N R. Bhanumurthy · K. Shanmugan Shriram Nerlekar · Sandeep Hegade Editors Advances in Finance & Applied Economics Advances in Finance & Applied Economics N R Bhanumurthy K Shanmugan Shriram Nerlekar Sandeep Hegade • • Editors Advances in Finance & Applied Economics 123 Editors N R Bhanumurthy National Institute of Public Finance and Policy (NIPFP) New Delhi, India K Shanmugan Maharaja Sayajirao University of Baroda Vadodara, India Shriram Nerlekar Marathwada Mitra Mandal’s Institute of Management Education and Research Pune, India Sandeep Hegade Marathwada Mitra Mandal’s Institute of Management Education and Research Pune, India ISBN 978-981-13-1695-1 ISBN 978-981-13-1696-8 https://doi.org/10.1007/978-981-13-1696-8 (eBook) Library of Congress Control Number: 2018948622 © Springer Nature Singapore Pte Ltd 2018 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Contents Part I Development Economics Federalism in India: An Economic Analysis Sripriya Srivatsa Econometric Analysis of Growth Inclusiveness in India: Evidence from Cross-Sectional Data Paramasivan S Vellala, Mani K Madala and Utpal Chattopadhyay Part II 19 Environmental Economics Assessing an Investor’s Decision to Invest in Solar Power Across Indian States Suramya Sharma and Srishti Dixit 41 Urban Sprawl and Transport Sustainability on Highway Corridors Using Stake Holder Analysis Twinkle Roy and Rahul Budhadev 63 Policy Interventions for Sustainable Solid Waste Management in Developing Countries Malladi Sindhuja and Krishnan Narayanan 73 Part III Monetary Economics MUDRA: The Transformation of Microfinance in India: Review, Experiences and Future Prospect Manas Roy 93 Impact of Fiscal Policy Initiatives on Inflation in India 105 Amrendra Pandey and Jagdish Shettigar Monetary Policy and Private Investment in India: The MIDAS Experience 119 Debasis Rooj and Reshmi Sengupta v vi Part IV Contents Public Economics Incidence of Specific or Employment Tax in Non-Walrasian Fixed Price Model with Efficiency Wage 135 Sucharita Roy and Arpita Ghose Married Women’s Education Levels and Agency Outside the Home: Evidence from Rural India 161 Nisha Vernekar and Karan Singhal Part V Behavioral Economics Mental Accounting—Saving with Virtual Shoeboxes 177 Chinmayi Srikanth, Ketan Reddy and S Raja Sethu Durai Trading Behaviour of Investor Categories and Its Impact on Indian Equity Market 185 Saji George and P Srinivasa Suresh Part VI Corporate Finance Ownership Classification and Technical Efficiency in Indian Manufacturing Firms: A Stochastic Frontier Approach 199 Sanjeev Kumar and K S Ranjani Investment Trends in Venture Capital and Private Equity in India 213 Poonam Dugar and Nirali Pandit Impact of Firms’ Market Value on Capital Structure Decisions: Panel Data Evidence from Indian Manufacturing Firms 237 Dhananjaya Kadanda Corporate Governance and Cash Holdings: An Empirical Investigation of Indian Companies 255 Amitava Roy Part VII Financial Risk Management Empirical Analysis of the Determinants of Dividend Payouts of Indian Banking Stocks Using Panel Data Econometrics 283 R Venkataraman and Thilak Venkatesan Asset Liability Management in Commercial Banks in India 299 Khushboo Thakker and Tanupa Chakraborty About the Editors N R Bhanumurthy is currently Professor at the National Institute of Public Finance and Policy (NIPFP), New Delhi, India Prior to joining NIPFP, he worked as Assistant and Associate Professor at the Institute of Economic Growth (IEG), New Delhi He has worked as Macroeconomist at UNESCAP, Bangkok, and the UNDP Regional Centre for the Asia-Pacific region, Colombo He has been Visiting Fellow at the Maison des Sciences de l’Homme, Paris, France, and at McGill University, Montreal, Canada He has also been a consultant to ILO, the World Bank and the Asian Development Bank Currently, he is Secretary of the Indian Econometric Society and Managing Trustee of the Indian Economic Association Trust for Research and Development K Shanmugan is currently working as Associate Professor at the M S University of Baroda, India He is also Treasurer of the Indian Econometric Society (TIES), New Delhi He has chaired numerous conferences and seminars