Name: - Shivam Goyal
Roll No - 209
Section - D
PGDM - Finance
ICICI BANK
Sector - Banking
BUSINESS RESEARCH
METHOD
List of Contents
1) Introduction
2) Various Related Statistics
2.1) Table Showing Assets, Market Capitalization, Revenue, Borrowing, Net Profit of Last Ten Years
2.2) Table Showing Share Book Value, Market Value and Nifty Value for Last Ten Years
3) Other Related Information about company
3.1) Mergers and Acquisitions
3.2) CSR Initiative
3.3) Growth Rate in Last Ten Years
3.4) Awards
4. Analysis of Share price
4.1) Five Number Box Plot
4.2) One Sample T-Test
4.3) Paired Sample T test
4.4) Multiple Regression
4.5) Sequence Chart
4.6) Seasonal Decomposition
4.7) Time Series Modeler
5. Conclusion
6. References
1. INTRODUCTION
ICICI Bank is an Indian multinational banking and financial services company. As of 2014 it is the second
largest bank in India in terms of assets and market capitalization. ICICI Bank is one of the Big Four banks of
India, along with State Bank of India, Punjab National Bank and Bank of Baroda.
? Year of Establishment - 1994
? MD & CEO - Ms.Chanda Kocher
? Chairman - K.V.Kamath
? Headquarters - Mumbai, Maharashtra, India
? Corporate Office - ICICI Bank Towers, Bandra-Kurla Complex, Mumbai 400 051
? Auditors - S.R. Batliboi & Co. LLP (Chartered Accountants)
? Branches - 2,791 branches in India and 10,021 ATM in India
Outside India has branches in United States, Singapore, Bahrain, Hong Kong, Sri Lanka, Qatar and
Dubai. Total number of 10 branches outside India.
? Product Lines - Credit Cards, Consumer Banking, Corporate banking, Finance and Insurance,
Investment Banking, Mortgage Loans, Private Banking, Wealth Management
? Shares –
Total: 1,154,832,769
Resident Holding: 354,184,495
Non Resident Holding: 800,648,274
? Facilities - Supplementary Savings, Free Anywhere banking, Free collection of outstation cheques, Free
collection of outstation cheques, Free multi city Cheque Book facility, Internet Banking.
? Subsidiaries –
Domestic: ICICI Prudential Life Insurance Company Limited,ICICI Lombard General Insurance
Company Limited,ICICI Prudential Asset Management Company Limited,ICICI Prudential Trust
Limited,ICICI Securities Limited,ICICI Securities Primary Dealership Limited,ICICI Venture Funds
Management Company Limited,ICICI Home Finance Company Limited,ICICI Investment Management
Company Limited,ICICI Trusteeship Services Limited,ICICI Prudential Pension Funds Management
Company Limited
.
International: ICICI Bank UK PLC, ICICI Bank Canada, ICICI Bank Eurasia Limited Liability
Company , ICICI Securities Holdings Inc., ICICI Securities Inc. ,ICICI International Limited.
2. Various Related Statistics
2.1 Table Showing Assets, Market Capitalization, Revenue, Borrowing, Net Profit of Last Ten
Years
In Crs Fixed Assets Total Assets Market Capitalization Revenue Borrowing Net Profit
2014 4678.14 594641.60 219020.23 54606.02 154759.05 9810.48
2013 4647.06 536794.69 115087.08 48421.30 145341.49 8325.47
2012 4614.69 489068.80 106262.98 41045.41 140164.91 6456.26
2011 4744.26 406233.67 102507.74 32621.94 109554.28 5151.38
2010 3212.69 363399.71 105807.18 32999.36 94263.57 4024.98
2009 3801.62 379300.96 68025.18 39210.31 67323.69 3758.13
2008 4108.89 399795.07 96720.36 39667.19 65648.43 4157.73
2007 3923.42 344658.11 96221.53 29957.24 51256.03 3110.22
2006 3980.72 251388.95 62510.13 18821.12 38521.91 2540.07
2005 4038.04 167659.40 28975.04 12826.12 33544.50 2005.20
2.2 Table Showing Share Book Value, Market Value and Nifty Value for Last Ten Years
Shares Nifty Index
Book Value High Low Average High Low Average
2014 633.92 1681.65 943.60 1312.62 8160.90 6067.35 7114.12
2013 578.21 1238.40 756.90 997.65 6415.75 5274.25 5845.00
2012 578.21 1158.65 685.00 921.85 5917.80 4770.35 5344.07
2011 478.31 1139.00 641.00 890.00 6181.35 4555.90 5368.45
2010 463.01 1186.15 712.00 949.08 6335.90 4692.75 5514.32
2009 444.94 969.80 252.30 611.05 5181.95 2663.90 3922.92
2008 417.64 1455.40 283.10 869.25 6357.10 2252.75 4304.92
2007 270.37 1349.00 791.00 1070.00 6175.65 3811.75 4993.7
2006 249.55 925.00 480.00 702.50 4036.20 2638.10 3337.15
2005 170.35 614.95 325.20 470.08 2853.45 1896.30 2374.87
3. Other Related Information about company
3.1 Mergers and Acquisitions
? 1996: SCICI Ltd. A diversified financial institution with headquarters in Mumbai
? 1997: ITC Classic Finance. Incorporated in 1986, ITC Classic was a non-bank financial firm that
engaged in hire, purchase, and leasing operations.
? 1998: Anagram Finance. Anagram had built up a network of some 50 branches in Gujarat, Rajasthan,
and Maharashtra that were primarily engaged in retail financing
? 2001: Bank of Madurai
? 2002: The Darjeeling and Shimla branches of Grindlays Bank
? 2005: Investitsionno-Kreditny Bank (IKB), a Russian bank
? 2007: Sangli Bank. Sangli Bank was a private sector unlisted bank.
