CHAPTER 7.pdf

BANKS AND FINANCIAL INSTITUTIONS ISBN:978-93-94676-00-8 Page 117
CHAPTER-7
MODEL AND CAMEL ANALYSIS OF BANKING
1.Bhadrappa Haralayya
Post Doctoral Fellowship Research Scholar,
Srinivas University, Mangalore, India.
bhadrappabhavimani@gmail.com
Orcid id-0000-0003-3214-7261
2.P. S. Aithal,
Professor, College of Management and Commerce,
Srinivas University, Mangalore, India.
psaithal@gmail.com
Orcid id-0000-0002-4691-8736
ABSTRACT
The banking writing has been commanded by three noteworthy methodologies, in particular, the
creation approach (benefit arrangement approach); the intermediation approach (resource approach) and
present day approach The initial two customarily recognized methodologies vary just in the specification
of banking activities. The generation approach expect that banks deliver credits and stores account
services, utilizing work and capital as inputs. In addition, the number and sort of exchanges or reports
handled are estimated as outputs. The second methodology sees banks as the money related middle
people among savers and speculators. It has been contended that these methodologies can't catch the
double idea of the banking system. However, under the intermediation approach, banks are considered
as go-betweens among savers and speculators and inside this methodology, the stream is commonly
expected to be proportional to the sock of money related estimation of accounts, for example, number of
advances (in '),stores, borrowings, ventures, and so forth. Then again, the third methodology
incorporates number of explicit activities of banking into the established hypothesis and along these
lines, adjusting them. recommended that the creation approach ascertains better as far as data given by
the banks at branch level,whereas, the intermediation approach is most appropriate for the money related
middle people at the whole dimension. Further, there dependably emerge impediments in gathering the
information for the quantity of exchanges and reports prepared. Subsequently, the intermediation
approach is for the most part supported in the banking literature. The accessible writing on the
identification of outputs and inputs in the banking system prompted the foundation of the benefit, client
cost and esteem included methodologies, which are seen as the variations of intermediation approach.

Every one of the methodologies center around different money related activities performed by the banks
and basically utilize the budgetary information given by the banks. This methodology is best spoken to
through proportion based CAMEL (Capital sufficiency, Resource quality, The board, Income, Liquidity)
approach. To quantify the execution of banks, different parts of CAMEL are gotten from the asset report
given by the banks toward the finish of the money related period
Key words: DEA, MODELS, CAMEL Analysis.
7.1 INTRODUCTION
This part presents and legitimizes kind of systems received in assessing the dimension of effectiveness
and TFP of banks in India. The present part of the examination is isolated into three segments. The area
I talks about problem, need and targets of the investigation. The segment II talks about the organization
of the information and determination of data sources and yields to evaluate the outcomes as far as
proficiency and efficiency markers. At last, the segment III tosses light on models embraced to quantify
effectiveness and TFP level along with determinants.
7.2 STATEMENT OF PROBLEM
The keeping money segment, center of country's financial system, performs crucial capacity of financial
intermediation through liquidity and hazard change. A productive financial intermediation will give a
quality towards the advancement of monetary recuperation by channelizing assets to various segments
of economy at the least conceivable expense. The enhancement in the profitability and effectiveness
levels and the resultant decrease in the expense of giving the financial administrations will help in
financial incorporation activities. The profitability and proficiency of banks, therefore, basically ascribes
efficiency and effectiveness of all the monetary exercises and is the matter of worry for the arrangement
producers and economy watchers. The worldwide managing an account emergency has featured risks of
flighty keeping money and through the subject of profitability brilliance the saving money division
needs to concentrate on ad libbing their profitability and thusly, profitability (Indian Banking ,
2011).Thus, proficiency and efficiency of banks has a vital bearing on the generally speaking monetary
wellbeing of a country. In addition, the effect of financial deregulation on effectiveness and efficiency of
banks is restrictive on the nature of direction and supervision of the saving money system Some contend
that financial changes enhance the condition for market rivalry, bringing about increasingly effective

designation of rare financial assets Then again, a few specialists called attention to antagonistic impact
of financial changes on banks' proficiency and efficiency. Subsequently, opposite perspectives on the
connection between the financial changes and the performance as far as proficiency and efficiency of the
Indian saving money segment requires an inside and out observational analysis. The Indian setting aside
extra cash section is of unequivocal excitement for different reasons. Immediately, because of rational
and continuing advancement of Indian setting aside some cash division after the introduction of
budgetary changes with an objective to progress expanded, gainful and aggressive monetary framework.
Furthermore, the distinctive belonging structure of open, private and remote banks offers a chance to test
whether the effectiveness and productivity dimension of banks contrast among these proprietorship
structures. Thirdly, in spite of the fact that the present study identifies with the Indian experience, it has
a more extensive application. The most striking is its broad reach. It is never again bound to just
metropolitans and urban territories in-certainty it has in contact with the remote corners of the nation
moreover. In this manner, the study worried about far reaching estimation of the Indian division as far as
effectiveness and productivity level presumes massive criticalness
7.3 NEED OF THE STUDY
In the present demanding business condition, enhanced effectiveness will assist keeps money with
attaining monetary wellbeing and profitability. Along these lines, to create solidness and flexibility of
the whole money related framework, enhanced productivity and proficiency of the saving money
framework is a distinct positive. The present study is an endeavor toward this path to report the
proficiency and productivity dimension of the saving money area in India amid post-deregulation period.
To be sure, an endeavor has been made to give a knowledge in deciding different elements that will cut
down the intermediation cost, enhance productivity, increment wellspring of salary and benchmark
banks for different information sources/yields in order to enhance in general execution of managing an
account segment in India. In such manner, an appraisal of the execution of banks assumes urgent job, as
the saving money part in India has gone under monetary developments and stuns (either because of
changing directions or unforeseen stuns). Specifically, the present study looks for entomb alia to address
the accompanying inquiries:
What is the dimension of cost productivity in Indian managing an account segment and how has it
shifted after some time?

1. Have the managing an account changes executed by the Indian government enhanced Indian
keeping money effectiveness level? Does the dimension of proficiency and productivity shift
crosswise over various banks?
2. Are there any economies or diseconomies of scale in the Indian managing an account area?
3. What are the principle determinants of the execution of the Indian managing an account
industry?
4. How deregulation has influenced the productivity dimension of the Indian keeping money
division?
5. What decides the productivity development of the Indian keeping money division?
6. In this manner, the exchange gives the accompanying objectives to concentrate on estimating
the execution of the keeping money area in India.
7. To study the development, advancement and structure of managing an account industry in
India with reference to post-change period.
8. To study the between bank investigation of different proportions of effectiveness, productivity
and their segments over the timeframe.
9. To inspect the impact of different determinants on effectiveness and productivity of Indian
business banks.
10. To give proposals for the enhancements in the methodology and working of the business
banks in the focused time.
7.4.DATA AND SPECIFICATION OF INPUT-OUTPUT VARIABLES
7.4.1Data Description In the present commitment,
Results depended on the information surveyed from auxiliary sources. In like manner, the reports given
by Hold Bank of India in type of "Essential Factual Returns of Booked Business Banks in India,
Measurable Tables Identifying with Banks in India, A Profile of Banks", "Indian Banking Affiliation"
and "Financial Overview" has been thought about. The examination is kept to the booked business banks
(SCBs) that incorporate open, private and remote area banks pulling back the territorial country banks.