organized by some of the most prestigious institutions in India and has 27 years of teaching experience Shriram Nerlekar is Faculty of Finance at the Savitribai Phule Pune University (SPPU), India He is currently serving as Director at the Institute of Management Education Research and Training (IMERT), Pune He has chaired numerous academic and administrative committees formed by SPPU He is the recipient of the ‘Best Professor in Financial Management Award’ (awarded in 2010 by Mumbai-based Business School Affaire’s Dewang Mehta Business School Awards) and of the ‘Young Achiever Award’ (awarded in 2012 by Mumbai-based World Education Congress) Sandeep Hegade is Founder Director of the Centre for Advanced Studies in Policy & Research, Pune He is Editor-in-Chief of IMERT’s journal Arthkalp: Journal of Finance & Economics He is the recipient of the National Research Fellowship— Dr Babasaheb Ambedkar National Research Fellowship (BANRF) at Dr Babasaheb Ambedkar Research and Training Institute (BARTI), Pune He is a researcher, author and blogger on various topics related to finance and economics vii Part I Development Economics Federalism in India: An Economic Analysis Sripriya Srivatsa Abstract The outcomes of federalism have played out in very different manners in various societies that have chosen to adopt this design of organising themselves The Indian context is particularly interesting because of how Indian states have formed, evolved or have carved out of one another into existence In this paper, I explore whether smaller states could perform better on governance outcomes The measure of governance is legislative activity in Indian state parliaments The results indicate that as states become smaller units to govern, the legislative in activity in their respective parliaments does increase Introduction and Literature Review An important classical argument favouring federalism is laid out by Friedrich Hayek His thesis is that in a heterogeneous society, apart from for truly national public goods such as defence or energy, the central government does not possess relevant information to frame policies that are suitable for all (Hayek 1948) However, when I observe how federalism has played out in various parts of the world, I see vastly contrasting outcomes The United States of America, which is a federal state, is one of the wealthiest and least corrupt nations in the world, while countries such as India, Mexico and Argentina, which are all federal states, have governments plagued with corruption and poor economies (Parikh and Weingast 1997) Comparing federal countries with other non-federal systems may not allow for accounting of several unobservable factors that vary between countries In this paper, I focus within India’s federal structure, hence mitigating this problem of unobservable discrepancies I am looking at different regions within a country, thus balancing the need for sufficient variation as well as the ability to control for regional idiosyncrasies and time-fixed effects S Srivatsa (B) Candidate for Master of Science, Political Economy of Development, School of Oriental and African Studies, University of London, London, UK e-mail: sripriya.srivatsa@tutanota.com © Springer Nature Singapore Pte Ltd 2018 N R Bhanumurthy et al (eds.), Advances in Finance & Applied Economics, https://doi.org/10.1007/978-981-13-1696-8_1 S Srivatsa Over time since independence, Indian states have been splitting into smaller and more homogenous units The purpose of this paper is to understand whether these splits have resulted in better outcomes for governance I measure governance using a new data set I collected on the number of bills and amendments passed in each state parliament(s) between 1956 and 2014 In view of state splits, amendments may be seen as the refinement of older existing laws to tailor to the needs of a newer, more homogenous society Laws may be viewed as fresh legislative activity that actually results in new governance outcomes The Indian context is particularly unique because the idea of federalism can be tested in a relatively exogenous sense, i.e a central government ensuring similar