines the effect of financial and macroeconomic performance on mudharabah financing in the short and long term in Indonesia.

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Section: Non-performing Financing (Npf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Analysis of factor affecting Islamic commercial bank financing for the agricultural sector in Indonesia

Wicaksono

Fitriyani

2020

AJIM

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“…This study contributes to empirical literatures in some ways. First, some previous studies have examined the Islamic commercial banks' nonperforming financing (Kabir & Worthington, 2017;Hassan et al, 2019;Widarjono, 2020;Rahmah & Armina, 2020) as well as conventional banks such as Rachman et al (2018), Tran & Nguyen (2020), Le & Diep (2020). However, to the best of our knowledge, they have not addressed the impact of financing diversification on NPF.…”

Section: Introductionmentioning

confidence: 99%

Financing diversification and Indonesian Islamic bank's non-performing financing

Widarjono

Rudatin

2021

JEKI

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Purpose -This study empirically analyzes the effect of the financing diversification with some control variables including both bank-specific variables such as bank's size, CAR, efficiency and the macroeconomic variables such as the inflation and exchange rate, on the Islamic bank's non-performing financing (NPF).Methodology -The aggregate Islamic bank data encompassing Islamic commercial banks and Islamic business units are used. The Autoregressive Distributed Lag Model (ARDL) is employed using the monthly data covering January 2011 to December 2019.Findings -The cointegration test indicates that the long-run relationship among variables being studied exists. Our results document that higher concentrated financing generates high NPF. Higher asset significantly contributes to reducing NPF. In addition, higher operating efficiency can reduce NPF. The instability of the exchange rate also generates the high NPF.Practical implications -Our results suggest that Islamic banks must lessen the high concentrated financing by optimizing both PLS and non-PLS contracts to reduce Islamic banks' financing risk.Research limitations -This study employs aggregate data but applying them may conceal for individual Islamic bank.Originality -Our study includes financing diversification to examine Islamic bank's financing risk. The existing empirical studies, to the best of our knowledge, have not addressed the impact of financing diversification on financing risk.

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“…Similarly, Al-Harbi (2020) also declared that GDP and the profitability of Islamic bank in Iran and Sudan are moves in the same direction. Specifically, in Indonesia's context, Rahmah & Armina (2020) revealed that GDP has a positive impact on the banks' profitability. These studies explained that the increase in GDP measured improving the quantity of production, consumption, and expenditure.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, Indonesia, as the largest E-ISSN: 2502-0633, P-ISSN: 2502-4647 http://jurnal.unissula.ac.id/index.php/ijibe DOI: http://dx.doi.org/10.30659/ijibe. 6.1.37-53 Muslim population country (Rahmah & Armina, 2020), the contribution of Islamic banks on promoting the business cycle, is promoting (Abduh & Azmi Omar, 2012). Based on these explanations, Islamic banks' performances become an essential topic to study to evaluate Islamic banks' contribution to national economic conditions.…”

Section: Introductionmentioning

confidence: 99%

Examining the Correlation Between Islamic Banks Profitability and the Business Cycle in Indonesia

Sholikhin

Supriani

Amijaya

2021

IJIBE

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This study aims to examine the correlation between Islamic banks' profitability in Indonesia and the business cycle, which measured by composite leading Indicator (CLI).�This study used several annual data covering the period from 2004-2018. This study utilizes Islamic banks profitability represent by Return on Asset (ROA) data as the dependent variable, CLI data as the dependent variable, and six control variables including Gross Domestic Product, Inflation, Financial to Deposit Ratio, Capital Adequacy Ratio, Non-Performing Finance, and Operating Costs Operating Income. This study adopted multiple regression analysis by using EViews 9.0 software. This study reveals that CLI has a positive and significant impact on ROA, which indicated that the expansion of business cycle activities would increase Islamic banks' profitability. Thus, this study suggests that Islamic bank should engage with the companies that relied on their business activities in the rill sector to boost their profitability. The limitation of this study is this study adopted Indonesian Islamic banks in general. Therefore, this study does not capture the correlation amongst the variables in the specific Islamic banks and region, which might be different from this result due to the differences in banks' internal conditions, culture, and the business cycle in the various region.

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Macro and micro determinants of the non-performing finance: The case of Indonesian Islamic bank

Cited by 17 publications

References 13 publications

Analysis of factor affecting Islamic commercial bank financing for the agricultural sector in Indonesia

Analysis of factor affecting Islamic commercial bank financing for the agricultural sector in Indonesia

Financing diversification and Indonesian Islamic bank's non-performing financing

Examining the Correlation Between Islamic Banks Profitability and the Business Cycle in Indonesia

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