? 2010: The Bank of Rajasthan (BOR) was acquired by the ICICI Bank.
3.2 CSR Initiative
? Go Green Initiative - The Go Green Initiative is an organization wide initiative that moves beyond
moving people, processes and customers to cost effective automated channels to build awareness and
consciousness of our environment, our nation and our society
? Read to Lead Phase: It initiative of ICICI Bank to facilitate access to elementary education for
underprivileged children in the age group of 3–14 years including girls and tribal children from the
remote rural areas. The Read to Lead initiative supports partner NGOs to design and implement
programmes that mobilize parent and community involvement in education, strengthen schools and
enable children to enter and complete formal elementary education.
3.3 Growth Rate In Last Ten Years
In 2014 bank growth at rate of 17.2 percent which was low as compared to 2013 which was almost 27 percent.
Comparing year 2008 and year 2009 we can see that profit remained dropped. It is due to effect for recession.
Other than this year on average we can see that company is showing growth rate greater than 10 percent which
is positive sign for bank.
3.4 Awards
? In 2013, ICICI Bank has been adjudged winner at the Express IT Innovation Award under the Large
Enterprise category
? In 2012 ICICI Bank is the only Indian brand to figure in the BrandZ Top 100 Most Valuable Global Brands
Report, second year in a row.
? In 2009, ICICI Bank bags the "Best bank in SME financing (Private Sector)" at the Dun & Bradstreet
Banking awards.
1. Analysis of Share price
4.1 Five Number Box Plot
A box plot is a one-dimensional graph of numerical data based on the five-number summary. This summary
includes the following statistics: the minimum value, the 25th percentile (known as Q1), the median, the 75th
percentile (Q3), and the maximum value. In essence, these five descriptive statistics divide the data set into four
parts, where each part contains 25% of the data.
Here we took average share price of ICICI bank for last 5 years from and using SPSS made box plot.
We can see that for 2014, it has greatest variation i.e. more fluctuation in price of shares as compared to other
year prices.
4.2 One Sample T Test
One-sample t-test is used to determine whether a sample comes from a population with a specific mean. This
population mean is not always known, but is sometimes hypothesized. If the P value is large, the data do not
give you any reason to conclude that the population mean differs from the hypothetical value you entered. This
is not the same as saying that the true mean equals the hypothetical value. You just don't have evidence of a
difference.
We Checked for confidence interval 95% i.e. ?p‘ should be greater than .05 .We took share price average of last
5 years and tested against our sample where we took average price of shares from 1
st
day of every month for 5
years. P value is .696 which is greater than .05. Conclusion was that sample is from data from population.
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Average Share Price 50 1.077326E3 204.3450604 28.8987556
One-Sample Test
Test Value = 1065.96
t df Sig. (2-tailed) Mean Difference
95% Confidence Interval of the
Difference
Lower Upper
Average Share Price .393 49 .696 11.3665000 -46.707724 69.440724
4.3 Paired Sample T test
Paired t-test is used to compare two population means where you have two samples in which observations in
one sample can be paired with observations in the other sample. Examples of where this might occur are: -
Before-and-after observations on the same or a comparison of two different methods of measurement or two
different treatments where the --measurements/treatments are applied to the same subjects.
Here we took high and low value of share price of every month to test for paired sample test. We can see by
looking at value of P that no statistically significant difference between your two conditions and correlation
between two samples is 88.1 %.
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Open 1074.47 50 200.698 28.383
Closing 1081.65 50 218.153 30.852
Paired Samples Correlations
N Correlation Sig.
Pair 1 Open & Closing 50 .881 .000
Paired Samples Test
Paired Differences
t df
Sig. (2-
tailed)
Mean Std. Deviation Std. Error Mean
95% Confidence Interval of the
Difference
Lower Upper
Pair 1
Open - Closing -7.188 103.619 14.654 -36.636 22.260 -.491 49 .626
4.4 Multiple Regression
For using MULTIPLE REGRESSION, the basic conditions are that the data should be on same time frame,
should be Linear and should not possess correlation between them. The first condition was fulfilled by all the
data as the data was collected on the same time frame. The next condition is that the data should belinear, but
this condition was not fulfilled by the factors like Repo Rate, Reverse Repo Rate and Cash Reserve Ratio. So
these factors were dropped for the analysis as they were not linear. Again the factors like Operating Profit and
Net Profit, Close of Nifty and Close of Bank Nifty and Borrowings and Deposits possessed correlation between
them. Thus, Operating Profit, Close of Nifty and Borrowings were dropped for the analysis part and Net Profit,
Close of Bank Nifty and Deposits were taken for the analysis. Though Borrowings is used in analysis as it did
not possess correlation there.
Average Share Price as Dependent variable and Interest Income, Advances, Net Profit, Close of Bank Nifty,
Deposit and Borrowings as Independent Variables.
In this case the value of R is .862. This shows that the Predictors have 86.2% influences on the output
In this case the value of R2is .744 this means that the predictors account for 74.4%variation in the Average
Price of the shares.
Here the difference between value of R2and the Adjusted R2 is 0.062 (.744-.682) which is small. This
shrinkage means that if model is derived from the population rather than a sample it would account for
approximately 6.2% less variance in the outcome.
Analysis of Variance (ANOVA) table shows the Sum of Squares here is 1821276; the value of Residual
Sum of Squares is 627348.6 and the value of F which is 12.096. Here we can conclude that the value of F is
significant and our ability to predict the Outcome Variable is good.
