1. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 – 6510(Online),
International Journal of Management (IJM)
Volume 2, Number 1, Jan- April (2011), © IAEME
ISSN 0976 – 6502(Print), ISSN 0976 – 6510(Online) IJM
Volume 2, Number 1, Jan- April (2011), pp. 79-83
© IAEME, http://www.iaeme.com/ijm.html ©IAEME
OPERATIONAL EFFICIENCY AND TIMES SERIES CHANGES IN TAICO
BANK – AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)
MODEL
Dr. S. RAJAMOHAN
Professor, Alagappa Institute of Management
Alagappa University, Karaikudi-630 004
S. PASUPATHI
Associate Professor in Commerce, Vivekananda College
Thiruvedakam (West), Madurai -625 217
ABSTRACT
The Tamilnadu Industrial Cooperative Bank established in 1962 provides credit to
industrial cooperatives like tea factories, match factories, coir industries and the like in
the state. It has 32 branches located at district headquarters. In this paper an attempt is
made to know the operational efficiency and the times series changes in overall
functioning of the bank during the period of analysis through a model called Auto
Regressive Integrated Moving Average (ARIMA). It was found that the financial
performance of the bank is consistent for the first five years (1998-99 to 2002-03) and a
radical change is occurred in the overall functioning of the bank during the last six years
of the study (2004-04 to 2008-09). Moreover, out of the 47 ratios, two thirds of the ratios
show an increasing trend and the rest of them shows a decreasing trend during the period
analysis. Also there is a constant increase and significant changes in the five variables
namely operating profit, gross income, capital employed, operating expenses and interest
expenses (11.14% each year). Thus the TAICO Bank has performed financially well
during the period of analysis.
INTRODUCTION
The Tamilnadu Industrial Cooperative Bank was established and started
functioning from November, 1962. It provides a wide range of financial assistance to
various industrial cooperatives, small scale industries, partnership firms, joint stock
companies and the like engaged in small, tiny, cottage and village industries in the non
farm sectors. In this paper an attempt has been made to identify the time series changes
in the overall functioning of the bank through a model called ARIMA model.
SCOPE OF THE STUDY
The present research study is pursued to analyse the financial performance of the
bank, its time series changes and prediction about the trends in the overall functioning to
the extent possible.
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2. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 – 6510(Online),
Volume 2, Number 1, Jan- April (2011), © IAEME
REVIEW OF LITERATURE
D. Ilangovan and K. Padmanaban1 (2002) analysed the performance of PACBs in
Tamilnadu by taking the different kinds of loans, branch expansion, level of deposits,
position of reserves, working capital, overdues as the criterion. They concluded that
cooperative banks are the suitable institutions for providing short term credit to
agriculture, small scale industries and industrial cooperatives.
H. Srinivas Rao2 (2006) in his article analysed the working of the Andhra
Pradesh State Cooperative Bank. The findings were that there was a steep increase in the
figures of interest earned, interest paid, deposits, fixed assets and liquid assets. Fixed
assets to net worth ratio showed a fluctuating trend and there was a perfect positive
relation between current assets and current liabilities.
A. Khan3 (2010) evaluated the “Performance of Dimapur District Central
Cooperative Bank (Patna)”. He pointed out that the overall performance of the bank is
very good during the reference period. He also suggested ways to reduce NPAs,
administrative expenses, deployment of funds in profitable sources and to increase the
non fund based (non interest income) activities of the bank.
R. Latha4 (2003) in her dissertation entitled. “A Comparative Study on the
Financial Performance of Associate Banks of State Bank of India” has made an inter-
bank comparison of the financial performance of associate banks of SBI. For analyzing
the financial performance she has used eight parameters like deposits, advances,
investments, branch expansion, NPAs, total income, total expenditure, net profit into
account. She has used ratio analysis for accessing the performance of the bank. She has
also used growth rate and percentage analysis for analyzing the financial performance.
Finally she has ranked all the seven banks under 24 parameters. State Bank of Hyderabad
secured first rank in 11 parameters and State Bank of Indore secured last rank in eight
parameters.
D. Suryachandra Rao5 (2009) in his article evaluated the performance of
commercial banks by taking the indicators like spread, return on assets, return on equity,
profit per branch, business per employee, deposits, advances and the like for a period of
11 years (1992-93 to 2002-03). He suggested that the banks should device strategies to
cut down and control the costs, earn more revenues from non interest sources and
reduce the dependence of interest income, adopt latest and cost-effective technologies to
improve the profitability.