The board information of 62 banks has been consolidated and inspected for the period. The present
examination endeavored to catch the delayed consequences of deregulation process that has been started
by permitting passage of new private and remote area banks amid mid nineties. The other discernible
attributes of deregulation process for the banking division in India has been remittance of 74 percent
outside interest in private area banks, authorizing of branches for planned business banks in staged way
and deregulation of interest rates with broadening of money related markets in India .
Also, there are number of private and outside area banks that were not in activities before. These banks
are directly having 15.9 percent and 7.2 percent piece of the overall industry in business banking part in
India. Subsequently, it ends up basic to join these banks in the last example. Be that as it may, to have
increasingly exact and effective insight to the outcome, information removed from the referenced reports
has been made into the reasonable board information shape to have a far reaching information base. The
critical favorable position of board information is that given reliably substantial timeframes, it grants
predictable estimation for effectiveness and profitability of individual manages an account with better
degrees of opportunity .
7.4.2 Specification of Inputs and Outputs In registering effectiveness score of banks,
The most challenging undertaking that investigators and specialists experience is determination of fitting
inputs and outputs for demonstrating the bank conduct. There has been long standing discussion in
banking productivity writing on what establish inputs and outputs of a bank affirmed in one of the
investigation that "there have been nearly the same number of presumptions of inputs and outputs as
there have been utilizations of DEA". Accordingly, it has been ordinarily acknowledged that the
decision of input and output variables in the effectiveness studies altogether influences the consequences
of the investigation. Further, the expense and output estimation in the banking division are, in particular,
difficult for the reason that a large number of the monetary services are commonly created and costs are
regularly alloted to a heap of money related services
What's more, one more methodology talked about in the cutting edge banking writing is working
methodology or income-based methodology. This methodology consider bank as specialty unit which is
concerned with the motivation behind producing the income from aggregate expense brought about for
maintaining the business activities (Leightner and Lovell, 1998). In this way, it considers add up to

income as an output that draws in interest and non-interest income and aggregate expense including
interest and non-interest costs as inputs for the banks .
Table: 7.1: classifications of inputs and outputs
Categories Sub-Categories Input Output
Assets Current/Liquid Assets - 16
Loans Less than One Year 8 230
Long-Term Loan - 6
Fixed Assets/Physical
Capital 99 2
Investments 1 45
Other Assets - 4
Security 1 34
Off-Balance Sheet - 11
Total Assets 10 1
Liability/ Deposit Current Liabilities/Deposits 148 48
Equity/Share
Capital/Financial 1 1
Income Statement Items Interest Income 1 39
Income
Non-Interest Income 1 41
Total Income - 3

Expenses Interest Expense 26 -
Non-Interest Expense 171 -
Profit Operating Profit - 2
Net Profit - 5
Others Number of Transactions 1 11
Other Input/ Output
Category 9 7
Source: Anouze, 2010, p.48.
Consequently, a large portion of the examinations use advances, propels and other acquiring
resources/investments as their yields Be that as it may, other procuring resources ordinarily incorporate
things under government securities, investment securities, exchanging securities, different securities,
value investment and other investment. there, examines likewise affirm disaggregation of advance
segments into related parts while making them into the consideration for yield vector, for example,
lodging and different credits; land, business and individual advances present moment and long haul
advances shopper and non-customer advances Others have attempted to disaggregate other winning
resources into investment and fluid resources or investment into government securities, treasury bills
and investment into open and private endeavors The other extra yields that are being utilized with
various blends in order to make extra an incentive to the clients along with advances and investment or
other acquiring resources, incorporate number of branches .
From the above exchange, it has been affirmed that intermediation approach is most appropriate for
estimating the execution of banks as far as effectiveness and efficiency measures and the equivalent has
likewise been affirmed by Then again, various different methodologies like esteem included
methodology, salary approach have additionally been utilized amid the most recent couple of years
ceaselessly Along these lines, the suitability of each methodology differs as per the conditions. This
makes it clear that banks attempt concurrent functions. However, in view of the down to earth
considerations and to analyze the heartiness of the evaluated in general or monetary or cost productivity

scores under various elective methodologies, the present investigation for the most part centers around
three methodologies i.e., intermediation, production and esteem included methodology and afterward
pick the proper methodology in accordance with reasonable info and yield blends utilizing the sensitivity
analysis .
7.3 SENSITIVITY ANALYSIS
It is to be kept into consideration that the productivity score estimated with the assistance of various
models are especially delicate to the selection of data sources and yields made in assessing the score.
Consequently, the determination of sources of info and yields made in evaluating the outcomes ought to
be made cautiously. In this way, so as to pick the suitable blend of data sources and yields, the present
examination drew nearer with sensitivity analysis for eight mixes. Three fundamental methodologies, to
be specific, intermediation, production and pay were used to assemble distinctive models.
Further, it merits referencing that the blends of data sources and yields referenced in the present
undertaking are regularly utilized in the writing and have been considered by writers in various
economies to gauge the execution of the managing an account segment The model M1 contains three
yields (Other Pay, Investments, Advances) and four data sources (Number of Representatives, Loan able
Assets, Settled Resources, Value). The model 2 consolidates three yields (Other Pay, Investments,
Gaining Advances/Performing Advance Resources) and four sources of info Number of Workers,
Stores, Settled Resources, Value (semi).
The model 3 incorporates two yields (Investments, Advances) and three information sources (Number of
Workers, Loan able Assets, Settled Resources). The model 4 includes two yields (Investments,
Acquiring Advances) and three data sources (Number of Representatives, Stores, Settled Resources).
The model 5 determines four yields (Other Salary, Investments, Stores, Winning Advances) and four
information sources (Number of Workers, Borrowings, Settled Resources, Value).
The model 6 makes reference to three yields (Investments, Stores, Acquiring Advances) and four
sources of info (Number of Workers, Borrowings, Settled Resources). The model 7 approaches yields
(Other Pay, Net Intrigue Salary and three sources of info (Number of Workers, Loan able Assets, Settled
Resources). At long last, the model 8 involves two yields (Other Salary, Net Intrigue Pay and three
sources of info (Loan able Assets, Settled Resources, Value).

