scholarly journals The Panel Data Analysis to Identify the Factors Affecting Turkish Currency Assets of Foreign Deposit Banks

2021 ◽  
pp. 121-128
Author(s):  
Ersan Özgür
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Banking Sector ◽  
Free Market ◽  
Panel Data Analysis ◽  
Foreign Banks ◽  
Significance Level ◽  
Factors Affecting ◽  
Independent Variables ◽  
Study Results

With the implementation of free market economy in Turkey starting from 1980, restrictions on foreign capital flows began to be abolished. Within the scope of international expansion in financial aspects, steps for integration with global financial markets were taken, and regulations were made. Accordingly, the number of foreign banks in Turkish banking system have increased since 1980, and reached an important scale in the sector. The share of foreign deposit banks’ total assets in the entire banking sector is at 22,8% level as of 2019. In this study, panel data analysis was performed to identify the factors affecting the Turkish currency assets of foreign deposit banks. The 11-year data for the 2009-2019 period were utilized in the study. Turkish Currency Assets / Total Assets was determined as the dependent variable in the analysis. The factors affecting the Turkish currency assets of foreign deposit banks were identified as Turkish Currency Liability / Total Liability [TPYUK], Turkish Currency Deposits / Total Deposits [TPMEV], and Turkish Currency Loans / Total Loans [TPKREDI]. Based on the study results the model formed was significant, and the ratio of independent variables for explaining the dependent variable in the model was approximately 48%. The independent variables TPYUK and TPKREDI were revealed to have a statistically significant positive effect on the dependent variable at 5% significance level. A 1-unit raise in TPYUK increased the dependent variable by 0,436 unit, and a 1-unit raise in TPKREDI by 0,033 unit. No statistically significant effect of TPMEV as the other independent variable was identified on the dependent variable.

scholarly journals Factors affecting hospital mortality rate in Iran: a panel data analysis

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Mohsen Bayati ◽  
Mehrnoosh Emadi
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Length Of Stay ◽  
Death Rate ◽  
Panel Data Analysis ◽  
Negative Relationship ◽  
Teaching Hospitals ◽  
Hospital Death ◽  
Factors Affecting ◽  
Hospital Deaths

Abstract Objective Hospital deaths account for a large number of community deaths. Moreover, one of the main indicators of inpatient services quality is the hospital death. This study was performed to investigate the factors affecting hospital death rate in Iran using panel data analysis. Results The net death rates in teaching and not-teaching hospitals were 6.24 and 5.58 per 1000 patients, respectively. Models' estimates showed, in teaching hospitals the number of surgeries (P < 0.05) and special beds (P < 0.01) had a significant positive relationship with death rate. In non-teaching hospitals, outpatient admissions (P < 0.01), number of surgeries (P < 0.05), number of special beds (P < 0.01), and length of stay (P < 0.01) had a positive and the number of inpatient admissions (P < 0.05) and active beds (P < 0.01) had a negative relationship with death rate. Policy-making towards optimization of hospital service size and volume, standardization of length of stay, interventions to control nosocomial infections, and planning to control the complications of surgeries and anesthesia could effectively reduce hospital death rate.

scholarly journals A model proposal for estimating banks’ future value: Evidence from Turkey

2021 ◽  
Vol 16 (4) ◽  
pp. 169-178
Author(s):  
Burhan Günay ◽  
Ayten Turan Kurtaran ◽  
Sara Faedfar
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Explanatory Power ◽  
Performance Criteria ◽  
Past Performance ◽  
Ols Regression ◽  
Significance Level ◽  
The Future ◽  
The Impact

Investors make solid decisions when evaluating their investments based on positive indicators the firm may show in the future, rather than based on its past performance. Accordingly, this study aims to investigate the relationship between performance criteria and the most significant value-based criterion; Economic Value Added (EVA). Further, it evaluates the impact of future EVA values on the bank value. Panel Data Analysis and the OLS Regression model are used to estimate the regression equation. The analysis is performed using data of 10 banks on the BIST Banks Index over the period 2011 to 2020. Furthermore, the EVA criterion was converted into standardized EVA(SEVA) by dividing EVA by total assets. The OLS regression analysis results revealed that the model’s explanatory power for the SEVA variable is 71.92%. The three variables that have positive correlation with SEVA are earnings per share (EPS) and TOBINQ rates at the 1% significance level and the price to sales growth rate with a degree of significance at 10%. Regarding the Panel Data Analysis results, while the explanatory power of the SEVA variable is 72.14%, its association with the EPS and TOBINQ criteria was found to be significant at the 1% significance level. The empirical investigations reveal that the model developed using the future SEVA as a proxy for bank value is found to be promising, and it is accepted that the SEVA variable can be used instead of the bank value.

Determinants of banks’ profitability in Angola

2019 ◽  
Vol 10 (1) ◽  
pp. 116-128
Author(s):  
Maria Teresa Medeiros Garcia ◽  
Maria José Trindade
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Design Methodology ◽  
Panel Data Analysis ◽  
Control Variables ◽  
Content Type ◽  
Independent Variables ◽  
Two Measures ◽  
Ability And Willingness

Purpose The purpose of this paper is to analyze the factors that influence the profitability of 17 banks in Angola between 2010 and 2016, as low profitability weakens the ability and willingness of banks to finance the wider economy. Design/methodology/approach The paper conducts panel data analysis, using two measures of profitability: the return on average assets and the return on average equity. Several control variables were included concerning both bank-specific and macroeconomic characteristics which have not been considered in previous studies. Findings The authors conclude that several independent variables have an impact which is different from expected, especially regarding ownership, which shows positive statistically significant effect on banks’ profitability. Originality/value To the best of the authors’ knowledge, this is the first attempt to examine determinants of banks’ profitability in Angola, both internal and external, which have not been considered in previous studies.

scholarly journals ANALISIS PEMBIAYAAN DAN KREDIT SEKTOR KONSTRUKSI DI INDONESIA: STUDI PERBANKAN SYARIAH DAN KONVENSIONAL

2018 ◽  
Vol 6 (1) ◽  
pp. 21-40
Author(s):  
Nidaa Nazaahah Kusumawati ◽  
Nunung Nuryartono ◽  
Irfan Syauqi Beik
Keyword(s):  
Panel Data ◽  
Interest Rate ◽  
Price Index ◽  
Panel Data Analysis ◽  
Wholesale Price ◽  
Third Party ◽  
Construction Sector ◽  
Factors Affecting ◽  
Study Results ◽  
Regional Product

The construction sector is an important sector in supporting development projects in Indonesia. The development of the construction sector requires the role of the banking sector to provide access of capital through credit or financing. This study aims to analyze the factors affecting construction financing and credit in Islamic and Conventional Banking in Indonesia and among regions in Indonesia. This study uses Vector Autoregression/ Vector Error Correction Model (VAR/VECM) with monthly data from 2006 until 2014 and panel data analysis with yearly data from 2009 until 2013. The study results that the factors affecting financing and credit on Construction Sector in Indonesia are Third Party Funds (DPK), Wholesale price index, fee of SBIS (interest rate of SBI), percentage of Non Performing Financing (Non Performing Loan), Consumer Price Index and equivalent rate of financing (Interest rate of Credit). Furthermore, the factors affecting financing and credit on Construction Sector among regions in Indonesia are Third Party Funds, Gross Domestic Regional Product of Construction Sector, Gross Domestic Regional Product per Capita and percentage of Non Performing Financing (Non Perfoming Loan). Keywords: Construction, Credit, Financing, Panel data, VAR/VECM

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