applications of statistics on banking and pharmaceutical companies

7/29/2019 Applications of Statistics On Banking and Pharmaceutical Companies

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Course Title: Business Statistics IICourse Code:F-207

S U B M I T T E D T O

Ms. Nusrat Khan

Lecture

Department of Finance

University of Dhaka

S U B M I T T E D B Y

Section A

BBA 16th Batch

Department of Finance

University of Dhaka

Date of Submission:November 2, 2011


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ABOUT US

SLNO.

NAME ID1 Md. Rabin Islam 16-0132 Mohammad Junaid Shawon 16-0173 Md. Zahidul Islam 16-0514 Md. Rashed Karim 16-1255 Rashid Muntasir 16-1656 Md. Zahirul Islam Khan 16-1717 Md. Sanowar Hossain 16-1758 Ariful Hossain 16-2629 Md. Mahbubur Rahman Khan 16-263


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LE TTE R OF TR AN S M I TTAL

November 2, 2011

Ms. Nusrat KhanLecturerDepartment of FinanceUniversity of Dhaka

Dear Madam,

It is our satisfaction to submit this report, which you assigned us. The topic of our reportis Applications of Statistics On Banking and Pharmaceutical Companies as a part of therequirements under the Course of Business Statistics II. Working on this report was agreat opportunity for us to acquire knowledge about many applications of descriptive &inferential statistics.

We are thankful to you, as you allowed us to perform the study and to submit the report.We hope that the report will meet the standard and will serve its purpose.

Sincerely Yours,

Md. Zahidul IslamOn behalf of the group


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ACKN OW LE D GE M E N T

For the completion of this study we cant deserve all praise. There were a lot of people

who helped us by providing valuable information, advice and guidance for the

completion of this Report in the scheduled time.

Course Report is an essential part of BBA program as one can gather practical knowledge

within the short period of time by observing and doing the works of chosen

organizations. In this regard our Report has been arranged.

At first we like to pay our heartiest thanks to ALMIGHTY, for helping us to do all the

works with perfection. Having prepared this report, we would like to give our thanks to

the Department of Finance and all the faculties of the department as they have given us

the opportunity to be a part of the BBA Program. We also thank them for preparing such

a curriculum as a part of which we have been able to accomplish this report.

We would like to pay our gratitude to our supervising Course teacher Ms. Nusrat Khan

who instructed us in the right way and gave us proper guidelines for preparing this

Report.


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EXECUTIVE SUMMARY

We have prepared this report on the study of Applications of Statistics On Bankingand Pharmaceutical Companies in which we have discussed the applications of somestatistical tools in business and commerce. These tools are-

Time series & Forecasting

Multiple regression & correlation analysis

The report starts with introducing the origin, objectives & methodology of the report at

the first part. The origin section describes the background of the preparation of this

report, while the objectives section explains the objectives of the preparation of this

report. The methodology section deals with the means of preparation of this report and

the processes that we have followed. Then the report describes various important topics

of time series analysis & multiple regression & correlation analysis in the theoretical

knowledge at the second part.

Thereafter at the third part there are Company profiles of four selected companies from

two different industries. Then we have shown the application of some statistical tools on

these companies.


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INTRODUCTION

Origin of the Report:

This Report is generated under the academic supervision of Ms. Nusrat Khan, Lecturer,

Department of Finance, University of Dhaka. The report is a prerequisite for the

completion of the course Business Statistics II under BBA Program. Each student has

to work on a report within a predetermined group over the period of this Program. The

assigned topic for our Report is Applications of Statistics On Banking and

Pharmaceutical Companies

Objectives of the Report:

The objective of preparing this report is to fulfill the requirement of the course Business

Statistics II through gaining the theoretical knowledge about various important topics

of business statistics and viewing the applications & tabular presentation of those topics.

Methodology of the Report:

The report in this study is basically an analytical one. Here, both the primary and the

secondary data have been used. The primary data has been collected from lectures given

on this topic in the classroom. The secondary sources of data are our textbook, reference

book, annual reports of the selected company & different websites. We used the

sophisticated data editorSPSS for our analyses.


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TH E OR E TI CAL KN OW LE D GE

Time Series & Forecasting:

A time series is a collection of data recorded over a period of time; weekly, monthly,quarterly or yearly. Time series can be used by management to make current decisions &

plans based on long-term forecasting.

Components of a Time-series:

Secular trend: The smooth long-term direction of a time series.

