Subtopic Deep Dive
Non-Performing Assets Management
Research Guide
What is Non-Performing Assets Management?
Non-Performing Assets Management examines determinants, impacts, and mitigation strategies for NPAs in commercial banks, focusing on provisioning norms, recovery mechanisms, and influences from macroeconomic and bank-specific factors.
Research analyzes NPA ratios in developing markets like India and Bangladesh, linking high NPLs (11.60% in Bangladesh banks) to profitability declines (Rozina Akter and Jewel Kumar Roy, 2017, 57 citations). Studies employ DEA, Malmquist, SFA for efficiency with bad outputs (Ather Hassan Dar et al., 2021, 41 citations), and CAMELS ratings for solvency (Pawan Kumar et al., 2023, 38 citations). Over 10 key papers since 2001 explore bank-specific microeconomic factors and reforms (P. Ganesan, 2001, 23 citations).
Why It Matters
NPAs erode bank profitability and solvency, as shown in Dhaka Stock Exchange banks where NPLs reached 11.60% of classified loans, trapping the sector in gridlock (Rozina Akter and Jewel Kumar Roy, 2017). In India, public sector banks face efficiency challenges from NPAs, measured via DEA and CAMELS, impacting financial stability amid reforms (Ather Hassan Dar et al., 2021; Pawan Kumar et al., 2023). Mitigation strategies inform credit risk policies in volatile economies, with GCC studies identifying causes like management quality to guide recovery (Mohammad Omar Farooq et al., 2019).
Key Research Challenges
Quantifying NPA Determinants
Identifying bank-specific factors like loan management drives NPLs in SMEs, requiring stratified sampling across datasets (Amir Ikram et al., 2016). Macroeconomic volatility complicates isolation of microeconomic impacts. Empirical models struggle with data from developing markets.
Measuring Efficiency with NPAs
Incorporating NPAs as bad outputs in DEA, Malmquist, and SFA analyses reveals Indian bank inefficiencies post-reforms (Ather Hassan Dar et al., 2021). Ownership effects vary, challenging performance comparisons (Padmasai Arora, 2014). Statistical significance tests show reform-era differences (Aparna Bhatia and Megha Mahendru, 2015).
Developing Recovery Strategies
Provisioning norms and recovery mechanisms lag in high-NPA contexts like Bangladesh and GCC countries (Rozina Akter and Jewel Kumar Roy, 2017; Mohammad Omar Farooq et al., 2019). Profit function approaches link NPAs to viability but lack predictive power (P. Ganesan, 2001). CAMELS and Z-index ratings highlight solvency gaps without actionable mitigations (Pawan Kumar et al., 2023).
Essential Papers
The Impacts of Non-Performing Loan on Profitability: An Empirical Study on Banking Sector of Dhaka Stock Exchange
Rozina Akter, Jewel Kumar Roy · 2017 · International Journal of Economics and Finance · 57 citations
The Banking sector of Bangladesh is trapped in a gridlock of non-performing loans (NPLs) so much so that NPL accounts for 11.60 percent of the total volume of classified loans. This problem has sta...
The Efficiency of Indian Banks: A DEA, Malmquist and SFA Analysis with Bad Output
Ather Hassan Dar, Somesh K. Mathur, Sila Mishra · 2021 · Journal of Quantitative Economics · 41 citations
The Financial Performance and Solvency Status of the Indian Public Sector Banks: A CAMELS Rating and Z Index Approach
Pawan Kumar, Poonam Verma, Mukul Bhatnagar et al. · 2023 · International Journal of Sustainable Development and Planning · 38 citations
Recently, there has been much discussion about the importance of sound financial regulation and supervision in light of the increasingly integrated financial markets.CAMELS rating is an established...
A comparative study of performance of commercial banks in ASIAN developing and developed countries
Asima Siddique, Omar Masood, Kiran Javaria et al. · 2020 · Insights into Regional Development · 30 citations
International audience
Determinants of Profits and Profitability of Public Sector Banks in India: A Profit Function Approach
P. Ganesan · 2001 · Journal of Financial Management and Analysis · 23 citations
Introduction The viability of banks depends largely on the adequacy of profits and profitability. This is so because adequate profit gives more room for manoeuvrability to strengthen the operation ...
