1. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 5, September - October (2013)
6
ASSET-LIABILITY MANAGEMENT IN INDIAN PRIVATE SECTOR
BANKS-A CANONICAL CORRELATION ANALYSIS
A. Karthigeyan*
, Dr V. Mariappan**
and Dr B. Rangaiah***
*Corresponding Author and Research Scholar, Department of Banking Technology,
Pondicherry University, India
** Associate Professor, Department of Banking Technology, Pondicherry University, India
*** Associate Professor, Department of Applied Psychology, Pondicherry University, India
ABSTRACT
Banks generally mobilize resources in the form of deposits and lend them as loans and
advances. The resources mobilized by the banks are generally short-term in nature and the
deployments of funds are medium and long-term, hence there is always a mismatch between the
maturity and repayment of funds in banks. The mismatch risk the banks due to the incurrent risks
involved in the business mechanisms and leads to liquidity crunch and loss of margins at times.
There are many risks involving the banking business - such as credit risk, liquidity risk, interest rate
risk, operational risk, exchange risk, and regulatory risk. However, the major risks faced by the
banks are liquidity risk and interest rate risk. A systematic management of assets and liability of the
banks help the banks to address these risks effectively and leads the banks towards a sustainable
growth. For this purpose, the sample of three Old & New generation Private sector Banks was taken
and the Canonical correlation technique has been applied to capture the predictor variables, liability
and predictive variables assets in these banks. The finding of the paper reveals that except ICICI
banks, all other banks are in safer zone and mostly the predictor variables are long-term and the
predictive variables are also long-term, and short- term in nature.
Keywords: Asset-Liability Management, Canonical correlation, Deposits, Advances.
INTRODUCTION
Risk and risk taking is an inherent part of any business activity. Banking business due to its
nature and presence is exposed to various types of risks. A country like India with a fast growing
economy exposed to higher risks. In a competitive environment risk taking is an essential part of the
INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)
ISSN 0976-6502 (Print)
ISSN 0976-6510 (Online)
Volume 4, Issue 5, September - October (2013), pp. 06-13
© IAEME: www.iaeme.com/ijm.asp
Journal Impact Factor (2013): 6.9071 (Calculated by GISI)
www.jifactor.com
IJM
© I A E M E

2. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 5, September - October (2013)
7
financial decision for the survival and grow of the entity. Recognizing the need of asset liability
management to develop a strong and sound banking system, the RBI introduced ALM guidelines for
banks and financial institutions in April 1999. There are separate norms prescribed for Scheduled
Commercial Banks and RRBs and for financial institutions for best management of banks resources.
The objective of the efforts is to effective management of banks resources and to optimize the returns
by minimizing the risk. While sound financial system is considered as the backbone of any country
and its economy, safety and stability of the financial institutions are even most important for the
growth of the industry, economy and economic well-being of the people. Though world over all
Governments and their respective Central Banks are aware of these truths and put in place necessary
risk management policies and procedures to protect their financial system and economy, certain
unforeseeable risks inherent in the business of banks shaken the countries’ economies in the past.
The recent failure of banks and banking system in parts of Europe and USA is a telling evidence of
these facts.
IMPORTANT RISKS IN BANKING BUSINESS
Due to the increased competition and expansion of business activities across geographies,
banks today are exposed to several risks - such as credit risk, market risk, liquidity risk, operational
risk, foreign exchange risk, technology risk, regulatory risk, country risk, reputation risk and so on.
However, most of the major risks confronted by banks across the globe are covered under few risk
categories such as credit risk, market risk and operational risk.
Credit Risk
Credit risk arises from a bank’s dealings with an individual customer, corporate companies,
banks, financial institutions or a sovereign. Credit Risk is inherent to banking and it is as old as
banking business itself. Credit risk in simple terms is the potential of the borrower fail to meet their
obligations; as a result the loan facility becomes default due to non repayment.
Later, the focus turned towards ownership, that the private ownership is performing better
than public ownership. The focus on the ownership and financial performance of banks in emerging
markets concern mainly about the possibility of inefficient allocation of bank’s financial resources,
it’s because of external threats like political interventions and public sector bank official’s references
to the disbursement of loans. It leads to significant accumulation of nonperforming assets (NPAs) in
the banking book. There is evidence in Latin America, the foreign banks are risk averse, market
penetration by these banks is significant and it affects developing economy in the context of credit
disbursal to small and medium enterprises (Clarke, Cull, D’Amato and Molinari, 1999; Clarke, Cull,
and Peria 2001; Clarke, Cull, Peria and Sanchez, 2002).