Cyclical variation: The rise & fall of a time series over periods longer than oneyear.

Seasonal variation: Patterns of change in a time series within a year.

Irregular variation: Episodic & residual fluctuations.

Linear Trend:

The long-term trend of many business series, such as sales, exports & production oftenapproximates a straight line. The equation to describe this growth is:

= a+ bt

Where:

is the projected value of the y variable for a selected value of t.

a is the Y-intercept. It is the estimated value of y when t = 0.

b is the slope of the line or the average change in for each increase of one unit in t.

t is any value of time that is selected.


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Growth Rate: It indicates that during the period of 200X to 200Y dependent variableincreases or decreases at an average of growth rate per year.

Acceleration Rate:It indicates that in the years to come growth rate will be increasedor decreased at an average rate of acceleration rate per year of the previous years growthrate.

Multiple Regression & Correlation Analysis:

Multiple Regressions: The general descriptive form of a multiple regression equation

isy = a + b1X1+ b2X2++b3X3+. + bkXk

where a is the Y-intercept when all Xs are zero, b refers to the sample regression co-efficient & X refers to the value of the various independent variables.

Multiple Standard Error of Estimate: It is similar to the standard deviation. It ismeasured in the same units as the dependent variables. It is based on squared deviations

from the regression equation.

Coefficient of multiple determinations: The coefficient of multiple determinationreports the percent of the variation in the dependent variable explained by the set ofindependents variable. It may range from 1 to 0.

It is also based on squared deviations from the regression equation. It is found by thefollowing equation:

=


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ANOVA Table: An ANOVA table summarizes the multiple regression analysis. Itreports the total amount of the variation in the dependent variables & divides thisvariation into that explained by the set of independent variables & that not explained. Italso reports the degrees of freedom associated with the independent variables, the error

variation & total variation.

Correlation Matrix: A correlation matrix shows all possible simple correlationcoefficients between pairs of variables.

Global Test: A global test is used to investigate whether any of the independentvariables have significant regression coefficients. The test statistics is the F distribution.

Individual Regression Coefficient: The test for individual variables determineswhich independent variables have nonzero regression coefficients. The variables thathave zero regression coefficients are usually dropped from the analysis. The test statisticsis the t distribution with n - (k + 1) degrees of freedom.


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COM PAN Y PR OFI LE

Established as the first private sector bank fully owned by Bangladeshi entrepreneurs,NBL has been flourishing as the largest private sector Bank with the passage of time afterfacing many stress and strain. To keep pace with time and in harmony with national andinternational economic activities and for rendering all modern services, NBL, as afinancial institution, automated all its branches with computer networks in accordancewith the competitive commercial demand of time. Moreover, considering its forth-

coming future, the infrastructure of the Bank has been rearranging. The expectation of allclass businessmen, entrepreneurs and general public is much more to NBL. At present wehave 145 branches under our branch network

National Bank Limited was born as the first hundred percent Bangladeshi owned Bank inthe private sector. From the very inception, it was the firm determination of NationalBank Limited to play a vital role in the national economy. We are determined to bring

back the long forgotten taste of banking services and flavors. We want to serve each onepromptly and with a sense of dedication and dignity.

At present, NBL has been carrying on business through its 130 branches and 15 SME /Agri Branches (total 145 service locations) spread all over the country. Since the very

beginning, the bank has exerted much emphasis on overseas operations and handled asizable quantum of home bound foreign remittance. It has drawing arrangements with415 correspondents in 75 countries of the world, as well as with 37 overseas ExchangeCompanies located in 13 countries. NBL was the first domestic bank to establish agencyarrangements with the world famous Western Union in order to facilitate quick and saferemittance of the valuable foreign exchanges earned by the expatriate Bangladeshinationals. This has meant that the expatriates can remit their hard-earned money to thecountry with much ease, confidence, safety and speed


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NATIONAL BANK- MULTIPLE LINEAR REGRESSIONANALYSIS AND INTERPRETATION

In our report, we have been told to draw a Multiple linear regression between following

variables where,The only dependent variable - Share priceThe 4 independent variables.-