Among the eight models, show 1, 2, 3 and 4 are considered under intermediation approach, demonstrate
5 and 6 under production approach and model 7 and 8 under salary approach. Further, the model 2 has
been considered as the center model, though, the staying seven models have been utilized in the
sensitivity analysis so as to catch the various parts of TE.
Table 7.4.2: Sensitivity Analysis Using Spearman’s Correlation
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Model 1 1.000 0.890** 0.822** 0.802** 0.649** 0.506** 0.782** 0.728**
Model 2 1.000 0.829** 0.906** 0.560** 0.392** 0.802** 0.741**
Model 3 1.000 0.869** 0.455** 0.471** 0.612** 0.565**
Model 4 1.000 0.454** 0.399** 0.706** 0.643**
Model 5 1.000 0.925** 0.545** 0.514**
Model 6 1.000 0.375** 0.342**
Model 7 1.000 0.974**
Model 8 1.000
Note: ** represent correlation is statistically significant at the 1.0 per cent level (2-tailed).
The data set in every one of the models comprise of 62 booked business banks amid the timespan and
the basis for dismissal seems to have the abnormal state of relationship with the Model 2. Following the
standard for dismissal and upheld by the evaluations of relationship coefficient have been processed in
the present examination. Consequently, the high relationship coefficient between the base model and
different models demonstrate that comparative outcomes can be given by these models moreover. Thus,
the consequence of affectability analysis affirms that all models are having factually noteworthy
relationship coefficient with model 2. Therefore, the aftereffects of the various models have been

dismissed for model 2 based on aforementioned paradigm. In totality, show 2 (Other Pay, Speculations,
Gaining Advances/Performing Advance Resources and four data sources Number of Employees, Stores,
Settled Resources, Value) has been considered as the most favored case. Hence, the decision of
information and yield factors has likewise been viewed as suitable based on unfair intensity of the
model. In this manner, other pay is salary from shaky sheet exercises, speculation incorporates venture
made by banks crosswise over various tasks and gaining propels incorporate advances short non-
performing resources. Then again, factors including number of employees working in the banks, stores
(time and request), settled resources and value (add up to capital + stores and excess) have been joined
as the sources of info.
7.5 MODELS
The present sub-area gives a diagram about the effectiveness and profitability measurement models that
are generally used to quantify the performance of basic leadership units. The audits about different
models have been done for appropriate comprehension about the structure for the further analysis.
Likewise, the segment additionally gives an insight into the To bit approach and granger causality way
to deal with confirm the determinants in charge of enhancing the performance of the banks in India.
7.5.1 CAMEL METHODOLOGY
Prior the strategy creators in the saving money part depended on monetary proportions to decide relative
performance of banks. The purpose for utilizing these proportions is to gauge the performance of the
banks that are of comparable size to control a few highlights of keeping money tasks with certain
benchmark The writing recommends utilization of various proportions as far as spread, resource quality,
liquidity, edition cost, business per worker, returns on resources, and so forth so as to quantify the
performance of managing an account part One of the techniques that have been generally embraced to
quantify the performance of banks utilizing the money related bookkeeping proportions is the CAMEL
approach. This methodology is a proportion based model to assess performance of the banks in order to
offer exceptional point of view in setting with the elements important productivity of banks. The
CAMEL approach rates performance of the banks utilizing five key measurements: capital sufficiency
(C), resource quality (A), management (M), profit (E), and liquidity (L). Capital sufficiency and
liquidity merit significance as administrative imperatives, while income merit significance as a
noteworthy target. The benefit quality is a prime pointer of anagement quality and management is

behind great and terrible choices that influence other CAMEL viewpoints. There have been sufficient
confirmations in created and creating economies for estimating the performance of the saving money
division utilizing CAMEL approach To gauge the outcomes, following markers have been utilized in the
present arrangement. C=Total Advances/Add up to Resources; A=Non-performing resources/Net
advances; M=Total progresses/Add up to stores; E=Interest salary/Add up to pay and L=Government
securities/Add up to resources. Nonetheless, add up to propels incorporate the every one of the advances
given by banks inside India or outside, non-performing resources incorporate information with respect to
the benefits that stayed dicey for over 90 days. Net advances are add up to propels short significant
reasoning‟s. Moreover, add up to stores incorporate sort and request stores; premium salary incorporates
enthusiasm from procuring resources and aggregate pay involves pay from both intrigue sources and
cockeyed sheet exercises. At last, government securities incorporate ventures made by banks in
securities under supervision of government. To have a comprehensive image of the keeping money
division in India utilizing CAMEL approach data have been incorporated at total dimension for open,
private and foreign area banks .
7.5.2 EFFICIENCY MEASUREMENT MODELS
The money related markers of banks as far as proportions, for example, working cost isolated by
aggregate resources, returns on value or resources, and so forth have been utilized to analyze
productivity of basic leadership units. Be that as it may, the utilization of such money related
proportions has certain confinements. The primary issue is that money related proportions are viewed as
deceiving pointers of proficiency on account of no control for item blend or info costs. Furthermore,
utilizing the expense to-resource proportion expect that all benefits are similarly exorbitant to create
significant yields and subsequently all areas have break even with expenses for working together. At
last, the utilization of straightforward proportions can't recognize X-effectiveness, scale and degree
proficiency gains Notwithstanding, there are different techniques additionally which can be utilized for
estimating performance of the banks as far as productivity and different parameters As various sorts of
estimation approaches are available, they contrast from each other based on the self-assertive
suppositions. Be that as it may, two basic methodologies i.e., parametric and non-parametric, have been
broadly connected to gauge the overall performance of basic leadership units. The parametric
econometric methodologies incorporate Stochastic Boondocks Approach, Thick Outskirts Approach,

and Dissemination Free Methodology and non-parametric (straight programming methodology)
incorporates Free Transfer Body, Data Envelopment Analysis, and so forth.
7.5.3 DATA ENVELOPMENT ANALYSIS
Utilizing the prior work of Farrell () amid the year 2011 and 2013, Charnes, Cooper and Rhodes (CCR)
created DEA demonstrate as a use of straight programming method for the performance measurement of
DMUs, having numerous info and yields. The direct programming system intends to gauge the
effectiveness of DMUs and complete the analysis that gives information with respect to the utilization of
accessible assets to deliver the yields. Contrasted with SFA, DEA demonstrates a few focal points. Right
off the bat, it handles various sources of info and yields in a non-complex manner. Furthermore, it
doesn't require any underlying presumption about an explicit utilitarian form connecting data sources
and yields like stochastic outskirts analysis .However, there are a few issues which should be settled
when DEA is to be completed. Right off the bat, in the decision of DEA, different models are arranged
based on input or yield introduction. Incase, the information sources are unbendable and not under the
control, yield based model has been viewed as progressively fitting. Despite what might be expected,
when the sources of info are under the control of management, the information based formulations are
proper for subtleties. Furthermore, the decision and number of DMUs ought to likewise be mulled over.
DMUs incorporate assembling units, banks, healing centers, inns, police headquarters, impose
workplaces, colleges, guard base, control plants, and so forth. From the most recent four decades, the
use of DEA has been utilized in various DMUs. There has additionally been a wide application on non-
benefit associations however their performance measurement isn't exactly simple like the assembling
units (where the performance can be estimated as far as benefits per annum).The extreme goal of DMU
is to make an incentive through the transformation of contributions to yields. In practices, the blend of
information sources and yields are plausible just if the yield amount can be created from the given
arrangement of data sources. The two basic ideas broadly used to assess the performance of DMUs are
profitability and effectiveness. Debreu (2012) and Koopmans (2013) started these two terms in the
monetary writing. These two terms are regularly treated as comparable to one another, which is for the
most part not true. The work has been further, reached out by Farell (2013) for the measurement of
performance of basic leadership units. Efficiency essentially can be the proportion of scalar proportion
of yields to inputs. It tends to be estimated as far as incomplete efficiency like work profitability (yield
per individual), arrive profitability (yield per hectare), and so forth or TFP which is the proportion of