Banerjee and Duflo (2002) came with an excellent argument by using Indian data, they
showed that, whether it is public or private the behaviour of banks in emerging economy ownership
does not affect or change the performance of credit market. However data used by Banerjee and
Duflo is limited it is restricted to one Indian public sector bank that accounted for about 5 percent of
banking sector assets. Sumon Kumar Bhaumik and Jenifer Piesse (2005) estimated a model that
expresses credit-to-deposit ratio (CDR) as a function of the credit risk associated with the banks’
potential borrower pool, and the risk averseness of the banks. B S Bodla and Richa Verma (2009)
had analysed Credit Risk Management framework of Scheduled commercial banks (SCB) in India.
Risk rating, credit administration, prudential limits and loan review mechanisms are very important
instruments of credit risk management.

3. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 5, September - October (2013)
8
Market risk
Market risk is primarily concerned with describing uncertainty about prices or returns due to
market price fluctuations. The value of on-/off-balance sheet positions get affected adversely by
movements in equity and interest rate markets, currency exchange rates and commodity prices. The
banks today require a strong market risk management policy for a comprehensive and dynamic frame
work for measuring, monitoring and managing liquidity, interest rate, foreign exchange and equity as
well as commodity price risk. The market risk management needs to be closely integrated with the
bank’s business strategy.
Ila Patnaik & Ajay Shah (2002) had analysed the Interest-Rate Risk in the Indian Banking
System by measuring the interest rate risk profile of major banks in India. It consists of estimating
the impact upon equity capital of standardized interest rate shocks and measuring the elasticity of
bank stock prices to fluctuations in interest rates. They found that the BIS notion of a 99% percentile
movement on a one–year holding period implies a shock of 320 bps in assessing interest-rate risk.
Finally, they suggested that, the banks and their supervisors may benefit from computing interest rate
exposure through these two approaches. But they have used structural liquidity report for calculating
interest rate risk; there is a huge difference between these two reports. Ahmed.S, Anne Beatty, and
Carolyn Takeda (1997) made a study of the Interest Rate Risk management activities of commercial
banks including their use of derivatives. The study had shown that the banks primarily focus on
managing interest rate sensitivity of net income rather than the interest rate sensitivity of stock
returns and the level of Interest rate risk taken by banks is directly related to liquidity, and inversely
related to managerial quality and bank size. It also reveals that derivative users as a group have lower
mean and median exposure than non-users, and derivative usage reduces exposure. But most of our
Indian banks not familiar in using Derivatives. Still it is in nascent stage in Indian financial market.
Operational Risk
Operational Risk is defined as “the risk of loss resulting from inadequate or failed internal
processes, people and systems or from external events. This definition includes legal risk, but
excludes strategic and reputational risk,” [Basel Committee (2004)]. As operational risk is inherent
in all business processes, the evolving banking practices highlight the importance of focusing on
management of operational risk separately. Globalization of financial services with growing
sophistication of financial technology and deregulation, as well as corporate restructuring had a large
impact on the activities of the bank more complex.
Usha Janakiraman (2008) assessed the status of operational risk management in Indian
banking system in the context of Basel II. Survey result shows that majority of the banks using self-
assessment- a qualitative factor, as an important tool in their operational risk framework. Many
banks had started the operational risk loss data collection exercise for moving over to the advanced
approaches through these internal assessments. But, in Indian scenario internal assessment/audit is
not in full fledged, still in the formative stages. Laviada (2007) emphasises that, the role of internal
audit and its effective implementation in managing operational risk. Laker (2006) argues that greater
complexity of banking activity and increasing dependence on technology and specialist skills has
made operational risk as one of the most important risk, faced by the banking institutions. According
to the author, outsourcing and technology are two major sources of operational risk.

4. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 5, September - October (2013)
9
Liquidity Risk
Liquidity risk is one of the major risks faced by banks in addition to credit risk, market risk
and operating risk. Yoram Landskroner and Jacob Paroush (2008) had studied the relationship
between the banks liquidity position and competition among the banks. They formulated a stylized
model of bank management, where the asset and liabilities structures are key factors in determining
the bank's exposure to liquidity risk. The main results of this model are that liquidity risk increases
when competition in the credit market increases, which means that more demand in the credit market
increases the lending rate which results in increase in spread i.e. interest margin. While increasing
competition in the deposit market will decrease the liquidity shortage. This means that mobilization
of resources in the form of deposit will increase the liquidity position of the bank. They concluded
that, the banks faced increased liquidity risk due to the recent developments in the financial markets.