1. Net income2. Return on equity3. Price earnings ratio4. Dividend per share

NATIONAL BANK-DATA SUMMARY

We were required to collect last 10 years data. That is given below-

YEAR SHAREPRICE

NET PROFIT DPS ROE P/E

2000 431.5 11.34 30.0 5.34 37.562001 615.25 17.43 40.0 4.12 67.232002 650.0 16.25 50.0 4.16 72.562003 1261.0 22.57 70.0 5.55 105.562004 3200.0 25.0 70.0 12.27 145.592005 3000.0 26.0 70.0 10.43 192.572006 3099.25 24.65 70.0 23.14 242.132007 7491.25 26.29 70.0 40.31 358.022008 7789.25 26.06 75.0 32.5 438.672009 12051.5 27.34 85.0 36.09 594.48

Data Analysis:

Now we are prepared to use SPSS to compute all the statistics needed for the analysis.The Regression equation intercept is labeled as constant in the SPSS:


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Coefficientsa

Model

Unstandardized

Coefficients

Standardiz

ed

Coefficient

s

t Sig.

95% Confidence

Interval for B Correlations

Collinearity

Statistics

B Std. Error Beta

Lower

Bound

Upper

Bound

Zero-

order Partial Part

Toleranc

e VIF

1 (Constant)-404.174 1412.711 -.286 .786 -4035.664

3227.31

5

ROE 3.834 199.776 .005 .019 .985 -509.707 517.375 .717 .009 .001 .067 14.837

DPS -9.230 66.177 -.041 -.139 .895 -179.343 160.883 .743 -.062 -.010 .061 16.500

PE 15.083 54.317 .055 .278 .792 -124.542 154.709 .912 .123 .020 .130 7.720

N.P 20.391 4.703 .963 4.335 .007 8.300 32.481 .986 .889 .312 .105 9.558

a. Dependent Variable: S.Price

In this case the estimated regression equation is =-404.174+3.834X1-9.230 X2+15.083 X3+20.391X4Here, r =0.987 This is the multiple regressions and it indicates a positive relation betweenvariables. It is close to 1 so we can say, there is strong positive relationship.Here, b1=3.834 it means that if the independent variable (Net income) increases by 1 takathen the dependent variable will increase by 3.834 taka. We can interpret other b by the

same.We can now estimate the share price in next year as shown before.The regression coefficient and their algebraic signs, also provide information about theirindividual relationship with the next year share price. The regression coefficient for Netincome is .005659. It is positive and shows a positive relationship between share priceand net income. It is logical. Cause, net income and share price tend to move in samedirection.

MULTIPLE STANDARD ERROR OF ESTIMATE

in the National Bank`s case we get the standard error of estimation is an amount equal to307.3944.we interpret this result as, It is the typical error when we use the equation to

predict the cost.First, the units are same as the dependent variable that is taka.Second, we expect the residuals to be approximately normally distributed. So, about 68%of the residuals will be within 307.3944and about 95% within 2(307.3944) =614.7888.here in National Bank`s case, the values are very close to this guideline.


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THE ANOVA TABLEANOVA

b

Model Sum of Squares df Mean Square F Sig.

1 Regression 1.332E8 4 3.330E7 47.163 .000a

Residual 3530134.188 5 706026.838

Total 1.367E8 9

a. Predictors: (Constant), N.P, ROE, PE, DPS

b. Dependent Variable: S.Price

Here, total variation is divided into 2 components:1. variation in the dependent variable by the regression model( the independent

variables)2. The residual of error variation. That is the random error due to sampling.

There are 3 categories identified in the 1st or source column in the Anova table:

i. Regressionii. Residualiii.Total

The 2nd column is labeled dfin Anova table. It is the degree of freedom. Here totaldegrees of freedom are n-(k+1) = 10-(4+1) =5The heading SS in the 3rd column of Anova table is the sum of squares or the variation.total variation= SStotal=1.367E8Residual error= SSE= 3530134.188Regression variation= 1.332E8

The 4th column heading MS or mean square. is obtained by dividing the SS quantity bythe matching df. Thus the MSR , the mean square regression is equal to SSR/K.Similarly, MSE , the mean square error is SSE/ n-(k+1). (3530134.188/5)

4.76

Sig. level:0.05

Calculate

value:47.163


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COEFFICIENT OF DETERMINATION

r2= SSR total/SS total =.974We interpret this value as, independent variables explain or account for, 97.4% of the

variation in share price.