total yields to inputs. Then again, effectiveness is the correlation of the watched and the ideal estimation
of basic leadership unit for yields and data sources.
7.5.4 INPUT ORIENTED VARIABLE RETURNS
The straight programming system DEA has been broadly received to quantify the dimension of
proficiency in respect to surface .Since the appropriation of DEA by extensive number of studies has
been completed to gauge the dimension of productivity crosswise over various DMUs in various
fragments of economy by using DEA approach. proposed a model with a supposition of steady returns
to scale i.e., CRS to bring into thought the information oriented approach. The presumptions of CRS are
fitting just when all the DMUs are working at the ideal scale. The conditions for flawed challenge like
budgetary limitations, natural imperatives, and so on may make issues for the DMUs. Along these lines,
to come over his deterrent, Financier, {BCC} proposed an expansion model for CRS-DEA which think
about the supposition of variable returns to scale (VRS). The CRS-DEA model expresses that an
expansion in the information sources will lead towards same proportionate increment in the yield.
Further, in the event that DMU don't work at ideal scale level, utilizing CRS for such DMUs will give
TE score influenced by scale effectiveness (SE). Along these lines, the utilization of VRS can be
connected to empty the SE impacts. To defeat the impediments of CCR-DEA model, BCC built up a
model to figure the VRS-TE score of the DMUs. VRS suggests that an expansion in sources of info may
results in either pretty much proportionate increment in the yield. The VRS model uses double of CRS
model with an additional imperative of convexity i.e., N1'λ=1.Further, the proportion of proficiency
utilizing DEA approach can be isolated into info and yield oriented measures. As referenced before,
input oriented measure clarify the technique about the corresponding reduction in contributions without
modifying the dimension of yields. In the meantime there can be corresponding extension of yield
without adjusting the information sources, hence, such cases can be considered as yield oriented
measure. The present investigation utilized info oriented VRS model to appraise the outcomes. The
yields are chosen by the objectives set by the administration staffs and the execution of DMUs will
likewise be needy upon the scale of tasks attributable to which input oriented VRS model has been used
in the present piece. Consequently, following model can be formulated Min θ, λ θsubject to the
constraints
θxi - Xλ >= 0

N1’λ=1
λ >= 0
Where, θ is efficiency score; xi* is the arrangement of information vectors for the ith firm; yi is the
vector of yields. The info and yield lattice is characterized by X and Y for N firms; λ is vector of Nx1
limitations. Further, the convexity requirement N1'λ=1 guarantees the nearness of wasteful firms
benchmarked against the organizations with comparative size. Also, the yield and info arranged models
are equal measures of TE just when CRS exists Given the VRS innovation, one can acquire the SE
measure for the DMUs. This measure of efficiency can be determined by measuring the efficiency score
with the two CRS and VRS-DEA models. The CRS-TE scores can be disintegrated into VRS-TE or PTE
(unadulterated specialized efficiency) and SE. The scale efficiency can be generally measured as theratio
of TECRS to TEVRS. In any case, hecontrast between CCR model and BCC model can be illustrated as
Source: Webb (2003), p.310
The scale efficiency measure, the CRS and VRS wildernesses are represented in the above figaure. The
beam Operation in the demonstrates all DMUs working under CRS. In any case, loosening up the
supposition and utilizing VRS show, it tends to be said that efficiency levels are disaggregated into
unadulterated specialized efficiency and scale efficiency. An interpretation of VRS is appeared in which
is the arched outskirts XXi. Any DMU, on the proficient outskirt Operation is by and large effective like
U3. Any DMU not showing up on this wilderness will show up as wasteful. In the wake of representing
VRS, U1 and U5 are accounted for as proficient, with U1 working under expanding comes back to scale
(IRS) and U5 working under diminishing comes back to scale (DRS). The accompanying relationship

exists with all measures being bound by zero and one: DC/DU2DC/DVIn expansion, the accompanying
relationship additionally exists:OTE = PTE x SEThus, in the present study, the dimension of efficiency
and profitability of the managing an account division in India has been determined utilizing VRS-DEA
show. With the accessibility of the value information for the arrangement of data sources and yields that
are utilized to measure the efficiency dimension of DMUs, it is conceivable to consider conduct targets
like minimization of cost, sohat both specialized and allocative measure of efficiency can be determined.
So as to decide cost minimization by embracing VRS show, one can without much of a stretch get TE
and allocative efficiency parameters. The accompanying DEA show has been embraced in the present
study for cost minimization.Min λi, xi* wi xi*
Färe-Primont Method
Where, Do and Di are input and yield separate capacities All the expressed indexes are notable in
profitability writing. Among these indexes, the most prominent is MPI propounded by There are
whirlwind of studies which are focused on particulars of MPI to assess execution of DMUs However,
extremely insufficient consideration has been paid to assess the dimension of effectiveness and TFP of
business banks using multiplicatively entire indexesike Hicks-Moortseen add up to factor profitability
(HMTFP) index and Admission Primont TFP (FPTFP) index. Hence, to keep away from issues related
with DEA strategy, O' proposed another method for decomposing multiplicatively total TFP index into
proportions of specialized change and different proportions of productivity change. O' Donnel (2008)
uncovered that fulfillment is a basic prerequisite for an economically-meaningful deteriorations for TFP
change. There are fundamentally two main methodologies in the writing for decomposing TFP
development. First methodology focuses on the general proportions of proficiency change and combines

them to make the TFP index (Recoil, 2001). Second, approach begins with a conspicuous TFP index
pursued by decay into a meaningful way Beam and The two kinds of fulfillment identified with
proportion type and distinction type index system are defined by include Multiplicative Culmination and
Added substance Culmination which determine contrast in TFP between the two timeframes. O'Donnell
(2010b) explained that the Main TFP index of is neither additively nor multiplicatively total, owing to
which it might be considered as a problematic proportion of TFP change. Admission et al. delineated
that yield situated Malmquist coincides with HMTFP indices if and just if the innovation is inversely
homothetic and demonstrates steady comes back to scale Against this setting, the present investigation
examines the proficiency and profitability estimation of business banks in India using the index that
fulfills all economically-significant adages and trial of index number hypothesis alongwith personality
aphorism and a transitivity test named as HMTFP Index. Additionally, an endeavor has been made in the
present investigation to distinguish the similar consequence of profitability examination based on three
indexes, to be specific, MPI, HMTFP and FPTFP index for the banking sector in India.
7.5.5 Tobit Regression Analysis
To estimate the determinants of cost inefficiency, the present study incorporates Tobit regression
technique. The Tobit model, also known as censored regression model, is designed to estimate linear
relationships between variables when there is either left or right-censoring in the dependent variable
(also known as censoring from below and above, respectively). Censoring from above takes place in
cases where a value is at or above some threshold limit. In such cases, the true value might be equal to
the threshold or higher than that. In the case of censoring from below, values which fall at or below
some threshold are censored.The Tobit regression model is an econometric technique proposed by Tobin
(1958) to describe relationship between non-negative dependent variable (Yi) and an independent
variable (or vector) (Xi). The model proposes that there is a latent (i.e. unobservable) variable which is
linearly dependent on Xi via a parameter (vector) (β), which determines the relationship between the
independent variable (vector) Xi and the latent variable (just as in a linear model). In addition, there are
normally distributed error terms (ui) which capture random influence on this relationship. The
observable variable Yi is defined to be equal to the latent variable whenever the latent variable is above
zero or it is zero otherwise. Mathematically the equation can be written down as:

Where, is the random term having typical circulation with fluctuation σ2, y* is the dormant variable, βi
is the vector of obscure parameters and xi is the vector of informative variables. The essential powerful
variables considered in the present examination are other than information and yields variables utilized
in evaluating cost productivity and its parts amid the period. Every one of the variables considered in the
present investigation are of quantitative nature. The present examination joins the models connected that
wherein subordinate variables incorporate cost wastefulness (CIE), specialized wastefulness (TIE) and
allocative wastefulness (AIE).Therefore, wastefulness has been clarified as opposed to effectiveness in
all models in the present investigation. The main differences in the clarification of the outcomes by the
coefficients of autonomous variables are reflected in the sign related with them. A positive coefficient
shows an expansion in the dimension of wastefulness or decline in the dimension of proficiency, though,
negative coefficients infers decline in the dimension of wastefulness or enhancement in the dimension of
effectiveness. The illustrative variables which are viewed as appropriate in the Tobit show in order to
assess the components that decide the effectiveness of chose banks in India are returns on value (ROE),
proportion of ad costs to working costs (An/OE), proportion of correspondence costs to working costs
(COM/OE), proportion of advances to need part (PS/Promotion), bank broadening (BD), the board
measure (MGT), size of bank (Estimate), staff efficiency (SP), credit quality (NPA/NA), net premium
edge (NIM), piece of the pie (MS), dummy variable for ownerships(DPub, DPrv). Following Tobit
model is used to develop the relationship between cost inefficiency and the variables affecting it.
Model 1: Cit= α1+ β1(ROE)it
Model 2: Cit= α2+ β2(A/OE)it
Model 3: Cit= α3+ β3(COM/E)it
Model 4: Cit= α4+ β4(PS/AD)it
Model 5: Cit= α5+ β5(BD)it
Model 6: Cit= α6+ β6(MGT)it

Model 7: Cit= α7+ β7(Size)it
Model 8: Cit= α8+ β8(SP)it
Model 9: Cit= α9+ β9(NPA/NA)it
Model 10: Cit= α10+ β10(NIM)it
Model 11: Cit= α11+ β11(MS)it
Model 12: Cit= α10+ β12(DPub)it+ β13(DPub)it
Model 13: Cit= α + β1(ROE)it+ β2(A/OE)it+ β3(COM/OE)it+ β4(PS/AD)it+ β5(BD)it+ β6(MGT) it + β7
(Size) it + β8 (SP) it + β9 (NPA/NA) it + β10 (NIM) it + β11 (MS) it + β12 (DPub) it + β13 (DPrv) it
Model 13: Tit= α + β1(ROE)it+ β2(A/OE)it+ β3(COM/OE)it+ β4(PS/AD)it+ β5(BD)it+ β6(MGT) it + β7
(Size) it + β8 (SP) it + β 9 (NPA/NA) it + β10 (NIM) it + β11 (MS) it + β12 (DPub) it + β13 (DPrv) it
Model 13: Ait= α + β1(ROE)it+ β2(A/OE)it+ β3(COM/OE)it+ β4(PS/AD)it+ β5(BD)it+ β6(MGT) it + β7
(Size) it + β8 (SP) it + β9 (NPA/NA) it + β10 (NIM) it + β11 (MS) it + β12 (DPub) it + β13 (DPrv) it
Where, α = constant; Cit, Tit and Ait = cost, technical and allocative inefficiency scores; ROE = returns
on equity of i-th bank in time period t; A/OE = ratio of advertisement expenses to operating expenses of
i -th bank in time period t; COM/OE = ratio of communication expenses to operating expenses of i-th
bank in time period t; PS/AD = ratio of priority expenses to total advances of i-th bank in time period t;
BD = ratio of other income to total assets of i-th bank in time period t; MGT = ratio of operating
expenses to total assets of i-th bank in time period t; Size = log of assets of i-th bank in time period t; SP
= ratio of (deposits + advances) to number of employees of i-th bank in time period t; NPA/NA = ratio
of non-performing assets to net advances of i-th bank in time period t; NIM = interest earned – interest
expended of i-th bank in time period t; MS = deposit of ith bank/ total deposit of 62 banks X 100; DPub
= Dummy variable for public sector banks; DPrv = Dummy variable for private sector banks It has been
suggested from the current writing that with the exception of a few factors there exist unidirectional
connection between the dimension of cost productivity and the determinants. For example the proportion
of nonperforming credits to add up to advances can be considered as the indicator of advance quality
and a negative relationship has been anticipated between the variable and the dimension of cost

effectiveness The explanation for that the lower an incentive for the proportion will help in upgrading
the dimension of cost proficiency of the banks over the timeframe. One of the important determinants of
the cost productivity is measure however the sign in regards to the coefficient has not been sure in the
literature. It may have presence of both positive and also negative sign. In the event that negative
coefficient exits, it is suggested that the little banks have more prominent access to the clients since the
board is near the general population, though, positive sign delineates that the administration can put
resources into the less hazardous portfolios and can create abnormal state of benefits and
incomes.urthermore, the two fundamental indicators specifically promotion and correspondence costs
have been incorporated into the present examination for breaking down the idea of operational
proficiency of banks at individual dimension. The normal signs in both the cases can be positive or
negative as the costs covering all the notice consumption and the correspondence uses may influence the
dimension of cost proficiency in positive or negative manner both
7.6 PANEL GRANGER CAUSALITY TEST
The majority of the study that has been consulted to identify the relationship among the financial
variables has focused on the capital market indicators of different countries. In terms of banking
efficiency literature, the relationship among efficiency of banks with management quality, loan quality,
bank capital and competition has been investigated. The present study is an endeavor for re-
establishment of relationship between TFP score and Holtz-Eakins
7.6.1 CROSS-SECTION DEPENDENCY TEST
There are different set of cross-section dependency tests to test the null hypothesis of zero dependency
across the panel decision making units. These tests are applicable to the panel ationary and unit root
dynamic heterogeneous panel with structural breaks and are presented with small T (time period) and
large sample (N) across cross-sections. Some of the tests include LM Test CD test statics Friedman‟s
test and Frees test Among these test statistics Friedman test statistics, a non-parametric test based on
Spearman‟s rank correlation coffecint has been used to estimate the cross-sectional dependency for the
estimates in the present study. The Friedman‟s test statistics based on the average Spearman‟s
correlation is given as:

Correlation estimates of residuals. Large value of Rave indicates the presence of non-zero cross-sectional
correlations. The Friedman‟s test statistics depicts an asymptotically χ2
distribution with t-1 degrees of
freedom, for fixed T and N.
7.6.2 PANEL UNIT ROOT TEST
In order to check the stationarity of data set, the present study uses panel unit root test rather than simple
Augmented Dickey Fuller (ADF) test statistics. The Panel unit root tests are although similar, but not
identical, to unit root tests carried out on a time series data.For testing unit root in panel data, two
assumptions can be made i.e., either the persistence parameters are common across cross sections (ρi = ρ
for all i, where, ρi are the autoregressive coefficients, i = 1, 2,…..N cross sections units or series) or ρi
vary freelyacross cross-sections. Therefore, the present study uses individual panel unit root test like Im,
Pesaran, Shin (IPS)”, Fisher-ADF”, Fisher-Philip Peron (PP)” rather than common unit root test i.e.,
Levin, Lin, Chu (LLC) test statistics.The assumptions regarding common unit root indicates that the
tests are estimated assuming common autoregressive structure for all of the series incorporated in the
panel structure. On the other hand, the individual unit root process allows for differentautoregressive
coefficients in each series involved in the panel. IPS begins by specifying the separate ADF regression
across the cross sections:
The null hypothesis regarding this equation can be written as,
H0 = αi = 0, for all i
Whereas the alternative hypothesis for the above equation can be written as

After estimating the separate ADF regressions, the average of the t-statistics for αi from the individual
ADF regressions is adjusted to calculate the desired test statistics. Having the data set of banks at
individual level over the period of time indicates the presence of effect on the operations and other
activities of banks individually and it might not be compulsory that the banks in one cross section is
going to have effect on the banks in the other cross sections over the period of time. Hence, the
appropriate unit root test model for the present study is individual test statistics. In addition to this, IPS
test is also made at individual level because selecting an individual test type helps better control over the
computational method and provides additional detail on the test results.Another important indicator is
regarding the lag values. Hence, for the group or pool unit root test, the automatic selection of lags has
been incorporated which involves information matrix criterion based on the number of lag difference
terms and the Andrews or Newly-West method for bandwidth selection. The null hypothesis for the IPS,
ADF and tests in the present study, includes that the data series of different determinants namely
business per branch, business per employee, ratio of net interest margin to total assets, profit per
employee, profit, return on assets and dTFP score have unit root.
7.6.3 PANEL CAUSALITY TEST
To test the casual relationship between performance indicators and TFP score, a pair-wise Dumitrescu
Hurlin Panel Causality tests statistics has been estimated after checking the unit root. This approach has
been initiated by the study of Dumitrescu-Hurlin, assuming all coefficients to be different across cross-
sections. This test statistics can be easily computed by simply running standard Granger Causality
regressions approach introduced in Granger for each cross-section individually. In the panel data settings
the commonly used least squares regression can take a number of different assumptions made about the
structure of the panel data.
−1forms, depending upon me period dimension of the panel, and i is the cross-sectional dimension. As
stated earlier also that there are alternative approaches to run causality tests in panel data models.
Therefore, in the present study, the approach proposed by Hurlin and Venet (2011); Hurlin (2014a);
Hurlin (2014b) that treats the autoregressive coefficients and regression coefficient slopes as constant
has been incorporated.

The different forms of panel causality test differ on the assumptions made about the homogeneity of the
coefficients across cross-sections. The first is to treat the panel data as one large stacked set of data, and
then perform the Granger Causality test in the standard way, with the exception of not allowing data
from one cross-section to enter lagged values of data from the next cross-section. This method assumes
that all coefficients are same across all cross-sections,
A second approach adopted by Dumitrescu-Hurlin (2012), makes an extreme opposite thereby, assuming
all coefficients to be different across cross-sections
The test is calculated by simply running standard Granger Causality regressions for each cross-section
individually. The next step is to take the average of the test statistics, which are termed the Wbar
statistic. When the standardized version of this statistic, appropriately weighted in unbalanced panels,
follows a standard normal distribution, it is termed the Zbar statistic. The pairwise Dumitrescu Hurlin
Panel causality tests may indicate which of the hypotheses are generally consistent or inconsistent with
the data. The following hypothesis has been used in the present study to empirically examine the
relationship:
H0: Business per Branch does not homogeneously cause dTFP
H0: dTFP does not homogeneously cause Business per Branch
H0: Business per Employee does not homogeneously cause dTFP
H0: dTFP does not homogeneously cause Business per EmployeeH0: NIM/TA does not homogeneously
cause dTFPH0: dTFP does not homogeneously cause NIM/TA
H0: Profit per Employee does not homogeneously cause dTFP
H0: dTFP does not homogeneously cause Profit per Employee

H0: ROA does not homogeneously cause dTFPH0: dTFP does not homogeneously cause ROA
7.6 CONCLUSION
It has been seen that planned business banks in India have reacted decidedly in the field of gainfulness,
profitability, resource quality, standards of prudential controls of bookkeeping, presentation of CAMEL
supervisory rating framework, and consistent up-degree of innovation to give better and productive
administrations to clients (Reserve Bank of India, 2008; Kaur, 2012; Sinha, 2012). The examination
found that SCBs in India are performing great on the markers referenced under CAMEL approach with
special case to the issue of NPAs. The decrease in NPAs at first has been because of the change over to
the framework based NPAs, though, decrease as of late is credited to the stoppage of financial
development and forceful loaning by banks amid great occasions. With utilization of DEA display, the
estimation of cost efficiency and its parts have been made for the Indian managing an account area amid
the post-deregulation period. It tends to be finished up from total dimension investigation that banks on
a normal can work at the generally speaking effective cost boondocks by making the utilization of just
48.4 percent (PSBs), 47.9 percent (PrSBs) and 61.5 percent (FSBs) of their inputs to deliver a similar
dimension of yields than they are by and by utilizing. Consequently, there is potential in cost sparing by
51.6 percent for PSBs, 52.8 percent for PrsBs and 38.5 percent for FSBs, individually. The outcomes
reveals that banks in India over the timeframe have attempted to get balanced with the advancement
approach, upgraded rivalry and prudential controls actualized for making the sound activity in the
economy.
REFERENCES
1. BHADRAPPA HARALAYYA , P.S.AITHAL , PERFORMANCE AFFECTING FACTORS OF INDIAN
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07 Issue -01, April-June 2021, PAGE No : 607-621.
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(TOJQI), Vol.- 12 Issue -03, June 2021, PAGE No : 603-620,
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DEVELOPING COUNTRY, International Research Journal of Humanities and Interdisciplinary Studies
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5. Bhadrappa Haralayya ; P. S. Aithal . "Study on Model and Camel Analysis of Banking" Iconic Research And
Engineering Journals ,Volume 4 ,Issue 11 ,May 2021 Page 244-259.
6. Bhadrappa Haralayya and Aithal, P. S.. “Analysis of cost efficiency on scheduled commercial banks in
India”. International Journal of Current Research, Volume 13, Issue 06, June 2021, pp 17718-17725
7. Bhadrappa Haralayya and P. S. Aithal, “A Study On Structure and Growth of Banking Industry in
India”, International Journal of Research in Engineering, Science and Management ,Volume 4, Issue 5, May
2021.Page no 225–230.
8. Bhadrappa Haralayya, Retail Banking Trends in India ,International Journal of All Research Education and
Scientific Methods (IJARESM), Volume: 9, Issue: 5, Year: May 2021, Page No : 3730-3732.
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Engineering Development Research (www.ijsdr.org), Vol.6, Issue 6, June-2021, page no.1 - 6, ,
10. BHADRAPPA HARALAYYA , P.S.AITHAL , IMPLICATIONS OF BANKING SECTOR ON
ECONOMIC DEVELOPMENT IN INDIA, flusserstudies, Volume 30, June 2021,Page No:1068-1080,
11. BHADRAPPA HARALAYYA, P.S.AITHAL, STUDY ON PRODUCTIVE EFFICIENCY OF FINANCIAL
INSTITUTIONS, International Journal of Innovative Research in Technology, Volume 8, Issue 1, June-2021
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12. BHADRAPPA HARALAYYA , STUDY OF BANKING SERVICES PROVIDED BY BANKS IN
INDIA, International Research Journal of Humanities and Interdisciplinary Studies
(www.irjhis.com), Volume: 2, Issue: 6, Year: June 2021,Page No : 06-12,
13. BHADRAPPA HARALAYYA, P.S.AITHAL , ANALYSIS OF BANK PERFORMANCE USING CAMEL
APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org |
UGC and issn Approved), Vol.8, Issue 5, May-2021, page no 305-314,
14. BHADRAPPA HARALAYYA, P.S.AITHAL, ANALYSIS OF BANK PRODUCTIVITY USING PANEL
CAUSALITY TEST, Journal of Huazhong University of Science and Technology, Volume 50, Issue 6, June-
2021 , Page no: 1 – 16,
15. BHADRAPPA HARALAYYA, P.S.AITHAL, INTER BANK ANALYSIS OF COST EFFICIENCY USING
MEAN, International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET),
Volume 10, Issue 6, June-2021 ,Page no: 6391-6397,
16. BHADRAPPA HARALAYYA, P.S.AITHAL , ANALYSIS OF TOTAL FACTOR PRODUCTIVITYAND
PROFITABILITY MATRIX OF BANKS BY HMTFP AND FPTFP, Science, Technology and Development
Journal, Volume 10, Issue 6, June-2021, Page no: 190-203, Available at: http://journalstd.com/gallery/23-

