Asset quality determinants of Indian banks: Empirical evidence and policy issues

Arrawatia, Rakesh; Dawar, Varun; Maitra, Debasish; Dash, Saumya Ranjan

doi:10.1002/pa.1937

Cited by 9 publications

(5 citation statements)

References 52 publications

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“…The independent sample t ‐test is applied, and the results are in Table 14. Among the 14 hypotheses tested, we could find statistically significant evidence for the following five hypotheses. Returns of in‐sample SIM portfolios are significantly different from the returns of in‐sample sectoral indices. Returns of out‐sample SIM portfolios are significantly different from the returns of in‐sample SIM portfolios. The risk of out‐sample SIM portfolios is significantly different from the risk of out‐sample sectoral indices. Returns of in‐sample MPT portfolios are significantly different from the returns of out‐sample sectoral indices. Returns of out‐sample MPT portfolios are significantly different from the returns of in‐sample MPT portfolios. Our findings in BFS sectors are in support of the studies by Arrawatia et al (2019); pharmaceuticals (Lal, 2016); private banks, and IT (Poornima & Remesh, 2015); inter‐sector portfolios (Sen & Fattawat, 2014). Also, we could not evidence support from the studies on inter‐sector out‐sample or mimic portfolios (Kumar & Bogarappu, 2016); automobile and FMCG (Mallikharjunarao & Anithadevi, 2017).…”

Section: Resultssupporting

confidence: 90%

“…The research was conducted using the monthly prices of five companies, each listed in BSE Sensex for 2016–2017. Among the sectoral studies, banks and financial services have by far been studied mainly due to their significance to the financial system, debatable asset quality, prompt corrective action by the Reserve Bank of India, and inconsistent performance between public and private sector banks (Arrawatia et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

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Does portfolio optimization favor sector or broad market investments?

Senthilkumar

Namboothiri

Rajeev

2021

Journal of Public Affairs

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In the Indian context, we have applied the Sharpe Single Index model with Elton's portfolio optimization framework and Markowitz Portfolio Theory framework for optimizing intra and inter‐sectoral equity portfolios. Literature provide less evidence on the inter and intra‐sector relationships to which we contribute. We compute 11 sector‐specific optimal portfolios and one inter‐sector optimal portfolio using the NSE sectoral indices like automobiles, banking, financial services, FMCG, IT, media, metals, pharmaceuticals, private banks, PSU banks, realty, and broad‐market inter‐sector index Nifty50. We use the month‐end adjusted closing returns data comprising 15,834 observations from 191 scrips during 2013 to 2019. We create in‐sample optimal portfolios and out‐sample mimic portfolios under the Sharpe and Markowitz models for comparison and validation with optimized weights. Our results show that inter‐sector portfolio performs better than some of the intra‐sector portfolios. Also, the intra‐sector portfolios like realty, metals, information technology, and media generated higher returns with relatively moderate or less risk than their sectoral peers and benchmark indices. We found that Sharpe's model performs better than Markowitz's model. We validated our findings with statistical testing. Thereby, we documented the applicability of the Sharpe Single Index for portfolio optimization in the Indian context. Our research has several implications. Fund managers can make use of our optimal portfolio performance matrix to recommend portfolios to their clients. With our optimized portfolio weights, investors can make sector‐level or inter‐sector investments. Regulators can get a synoptic view of the performance of industry‐level portfolios. Researchers can extend the literature by improvising our portfolio optimization procedure.

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Section: Resultssupporting

confidence: 90%

Section: Literature Reviewmentioning

confidence: 99%

Does portfolio optimization favor sector or broad market investments?