ADJUSTED COEFFICIENT OF DETERMINATION

The number of independent variable in a multiple linear regression makes the coefficientof determination larger. Each new variable causes the predictions to be more accurate.That in turn makes SSE smaller, and SSR larger.To balance the effect of the number of independent variable has on coefficient ofdetermination the statistical software or here SPSS used an adjusted coefficient ofdetermination.Here, the value is = .954

INFERENCES IN MULTIPLE LINEAR REGRESSIONS

Multiple regression analysis is not only a way to describe the relationship between adependent variable and several independent variables. The least square method also hasthe ability to draw inferences or generalization about the relationship for an entire

population.the model of relationship-=+ 1X1+2X2++kXkhere, the coefficients are reported as Greek letter. - ,measures the strength of influence ofindependent variable on dependent variable. =198.7785+.005X1-.041 X2+.055 X3+.963 X4

in our equation, 1= .0052= -.0413= .0554= .963

4 is the largest. So, we can say that return on equity has the highest influence on shareprice.

Model Summaryb

Model R

R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

Durbin-

Watson

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .987a

.974 .954 840.25403 .974 47.163 4 5 .000 2.660

a. Predictors: (Constant), N.P, ROE, PE, DPS



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TESTING THE MULTIPLE REGRESSION MODEL

The critical value if F is found in Appendix b.4.using the table for the .05 significancelevel, move horizontally to 4 degrees of freedom in numerator and down to 5 degrees ofdenominator we found the critical value= 5.19 . if computed value exceeds this, the nullhypothesis will be rejected.

here, we found f value- 1.2387.so the decision is not to reject null hypothesis.

EVALUATING INDIVIDUAL REGRESSION COEFFECIENTS

The critical value for a t is in appendix b.2. For a 2 tailed test with 5 degrees of freedomusing .05 significance level, null hypothesis is rejected if t is less than 2.571 or greaterthan 2.571.Here t-stat for all independent variable is not in rejection area except dividend per share.

Thus 4 couldn`t be 0. We can say, none of the independent variables are significantpredictor for share price except dps. The dividend per share is an important predictor forshare price. We can drop others and rerun the analysis with dividend per share asindependent variable.Here t=.547


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COM PAN Y PR OFI LE

AB Bank Limited, the first private sector bank was incorporated in Bangladesh on 31st December 1981

as Arab Bangladesh Bank Limited and started its operation with effect from April 12, 1982.

AB Bank is known as one of leading bank of the country since its commencement 29 years ago. It

continues to remain updated with the latest products and services, considering consumer and client

perspectives. AB Bank has thus been able to keep their consumers and clients trust while upholding

their reliability, across time.

In spite of adverse market conditions, AB Bank Limited which turned 28 this year, concluded the 2008

financial year with good results. The Banks consolidated profit after taxes amounted to Taka 230 cr

which is 21% higher than that of 2007. The asset base of AB grew by 32% from 2007 to stand at over Tk8,400 cr as at the end of 2008.

AB Bank believes in modernization. The bank took a conscious decision to rejuvenate its past identity

an identity that the bank carried as Arab Bangladesh Bank Limited for twenty five long years. As a result

of this decision, the bank chose to rename itself as AB Bank Limited and the Bangladesh Bank put its

affirmative stamp on November 14, 2007.

AB Bank commits to nation to take a lead in the Banking sector through not only its strong financial

position, but also through innovation of products and services. It also ensures creating higher value for

its respected customers and shareholders. The bank has focused to bring services at the doorstep of its

customers, and to bring millions into banking channels those who are outside the mainstream bankingarena. Innovative products and services were introduced in the field of Small and Medium Enterprise

(SME) credit, Womens Entrepreneur, Consumer Loans, Debit and Credit Cards (Local & International),

ATMs, Internet and SMS Banking, Remittance Services, Treasury Products and Services, Structured

Finance for Corporate, strengthening and expanding its Islamic Banking activities, Investment Banking,

specialized products and services for NRBs, Priority Banking, and Customer Care. The Bank has

successfully completed its automation project in mid 2008. It envisages enabling customers to get

banking services within the comfort of their homes and offices.

AB is recognized as the peoples choice, catering to the satisfaction of its cliental. Their satisfaction is

ABs success.


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AB BANK-DATA SUMMARY

We collected 10 years data below from 2000 to 2009 of AB Bank Ltd.

AB Bank Ltd.