NEED FOR THE STUDY
Banking sector in the current scenario have undergone lot of changes, deregulation has
opened up lot of business avenues. Risk and risk taking are the inherent part of any business activity.
It has been a universal observation that banks do not show much cautious while managing this risk,
especially the interest rate risk and liquidity risk have crumbled without giving adequate notice to the
management, regulator and the depositors.
However still, the level of awareness regarding Asset liability management in the Indian
Banks is almost negligible. Risk management is the core activity of ALM. So, ALM is of great
significance to organizations in the financial sector. So the present study evaluates the changing
perspectives of the select private sector banks in identifying and facing the risks and also maintaining
the asset quality to ensure profitability by using asset-liability management techniques.
DATA AND METHODOLOGY
The study is basically rests on secondary data collected from the select banks records and
CMIE data bank and other secondary sources. A sample of three Old and New generation private
sector banks will be selected for the study. The collected secondary data were put to a critical
statistical examination with help of Canonical Correlation, a Multivariate Statistical tool. The study
uses canonical correlation analysis to assess the nature and strength of asset-liability management of
different bank groups, as described in Seema Jaiswal (2010), Suman Chakraborty and Subhalaxmi
Mohapatra (2009) Mihir dash and Ravi pathak (2008), P. K. Jain and v. Gupta (2004). To this end,
the assets and liabilities of the banks were first reclassified as discussed below. The canonical
correlation technique was applied to these reclassified assets and liabilities. The results of the
canonical correlation analysis were then interpreted in terms of their implications on profitability,
liquidity, and interest rate sensitivity.
RECLASSIFICATION OF ASSETS & LIABILITIES
The assets and the liabilities of the banks were regrouped into various sub-heads, guided by
the liquidity-return profile of the assets and the maturity-cost profile of the liabilities (P. K. Jain and
v. Gupta (2004)). The reclassified assets and liabilities covered in the study excluded ‘other assets’
on the asset side and ‘other liabilities’ on the liabilities side. This was necessary to deal with the
problem of singularity - a situation that produces perfect correlation within sets and makes

5. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 5, September - October (2013)
10
correlation between sets meaningless. For example, the assets and liabilities for private banks for
year 2002-12 have been reclassified and presented in Tables 1 and 2.
Table 1: Classification of Assets
Fixed Assets Fixed Assets
Liquid Assets Cash in hand, Balances with RBI, Balances with other banks, Money at
call and short notice
Term Loans Term loan
Short term loans Advances not in term loans, Bills purchased and discounted, Cash credits,
Overdrafts and loans
Investments Other than SLR like shares, debentures, bonds etc
SLR Securities Govt. securities and other approved securities
Table 2: Classification of Liabilities
Net Worth Capital , Reserve and Surplus
Short term deposits Saving bank deposits and Demand deposits
Long term deposits Deposits not included in short term
Borrowings From RBI, other banks, other financial
institutions both from India and abroad
CANONICAL CORRELATION
Canonical correlation is a multivariate statistical technique that has been used to assess the
correlation between each set of assets and each set of liabilities, which indicates the relationship
between assets and liabilities.
Hotelling (1935) was the first to describe the mathematics of canonical correlation. An
excellent introduction into this procedure was given by Marinell (1990), Thompson (1984), Hair et
al. (1998), and Stevens (2002). The main objective of the canonical correlation procedure is to
identify significant relationships between two sets of variables. Each of these sets consists of at least
two variables. Being a true multivariate approach for both sides of the equation, it has clear
advantages compared to the more commonly used pair-wise correlations or multiple regressions.
Canonical correlation provides a means to explore all of the correlations concurrently. The
technique reduces the relationship into a few significant relationships. The essence of canonical
correlation measures the strength of relationship between two sets of variables by establishing linear
combination of variables in one set and linear combination of variables in other set. It produces an
output that shows the strength of relationship between two variates as well as individual variables
accounting for variance in the other set. This is expressed mathematically as: A =A1 *(Liquid Assets)
+ A2 * (SLR securities) + A3 * (Investments) + A4 * Long-term Loans) + A5 * (Short-term Loans) +
A6 * (Fixed Assets) and L= L1 * (Net worth) + L2 *(Short- term Deposits) + L3 * (Long-term
Deposits) + L4*(Borrowings). To begin with, A and L (called canonical variates) are unknown. The
technique tries to compute the values of Ai and Li such that the correlation between A and L is
maximized.

6. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 5, September - October (2013)
11
Table 3 canonical correlation analysis for Axis Bank
Axis
Bank
HDFC
Bank ICICI Bank
Karur
Vysya
Bank
Lakshmi
Vilas
Bank
City
Union
Bank Overall
Wilk’s Lambda
0.000 0.000 0.000 0.000 0.000 0.000 0.000
Squared
Canonical
Correlation
(Rc2
)
0.999 0.999 0.9997 0.999 0.9999 0.999 0.9997
Canonical Loadings: ASSETS
Liquid Assets -0.05318 -0.13477 0.00385 -0.02587 0.00038 -0.04402 -0.09183
SLR Securities -0.16253 -0.20487 -0.23661 -0.26052 -0.20651 -0.1806 -0.22585
Investment -0.11657 -0.05079 -0.97919 -0.02407 0.00729 -0.05161 -0.15671
Short-term
Loan
-0.45307 -0.31714 -0.05886 -0.10983 -0.35582 -0.29553 -0.45244
Long-term
Loan
-0.21426 -0.27159 0.20787 -0.56578 -0.44342 -0.44576 -0.12398
Fixed Asset -0.00891 -0.06384 0.1174 -0.02776 -0.00658 0.00098 0.01689
Canonical Loadings: LIABILITIES
Net worth -0.12513 -0.25075 -0.03371 -0.0641 0.03807 -0.19197 -0.12171
Short-term
Deposits
-0.32087 -0.45186 -0.93869
-0.16417
-0.30452 -0.11586 -0.36405
Long-term
Deposits
-0.44515 -0.31549 0.27427
-0.73518
-0.75359 -0.66806 -0.3555
Borrowing -0.11245 0.01417 -0.24128 -0.05077 0.01601 -0.03111 -0.21866
Redundancy
ASSETS (A) 0.049015
0.040188 0.179211561 0.06701 0.0609954 0.053876 0.050726
LIABILITIES
(L)
0.082355
0.091697 0.253929 0.143531 0.165584 0.124387 0.080385
Cause- Effect Relationship
Independent
Set
A A A A A A A
Dependent Set L L L L L L L
ANALYSIS AND INTERPRETATION
The analyses for the present data have been presented in the above table, which shows the
results of canonical correlation. The criterion variables were net worth, Short-term loan, Long-term
loan, and Borrowings; whereas predictive variables were Liquid asset, SLR securities, Investment,

7. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 5, September - October (2013)
12
Short-term deposits, Long-term deposits, and Fixed assets. The Rc2
was found to be significant for
each bank as well as for the overall data. The present analysis was done for two set of canonical
variables. The overall model was found to be significant with Wilks λ = .002 criterion, F (24, 765.21)
= 712.15, p >.000.
Table 3 presents standardized canonical function coefficients for the first function for various
banks mentioned above. The careful observation of the table reveals that for the Axis bank the
relevant criterion variables were primarily short term loan, with long term loan being secondary.
Regarding to predictor variables the primary contributors was long term deposits with short term
deposits as secondary contributor. The predictive variables were positively related with criterion set.
Long term deposits related with short term loan, followed by long term loan. This explains that Axis
bank asset liability management related to long term deposit with short term loan.
The study reveals that for HDFC bank, the appropriate criterion variables were primarily
same as like that of Axis bank short-term loan, with long-term loan being secondary. The primary
contributor was short term deposits with long term deposits as secondary contributor for predictor
variable. The predictive variables were positively related with criterion set. Short term deposits
related with short term loan, followed by long term loan. This explains that HDFC bank asset
liability management related to short term deposit with short term loan, which is different from Axis
Bank.
For ICICI bank the relationship between assets and liabilities revealed that the banks
appropriate criterion variable was primarily very high Investment, with Net worth being secondary
contributor. Regarding to predictor variable the primary contributor was short term deposits with
long term deposits as secondary contributor. The predictive variables were positively related with
criterion set. Short term deposits related with Investment, followed by Net worth. This explains that
ICICI bank asset liability management related to short term deposit with Investment.
The careful observation of the table reveals that for the Karur Vysya bank the relevant
criterion variable was primarily Long-term loan, with SLR securities being secondary contributor.
Regarding to predictor variables the primary contributor was long term deposits with short term
deposits as secondary contributor. The predictive variables were positively related with criterion set.
Long-term deposits related with Long-term loan, followed by SLR securities. This explains that
Karur Vysya bank asset liability management related to long-term deposit with long-term loan.