This part of model is concerned with the parameters of the model. The Coefficients table shows us the
parameters. We can define the equation here as follows:
Average Price = b0+ b1 (Interest Income) + b2 (Advances) + b3 (Net profit) + b4 (Close of Bank Nifty) + b5 (Deposits)
+ b6 (Borrowings).
Average Price = 472.069 + .079 (Interest Income) + .005 (Advances) + (-.272) (Net profit) + .009 (Close of Bank Nifty) + (-.002) (Deposits) + (-
.016) (Borrowings)
The B values tell us to what degree each predictor affects the outcome if the effects of all other predictors
are held constant. Here is the analysis.
Interest Income (b=.079) :This value indicates that as the interest income increases by Rs. 1 crore, the
Average Price increases by Rs. 0.079Thisinterpretation is only true if the effect of other predictors is held
constant.
Advances (b=.005) : This value indicates that as the Advances are increases by Rs.1 crore , the Average
Price increases by Rs. 0.005This interpretation is only true if the effect of other predictors is held constant.
Net Profit (b=-.272) : This value indicates that as the Net Profit is increases by Rs.1 crore , the Average
Price decreases by Rs. 0.272. This interpretation is only true if the effect of other predictors is held constant.
Close of Bank Nifty (b=.009) : This value indicates that as the Close of Bank Nifty increases by 1 point the
Average Price increases by Rs.0.009..This interpretation is only true if the effect of other predictors is held
constant.
Deposits (b=-.002) : This value indicates that as the Deposits are increases by Rs. 1crore, the Average Price
decreases by Rs. 0.002.This interpretation is only true if the effect of other predictors is held constant.
Borrowings (b=-.016) : This value indicates that as the Borrowings are increases by Rs. 1 crore, the Average
Price decreases by Rs. 0.016.This interpretation is only true if the effect of other predictors is held constant.
For this model the value of t-statistic for different variables and their Sig. can be seen from the above table.
From the magnitude of t-statistics we can see that the t-statistics for Borrowings (.0230.05),
Close of Bank Nifty (.823>0.05), Deposits (.668>0.05), Advances (.247>0.05) and Net Profit (.311>0.05) is
more than 0.05, thus, they don‘t have a significant contribution to the model. We also look for t-values,
which if are above +2 and below -2. Here in this case only Borrowings match the criteria, thus, it has a
significant contribution in the Model.
The Standardized Beta Values as shown in the above table can be interpreted as follows:
Interest Income (?=.738) : This value indicates that as the interest income increases by 1 Standard Deviation
(2612.99), the Average Price increases by 0.738Standard Deviation. As the Standard Deviation for Average
Price is 281.0478, thus, a change of 1 Standard Deviation would bring a change of .738 in the price.
This interpretation is only true if the effect of other predictors is held constant.
Advances (?=1.413) :This value indicates that as the Advances are increases by 1Standard Deviation
(74660.49), the Average Price increases by 1.413 Standard Deviations the Standard Deviation for Average
Price is 281.0478, thus, a change of 1 Standard Deviation would bring a change of 1.413 in the price. This
interpretation is only true if the effect of other predictors is held constant.
Net Profit (?=-.325) : This value indicates that as the Net Profit is increases by 1Standard Deviation (335.05),
the Average Price increases by -.325 Standard Deviation. As the Standard Deviation for Average Price is
281.0478, thus, a change of 1 Standard Deviation would bring a change of -.325 in the price. This interpretation
is only true if the effect of other predictors is held constant.
Close of Bank Nifty (?=.077) : This value indicates that as the Close of Bank Nifty increases by 1 Standard
Deviation (2314.74), the Average Price increases by.077 Standard Deviation. As the Standard Deviation for
Average Price is281.0478, thus, a change of 1 Standard Deviation would bring a change of .077 in the price.
This interpretation is only true if the effect of other predictors is held constant.
Deposits (?=-.446): This value indicates that as the Deposits are increases by 1Standard Deviation (81172.88),
the Average Price increases by -.446 Standard Deviation. As the Standard Deviation for Average Price is
281.0478, thus, a change of 1 Standard Deviation would bring a change of -.446 in the price. This interpretation
is only true if the effect of other predictors is held constant.
Borrowings (=-.839) : This value indicates that as the Borrowings are increases by 1 Standard Deviation
(14793.88), the Average Price increases by -.839Standard Deviation. As the Standard Deviation for Average
Price is 281.0478, thus, a change of 1 Standard Deviation would bring a change of -.839 in the price.
This interpretation is only true if the effect of other predictors is held constant. Now talking about the rest of the
table, we have got zero-order correlations which are nothing but simple Pearson Correlation Coefficients. The
Partial Correlations represent the relationship between each predictor and the outcome Variable, controlling for
the effect of other predictors. The Part Correlations represent the relationship between each predictor and the
outcome Variable, controlling for the effect of other predictors on the outcome. In effect, these Part Correlations
represent the unique relationship that each predictor has with the outcome.
4.5 Sequence Chart
When there is only one variable upon which observations are made then we call them a single time series or
more specifically a univariate time series. It is type of time series prediction where we use sequence chart we to
see behavior of variable, how it has changes over period of time. We can see if its behavior is cyclic, trend or
have seasonality. Here we have use average price of share of last 5 years on monthly basis, and also to make
analyze trend more properly have also taken high and low values of share prices on monthly bais.Also to
differentiate the years, we have divided into different years.
We can see that price at beginning of our sample is around Rs 1100.Till December 2011,it showed downward
trend, then it started recovering and again in 2013 it show some change in trend i.e. fall in price but as 2014
began, it show regular behavior of upward trend.
So, with the help of sequence chart we have able to see how share prices reacted to different conditions of
market in last 5 years, to make more accurate we can also take on daily basis which will give more specific
information.