YEAR SHAREPRICE

NET PROFIT DPS ROE P/E EPS

2000 109.02 158.86 18.0 17.87 4.38 38.752001 213.35 263.28 25.0 24.45 2.79 64.222002 254.58 23.69 15.0 2.07 53.0 5.782003 211.66 17.12 5.0 1.53 53.89 3.632004 257.25 90.07 5.0 7.24 20.95 18.192005 315.14 162.45 10.0 10.64 11.68 31.262006 566.91 532.19 30.0 20.61 9.59 93.082007 1709.43 1903.49 200.0 42.19 10.0 85.37

2008 1934.14 2300.62 15.0 34.2 7.97 103.182009 831.85 3417.18 25.0 33.88 6.24 133.26

Data Analysis:

Descriptive Statistics

Mean Std. Deviation N

S.PRICE 6.4033E2 659.01034 10

NP 8.8690E2 1208.33008 10

DPS 34.8000 58.65492 10

ROE 19.4680 14.22683 10

PE 18.0490 19.29936 10

EPS 57.6720 44.81578 10


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Correlations

S.PRICE NP DPS ROE PE EPS

Pearson Correlation S.PRICE 1.000 .743 .582 .791 -.327 .657

NP .743 1.000 .345 .807 -.424 .868

DPS .582 .345 1.000 .633 -.224 .316

ROE .791 .807 .633 1.000 -.727 .885

PE -.327 -.424 -.224 -.727 1.000 -.695

EPS .657 .868 .316 .885 -.695 1.000

Sig. (1-tailed) S.PRICE . .007 .039 .003 .178 .020

NP .007 . .165 .002 .111 .001

DPS .039 .165 . .025 .267 .186

ROE .003 .002 .025 . .009 .000

PE .178 .111 .267 .009 . .013

EPS .020 .001 .186 .000 .013 .

N S.PRICE 10 10 10 10 10 10

NP 10 10 10 10 10 10

DPS 10 10 10 10 10 10

ROE 10 10 10 10 10 10

PE 10 10 10 10 10 10

EPS 10 10 10 10 10 10

Model Summaryb

Model R

R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

Durbin-

Watson

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .879a

.772 .488 471.73463 .772 2.713 5 4 .178 1.616

a. Predictors: (Constant), EPS, DPS, PE, NP, ROE

b. Dependent Variable: S.PRICE

Our findings from regression analysis:

R=.879; this suggests that there is very strong relationship among dependent variable that is share price

and independent variables such as net income, DPS, EPS, ROE and P/E ratio.

R2=.772; this indicates 77.2% of dependent variables: share price can be explained by independent

variables: net income, DPS, EPS, ROE, P/E ratio.


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Coefficientsa

Model

Unstandardized

Coefficients

Standardiz

ed

Coefficient

s

t Sig.

95% Confidence


Collinearity

Statistics

B Std. Error Beta

Lower

Bound

Upper

Bound

Zero-

order Partial Part

Tolera

nce VIF

1 (Constant) -

739.500887.853 -.833 .452

-

3204.5741725.574

NP .071 .356 .130 .199 .852 -.917 1.058 .743 .099 .047 .134 7.474

DPS -2.219 5.670 -.198 -.391 .715 -17.960 13.522 .582 -.192 -.093 .224 4.473

ROE 71.526 54.533 1.544 1.312 .260 -79.882 222.934 .791 .548 .313 .041 24.344

PE 18.319 18.094 .536 1.012 .369 -31.918 68.555 -.327 .452 .242 .203 4.932

EPS -5.700 11.713 -.388 -.487 .652 -38.220 26.821 .657 -.236 -.116 .090 11.144

a. Dependent Variable: S.PRICE

CoefficientsCo-efficient and constant of regression:

i. Regression equationY = -739.50+ .071x1-2.219x2+71.526X3+18.319X4-5.70X5

ii. Constant a= -739.50 indicates that share price is equal to -739.50 tk irrespective ofthe independent variables.

iii. b1=.071 suggests that for each million tk increase in net income share price isincreased by tk .071 m.

iv. b2=-2.219 suggests that for each additional % increase in DPS share price isdecreased by 2.219%.

v. b3=71.526 suggests that for each percent increase in ROE share price is increasedby 71.526%.

vi. b4=18.319 suggests that for each time increase in P/E ratio share price is increasedby 18.319 times.

vii. b5=-5.70 suggests that for each additional % increase in EPS share price isdecreased by 5.70%

1=.130, 2=-0.198, 3=1.544, 4=0.536 and 5=-.388 Beta measures strength of influence of the independent

variable over the dependent variable. Amongthe Betas 3 is the highest so that we can say upon the share

price influence ROE is the strongest.