economic climate in all states, but also sufficient diversity among states Also, there have been numerous state splits over time The key contribution of this paper is that so far, nobody has been looked at this issue through an empirical lens There has been no effort in the Indian context, to test systematically the effect of state splits on economic and governance outcomes This paper is a first step towards getting a better understanding in this field I find that a split causes legislative activity to increase by 12 bills (laws and amendments) approximately The effect of a split on state domestic product is positive, but not significant However, when I measure the effect of the split on state domestic product after 1990 (when the liberalisation reforms were implemented at the central level), I find that state domestic product increases by almost 33 lakh rupees The value added in manufacturing units/factories reduces by 752 crore rupees after a state split The vast body of work in the area of fiscal federalism has been segregated into firstand second-generation fiscal federalism The major difference being in an assumption made about public officials—the former treats them as benevolent, while latter treats them as working for their own good but who are held publicly accountable for their actions Second-generation fiscal federalism stresses on the importance of incentivising lower governments with sufficient tax revenues such that they provide ‘marketenhancing public goods’ This leads us to examine the concept of an ‘ideal’ form of federalism which is ‘market preserving’ See Weingast (2007) for a survey of literature on first- and second-generation fiscal federalism Weingast (2009) embarks on a comparative study across various federal units to understand the necessary elements to result in this ideal form of federalism His paper discusses the importance of inter-jurisdictional competition to reap the benefits of federalism Parikh and Weingast (1997) also present arguments for federalism that prevents different ethnic/religious groups from fighting one another over heatedly debated policy concerns In an ethnically diverse country like India, this feature is beneficial for a functioning democracy It can thus be seen in the several splits that are caused for reasons such as religion, language, ethnicity, as discussed in the background section below Drawing from the axioms of market-preserving federalism which forms a good base for any further comparative studies, I look at the data to see whether state splits are, in fact, resulting in governance improving outcomes and improvements in 302 K Thakker and T Chakraborty Research Methodology This study is carried out for the period 2007–2016 The top ten constituent banks of BSE BANKEX (Axis Bank, Bank of Baroda, Federal Bank, HDFC Bank ICICI Bank, IndusInd Bank, Kotak Mahindra Bank, Punjab National Bank, State Bank of India and Yes Bank) were taken as a sample to be analyzed The data for the study have been collected from Capitaline database This study analyses Rate Sensitivity Assets, Rate Sensitivity Liabilities, Interest Sensitivity Ratio, Net Interest Income Ratio and Net Interest Margin Ratio Gap Analysis is also carried out to understand the impact of Asset Liability Management on the profitability of banks Mean, standard deviation and co-efficient of variation has also been used for analyzing the data 3.1 Rate Sensitivity Assets Assets held by a bank that are vulnerable to changes in interest rates This change can occur either when the asset matures or when it is repriced according to an index rate The value of these assets is adjusted according to the rise or fall of a published rate or index Rate sensitivity assets have been calculated by adding advances and investments (Table 1) Rate Sensitivity Assets Advances + Investments The mean value of rate sensitivity assets of State Bank of India is Rs 1178871.78 crores followed by ICICI Bank is Rs 413069.54 crores, Punjab National Bank is Rs 359527.53 crores and Bank of Baroda is Rs 338961.84 crores Thus it can be noticed that Public Sector Banks on an average have higher Risk Sensitive Assets in