Determinants Of Non-Performing Loans: An Empirical Investigation Of Bank-Specific Microeconomic Factors
Amir Ikram, Qin Su, Faisal Ijaz et al. · 2016 · Journal of Applied Business Research (JABR) · 23 citations
The empirical study was undertaken to explore the determinants of non-performing loans (NPLs) of small and medium enterprises (SMEs) sector held by the commercial banks. Stratified sampling techniq...
Indian Banking Sector – Challenges and Opportunities
K. Ratna Manikyam · 2014 · IOSR Journal of Business and Management · 22 citations
The economic reforms initiated by the Government of India about two decades ago have changed the landscape of several sectors of the Indian economy.The Indian banking sector is no exception.This se...
Reading Guide
Foundational Papers
Start with P. Ganesan (2001, 23 citations) for profit functions linking NPAs to bank viability, then K. Ratna Manikyam (2014, 22 citations) on reform challenges, and Padmasai Arora (2014, 16 citations) on efficiency determinants.
Recent Advances
Study Ather Hassan Dar et al. (2021, 41 citations) for DEA with bad outputs, Pawan Kumar et al. (2023, 38 citations) for CAMELS solvency, and Asima Siddique et al. (2020, 30 citations) for Asian comparisons.
Core Methods
DEA and Malmquist for efficiency (Dar et al., 2021; Bhatia and Mahendru, 2015); CAMELS ratings and Z-index for performance (Kumar et al., 2023); stratified sampling regressions for NPL determinants (Ikram et al., 2016).
How PapersFlow Helps You Research Non-Performing Assets Management
Discover & Search
Research Agent uses searchPapers and exaSearch to find 250M+ OpenAlex papers on NPAs, then citationGraph traces influences from Rozina Akter and Jewel Kumar Roy (2017, 57 citations) to recent works like Pawan Kumar et al. (2023). findSimilarPapers expands to efficiency studies like Ather Hassan Dar et al. (2021).
Analyze & Verify
Analysis Agent applies readPaperContent to extract NPA ratios from Akter and Roy (2017), then runPythonAnalysis with pandas for regression on bank data, verified via verifyResponse (CoVe) and GRADE grading for empirical claims. Statistical verification confirms DEA results in Dar et al. (2021).
Synthesize & Write
Synthesis Agent detects gaps in NPA mitigation across Indian banks (Ganesan, 2001 vs. Kumar et al., 2023), flags contradictions in efficiency metrics, and uses exportMermaid for cause-effect diagrams. Writing Agent employs latexEditText, latexSyncCitations for 10+ papers, and latexCompile for reports.
Use Cases
"Run regression on NPA determinants from Ikram et al. 2016 and Indian bank data."
Research Agent → searchPapers → Analysis Agent → readPaperContent (Ikram et al.) → runPythonAnalysis (pandas regression on NPL factors) → researcher gets CSV of coefficients and p-values.
"Draft LaTeX report comparing NPA efficiency in Dar 2021 and Arora 2014."
Synthesis Agent → gap detection → Writing Agent → latexEditText (structure report) → latexSyncCitations (10 papers) → latexCompile → researcher gets PDF with diagrams.
"Find code for DEA models in banking efficiency papers."
Research Agent → paperExtractUrls (Dar et al. 2021) → paperFindGithubRepo → githubRepoInspect → researcher gets Python scripts for Malmquist/SFA replication.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ NPA papers: searchPapers → citationGraph → DeepScan (7-step with GRADE checkpoints on Akter 2017 claims). Theorizer generates mitigation theories from Ganesan (2001) profit functions and Farooq et al. (2019) causes, outputting structured hypotheses. DeepScan verifies NPA impacts via CoVe on profitability regressions.
Frequently Asked Questions
What defines Non-Performing Assets Management?
It covers determinants, impacts, and mitigation of NPAs in banks, including macroeconomic and bank-specific factors driving NPA ratios in developing markets.
What methods analyze NPAs?
DEA, Malmquist, SFA treat NPAs as bad outputs (Dar et al., 2021); CAMELS and Z-index assess solvency (Kumar et al., 2023); profit functions link to viability (Ganesan, 2001).
What are key papers on NPAs?
Akter and Roy (2017, 57 citations) on Bangladesh NPL impacts; Dar et al. (2021, 41 citations) on Indian efficiency; Ikram et al. (2016) on microeconomic determinants.
What open problems exist in NPA research?
Predictive recovery models lack strength beyond descriptive DEA/CAMELS; ownership effects on NPAs need cross-country data; real-time macroeconomic integration for NPA forecasting remains unaddressed.
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