4.6 Seasonal Decomposition
We realized that there is a seasonal component in the model. The seasonal decomposition model type should be
additive, because the amplitude of both the seasonal and irregular variations do not change as the level of the
trend rises or falls. The seasonal decomposition model type should be additive, because the amplitude of both
the seasonal and irregular variations do not change as the level of the trend rises or falls. The Seasonal
Decomposition procedure creates four new variables (series), with the following three-letter prefixes, for each
series specified:
SAF: Seasonal adjustment factors. These values indicate the effect of each period on the level of the series.
SAS: Seasonally adjusted series. These show values obtained after removing the seasonal variation of a series.
STC: Smoothed trend-cycle components. Values show the trend and cyclical behavior present in the series.
ERR: Residual or ?error? values. The values that remain after the seasonal, trend, and cycle components have
been removed from the series.
The Seasonal adjustment factors representing seasonal variation show fluctuation that is expansion and
contraction in price of share.
The seasonally adjusted series shows a clear upward trend. A number of peaks are evident, but they appear at
random intervals showing no evidence of an annual pattern.
The smoothed trend-cycle component, a smoothed version of the seasonally adjusted series that shows both
trend and cyclic components also show upward trend.
Using the Seasonal Decomposition procedure, we have removed the seasonal component of a periodic time
series to produce a series more suitable for trend analysis.
Conclusion: - The series exhibits a number of peaks, but they do not appear to be equally spaced. This suggests
that if the series has a periodic component, it also has fluctuations that are not periodic—the typical case for real
time series. Aside from the small scale fluctuations, the significant peaks appear to be separated by more than a
few months. Notice that the seasonal variations appear to grow with the upward series trend, suggesting that the
seasonal variations may be proportional to the level of the series.
4.7 Time Series Modeler
The Time Series Modeler procedure creates models for time series, and produces forecasts. It includes an
Expert Modeler that automatically determines the best model for each of your time series. For experienced
analysts who desire a greater degree of control, it also provides stools for custom model building.
The Time Series Modeler procedure estimates exponential smoothing, univariate Autoregressive Integrated
Moving Average (ARIMA), and multivariate ARIMA (or transfer function models) models for time series, and
produces forecasts. The procedure includes an Expert Modeler that automatically identifies and estimates the
best-fitting ARIMA or exponential smoothing model for one or more dependent variable series, thus
eliminating the need to identify an appropriate model through trial and error
Model Description
Model Type
Model ID Average Share Price Model_1 ARIMA(0,1,1)(0,0,0)
Model Fit
Fit Statistic Mean SE Minimum Maximum
Percentile
5 10 25 50 75 90 95
Stationary R-squared .252 . .252 .252 .252 .252 .252 .252 .252 .252 .252
R-squared .915 . .915 .915 .915 .915 .915 .915 .915 .915 .915
RMSE 60.112 . 60.112 60.112 60.112 60.112 60.112 60.112 60.112 60.112 60.112
MAPE 4.751 . 4.751 4.751 4.751 4.751 4.751 4.751 4.751 4.751 4.751
MaxAPE 14.588 . 14.588 14.588 14.588 14.588 14.588 14.588 14.588 14.588 14.588
MAE 48.609 . 48.609 48.609 48.609 48.609 48.609 48.609 48.609 48.609 48.609
MaxAE
137.073 . 137.073 137.073 137.073 137.073 137.073 137.073 137.073 137.073 137.073
Normalized BIC 8.272 . 8.272 8.272 8.272 8.272 8.272 8.272 8.272 8.272 8.272
We can see that on future forecast the price of share will be Rs1648.6.
Forecast
Model Dec 2014
Average Share Price-Model_1 Forecast 1.6846E3
UCL 1.8051E3
LCL 1.5641E3
For each model, forecasts start after the last non-missing
in the range of the requested estimation period, and end
at the last period for which non-missing values of all the
predictors are available or at the end date of the
requested forecast period, whichever is earlier.
5. Conclusion
The analysis of all the various factors affecting the ICICI Bank share prices leads us to some findings and
conclusion. While I was doing the project I came across I studied behavior of stock prices. I studied various
research reports, news articles, and from all that I could find out that each and every thing, whether a minute
one or a big one affects the Investors decision and thus affects the share prices. I have listed some factors in the
report which affect the share prices, also I have done a technical analysis for the same, but what comes out at
the end is that there are still so many unnoticed factors which affect the share prices. This list is not exhaustive;
still there is so much which needs to be studied and I tried to cover as much as I could. From the analysis, I
could find out and conclude that, the Share Prices are affected by each and every factor in varying degree. The
analysis shows that there is a very small impact of Interest Income, Advances, Deposits, Borrowings and a
slightly more impact of Bank Nifty Index on Share Prices. It is so because there are number of factors and it
was not possible to quantify each one of them and conduct the analysis, there was some technical difficulties
also which turn out to be the limitation of the project. But at the end of the analysis we can accept the
hypothesis, as most of the factors, do affect the Share Prices in some or other manner. A latest example I can
quote is the post election result session when Investors were happy that NDA government came into power and
the market jumped thousand points up.