comparison to Private Sector Banks During the study period, all the banks show an increasing trend in the Risk Sensitive Assets except for Bank of Baroda declining in the year 2016 ICICI Bank showed an increasing trend initially and at the later year but in the middle it was fluctuating The co-efficient of variation of rate sensitivity assets endowed by 95.90% in YES Bank followed by 72.89 in Kotak Mahindra Bank, 66.30% in IndusInd Bank Thus it can be observed that private sector banks are showing a rapid increase in their Risk Sensitive Assets in comparison to Public Sector banks It clears that the rate sensitivity assets of 38.70% in Federal Bank is constant against 95.90% in YES Bank The graphical representation of the rate sensitivity assets of various banks is given below 63773.64 93366.24 127887.12 160315.77 214399.45 262951.64 310703.49 343615.20 413425.86 460779.92 245121.83 136198.91 55.56 Source Capitaline database 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Mean SD CV (%) Axis Bank 118564.50 150571.39 196431.78 236217.65 299936.98 370586.72 449585.36 513118.48 550384.86 504220.67 338961.84 160567.91 47.37 Bank of Baroda Table Risk sensitive assets (Rs in crore) 21931.76 28931.25 34510.85 40004.76 46490.91 55158.47 65251.29 67553.95 71853.81 80307.64 51199.47 19812.90 38.70 Federal Bank 79122.91 113375.99 157700.59 184438.21 230912.04 292902.94 351334.24 423951.35 517136.78 628479.74 297935.48 181060.52 60.77 287123.43 337070.42 321369.15 302098.39 351051.84 413287.71 461643.03 515724.47 545651.26 595675.74 413069.54 110568.45 26.77 HDFC Bank ICICI Bank 16780.40 19425.01 23854.05 30952.43 39716.46 49635.90 67524.78 79955.79 98367.54 124173.64 55038.60 36488.07 66.30 IndusInd Bank 17763.05 24665.17 25706.31 33266.91 46435.62 60633.17 77329.07 78512.18 96294.72 169599.70 63020.59 45933.26 72.89 Kotak Mahindra Bank 143111.31 174413.48 218463.63 264658.90 337619.72 416922.04 442290.09 494243.63 532420.21 571132.33 359527.53 153999.60 42.83 Punjab National Bank 486485.38 606269.46 818457.13 917704.19 1052320.00 1179776.51 1396494.06 1608628.31 1781785.13 1940797.66 1178871.78 494130.15 41.92 State Bank of India 9362.85 14523.98 19520.11 32403.06 53192.48 65745.99 89975.60 96583.32 118778.31 249622.41 74970.81 71894.16 95.90 YES Bank Asset Liability Management in Commercial Banks in India 303 304 K Thakker and T Chakraborty 3.2 Rate Sensitivity Liabilities The short-term deposit held by a bank pays a variable rate of interest to the customer Interest sensitive liabilities include money market certificates, savings accounts and the super now account The rate sensitivity liabilities have been calculated by adding deposits and borrowing (Table 2) Rate Sensitivity Liabilities Deposits + Borrowings The mean value of rate sensitivity liabilities of State Bank of India is Rs 1170808.77 crores followed by ICICI Bank is Rs 398529.14 crores, Bank of Baroda is Rs 383714.02 crore and Punjab National Bank is Rs 365725.77 crores Thus, it can be seen that Risk Sensitive Liabilities are high for public sector banks in comparison to private sector banks During the study period, all the selected banks showed an increasing trend in Risk Sensitive Liabilities except for Bank of Baroda had a fluctuating trend ICICI bank also showed an increasing trend except for a fall in the year 2010 The co-efficient of variation of rate sensitivity liabilities endowed by 75.37% in Kotak Mahindra Bank, followed by 73.00% in YES Bank, 62.36% in IndusInd Bank It clears that the rate sensitivity liabilities of Federal Bank as 40.10%, State Bank of India as 42.41% and Punjab National Bank as 44.39% is constant The other selected banks’ rate sensitivity liabilities are moderate during the study period The graphical representation of the rate sensitivity liabilities is given below 63981.18 93250.26 132893.98 158469.77 215505.68 254175.97 296564.69 331235.50 402200.21 457193.94 240547.12 131841.88 54.81 Source Capitaline database 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Mean SD CV (%) Axis Bank 126058.54 155961.18 198033.04 254394.35 327747.30 408444.14 500460.57 605707.77 652823.77 607509.57 383714.02 199615.75 