On the profitability of commercial banks: the Sri Lankan case

Ariyadasa, Chatura; Siddique, Md. Abu Bakar; Selvanathan, Saroja

doi:10.1080/00036846.2016.1231909

Cited by 19 publications

(33 citation statements)

References 10 publications

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“…Finally, with regard to the macroeconomic determinants of bank profitability, the findings suggest that Chinese commercial banks have higher profitability in terms of ROA and NIM in a higher inflationary environment. This result is in line with the finding of Ariyadasa et al (2017) in terms of the Sri Lankan banking industry. The higher inflationary environment is associated with higher loan interest rate, which will increase bank profitability (Tan and Floros, 2012a).…”

Section: Datasupporting

confidence: 91%

The impacts of competition and shadow banking on profitability: Evidence from the Chinese banking industry

Tan

2017

The North American Journal of Economics and Finance

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This paper contributes to the empirical literature on banking profitability by testing the impacts of competition and shadow banking on bank profitability using a sample of 100 Chinese commercial banks over 2003-2013 with 417 and 395 observations. The current study fills the gaps in the empirical studies by examining the competition in different banking markets (i.e. deposit market, loan market and non-interest income market) in China and further evaluating their impacts on bank profitability. The findings show that the non-interest income market has a higher level of competition compared to the deposit market and loan market. It is further reported that a lower level of competition in deposit market leads to an increase in the profitability of Chinese commercial banks. Finally, the results suggest that shadow banking improves the profitability of Chinese banks.

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Section: Datasupporting

confidence: 91%

The impacts of competition and shadow banking on profitability: Evidence from the Chinese banking industry

Tan

2017

The North American Journal of Economics and Finance

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“…Tan et al (2016) and Djalilov and Piesse (2016) identified that the non-performing loans ratio negatively affects the profitability of banks by using a GMM approach. Similarly, Ariyadasa et al (2016) reached the same conclusion for Sri Lanka by using a vector error correction model. In addition to these studies, Nisar (2015) analyzed the determinants of bank profitability in Pakistan by using a regression method.…”

Section: Buchory (2015) Indonesia Regressionmentioning

confidence: 53%

“…Hence, this situation has a positive impact on profitability (Molyneux and Thornton 1992;Islam and Nishiyama 2016). On the other hand, if there is an unanticipated change in the interest rates, then there should be a negative relationship between these two variables (Noman et al 2015;Ariyadasa et al 2016). Last but not least, since economic growth shows the improvement in the economy, the effect of this variable on profitability should be positive (Djalilov and Piesse 2016;Javaid 2016;Saeed 2014;Ahmad et al 2016).…”

Section: Variables Used In the Analysismentioning

confidence: 99%

Determinants of Profitability in the Banking Sector: An Analysis of Post-Soviet Countries

et al. 2018

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Abstract:The purpose of this paper is to identify the determinants of bank profitability in 13 post-Soviet countries. Within this scope, annual data between 1996 and 2016 is analyzed by using fixed effects panel regression and the Generalized Method of Moments (GMM). It is concluded that loan amount, non-interest income and economic growth are significant indicators of profitability. Moreover, the 2008 global mortgage crisis has a negative influence on bank profitability in post-Soviet countries. According to the estimation results, there is a positive relationship between non-interest income and economic growth with profitability. This result shows that when non-interest income of the banks increases, such as credit card fees and commission, it affects the financial performance of the banks, positively, and contributes to bank profitability. Another result of this study is that economic growth positively influences bank profitability. This result allows us to conclude that higher GDP comes with higher bank profitability for post-Soviet countries. Lastly, there is a negative relationship between loan-to-GDP ratio and profitability of the banks in post-Soviet countries. This means that when the ratio of total loans to GDP increases, it affects financial performance of the banks in a negative way. While considering this result, it is recommended that banks in post-Soviet countries should focus on ways to increase their non-interest income. Additionally, it is also significant for these banks to be careful and risk averse when lending to their customers.

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“…ROA shows the earned profit from each dollar invested in the business. It is also an indicator of the efficiency of business management (Ariyadasa et al, 2017). ROA has been used as a profitability measure in many previous studies (Ariyadasa et al, 2017;Bucevska & Misheva, 2017;Knezevic & Dobromirov, 2016;Paolucci & Menicucci, 2016;and Chowdhury, 2015).…”

Section: Roamentioning

confidence: 99%

“…The studies on internal and external factors affecting bank profitability have grown rapidly, especially those concentrating on developed country economies (Iskandar, Che-Yahya & Wahid, 2019;Öhman & Yazdanfar, 2018;Fidanoski et al, 2018;Bongini et al, 2018;Garcia & Guerreiro, 2016). However, according to Ariyadasa, Selvanathan, Siddique & Selvanathan, (2017) not many studies have been conducted on developing countries and one of these countries is Jordan. In Jordan, the financial system has played an important role in enhancing the country's economic development in past years.…”

Section: Introductionmentioning

confidence: 99%

Effects of Internal and External Factors on Profitability of Jordanian Commercial Banks: Panel Data Approach

Bekhet¹,

Al-smadi²,

Khudari³

2020

IJFR

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This article assesses the effects of internal and external factors on the profitability of Jordanian commercial banks. A panel data set of thirteen commercial banks between 2000 and 2018 was used. Pooled ordinary least squares, random and fixed models were applied. Moreover, a Hausman test was performed to confirm the suitability of models, which was preferred on the random effect model. Also, a Wooldridge test for serial correlation and a modified Wald test for groupwise Heteroskedasticity were used and both of their null hypotheses were rejected. However, to deal with these problems, a robustness analysis was performed using feasible generalized least square. The findings suggested that internal factors and in particular, bank size and diversification, had positive effects on bank profitability, while credit risk, operational risk and leverage risk were negatively related to bank performance. However, capital risk had a positive but insignificant impact on bank profitability. As for the effect of external factors, the results suggested that financial development and inflation had a positive and significant impact on bank profitability, while market concentration and stock market volatility had a significant negative effect on bank profitability. Further, a negative and insignificant impact were found for GDP and refugee crisis on bank profitability in Jordan. The findings would help managers of commercial banks, investors, government, policy makers and shareholders to make better decisions and improve performance by highlighting areas of weaknesses. In general, policy makers should become more aware with these insights on profit determinants in Jordanian commercial banks.

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On the profitability of commercial banks: the Sri Lankan case

Cited by 19 publications

References 10 publications

The impacts of competition and shadow banking on profitability: Evidence from the Chinese banking industry

The impacts of competition and shadow banking on profitability: Evidence from the Chinese banking industry

Determinants of Profitability in the Banking Sector: An Analysis of Post-Soviet Countries

Effects of Internal and External Factors on Profitability of Jordanian Commercial Banks: Panel Data Approach

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