june2021.pdf
17. BHADRAPPA HARALAYYA, P.S.AITHAL , ANALYSIS OF BANKS TOTAL FACTOR
PRODUCTIVITY BY AGGREGATE LEVEL, Journal of Xi'an University of Architecture & Technology,
Volume 13, Issue 6, June- 2021 ,Page no: 296-314,
18. Bhadrappa Haralayya, P S Aithal, "ANALYSIS OF BANKS TOTAL FACTOR PRODUCTIVITY BY
DISAGGREGATE LEVEL", International Journal of Creative Research Thoughts (IJCRT), Volume.9, Issue
6, June 2021, pp.b488-b502,
19. Haralayya B. Importance of CRM in Banking and Financial Sectors Journal of Advanced Research in Quality
Control and Management 2021, 6(1): 8-9
20. Haralayya B. How Digital Banking has Brought Innovative Products and Services to India. Journal of
Advanced Research in Quality Control and Management 2021; 6(1): 16-18
21. Haralayya B. Top 5 Priorities That will Shape The Future of Retail Banking Industry in India. Journal of
Advanced Research in HR and Organizational Management 2021; 8(1&2): 17-18.
22. Haralayya B. Millennials and Mobile-Savvy Consumers are Driving a Huge Shift in The Retail Banking
Industry. Journal of Advanced Research in Operational and Marketing Management 2021; 4(1): 17-19
23. Haralayya B. Core Banking Technology and Its Top 6 Implementation Challenges. Journal of Advanced
Research in Operational and Marketing Management 2021; 4(1): 25-27
24. Nitesh S Vibhute ; Dr. Chandrakant B. Jewargi ; Dr. Bhadrappa Haralayya . "Study on Non-Performing
Assets of Public Sector Banks" Iconic Research And Engineering Journals Volume 4, Issue, 12 June 2021,
Page 52-61
25. Haralayya, Dr. Bhadrappa and Saini, Shrawan Kumar, An Overview on Productive Efficiency of Banks &
Financial Institution (2018). International Journal of Research, Volume 05 Issue 12, April 2018.
26. Haralayya, Dr. Bhadrappa, Review on the Productive Efficiency of Banks in Developing Country (2018).
Journal for Studies in Management and Planning, Volume 04 Issue 05, April 2018,
27. Haralayya, Dr. Bhadrappa, The Productive Efficiency of Banks in Developing Country With Special
Reference to Banks & Financial Institution (april 30, 2019).
28. Haralayya, Dr. Bhadrappa, Study on Performance of Foreign Banks in India (APRIL 2, 2016).
29. Haralayya, Dr. Bhadrappa, E-Finance and the Financial Services Industry (MARCH 28, 2014).
30. Haralayya, Dr. Bhadrappa, E-payment - An Overview (MARCH 28, 2014).
31. Bhadrappa Haralayya . "Customer Satisfaction at M/s Sindol Bajaj Bidar" Iconic Research And Engineering
Journals, Volume 4 ,Issue 12, June 2021, Page 157-169
32. Bhadrappa Haralayya . "Ratio Analysis at NSSK, Bidar" Iconic Research And Engineering Journals, Volume
4, Issue 12,June 2021, Page 170-182
33. Bhadrappa Haralayya . "Financial Statement Analysis of Shri Ram City Union Finance" Iconic Research And

Engineering Journals, Volume 4, Issue 12,June 2021, Page 183-196
34. Bhadrappa Haralayya . "Employee Job Satisfaction at Big Bazaar" Iconic Research And Engineering
Journals, Volume 4, Issue 12, June 2021, Page 197-206
35. Bhadrappa Haralayya . "Effect of Branding on Consumer Buying Behaviour at Vijay Bharat Motors Pvt Ltd,
Bidar" Iconic Research And Engineering Journals, Volume 4, Issue 12, June 2021, Page 207-222
36. Bhadrappa Haralayya . "Study on Customer Perceptions Guru Basava Motors, Bidar" Iconic Research And
Engineering Journals, Volume 4, Issue 12,June 2021, Page 223-231
37. Bhadrappa Haralayya . "Study on Loans and Advances for DCC Bank Main Branch Nayakaman, Bidar"
Iconic Research And Engineering Journals, Volume 4, Issue 12, June 2021, Page 232-242
38. Bhadrappa Haralayya . "Work Life Balance of Employees at Karanja Industries Pvt Ltd, Bidar" Iconic
Research And Engineering Journals, Volume 4, Issue 12, June 2021, Page 243-254
39. Bhadrappa Haralayya . "Working Capital Management at TVS Motors, Bidar" Iconic Research And
Engineering Journals, Volume 4, Issue 12, June 2021, Page 255-265
40. Haralayya, Dr. Bhadrappa, Testing Weak Form Efficiency of Indian Stock Market – An Empirical Study on
NSE (April 30, 2021). Emerging Global Strategies for Indian Industry (ISBN: 978-81-910118-7-6), 2021,
41. Bhadrappa Haralayya . "Advertising Effectiveness With Reference to Big Bazaar" Iconic Research And
Engineering Journals, Volume 5, Issue 1, July 2021, Page 101-110
42. Bhadrappa Haralayya . "Analysis of Non Performing Asset on Urban Cooperative Bank in India" Iconic
Research And Engineering Journals, Volume 5, Issue 1,July 2021, Page 111-121
43. Bhadrappa Haralayya . "Ration Analysis With Reference to DCC Bank" Iconic Research And Engineering
Journals, Volume 5, Issue 1, July 2021, Page 122-130
44. Bhadrappa Haralayya . "Consumer Buying Behavior With Reference to Bajaj Auto Ltd" Iconic Research And
45. Bhadrappa Haralayya . "Sales Promotion With Reference to Yamaha Motor" Iconic Research And
46. Bhadrappa Haralayya . "Financial Statement Analysis Using Common Size on Mahindra Sindol Motors"
Iconic Research And Engineering Journals, Volume 5, Issue 1, July 2021 , Page 150-159
47. Bhadrappa Haralayya . "Loans And Advances with Reference to PKGB Bank" Iconic Research And
48. Bhadrappa Haralayya . "Study on Trend Analysis at John Deere" Iconic Research And Engineering Journals,
Volume 5, Issue 1, July 2021, Page 171-181
49. Haralayya B, Aithal PS. Study on Cost Efficiency in Indian and Other Countries Experience. Journal of
Advanced Research in HR and Organizational Management 2021; 8(1&2): 23-30.
50. Haralayya B, Aithal PS. Study on Theoretical Foundations of Bank Efficiency. . Journal of Advanced