For Lakshmi Vilas bank, the nature and strength of relationship between assets and liabilities
suggest us that the relevant criterion variable was primarily Long-term loan, with short-term loan
being secondary contributor. Regarding to predictor variables the primary contributor was long term
deposits. The predictive variables were positively related with criterion set. Long-term deposits
related with Long-term loan, followed by short-term loan. This explains that Lakshmi Vilas bank
asset liability management related to long-term deposit with long-term loan.
For City Union bank, the careful observation of the table reveals that for the bank the relevant
criterion variable was primarily long-term loan. Regarding to predictor variables the primary
contributor was long term deposits. The predictive variables were positively related with criterion
set. Long-term deposits related with long-term loan. This explains that City Union bank asset
liability management related to long term deposit with long-term loan.
The above table reveals that on the whole, overall results show that the primary criterion
variable was short term loan with Slr securitites being the secondary contributor. With regard to the
predictor variable short term deposit behaved as a primary contributor, where as long term deposit as
a secondary contributor. Hence, overall banks asset-liability management was related to short term
deposits with short term loans. However, there are differences in the focus of each bank, which has
been portrayed in the table 3 as well as the explanation in the preceding paragraphs. Further, a
similar cause-effect relationships have been found in the redundancy effects.

8. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 5, September - October (2013)
13
DISCUSSION
Based on the empirical findings, it can be concluded that the practices and implementation of
ALM in private banks differs from category i.e. old generation and new generation private banks.
The study finding shows in depth about the deployment of funds and its maturity of the select private
banks. The redundancy factors shows that, the independent and dependent sets for select banks can
be identified, select banks having assets as their independent set, which means that during the period
2002-2012, these banks were actively managing assets and liability was dependent upon how well
the assets are managed; it reveals that the private sector banks throughout their course of business
actively managing their assets.
Careful observation of the table 3 presents that, when compared to Axis bank and HDFC
bank, the ICICI bank follows aggressive ALM strategy; it uses short-term deposits for their
Investment purpose. It is more risky, there should be a high possibility for the bank fall in liquidity
risk. In other side, during the study period select old generation private banks like Lakshmi Vilas
Bank, Karur Vysya Bank and City Union Bank were in safer side. The predictor variables are mostly
long-term deposits and the predictive variables are long-term loans, it is less risky when compared to
new generation banks, particularly, ICICI bank. Thus, study has shown the importance of asset-
liability management. However, the present study has had a few demerits also because the study data
was secondary in nature, and it included only select private banks and not public and foreign banks.
ACKNOWLEDGEMENTS
We would like to thank Shri. Kadalarasane Touloucaname and Shri. Rameshbabu Tamarana,
for their contribution in completion of this article.
REFERENCES
1. Reserve Bank of India (2000). Asset Liability Management (ALM) System. Ref DBS.FID
No. C-11 /01.02.00/99-2000.
2. Reserve Bank of India (2001). Asset Liability Management (ALM) System – amendments to
ALM Guidelines. Ref DBS.FID No. C.5 /01.02.00/ 2001-02
3. Bank for International Settlements (2004). International convergence of capital Measurement
and capital standards: A revised framework, available at www.bis.org.
4. Reserve Bank of India (2006). Draft guidelines on improvements to banks’ Asset Liability
Management framework. DBOD. No. BP. 7 / 21.04.098/ 2005-06.
5. Reserve Bank of India (2007). Guidelines on Asset-Liability Management (ALM) System –
amendments. DBOD. No. BP. BC. 38 / 21.04.098/ 2007-08.
6. Reserve Bank of India (2008). Guidelines on Asset-Liability Management (ALM) System.
DBOD. No. BP. BC. 68 / 21.04.098/ 2007-08.
7. Reserve Bank of India (2011). Guidelines on Asset-Liability Management (ALM) System –
Draft Amendments. DBOD. No. BP. 256 / 21.04.098/ 2007-08.
8. Dr. C. Mahadeva Murthy and Prof. S.N. Pathi, “Risk Management in Banking: A Study with
Reference to State Bank of India (SBI) and Associates”, International Journal of Management
(IJM), Volume 4, Issue 4, 2013, pp. 119 - 130, ISSN Print: 0976-6502, ISSN Online:
0976-6510.
9. Dr. N. Kannan, “Risk and Technology Management in Banking Industry”, International
Journal of Management (IJM), Volume 1, Issue 1, 2010, pp. 43 - 58, ISSN Print: 0976-6502,
ISSN Online: 0976-6510.