METHODOLOGY
This study is based on secondary data. The data required for the study have been
collected from the annual accounts of the TAICO Bank, books, journals and the like.
Discussions have also been held with the official of the bank.
PERIOD OF THE STUDY
This study covers a period of 11 years commencing from 1998-99 to 2008-09.
ANALYSIS OF THE STUDY
The ARIMA Model is useful in identifying the Time Series changes and to
estimate the forecasts about the overall functioning of the bank.6 It automatically
identifies and estimates the best fitting Arima or exponential smoothing model for one or
more dependent variable series. In this present research work, the researcher identified a
number of 47 independent variables namely total loans and advances per employee,
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3. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 – 6510(Online),
Volume 2, Number 1, Jan- April (2011), © IAEME
deposits per employee, business per employee, total outside liabilities to networth,
deposits to equity, deposits to total assets, net NPAs to net advances, total liabilities to
owned funds, total assets to equity fund, liquid assets to total assets, cash to reserve, staff
cost to total income, cash to volume of business, net NPAs to total advances, total income
to total assets, total expenses to total income, interest expenses to total income, current
assets to volume of business, returns to average assets, liquid assets to total deposits,
liquid assets to demand deposits, operating expenses to total expenses, fixed deposits to
total deposits, net profit to owned funds, net profit to total deposits, net profit to total
income, net profit to working capital, net profit to total assets, net profit to spread, spread
to total income, current ratio, total income to working capital, total expenses to working
capital, burden to working capital, cash to current liabilities, working capital to volume of
business, current assets to total assets, non interest income to total income, interest
income to total income, networth to total assets, total advances to total deposits, total
assets to total liabilities, fixed assets to owned funds, networth to current assets, spread to
total assets, cash to current assets and demand liabilities to total liabilities against the five
dependent variables(operating profit, operating expenses, capital employed, interest
expenses and gross income). The details of the five dependent variables are depicted in
Table 1.
TABLE 1: Selected Variables for ARIMA Model
Net
Operating Capital Interest Gross
Year Operating
Expenses Employed Expenses Income
Profit
1998-1999 - 157.27 121.74 1392.72 329.91 664.19
1999-2000 - 273.32 129.85 1745.77 420.44 737.62
2000-2001 104.45 128.07 2024.48 456.51 943.02
2001-2002 150.89 268.53 1806.67 642.67 975.01
2002-2003 215.19 185.06 1859.14 905.75 1598.16
2003-2004 205.55 195.23 2010.40 1311.19 2234.79
2004-2005 243.19 230.51 2340.00 1566.76 2707.42
2005-2006 169.84 232.17 2631.47 1683.31 2691.51
2006-2007 45.35 260.54 3101.21 1854.86 3021.44
2007-2008 78.10 289.98 3362.64 2515.31 3571.44
2008-2009 91.04 361.24 3664.20 2871.74 3981.95
Source: Annual Accounts of the TAICO Bank
Table 1 shows that the three selected variables namely capital employed, interest
expenses and gross income show an increasing trend and the remaining two variables
namely net operating profit and operating expenses show a decreasing trend during the
period of analysis.
The ARIMA Model is executed in this context and the following result is
obtained and is presented in Table 2.
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4. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 – 6510(Online),
Volume 2, Number 1, Jan- April (2011), © IAEME
TABLE 2 Projections of Vital Ratios - ARIMA Model Values
Minimu
Fit Statistic Mean SE m Maximum Percentile
Particulars 5 10 25 50 75 90 95 5 10 25 50
Stationary R-
.786 .353 .156 .968 .156 .156 .528 .951 .961 .968 .968
squared
R-squared .786 .353 .156 .968 .156 .156 .528 .951 .961 .968 .968
RMSE 214.067 32.839 172.294 247.293 172.294 172.294 183.366 209.686 246.959 247.293 247.293
MAPE 18.016 8.588 9.544 31.503 9.544 9.544 10.738 17.177 25.713 31.503 31.503
MaxAPE 61.015 33.477 17.293 97.786 17.293 17.293 26.981 68.401 91.357 97.786 97.786
MAE 168.895 33.118 124.599 207.752 124.599 124.599 138.922 164.473 201.078 207.752 207.752
MaxAE 332.251 57.986 280.190 421.543 280.190 280.190 281.106 329.834 384.603 421.543 421.543
Normalized
11.149 .312 10.734 11.457 10.734 10.734 10.855 11.127 11.454 11.457 11.457
BIC
Source: Box, Jenkins and Reinsel, “An Over view of Multiple Regression Co-efficient”, American Journal
of Sunsehes, 1994, pp.141-170.
