# Resolution of Non-performing Assets of Commercial Banks: The Evidence from Banker’s Perspective in Indian Banking Sector

TL;DR: In this article , a structured questionnaire has been developed and data has been collected from officers in different banks in India, especially working in the credit department, which has been empirically tested for reliability and validity using confirmatory factor analysis (CFA) and also Z-test for checking the significance of explored and confirmed factors.

Abstract: The present article draws on the banker’s perspective and extracts some practical insights about the factors behind specific NPAs resolution strategies. Based on the thorough review of the perspective, conceptual and empirical literature, and using exploratory factor analysis (EFA), the study has identified 21 dimensions for ‘management of NPAs’. The empirical analysis of these dimensions has extracted 7 factors for management to be significant. A structured questionnaire has been developed and data has been collected from officers in different banks in India, especially working in the credit department. The questionnaire has been empirically tested for reliability and validity using confirmatory factor analysis (CFA) and also Z-test for checking the significance of the explored and confirmed factors. The present research work offers pragmatic suggestions for banking regulators, on improving the asset quality of banks in India and also throws new insights on effective credit management in banks. JEL Codes: G01, G21, G32, E44

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TL;DR: In this paper, a general formula (α) of which a special case is the Kuder-Richardson coefficient of equivalence is shown to be the mean of all split-half coefficients resulting from different splittings of a test, therefore an estimate of the correlation between two random samples of items from a universe of items like those in the test.

Abstract: A general formula (α) of which a special case is the Kuder-Richardson coefficient of equivalence is shown to be the mean of all split-half coefficients resulting from different splittings of a test. α is therefore an estimate of the correlation between two random samples of items from a universe of items like those in the test. α is found to be an appropriate index of equivalence and, except for very short tests, of the first-factor concentration in the test. Tests divisible into distinct subtests should be so divided before using the formula. The index
$$\bar r_{ij} $$
, derived from α, is shown to be an index of inter-item homogeneity. Comparison is made to the Guttman and Loevinger approaches. Parallel split coefficients are shown to be unnecessary for tests of common types. In designing tests, maximum interpretability of scores is obtained by increasing the first-factor concentration in any separately-scored subtest and avoiding substantial group-factor clusters within a subtest. Scalability is not a requisite.

37,235 citations

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TL;DR: A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models and two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes.

Abstract: Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model A drawback of existing indexes is that they estimate no known population parameters A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI) FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics An example illustrates the behavior of these indexes under conditions of correct specification and misspecification The new fit indexes perform very well at all sample sizes

21,588 citations

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TL;DR: This book deals with probability distributions, discrete and continuous densities, distribution functions, bivariate distributions, means, variances, covariance, correlation, and some random process material.

Abstract: Chapter 3 deals with probability distributions, discrete and continuous densities, distribution functions, bivariate distributions, means, variances, covariance, correlation, and some random process material. Chapter 4 is a detailed study of the concept of utility including the psychological aspects, risk, attributes, rules for utilities, multidimensional utility, and normal form of analysis. Chapter 5 treats games and optimization, linear optimization, and mixed strategies. Entropy is the topic of Chapter 6 with sections devoted to entropy, disorder, information, Shannon’s theorem, demon’s roulette, Maxwell– Boltzmann distribution, Schrodinger’s nutshell, maximum entropy probability distributions, blackbodies, and Bose–Einstein distribution. Chapter 7 is standard statistical fare including transformations of random variables, characteristic functions, generating functions, and the classic limit theorems such as the central limit theorem and the laws of large numbers. Chapter 8 is about exchangeability and inference with sections on Bayesian techniques and classical inference. Partial exchangeability is also treated. Chapter 9 considers such things as order statistics, extreme value, intensity, hazard functions, and Poisson processes. Chapter 10 covers basic elements of risk and reliability, while Chapter 11 is devoted to curve fitting, regression, and Monte Carlo simulation. There is an ample number of exercises at the ends of the chapters with answers or comments on many of them in an appendix in the back of the book. Other appendices are on the common discrete and continuous distributions and mathematical aspects of integration.

19,893 citations

01 Jan 2016

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TL;DR: This paper reviewed the major design and analytical decisions that must be made when conducting exploratory factor analysis and notes that each of these decisions has important consequences for the obtained results, and the implications of these practices for psychological research are discussed.

Abstract: Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed.

7,590 citations