6. References
M. Kabir Hassan, Gordon V. Karels, Manfred O. Peterson, Journal of Banking & Finance, Volume 18, Issue 3,
May 1994, Pages 575-593
Yaping Wang, Liansheng Wu, Yunhong Yang, Journal of Banking & Finance, Volume 33, Issue 1, January
2009, Pages 53-62
Siteshttp://www.unt.edu/rss/class/Jon/SPSS_SC/Manuals/v19/IBM SPSS Forecasting 19.pdfhttp://www-03.ibm.com/software/products/en/spss-forecasting
doc_888162780.docx
Roll No - 209
Section - D
PGDM - Finance
ICICI BANK
Sector - Banking
BUSINESS RESEARCH
METHOD
List of Contents
1) Introduction
2) Various Related Statistics
2.1) Table Showing Assets, Market Capitalization, Revenue, Borrowing, Net Profit of Last Ten Years
2.2) Table Showing Share Book Value, Market Value and Nifty Value for Last Ten Years
3) Other Related Information about company
3.1) Mergers and Acquisitions
3.2) CSR Initiative
3.3) Growth Rate in Last Ten Years
3.4) Awards
4. Analysis of Share price
4.1) Five Number Box Plot
4.2) One Sample T-Test
4.3) Paired Sample T test
4.4) Multiple Regression
4.5) Sequence Chart
4.6) Seasonal Decomposition
4.7) Time Series Modeler
5. Conclusion
6. References
1. INTRODUCTION
ICICI Bank is an Indian multinational banking and financial services company. As of 2014 it is the second
largest bank in India in terms of assets and market capitalization. ICICI Bank is one of the Big Four banks of
India, along with State Bank of India, Punjab National Bank and Bank of Baroda.
? Year of Establishment - 1994
? MD & CEO - Ms.Chanda Kocher
? Chairman - K.V.Kamath
? Headquarters - Mumbai, Maharashtra, India
? Corporate Office - ICICI Bank Towers, Bandra-Kurla Complex, Mumbai 400 051
? Auditors - S.R. Batliboi & Co. LLP (Chartered Accountants)
? Branches - 2,791 branches in India and 10,021 ATM in India
Outside India has branches in United States, Singapore, Bahrain, Hong Kong, Sri Lanka, Qatar and
Dubai. Total number of 10 branches outside India.
? Product Lines - Credit Cards, Consumer Banking, Corporate banking, Finance and Insurance,
Investment Banking, Mortgage Loans, Private Banking, Wealth Management
? Shares –
Total: 1,154,832,769
Resident Holding: 354,184,495
Non Resident Holding: 800,648,274
? Facilities - Supplementary Savings, Free Anywhere banking, Free collection of outstation cheques, Free
collection of outstation cheques, Free multi city Cheque Book facility, Internet Banking.
? Subsidiaries –
Domestic: ICICI Prudential Life Insurance Company Limited,ICICI Lombard General Insurance
Company Limited,ICICI Prudential Asset Management Company Limited,ICICI Prudential Trust
Limited,ICICI Securities Limited,ICICI Securities Primary Dealership Limited,ICICI Venture Funds
Management Company Limited,ICICI Home Finance Company Limited,ICICI Investment Management
Company Limited,ICICI Trusteeship Services Limited,ICICI Prudential Pension Funds Management
Company Limited
.
International: ICICI Bank UK PLC, ICICI Bank Canada, ICICI Bank Eurasia Limited Liability
Company , ICICI Securities Holdings Inc., ICICI Securities Inc. ,ICICI International Limited.
2. Various Related Statistics
2.1 Table Showing Assets, Market Capitalization, Revenue, Borrowing, Net Profit of Last Ten
Years
In Crs Fixed Assets Total Assets Market Capitalization Revenue Borrowing Net Profit
2014 4678.14 594641.60 219020.23 54606.02 154759.05 9810.48
2013 4647.06 536794.69 115087.08 48421.30 145341.49 8325.47
2012 4614.69 489068.80 106262.98 41045.41 140164.91 6456.26
2011 4744.26 406233.67 102507.74 32621.94 109554.28 5151.38
2010 3212.69 363399.71 105807.18 32999.36 94263.57 4024.98
2009 3801.62 379300.96 68025.18 39210.31 67323.69 3758.13
2008 4108.89 399795.07 96720.36 39667.19 65648.43 4157.73
2007 3923.42 344658.11 96221.53 29957.24 51256.03 3110.22
2006 3980.72 251388.95 62510.13 18821.12 38521.91 2540.07
2005 4038.04 167659.40 28975.04 12826.12 33544.50 2005.20
2.2 Table Showing Share Book Value, Market Value and Nifty Value for Last Ten Years
Shares Nifty Index
Book Value High Low Average High Low Average
2014 633.92 1681.65 943.60 1312.62 8160.90 6067.35 7114.12
2013 578.21 1238.40 756.90 997.65 6415.75 5274.25 5845.00
2012 578.21 1158.65 685.00 921.85 5917.80 4770.35 5344.07
2011 478.31 1139.00 641.00 890.00 6181.35 4555.90 5368.45
2010 463.01 1186.15 712.00 949.08 6335.90 4692.75 5514.32
2009 444.94 969.80 252.30 611.05 5181.95 2663.90 3922.92
2008 417.64 1455.40 283.10 869.25 6357.10 2252.75 4304.92
2007 270.37 1349.00 791.00 1070.00 6175.65 3811.75 4993.7
2006 249.55 925.00 480.00 702.50 4036.20 2638.10 3337.15
2005 170.35 614.95 325.20 470.08 2853.45 1896.30 2374.87
3. Other Related Information about company
3.1 Mergers and Acquisitions
? 1996: SCICI Ltd. A diversified financial institution with headquarters in Mumbai
? 1997: ITC Classic Finance. Incorporated in 1986, ITC Classic was a non-bank financial firm that
engaged in hire, purchase, and leasing operations.
? 1998: Anagram Finance. Anagram had built up a network of some 50 branches in Gujarat, Rajasthan,
and Maharashtra that were primarily engaged in retail financing
? 2001: Bank of Madurai
? 2002: The Darjeeling and Shimla branches of Grindlays Bank
? 2005: Investitsionno-Kreditny Bank (IKB), a Russian bank
? 2007: Sangli Bank. Sangli Bank was a private sector unlisted bank.