52.02 Bank of Baroda 22824.45 27175.31 33417.12 37604.71 44903.14 53178.15 62801.85 65419.24 73133.23 81348.28 50180.55 20120.10 40.10 Federal Bank Table Risk sensitive liabilities (Rs in crore) 71113.33 105363.51 151975.22 180320.13 222980.47 270552.96 329253.57 406776.46 496009.19 599442.66 283378.75 173489.05 61.22 281766.22 310079.48 311503.26 296280.16 335156.39 395664.86 437955.11 486672.71 533980.06 596233.10 398529.14 110688.30 27.77 HDFC Bank ICICI Bank 17461.55 20132.85 24927.21 31644.46 39890.79 51043.55 63576.28 75264.25 94752.42 115156.21 53384.96 33292.95 62.36 IndusInd Bank 16099.84 21542.90 22378.01 30026.98 40984.92 55132.04 71439.39 71967.91 87010.02 159618.36 57620.04 43430.72 75.37 Kotak Mahindra Bank 141808.53 171903.78 222220.16 268592.17 344488.41 416852.74 431180.98 499431.16 547973.42 612806.37 365725.77 162352.23 44.39 Punjab National Bank 475224.42 589131.35 826131.06 907127.85 1053501.76 1170652.94 1371922.33 1577539.38 1781943.55 1954913.09 1170808.77 496483.11 42.41 State Bank of India 9087.71 14259.37 19871.10 31547.65 52629.84 63308.20 87877.74 95506.31 117396.25 143378.51 63486.27 46346.55 73.00 YES Bank Asset Liability Management in Commercial Banks in India 305 306 K Thakker and T Chakraborty 3.3 Interest Sensitivity Ratio A measure of how much the price of a fixed-income asset will fluctuate as a result of changes in the interest rate environment Securities that are more sensitive will have greater price fluctuations than those with less sensitivity Normally this type of sensitivity must be taken into account when selecting a bond or other fixed-income instrument that the investor may sell in the secondary market The interest sensitivity ratio is calculated by division of Rate Sensitive Assets by Rate Sensitive Liabilities (Table 3) Interest Sensitivity Ratio Rate Sensitivity Assets/Rate Sensitivity Liabilities The mean value of interest sensitivity ratio of Kotak Mahindra Bank is 1.108 times followed by Yes Bank is 1.090 times and HDFC Bank is 1.057 times The co-efficient of variation of interest sensitivity ratio endowed by 21.055% in YES Bank followed by 5.923% in Bank of Baroda, 4.764% in IndusInd Bank It clears that the interest sensitivity ratio of State Bank of India as 1.323% which is moderate The graphical representation of the interest sensitivity ratio is given below 0.997 1.001 0.962 1.012 0.995 1.035 1.048 1.037 1.028 1.008 1.012 0.025 2.509 Source Capitaline database 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Mean SD CV (%) Axis Bank 0.941 0.965 0.992 0.929 0.915 0.907 0.898 0.847 0.843 0.830 0.907 0.054 5.923 Bank of Baroda Table Interest sensitivity ratio (in times) 0.961 1.065 1.033 1.064 1.035 1.037 1.039 1.033 0.983 0.987 1.024 0.035 3.411 Federal Bank 1.113 1.076 1.038 1.023 1.036 1.083 1.067 1.042 1.043 1.048 1.057 0.027 2.584 HDFC Bank 1.019 1.087 1.032 1.020 1.047 1.045 1.054 1.060 1.022 0.999 1.038 0.025 2.440 ICICI Bank 0.961 0.965 0.957 0.978 0.996 0.972 1.062 1.062 1.038 1.078 1.007 0.048 4.764 IndusInd Bank Kotak Mahindra Bank 1.103 1.145 1.149 1.108 1.133 1.100 1.082 1.091 1.107 1.063 1.108 0.027 2.472 Punjab National Bank 1.009 1.015 0.983 0.985 0.980 1.000 1.026 0.990 0.972 0.932 0.989 0.026 2.658 1.024 1.029 0.991 1.012 0.999 1.008 1.018 1.020 1.000 0.993 1.009 0.013 1.323 State Bank of India 1.030 1.019 0.982 1.027 1.011 1.039 1.024 1.011 1.012 1.741 1.090 0.229 21.055 YES Bank Asset Liability Management in Commercial Banks in India 307 308 K Thakker and T Chakraborty 3.4 Net Interest Income The difference between revenues generated by interest-bearing assets and the interestburdened liabilities are referred as Net Interest Income For banks, the assets typically include commercial and personal loans, mortgages, construction loans and investment securities The net interest income is calculated by subtracting Interest expended from Interest earned (Table 4) Net Interest Income Interest Earned − Interest Expenditure The mean value of Net Interest Income of State Bank of India is Rs 38099.09 crores followed by HDFC Bank is Rs 13221.26 crores and ICICI Bank is Rs 11977.88 crores During the study period, all the selected