It gives out Stationery R2 Values, Varying R2 values, Root mean Square Error (RMSE), MEAN
Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Maximum Absolute Error (MAE),
Maximum absolute Percentage Error (MAPE), Normalised Bayesian Information. The modified ARIMA
values are presented in Table 3.
TABLE 3 Projections of Vital Ratios - Modified ARIMA Model Values
Maximu
Fit Statistic Mean SE Minimum m Percentile
Particulars 5 10 25 50 75 90 95 5 10 25 50
Stationary R- -4.44E- -4.44E- -4.44E- -1.11E-
.375 .414 .814 .333 .782 .814 .814
squared 016 016 016 016
R-squared .923 .035 .857 .956 .857 .857 .905 .931 .945 .956 .956
RMSE 11668.4 29768.82 1122.6 9489.87 29768.8 29768.8
6005.887 341.950 341.950 341.950 797.316
42 1 09 8 21 21
MAPE 17.697 6.733 11.780 29.019 11.780 11.780 12.844 14.761 24.253 29.019 29.019
MaxAPE 54.167 28.293 28.838 95.421 28.838 28.838 31.098 42.908 86.163 95.421 95.421
MAE 8261.36 21157.20 867.89 7010.44 21157.2 21157.2
4353.603 231.653 231.653 231.653 584.499
9 8 4 2 08 08
MaxAE 25466.9 64584.27 2226.7 19943.3 64584.2 64584.2
12691.656 760.706 760.706 760.706 1156.41
09 2 79 7 72 72
Normalized BIC 15.319 2.979 12.130 20.833 12.130 12.130 13.634 14.381 17.249 20.833 20.833
Source: Box, Jenkins and Reinsel, “An Over view of Multiple Regression Co-efficient”, American
Journal of Sunsehes, 1994, pp.141-170.
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5. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 – 6510(Online),
Volume 2, Number 1, Jan- April (2011), © IAEME
From Table 3, it is found that the Mean, Standard Error with maximum and
minimum fit statistics are sharply estimated. Since the whole series is centered at mean
values, it can be concluded that collectively the five variables totally exhibit 78.6 per cent
variance in the past 11 years. The RMSE variance and NAPE variance are respectively
214.067 and 18.016 with normalized BIC variance 11.149. This implies that the five
variables have made significant changes, that is 11.14 per cent each year on the average.
Thus it can be concluded that the variation is above 50 per cent in the span of 11
years for TAICO Bank. It shows that the TAICO Bank has performed financially well
with respective increase in its operating profit and gross income. At the same time the
increase in operative expenses and interest expenses, capital employed shows its
significant financial development.
CONCLUSION
The TAICO Bank has been performing financial well during the period of
analysis. Efforts must be taken by the bank to ensure more total income and interest
income by reducing its operating expenses in the future years.
REFERENCES
1. D.Ilangovan and K. Padmanaban, “Performance of DACBs in Tamilnadu,”
Tamilnadu Journal of Cooperative, Vol.2, No.7, May 2002, pp.27-31.
2. H. Srinivas Rao, “Working of the Andhra Pradesh State Cooperative Bank – An
Evaluation” Finance India, Vol. XV, No 2, September 2006, pp.1351 – 1357.
3. A. Khan, “Performance of Dimapur District Central Cooperative Bank (Patna)”,
NCDC Bulletin, No.6, June 2010, pp.8-14.
4. R. Latha, “A Comparative Study on the Financial Performance of Associate
Banks of State Bank of India”, M.Phil. Dissertation Submitted to Alagappa
University, June 2003.
5. D. Suryachandra Rao, “An Evaluation Study of the Performance of Commercial
Banks”, Finance India, June 2009, Vol.XXI, No.2, pp.591- 597.
6. Box, Jenkins and Reinsel, “An Overview of Multiple Regression Co-efficient”,
American Journal of Sunsehes, 1994, pp.141-170.
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