? 2010: The Bank of Rajasthan (BOR) was acquired by the ICICI Bank.
3.2 CSR Initiative
? Go Green Initiative - The Go Green Initiative is an organization wide initiative that moves beyond
moving people, processes and customers to cost effective automated channels to build awareness and
consciousness of our environment, our nation and our society
? Read to Lead Phase: It initiative of ICICI Bank to facilitate access to elementary education for
underprivileged children in the age group of 3–14 years including girls and tribal children from the
remote rural areas. The Read to Lead initiative supports partner NGOs to design and implement
programmes that mobilize parent and community involvement in education, strengthen schools and
enable children to enter and complete formal elementary education.
3.3 Growth Rate In Last Ten Years
In 2014 bank growth at rate of 17.2 percent which was low as compared to 2013 which was almost 27 percent.
Comparing year 2008 and year 2009 we can see that profit remained dropped. It is due to effect for recession.
Other than this year on average we can see that company is showing growth rate greater than 10 percent which
is positive sign for bank.
3.4 Awards
? In 2013, ICICI Bank has been adjudged winner at the Express IT Innovation Award under the Large
Enterprise category
? In 2012 ICICI Bank is the only Indian brand to figure in the BrandZ Top 100 Most Valuable Global Brands
Report, second year in a row.
? In 2009, ICICI Bank bags the "Best bank in SME financing (Private Sector)" at the Dun & Bradstreet
Banking awards.
1. Analysis of Share price
4.1 Five Number Box Plot
A box plot is a one-dimensional graph of numerical data based on the five-number summary. This summary
includes the following statistics: the minimum value, the 25th percentile (known as Q1), the median, the 75th
percentile (Q3), and the maximum value. In essence, these five descriptive statistics divide the data set into four
parts, where each part contains 25% of the data.
Here we took average share price of ICICI bank for last 5 years from and using SPSS made box plot.
We can see that for 2014, it has greatest variation i.e. more fluctuation in price of shares as compared to other
year prices.
4.2 One Sample T Test
One-sample t-test is used to determine whether a sample comes from a population with a specific mean. This
population mean is not always known, but is sometimes hypothesized. If the P value is large, the data do not
give you any reason to conclude that the population mean differs from the hypothetical value you entered. This
is not the same as saying that the true mean equals the hypothetical value. You just don't have evidence of a
difference.
We Checked for confidence interval 95% i.e. ?p‘ should be greater than .05 .We took share price average of last
5 years and tested against our sample where we took average price of shares from 1
st
day of every month for 5
years. P value is .696 which is greater than .05. Conclusion was that sample is from data from population.
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Average Share Price 50 1.077326E3 204.3450604 28.8987556
One-Sample Test
Test Value = 1065.96
t df Sig. (2-tailed) Mean Difference
95% Confidence Interval of the
Difference
Lower Upper
Average Share Price .393 49 .696 11.3665000 -46.707724 69.440724
4.3 Paired Sample T test
Paired t-test is used to compare two population means where you have two samples in which observations in
one sample can be paired with observations in the other sample. Examples of where this might occur are: -
Before-and-after observations on the same or a comparison of two different methods of measurement or two
different treatments where the --measurements/treatments are applied to the same subjects.
Here we took high and low value of share price of every month to test for paired sample test. We can see by
looking at value of P that no statistically significant difference between your two conditions and correlation
between two samples is 88.1 %.
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Open 1074.47 50 200.698 28.383
Closing 1081.65 50 218.153 30.852
Paired Samples Correlations
N Correlation Sig.
Pair 1 Open & Closing 50 .881 .000
Paired Samples Test
Paired Differences
t df
Sig. (2-
tailed)
Mean Std. Deviation Std. Error Mean
95% Confidence Interval of the
Difference
Lower Upper
Pair 1
Open - Closing -7.188 103.619 14.654 -36.636 22.260 -.491 49 .626
4.4 Multiple Regression
For using MULTIPLE REGRESSION, the basic conditions are that the data should be on same time frame,
should be Linear and should not possess correlation between them. The first condition was fulfilled by all the
data as the data was collected on the same time frame. The next condition is that the data should belinear, but
this condition was not fulfilled by the factors like Repo Rate, Reverse Repo Rate and Cash Reserve Ratio. So
these factors were dropped for the analysis as they were not linear. Again the factors like Operating Profit and
Net Profit, Close of Nifty and Close of Bank Nifty and Borrowings and Deposits possessed correlation between
them. Thus, Operating Profit, Close of Nifty and Borrowings were dropped for the analysis part and Net Profit,
Close of Bank Nifty and Deposits were taken for the analysis. Though Borrowings is used in analysis as it did
not possess correlation there.
Average Share Price as Dependent variable and Interest Income, Advances, Net Profit, Close of Bank Nifty,
Deposit and Borrowings as Independent Variables.
In this case the value of R is .862. This shows that the Predictors have 86.2% influences on the output
In this case the value of R2is .744 this means that the predictors account for 74.4%variation in the Average
Price of the shares.
Here the difference between value of R2and the Adjusted R2 is 0.062 (.744-.682) which is small. This
shrinkage means that if model is derived from the population rather than a sample it would account for
approximately 6.2% less variance in the outcome.
Analysis of Variance (ANOVA) table shows the Sum of Squares here is 1821276; the value of Residual
Sum of Squares is 627348.6 and the value of F which is 12.096. Here we can conclude that the value of F is
significant and our ability to predict the Outcome Variable is good.