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ANOVAb


1 Regression 3018517.397 5 603703.479 2.713 .178a

Residual 890134.226 4 222533.556

Total 3908651.623 9

a. Predictors: (Constant), EPS, DPS, PE, NP, ROE


With df=5 and df=4 table value of F=6.26 and calculated value of F=2.713 where F


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COM PAN Y PR OFI LE

The Company started its operations as Pfizer (Bangladesh) Limited in 1972. For the nexttwo decades it continued as a highly successful subsidiary of Pfizer Corporation.However, by the late 1990s the focus of Pfizer had shifted from formulations to research.In accordance with this transformation, Pfizer divested its interests in many countries,including Bangladesh. Specifically, in 1993 Pfizer transferred the ownership of its

Bangladesh operations to local shareholders, and the name of the company was changedto Renata Limited.

In a gesture of corporate charity, Pfizer donated shares so that, along with a partialpayment from the SAJIDA Foundation, 51% ownership of Renata Limited would be heldby the Foundation. Today SAJIDAs microfinance and micro-insurance programssupport over 107,120 members and their families; thus far cumulative loan disbursementtotals BDT 5,750 million. Currently, SAJIDAs health program covers over 1 million

beneficiaries by delivering services through two 70 bed hospitals, panel doctors inSAJIDAs micro finance branches, and mobile health teams. To date, the SAJIDAFoundation holds the majority ownership in Renata Limited.


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RENATA LIMITED-DATA SUMMARYWe collected 10 years data below from 2000 to 2009 ofRenata Pharmaceuticals

Renata Limited

Year Market SharePrice

ROE DPS P/E NP

2000 450.5 12.34 25.0 4.34 35.562001 615.25 17.43 40.0 4.12 67.23

2002 650.0 16.25 50.0 4.16 72.56

2003 1261.0 22.57 70.0 5.55 105.56

2004 3200.0 25.0 70.0 12.27 145.59

2005 3000.0 26.0 70.0 10.43 192.57

2006 3099.25 23.65 70.0 23.14 242.13

2007 6491.25 26.29 70.0 40.31 358.02

2008 7789.25 26.06 75.0 32.5 438.67

2009 12051.5 25.34 80.0 35.09 590.48

Where market price is dependent variable and ROE, DPS, P/E, and NP are independentvariable.Data Analysis:

Model Summaryb

Model R

R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

Durbin-

Watson

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .990a

.980 .964 724.80515 .980 60.819 4 5 .000 2.493

a. Predictors: (Constant), NP, ROE, PE, DPS


INTERPRETATION OF THE COEFFICIENT OF MULTIPLE

DETERMINATIONS (R2)

The model summary shows some important indicators of the explaining power of themodel. The R-square value shows the percent of variation in the dependent variable is

explained by the set of independent variables. In this case, r-square value of .980 or

98.0% means that the independent variables (return on equity, dividend per share, price

earnings ratio, and net income) explain 98.0 percent of change in share price.


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INTERPRETATION OF ADJUSTED COEFFICIENT OF

DETERMINATION (R2

adj):

On the other hand, the adjusted r-square value shows the percent of variation in the

dependent variable is explained by the statistically significant independent variables. So

in this case, an adjusted r-square value of .964 or 96.4% means that the statisticallysignificant independent variables explain 96.4 percent of the variation in labor

productivity and it also indicates the model has the strong predictive power. If we

compare R-square (.980) to the adjusted R-square (.964), the difference in this case is

small.

ANALYSIS OF VARIANCE (ANOVA)

ANOVAb


1 Regression 1.278E8 4 3.195E7 60.819 .000a

Residual 2626712.517 5 525342.503

Total 1.304E8 9

a. Predictors: (Constant), NP, ROE, PE, DPS


Now we are testing the ability of the independent variables X1, X2Xk to explain thebehavior of the dependent variable y. this test is called global test. It mainly investigateswhether it is possible all the independent variables have zero regression coefficients. Wewill test whether the independent variable (DPS, net income, P/E ratio) effectivelyestimates the market price of share. In testing hypothesis, we first state the nullhypothesis and the alternate hypothesis. We have 4 independent variables such as b1, b2,

b3, b4 are simple regression coefficients. The corresponding coefficients in the populationare given in the symbol 1, 2, 3, 4.