banks are showing an increasing trend except for ICICI Bank which showed a slight decline in 2010 in Net Interest Income and Punjab National Bank’s Net Interest Income declined in the year 2016 The co-efficient of variation of Net Interest Income endowed by 80.01% in IndusInd Bank followed by 74.00% in YES Bank and 64.12% in Axis Bank It clears that the Net Interest Income of Federal Bank, Punjab National Bank and State Bank of India shows less volatility The graphical representation of the net interest income is given below 1468.34 2585.36 3686.21 5004.49 6562.99 8017.75 9666.26 11951.64 14224.14 16832.97 8000.02 5129.23 64.12 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Mean SD CV (%) Source Capitaline database Axis Bank NII 3577.53 3911.81 5123.41 5939.48 8802.25 10317.01 11315.26 11965.35 13187.24 12739.85 8687.92 3744.04 43.09 Bank of Baroda Table Net interest income (Rs in crore) 716.50 868.02 1315.46 1410.84 1746.58 1953.40 1974.66 2228.61 2380.41 2504.24 1709.87 616.53 36.06 Federal Bank 3468.48 5227.89 7421.16 8386.42 10543.13 12884.61 15811.12 18482.63 22395.66 27591.52 13221.26 7805.81 59.04 5637.09 7304.1 8366.61 8114.36 9016.9 10734.15 13866.42 16475.56 19039.61 21224.04 11977.88 5369.54 44.83 HDFC Bank ICICI Bank 271.40 300.80 459.03 886.41 1376.50 1704.24 2232.86 2890.71 3420.27 4516.57 1805.88 1444.81 80.01 IndusInd Bank 619.86 1225.80 1518.54 1858.14 2097.57 2512.49 3205.67 3720.05 4223.74 6900.37 2788.22 1831.40 65.68 Kotak Mahindra Bank 5514.57 5534.16 7030.86 8478.07 11807.34 13414.44 14849 16145.97 16555.57 15311.78 11464.18 4436.69 38.70 Punjab National Bank – 17021.23 20873.14 23671.44 32526.40 43291.08 44329.30 49282.16 55015.24 56881.82 38099.09 15041.62 39.48 State Bank of India – 330.57 509.30 787.95 1246.93 1615.64 2218.79 2716.26 3487.84 4566.72 1942.22 1437.26 74.00 YES Bank Asset Liability Management in Commercial Banks in India 309 310 K Thakker and T Chakraborty 3.5 Net Interest by Total Funds Ratio Net interest is a measure of the difference between the interest income generated by banks or other financial institutions and the amount of interest paid out to their lenders (for example, deposits), relative to the amount of their (interest-earning) assets It is similar to the gross margin (or gross profit margin) of non-financial companies It is usually expressed as a percentage of what the financial institution earns on loans in a time period and other assets minus the interest paid on borrowed funds divided by the Total Funds in that time period The net interest by Total Funds ratio is calculated as follows (Table 5): Net Interest Margin Ratio Net Interest Income/Total Funds The mean value of net interest by total funds ratio of Kotak Mahindra Bank is 4.68 times followed by HDFC Bank is 4.29 times and Federal Bank is 3.23 times The co-efficient of variation of net interest by total funds ratio accounted by 32.68% in IndusInd Bank followed by 18.13% in ICICI Bank and 14.44% in Bank of Baroda All the selected banks under study show a fluctuating trend except for ICICI Bank which has increased every year The co-efficient of variation of HDFC indicates that the Net Interest by total fund ratio had been less consistent during the study period The graphical representation of the net interest by total funds ratio is given below 2.39 2.83 2.86 3.05 3.10 3.03 3.09 3.30 3.37 3.41 3.04 0.30 9.91 Source Capitaline database 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Mean SD CV (%) Axis Bank 2.79 2.42 2.52 2.35 2.77 2.57 2.28 1.99 1.92 1.84 2.35 0.34 14.44 Bank of Baroda 3.13 3.01 3.68 3.41 3.66 3.48 2.99 3.05 3.01 2.87 3.23 0.30 9.28 Federal Bank Table Net interest income by total funds (in times) 4.21 4.66 4.69 4.13 4.22 4.19 4.28 4.14 4.14 4.25 4.29 0.21 4.85 1.89 1.96 2.14 2.18 2.34 2.40 2.70 2.91 3.06 3.10 2.47 0.45 18.13 HDFC Bank ICICI Bank 1.41 1.37 1.82 2.83 3.41 3.31 3.42 3.61 3.45 3.59 2.82 0.92 32.68 IndusInd Bank 4.12 5.08 5.33 5.62 4.75 4.31 4.29 4.34 4.36 4.62 4.68 0.50 10.79 Kotak Mahindra Bank 3.59 3.08 3.17 3.14 3.51 3.22 3.18 3.14 2.87 2.42 3.13 0.32 10.33 Punjab National Bank – 2.64 2.47 2.34 2.85 3.38 3.05 2.93 2.86 2.64 2.80 0.31 11.27 