This part of model is concerned with the parameters of the model. The Coefficients table shows us the
parameters. We can define the equation here as follows:
Average Price = b0+ b1 (Interest Income) + b2 (Advances) + b3 (Net profit) + b4 (Close of Bank Nifty) + b5 (Deposits)
+ b6 (Borrowings).
Average Price = 472.069 + .079 (Interest Income) + .005 (Advances) + (-.272) (Net profit) + .009 (Close of Bank Nifty) + (-.002) (Deposits) + (-
.016) (Borrowings)
The B values tell us to what degree each predictor affects the outcome if the effects of all other predictors
are held constant. Here is the analysis.
Interest Income (b=.079) :This value indicates that as the interest income increases by Rs. 1 crore, the
Average Price increases by Rs. 0.079Thisinterpretation is only true if the effect of other predictors is held
constant.
Advances (b=.005) : This value indicates that as the Advances are increases by Rs.1 crore , the Average
Price increases by Rs. 0.005This interpretation is only true if the effect of other predictors is held constant.
Net Profit (b=-.272) : This value indicates that as the Net Profit is increases by Rs.1 crore , the Average
Price decreases by Rs. 0.272. This interpretation is only true if the effect of other predictors is held constant.
Close of Bank Nifty (b=.009) : This value indicates that as the Close of Bank Nifty increases by 1 point the
Average Price increases by Rs.0.009..This interpretation is only true if the effect of other predictors is held
constant.
Deposits (b=-.002) : This value indicates that as the Deposits are increases by Rs. 1crore, the Average Price
decreases by Rs. 0.002.This interpretation is only true if the effect of other predictors is held constant.
Borrowings (b=-.016) : This value indicates that as the Borrowings are increases by Rs. 1 crore, the Average
Price decreases by Rs. 0.016.This interpretation is only true if the effect of other predictors is held constant.
For this model the value of t-statistic for different variables and their Sig. can be seen from the above table.
From the magnitude of t-statistics we can see that the t-statistics for Borrowings (.0230.05),
Close of Bank Nifty (.823>0.05), Deposits (.668>0.05), Advances (.247>0.05) and Net Profit (.311>0.05) is
more than 0.05, thus, they don‘t have a significant contribution to the model. We also look for t-values,
which if are above +2 and below -2. Here in this case only Borrowings match the criteria, thus, it has a
significant contribution in the Model.
The Standardized Beta Values as shown in the above table can be interpreted as follows:
Interest Income (?=.738) : This value indicates that as the interest income increases by 1 Standard Deviation
(2612.99), the Average Price increases by 0.738Standard Deviation. As the Standard Deviation for Average
Price is 281.0478, thus, a change of 1 Standard Deviation would bring a change of .738 in the price.
This interpretation is only true if the effect of other predictors is held constant.
Advances (?=1.413) :This value indicates that as the Advances are increases by 1Standard Deviation
(74660.49), the Average Price increases by 1.413 Standard Deviations the Standard Deviation for Average
Price is 281.0478, thus, a change of 1 Standard Deviation would bring a change of 1.413 in the price. This
interpretation is only true if the effect of other predictors is held constant.
Net Profit (?=-.325) : This value indicates that as the Net Profit is increases by 1Standard Deviation (335.05),
the Average Price increases by -.325 Standard Deviation. As the Standard Deviation for Average Price is
281.0478, thus, a change of 1 Standard Deviation would bring a change of -.325 in the price. This interpretation
is only true if the effect of other predictors is held constant.
Close of Bank Nifty (?=.077) : This value indicates that as the Close of Bank Nifty increases by 1 Standard
Deviation (2314.74), the Average Price increases by.077 Standard Deviation. As the Standard Deviation for
Average Price is281.0478, thus, a change of 1 Standard Deviation would bring a change of .077 in the price.
This interpretation is only true if the effect of other predictors is held constant.
Deposits (?=-.446): This value indicates that as the Deposits are increases by 1Standard Deviation (81172.88),
the Average Price increases by -.446 Standard Deviation. As the Standard Deviation for Average Price is
281.0478, thus, a change of 1 Standard Deviation would bring a change of -.446 in the price. This interpretation
is only true if the effect of other predictors is held constant.
Borrowings (=-.839) : This value indicates that as the Borrowings are increases by 1 Standard Deviation
(14793.88), the Average Price increases by -.839Standard Deviation. As the Standard Deviation for Average
Price is 281.0478, thus, a change of 1 Standard Deviation would bring a change of -.839 in the price.
This interpretation is only true if the effect of other predictors is held constant. Now talking about the rest of the
table, we have got zero-order correlations which are nothing but simple Pearson Correlation Coefficients. The
Partial Correlations represent the relationship between each predictor and the outcome Variable, controlling for
the effect of other predictors. The Part Correlations represent the relationship between each predictor and the
outcome Variable, controlling for the effect of other predictors on the outcome. In effect, these Part Correlations
represent the unique relationship that each predictor has with the outcome.
4.5 Sequence Chart
When there is only one variable upon which observations are made then we call them a single time series or
more specifically a univariate time series. It is type of time series prediction where we use sequence chart we to
see behavior of variable, how it has changes over period of time. We can see if its behavior is cyclic, trend or
have seasonality. Here we have use average price of share of last 5 years on monthly basis, and also to make
analyze trend more properly have also taken high and low values of share prices on monthly bais.Also to
differentiate the years, we have divided into different years.
We can see that price at beginning of our sample is around Rs 1100.Till December 2011,it showed downward
trend, then it started recovering and again in 2013 it show some change in trend i.e. fall in price but as 2014
began, it show regular behavior of upward trend.
So, with the help of sequence chart we have able to see how share prices reacted to different conditions of
market in last 5 years, to make more accurate we can also take on daily basis which will give more specific
information.