The null hypothesis is

Ho: 1= 2= 3= 4 = 0

The alternate hypothesis is

H1: Not all the s are 0


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If the null hypothesis is true, it implies the regression coefficients is 0 and logically are ofno use in estimating the dependent variable. We have to search some for otherindependent variable to predict share price. To test null hypothesis that the multipleregression are all 0, we have to find out a critical value. From appendix B.4 in our test

book and we have used 0.05 significant level. Here the degree of freedom in numerator is

4 because there are 4 independent variables and the degree of denominator is 5 foundedby {n-(k+1)} =10-(4+1) =5.

So the critical value is 5.41. The region where Ho is not rejected and the region where Hois rejected are shown in the following diagram.

Continuing with the global test, the decision tool is not to reject the null hypothesis. Thecomputed value of f is less than or equal 4.76. If the computed value is more than 4.76then we reject the Ho.

The value of F is found from the following equation: F =

= 60.819

The computed value of F is 60.81 which is in the rejection region. The null hypothesis

that the multiple regression coefficients are 0 is therefore rejected. This means that sumof the independent variables have the ability to explain the variation in the dependentvariable.

4.76

Regionwhere

Ho is not

rejected

Region of

rejection (0.05

level)

SSR/k

SSE/[m-(k+1)]

Calculated

value: 60.819


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Evaluating individual regression coefficient:

Our next step is to test the independent variables individually to determine whichregression coefficients may be 0 and which are not. If a could be equal to 0, it indicatesthat this particular independent variable is of no value in explain any variation in thedependent value. If there are coefficients for which Ho cant be rejected. We may want to

eliminate coefficient from regression equation.

We will now conduct four separate test of hypothesis:

For ROE For DPS For P/E ratioFor NP

Ho: 1 = 0 2 = 0 3 = 04 = 0

H1 1 0 2 0 3 04 0

We will test the hypothesis at 0.05 significant level.

The alternate hypothesis stated indicates that the test is two tail. The test statistic follows tdistribution with n-(k+1) degrees of freedom. The number of sample observation is n=10.The number of independent variables are k=4. Thus there are 5 degrees of freedom.

The critical value in Appendix B.2 for a two tailed test with degrees of freedom using

0.05 significant level Ho is rejected if t is less than -2.571 or more than 2.571.Coefficients

a

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

95% Confidence


Collinearity

Statistics

B Std. Error Beta

Lower

Bound

Upper

Bound

Zero-

order Partial Part Tolerance VIF

1 (Constant) -610.976 1387.980 -.440 .678 -4178.892 2956.941

ROE 70.914 175.092 .092 .405 .702 -379.175 521.003 .663 .178 .026 .077 12.9

DPS -29.198 49.956 -.136 -.584 .584 -157.615 99.219 .688 -.253 -.037 .075 13.3

PE -40.138 46.542 -.151 -.862 .428 -159.778 79.503 .880 -.360 -.055 .132 7.5

NP 24.041 3.774 1.158 6.370 .001 14.340 33.743 .988 .944 .404 .122 8.2

a. Dependent Variable: S.Price


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The regression model is Y= -610.976 + 70.914X1 -29.198X2 -40.138 X3 +24.041 X4

The intercept value of -610.976 indicates the regression intersect the Y-axis at -610.976when X1, X2, X3, and X4 are 0.

b1 of 70.914indicates that for each increase in 1% ROE, share price will increase by70.914Tk.

b2 of -29.198 indicates that for each increase in 1tk. DPS, share price will decrease by -29.198Tk.

b3 of -40.138 indicates that for each increase in 1tk. Price Earnings ratio, share price willdecrease by 40.138 Tk.

b4 of 24.041 indicates that for each increase in 1 million tk., Net Profit share price will

increase by 24.041Tk.

As b1 is highest, it means that influence of Net Profit is fluctuating over increasingmarket share price.

The sample distribution of coefficient follows the t distribution n-(k+1). Hence we areable to test independent variables individually to determine whether the regressioncoefficients differ from 0. The computed t ratio is 0.405 for ROE, -.584 for DPS, 0.862

for P/E ratio and 4.335 for Net Profit. The regression coefficient for Net Profit is not 0because it is right to the 2.571 and ROE, P/E ratio and DPS fall into the region within -6.370. So independent variable P/E ratio, DPS and ROE have no effect over dependentvariable. It can be dropped from the analysis. So we can conclude that only Net Profitmakes an impact over changing market share price.