Research in Operational and Marketing Management 2021; 4(2): 12-23.
51. Haralayya B, Aithal PS. Study on Profitability Efficiency in Indian and Other Countries Experience. Journal
of Advanced Research in Quality Control and Management 2021; 6(2): 1-10.
52. S. Vinoth, Hari Leela Vemula, Bhadrappa Haralayya, Pradeep Mamgain, Mohammed Faez Hasan, Mohd
Naved, Application of cloud computing in banking and e-commerce and related security threats, Materials
Today: Proceedings, 2021,ISSN 2214-7853,
https://doi.org/10.1016/j.matpr.2021.11.121.(https://www.sciencedirect.com/science/article/pii/S2214785321
071285).
53. Haralayya B, Jeelan BV, Vibhute NS. Capital Structure and Factors Affecting Capital Structure. J Adv Res
Eco Busi Mgmt 2021; 4(2): 4-35.
54. Vibhute NS, Haralayya B, Jeelan BV. Performance Evaluation of Selected Banks using Ratio Analysis. J Adv
Res Eco Busi Mgmt 2021; 4(2): 36-44
55. Jeelan BV, Haralayya B, Vibhute NS. A Study on Empirical Analysis of Relationship between FPI and
NIFTY Returns. J Adv Res Acct Fin Mgmt 2021; 3(2): 3-22
56. Jeelan BV, Haralayya B, Vibhute NS. A Study on Performance Evaluation of Initial Public Offering (IPO). J
Adv Res Pub Poli Admn 2021; 3(2): 12-26.
57. Basha VJ, Haralayya B, Vibhute NS. Analysis of Segment Reporting with Reference to Selected Software
Companies. J Adv Res Entrep Innov SMES Mgmt 2021; 4(2): 9-26.
58. Jeelan BV, Haralayya B, Vibhute NS. Co-Movement and Integration among Stock Markets: A Study of 10
Countries. J Adv Res Acct Fin Mgmt 2021; 3(2): 23-38.
59.Jeelan BV, Haralayya B, Vibhute NS. A Comparative Study on Selected Foreign Currencies. J Adv Res Eco
Busi Mgmt 2021; 4(2): 45-5
60. Bhadrappa Haralayya . "A Study on Customer Satisfaction at TVS Vanish Motors Bidar" Iconic Research
And Engineering Journals Volume 5 Issue 9 2022 Page 117-127
61. Bhadrappa Haralayya . "Consumer Buying Behavior at Kailash Motors Bidar" Iconic Research And
Engineering Journals Volume 5 Issue 9 2022 Page 128-137
62. Bhadrappa Haralayya . "Credit Risk of Canara Bank Bidar" Iconic Research And Engineering Journals
Volume 5 Issue 9 2022 Page 138-149
63. Bhadrappa Haralayya . "Effect of Branding on Consumer Buying Behaviour in Bharat Ford Bidar" Iconic
Research And Engineering Journals Volume 5 Issue 9 2022 Page 150-159
64. Bhadrappa Haralayya . "Employee Engagement at Kharanja Industry Pvt Ltd Humanbad" Iconic Research
65. Bhadrappa Haralayya . "Employee Performance Appraisal at Sri Veerabhadreshwar Motors Bidar" Iconic

66. Bhadrappa Haralayya . "Employees Traning and Development at Mgssk Ltd Bhalki" Iconic Research And
67. Bhadrappa Haralayya . "Impact of Financial Statement Analysis on Financial Performance in Lahoti Motors
Bidar" Iconic Research And Engineering Journals Volume 5 Issue 9 2022 Page 197-206
68. Bhadrappa Haralayya . "Impact of Ratio Analysis on Financial Performance in Royal Enfield (Bhavani
Motors) Bidar" Iconic Research And Engineering Journals Volume 5 Issue 9 2022 Page 207-222
69. Bhadrappa Haralayya . "Sales Promotion at Keshva Enterprise Bidar" Iconic Research And Engineering
Journals Volume 5 Issue 9 2022 Page 223-232
70. Bhadrappa Haralayya . "The Impact of Safety and Health Measures of Employees at KJD Pharma Bidar"
Iconic Research And Engineering Journals Volume 5 Issue 9 2022 Page 233-242
71. Bhadrappa Haralayya . "Comparative Analysis of Mutual Funds in Geojit Financial Services Ltd Gulbarga"
72. Bhadrappa Haralayya . "Cost Analysis at MGSSK Bhalki" Iconic Research And Engineering Journals
Volume 5 Issue 9 2022 Page 252-258
73. Bhadrappa Haralayya . "Employee Compensation Management at Vani Organic Pvt Ltd Bidar" Iconic
74. Bhadrappa Haralayya . "Employees Performance Appraisal of Chettinad Cement Gulbarga" Iconic Research
75. Bhadrappa Haralayya . "Ratio Analysis in Muthoot Finance Ltd Aurad" Iconic Research And Engineering
76. Bhadrappa Haralayya . "Study on Promotion Mix Strategy Towards Big Bazaar Bidar" Iconic Research And
77. Bhadrappa Haralayya . "Study on Sales Promotion Techniques Used by VKG Bajaj at Kalaburagi" Iconic
78. Bhadrappa Haralayya . "Working Capital Management in Hyundai Showroom Bidar" Iconic Research And
79. Bhadrappa Haralayya "Study on Performance Evaluation of Mutual Funds" Iconic Research And Engineering
80. Bhadrappa Haralayya . "The Performance of Mutual Fund Schemes in The Framework of Risk and Return"
81. Bhadrappa Haralayya . "Risk And Return Analysis of Mutual Funds with Reference to Banks" Iconic
82. Bhadrappa Haralayya . "Comparative Study on Performance Evaluation of Mutual Funds with Reference to
Banking Funds" Iconic Research And Engineering Journals Volume 5 Issue 10 2022 Page 56-64

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