4.6 Seasonal Decomposition
We realized that there is a seasonal component in the model. The seasonal decomposition model type should be
additive, because the amplitude of both the seasonal and irregular variations do not change as the level of the
trend rises or falls. The seasonal decomposition model type should be additive, because the amplitude of both
the seasonal and irregular variations do not change as the level of the trend rises or falls. The Seasonal
Decomposition procedure creates four new variables (series), with the following three-letter prefixes, for each
series specified:
SAF: Seasonal adjustment factors. These values indicate the effect of each period on the level of the series.
SAS: Seasonally adjusted series. These show values obtained after removing the seasonal variation of a series.
STC: Smoothed trend-cycle components. Values show the trend and cyclical behavior present in the series.
ERR: Residual or ?error? values. The values that remain after the seasonal, trend, and cycle components have
been removed from the series.
The Seasonal adjustment factors representing seasonal variation show fluctuation that is expansion and
contraction in price of share.
The seasonally adjusted series shows a clear upward trend. A number of peaks are evident, but they appear at
random intervals showing no evidence of an annual pattern.
The smoothed trend-cycle component, a smoothed version of the seasonally adjusted series that shows both
trend and cyclic components also show upward trend.
Using the Seasonal Decomposition procedure, we have removed the seasonal component of a periodic time
series to produce a series more suitable for trend analysis.
Conclusion: - The series exhibits a number of peaks, but they do not appear to be equally spaced. This suggests
that if the series has a periodic component, it also has fluctuations that are not periodic—the typical case for real
time series. Aside from the small scale fluctuations, the significant peaks appear to be separated by more than a
few months. Notice that the seasonal variations appear to grow with the upward series trend, suggesting that the
seasonal variations may be proportional to the level of the series.
4.7 Time Series Modeler
The Time Series Modeler procedure creates models for time series, and produces forecasts. It includes an
Expert Modeler that automatically determines the best model for each of your time series. For experienced
analysts who desire a greater degree of control, it also provides stools for custom model building.
The Time Series Modeler procedure estimates exponential smoothing, univariate Autoregressive Integrated
Moving Average (ARIMA), and multivariate ARIMA (or transfer function models) models for time series, and
produces forecasts. The procedure includes an Expert Modeler that automatically identifies and estimates the
best-fitting ARIMA or exponential smoothing model for one or more dependent variable series, thus
eliminating the need to identify an appropriate model through trial and error
Model Description
Model Type
Model ID Average Share Price Model_1 ARIMA(0,1,1)(0,0,0)
Model Fit
Fit Statistic Mean SE Minimum Maximum
Percentile
5 10 25 50 75 90 95
Stationary R-squared .252 . .252 .252 .252 .252 .252 .252 .252 .252 .252
R-squared .915 . .915 .915 .915 .915 .915 .915 .915 .915 .915
RMSE 60.112 . 60.112 60.112 60.112 60.112 60.112 60.112 60.112 60.112 60.112
MAPE 4.751 . 4.751 4.751 4.751 4.751 4.751 4.751 4.751 4.751 4.751
MaxAPE 14.588 . 14.588 14.588 14.588 14.588 14.588 14.588 14.588 14.588 14.588
MAE 48.609 . 48.609 48.609 48.609 48.609 48.609 48.609 48.609 48.609 48.609
MaxAE
137.073 . 137.073 137.073 137.073 137.073 137.073 137.073 137.073 137.073 137.073
Normalized BIC 8.272 . 8.272 8.272 8.272 8.272 8.272 8.272 8.272 8.272 8.272
We can see that on future forecast the price of share will be Rs1648.6.
Forecast
Model Dec 2014
Average Share Price-Model_1 Forecast 1.6846E3
UCL 1.8051E3
LCL 1.5641E3
For each model, forecasts start after the last non-missing
in the range of the requested estimation period, and end
at the last period for which non-missing values of all the
predictors are available or at the end date of the
requested forecast period, whichever is earlier.
5. Conclusion
The analysis of all the various factors affecting the ICICI Bank share prices leads us to some findings and
conclusion. While I was doing the project I came across I studied behavior of stock prices. I studied various
research reports, news articles, and from all that I could find out that each and every thing, whether a minute
one or a big one affects the Investors decision and thus affects the share prices. I have listed some factors in the
report which affect the share prices, also I have done a technical analysis for the same, but what comes out at
the end is that there are still so many unnoticed factors which affect the share prices. This list is not exhaustive;
still there is so much which needs to be studied and I tried to cover as much as I could. From the analysis, I
could find out and conclude that, the Share Prices are affected by each and every factor in varying degree. The
analysis shows that there is a very small impact of Interest Income, Advances, Deposits, Borrowings and a
slightly more impact of Bank Nifty Index on Share Prices. It is so because there are number of factors and it
was not possible to quantify each one of them and conduct the analysis, there was some technical difficulties
also which turn out to be the limitation of the project. But at the end of the analysis we can accept the
hypothesis, as most of the factors, do affect the Share Prices in some or other manner. A latest example I can
quote is the post election result session when Investors were happy that NDA government came into power and
the market jumped thousand points up.
6. References
M. Kabir Hassan, Gordon V. Karels, Manfred O. Peterson, Journal of Banking & Finance, Volume 18, Issue 3,
May 1994, Pages 575-593
Yaping Wang, Liansheng Wu, Yunhong Yang, Journal of Banking & Finance, Volume 33, Issue 1, January
2009, Pages 53-62
Siteshttp://www.unt.edu/rss/class/Jon/SPSS_SC/Manuals/v19/IBM SPSS Forecasting 19.pdfhttp://www-03.ibm.com/software/products/en/spss-forecasting
doc_888162780.docx