Senthilkumar

Namboothiri

Rajeev

2021

Journal of Public Affairs

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“…Deterioration of bank asset quality leads to high non-performing loans that affect banks' financial performance due to reduced interest income (Abata, 2014). An increase in loan defaults by borrowers results to a rise in non-performing assets which impairs the financial performance of banks and growth of the economy in general (Arrawatia et al, 2019). Strong asset quality on the other hand significantly impacts commercial banks financial performance (Eze and Ogbulu, 2016).…”

Section: Asset Quality and Financial Performancementioning

confidence: 99%

Financial soundness and performance: evidence from commercial banks in Kenya

Kirimi

Kariuki

Ocharo

2022

AJEMS

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PurposeThe study aims to analyze the effect of financial soundness on financial performance of commercial banks in Kenya.Design/methodology/approachThe study used dynamic panel model to analyze data from commercial banks for the period 2009 to 2020. The study was modeled on the concept of CAMEL approach using five CAMEL variables as financial soundness indicators. Four indicators that is, net interest margin (NIM), earnings per share (EPS), return on assets (ROA) and return on equity (ROE) were used as measures of financial performance.FindingsGeneralized method of moments results established that financial soundness had a statistically significant effect on NIM, ROA and ROE. It was also found that asset quality and earning quality had a statistically significant effect on net interest margin. In addition management efficiency had significant effect on ROE. However, the study established that capital adequacy, asset quality, earning quality and liquidity had a statistically insignificant effect on ROA and ROE respectively while capital adequacy, management efficiency and liquidity had statistically insignificant effect on NIM.Practical implicationsBank managers should put into place effective financial policies to govern changes in CAMEL variables to ensure optimal banks' financial soundness to facilitate positive growth in banks' financial performance.Originality/valueThe current study is modeled on the concept of the CAMEL approach by employing the five CAMEL variables as financial soundness indicators. In addition, the study contributes to local literature by examining banks in a developing economy to provide reliable and relevant information on their differences to monitor their dynamics in financial soundness and financial performance which could not be provided by regional or global studies.

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“…Kaushal and Ghosh (2018) reported that banks do contribute to the overall economic growth in India. Arrawatia et al (2019) found that any improvement in the GDP growth rate during crisis period has impacted NPLs positively. Kosmidou (2008) found that GDP growth plays a significantly positive role in determining banks' performance.…”

Section: External Determinantsmentioning

confidence: 98%

Analysis of Financial Performance of Public Sector Banks in India: CAMEL

Singh

Milan

2020

Arthaniti: Journal of Economic Theory and Practice

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Public sector banks have been merged by the government in the last few years. This is the rationale behind conducting this study. The purpose of this article is to determine the factors affecting the performance of public sector banks in India and the interrelationship between bank-specific determinants and performance of public sector banks. In this article, we shall analyse the financial data of all the public sector commercial banks for a period spread across 11 years (2009–2019); Capital adequacy, Assets quality, Management efficiency, Earning, and Liquidity (CAMEL) has been used as a performance determinant; system generalised method of moments (GMM) analysis has been used to find the effect of determinants on the performance measurement of public sector banks; and CCA (canonical correlation analysis) has been used to find the interrelationship between the bank-specific determinants and the performance of public sector banks. The finding has important implications in terms of performance in the banking sector. Certain limitations of this study are: It is based on secondary data. The study only covers the financial aspects and not the non-financial aspects. It is found that the asset quality is negatively related with performance of public sector banks. Liquidity and inflation are inversely related to performance of public sector banks in India. Capital adequacy is positively related with banks’ performance, but inversely related with banks’ interest margin. GDP growth has a significant positive impact on banks’ performance, but inversely related with banks’ interest income. Inflation rate is inversely related with banks’ performance. Banking sector reforms are insignificantly related with banks’ performance.

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Asset quality determinants of Indian banks: Empirical evidence and policy issues

Cited by 9 publications

References 52 publications

Does portfolio optimization favor sector or broad market investments?

Does portfolio optimization favor sector or broad market investments?

Financial soundness and performance: evidence from commercial banks in Kenya

Analysis of Financial Performance of Public Sector Banks in India: CAMEL

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