State Bank of India – 2.35 2.55 2.66 2.61 2.44 2.57 2.61 2.85 3.03 2.63 0.20 7.78 YES Bank Asset Liability Management in Commercial Banks in India 311 312 K Thakker and T Chakraborty 3.6 Gap Analysis Gap analysis is a method that conveys the difference between rate sensitive assets and rate sensitive liabilities over a period of time (Table 6) Gap Analysis Rate Sensitivity Assets − Rate Sensitivity Liabilities From the above table, we can observe that HDFC Bank and YES Bank have a positive Gap throughout the period of study followed by ICICI Bank, Federal Bank and Axis Bank Bank of Baroda has a negative Gap throughout the period of study All banks except for Bank of Baroda and Punjab National Bank have a negative mean as shown in the table above Axis Bank has a negative gap almost in all the years from 2007 till 2011, thereafter it has a positive gap for all the years covered in the study Federal Bank has a negative gap only in the year 2007, 2015 and 2016 ICICI Bank has a negative gap only in the year 2016 IndusInd Bank has a negative gap till 2012, thereafter it is positive Punjab National Bank has a negative gap in almost all the years except 2007, 2008, 2012 and 2013 State Bank of India has a positive gap except for the year 2009, 2011, 2015 and 2016 While considering the co-efficient of variation, Kotak Mahindra Bank has a less volatile gap and YES Bank has a more volatile gap The graphical representation of the gap analysis is given below Source Capitaline database −7494.04 −892.69 −5389.79 1755.94 −1601.26 1093.73 −18176.70 2400.05 −27810.32 1587.77 −37857.42 1980.32 −50875.21 2449.44 −92589.29 2134.71 −102438.91 −1279.42 −103288.90 −1040.64 −44752.18 1018.92 40716.60 1497.18 −90.98 146.94 −207.54 115.98 −5006.86 1846.00 −1106.23 8775.67 14138.80 12379.70 11225.65 3585.98 4574.71 6578.80 143.81 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Mean SD CV (%) Federal Bank Bank of Baroda Axis Bank GAP Table GAP analysis (in crore) 8009.58 8012.48 5725.37 4118.08 7931.57 22349.98 22080.67 17174.89 21127.59 29037.08 14556.73 8778.76 60.31 5357.21 26990.94 9865.89 5818.23 15895.45 17622.85 23687.92 29051.76 11671.20 −557.36 14540.41 9882.89 67.97 HDFC Bank ICICI Bank −681.15 −707.84 −1073.16 −692.03 −174.33 −1407.65 3948.50 4691.54 3615.12 9017.43 1653.64 3484.03 210.69 IndusInd Bank 1663.21 3122.27 3328.30 3239.93 5450.70 5501.13 5889.68 6544.27 9284.70 9981.34 5400.55 2702.90 50.05 Kotak Mahindra Bank 1302.78 2509.70 −3756.53 −3933.27 −6868.69 69.30 11109.11 −5187.53 −15553.21 −41674.04 −6198.24 14257.67 −230.03 Punjab National Bank 11260.96 17138.11 −7673.93 10576.34 −1181.76 9123.57 24571.73 31088.93 −158.42 −14115.43 8063.01 14123.85 175.17 State Bank of India 275.14 264.61 −350.99 855.41 562.64 2437.79 2097.86 1077.01 1382.06 106243.90 11484.54 33305.91 290.01 YES Bank Asset Liability Management in Commercial Banks in India 313 314 K Thakker and T Chakraborty Findings and Analysis of the Study • The Rate Sensitive assets are showing an increasing trend in general for all the selected banks in the study period Mean value of rate sensitive assets are high in public sector banks as compared to private sector banks It is observed that there is a very high covariance for private sector banks such as YES Bank (95.9), Kotak Mahindra Bank (72.89), IndusInd Bank (66.30) and HDFC Bank (60.77) in comparison to Public Sector banks like Bank of Baroda (47.37), State Bank of India (41.92) and Punjab National Bank (42.83) • It is noticed that trend from the rate sensitivity liabilities is also rising for almost all the banks under study Mean value of rate sensitive assets are high in public sector banks as compared to private sector banks It is observed that there is a very high covariance for private sector banks such as YES Bank (73), Kotak Mahindra Bank (75.37), IndusInd Bank (62.36) and HDFC Bank (61.22) in comparison to Public Sector banks like Bank of Baroda (52.07), State Bank of India (42.41) and Punjab National Bank (44.39) • It is observed from the interest sensitive ratio that the mean value are recorded as high in private sector banks such as Kotak Mahindra Bank (1.108), YES Bank (1.090), ICICI Bank (1.038) and Federal Bank (1.024) as compared to Public sector banks The co-efficient of variation indicates that the interest sensitivity ratio is highly volatile in YES Bank and Bank of Baroda and high volatile in ICICI Bank Asset Liability Management in Commercial Banks in India 315 • It could be noticed from the net interest income that the mean value recorded as high in Public sector banks such as State Bank of Indian and Punjab National Bank in comparison to private sector banks IndusInd Bank and YES Bank have a high volatility in net interest income • It is observed from the analysis of Net Interest Income by Total Funds Ratio that the mean value recorded as high in private sector banks such as Kotak Mahindra Bank and Federal Bank and it is lowest in Bank of Baroda The co-efficient of variation indicates that the Net Interest Income by Total Funds Ratio is less consistent in HDFC Bank and highly volatile in IndusInd Bank and ICICI Bank • It is found from the GAP analysis that the mean values of all the banks except Bank of Baroda and Punjab National Bank are positive While considering the co-efficient of variation, Kotak Mahindra Bank has less volatile and YES Bank has more volatile GAP during the study period Conclusion The growth rate of private sector banks such as YES Bank and Kotak Mahindra Bank is very high, where as ICICI Bank has maintained its normal position without much fluctuations in any of the elements All banks except bank of Baroda and Punjab National Bank show a positive gap The performance of Bank of Baroda for the year 2016 shows a decline in most of the variables considered in the study which needs further investigation The banks should try to integrate liquidity management as a part of banks asset liability management The bank’s asset and liability management policy should clearly define the role of liquid assets along with setting clear targets and limits The banks should reduce the ALM gap by increasing the deposits from the public All the selected banks should focus more on reducing the mismatches because it creates risks for the banks and it should be addressed immediately Limitations of the Study The present study has a few demerits also because the study data was selective in nature Only loans & advances and investments were taken as Risk Sensitive Assets and deposits and borrowings as Risk Sensitive Liabilities All other assets, liabilities and off balance sheet items were not included in the study The study considers only the total Risk Sensitive Assets and Liabilities at the end of the year of only selected private and public sector banks constituting the BSE SENSEX 316 K Thakker and T Chakraborty Future Research Possibilities Further studies can be carried out by applying improved methods for better management of bank’s maturity gap by dividing the Risk Sensitive Assets and Liabilities into different time periods based on its maturity during the year References Balanagagurunathan, K., Selvaraj, M., & Sathyakala, S (2016) Impact of asset liability management for the growth of selected private sector banks in India International Journal of Economic Research, 13(1), 2016 Baser, N (2013) Asset-liability management in the Indian commercial banks Asian Journal of Research in Banking and Finance, 3(10), 28–40 Charumathi, B (2008) Asset liability management in Indian banking 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Indian context ASCI Journal of Management, 29(1), 39–48 http://www.iimb.ernet.in/~vaidya/Asset-liability.pdf .. .Advances in Finance & Applied Economics N R Bhanumurthy K Shanmugan Shriram Nerlekar Sandeep Hegade • • Editors Advances in Finance & Applied Economics 123 Editors N R Bhanumurthy National Institute... in fact, resulting in governance improving outcomes and improvements in Federalism in India: An Economic Analysis economic indicators The main idea I test in this paper is whether the splitting... sripriya.srivatsa@tutanota.com © Springer Nature Singapore Pte Ltd 2018 N R Bhanumurthy et al (eds.), Advances in Finance & Applied Economics, https://doi.org/10.1007/978-981-13-1696-8_1 S Srivatsa Over time since independence,