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COM PAN Y PR OFI LE

The company was incorporated in 1976 and commenced operations in 1980 with the

manufacturing and marketing of products of Bayer AG, Germany and Upjohn Inc., USA under

licensing arrangements. In 1983, the company started manufacturing its own formulations and

it launched export operation in 1992. In 2005 Beximco Infusions Ltd, the company that

produces intravenous fluids, was amalgamated with the parent company. In the same year it

completed the state-of-the-art oral solid dosage plant in compliance with the US FDA and UK

MHRA standards, which has been approved by major global regulatory bodies. Today Beximco

Pharma is the largest exporter of pharmaceuticals in the country and the only company to win

National Export Trophy (Gold), the highest national accolade for export, for record three times.The company is the largest producer of Metered Dose Inhalers (MDIs) in the country, and the

first to produce CFC free inhalers. BPL is also the first company to produce anti-retroviral drugs

(ARVs) locally. As a public limited company, its shares are actively traded in Dhaka Stock

Exchange and Chittagong Stock Exchange, and Beximco Pharma has the unique distinction of

being the only company in the country listed on AIM of London Stock Exchange.

Beximco Pharmaceuticals Ltd (BPL) is a leading manufacturer of pharmaceutical formulations

and Active Pharmaceutical Ingredients (APIs) in Bangladesh. The company is the largest

exporter of pharmaceuticals in the country and its state-of-the-art manufacturing facilities are

certified by global regulatory bodies of Australia, Gulf nations, Brazil, among others. The

company is consistently building upon its portfolio and currently producing more than 400

products in different dosage forms covering broader therapeutic categories which include

antibiotics, antihypertensives, antidiabetics, antireretrovirals, anti asthma inhalers etc, among

many others.


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BEXIMCO PHARMA-DATA SUMMARY

We collected 10 years data below from 2000 to 2009 of Beximco Pharmaceuticals

Ltd.

Beximco Pharmaceuticals Ltd.

YEAR SHAREPRICE

NETPROFIT

DPS ROE P/E EPS

2000 49.75 398.3 20.0 10.58 9.4 7.122001 54.88 401.78 15.0 9.64 8.62 5.742002 42.83 341.68 15.0 7.7 8.57 4.882003 35.03 207.14 20.0 4.46 13.42 2.962004 57.1 329.38 30.0 6.81 19.55 4.712005 78.25 489.26 15.0 7.17 9.09 6.362006 52.41 470.66 15.0 5.92 13.06 4.112007 54.85 353.07 15.0 4.28 21.04 2.82008 104.48 545.34 30.0 5.22 46.45 3.61

2009 157.0 624.74 15.0 5.74 37.72 4.13

Data Analysis:

Descriptive Statistics

Mean Std. Deviation N

S.PRICE 68.6580 36.68643 10

NP 4.1614E2 120.08351 10

DPS 19.0000 6.14636 10

ROE 6.7520 2.09492 10

PE 18.6920 13.25298 10

EPS 4.6420 1.42170 10


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Correlations

S.PRICE NP DPS ROE PE EPS

Pearson Correlation S.PRICE 1.000 .863 .050 -.221 .774 -.094

NP .863 1.000 -.075 .011 .593 .157

DPS .050 -.075 1.000 -.125 .486 -.133

ROE -.221 .011 -.125 1.000 -.516 .916

PE .774 .593 .486 -.516 1.000 -.494

EPS -.094 .157 -.133 .916 -.494 1.000

Sig. (1-tailed) S.PRICE . .001 .446 .270 .004 .398

NP .001 . .419 .488 .035 .333

DPS .446 .419 . .366 .077 .357

ROE .270 .488 .366 . .063 .000

PE .004 .035 .077 .063 . .073

EPS .398 .333 .357 .000 .073 .

N S.PRICE 10 10 10 10 10 10

NP 10 10 10 10 10 10

DPS 10 10 10 10 10 10

ROE 10 10 10 10 10 10

PE 10 10 10 10 10 10

EPS 10 10 10 10 10 10

Model Summaryb

Model R

R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

Durbin-

Watson

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .954a

.910 .798 16.47965 .910 8.120 5 4 .032 1.678

a. Predictors: (Constant), EPS, DPS, NP, ROE, PE


Our findings from regression analysis:

R=.954; this suggests that there is very strong relationship among dependent variable that is share price

and independent variables such as net income, DPS, EPS, ROE and P/E ratio.

R2=.910; this indicates 91% of dependent variables: share price can be explained by independent variables:

net income, DPS, EPS, ROE, P/E ratio.


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Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

95% Confidence


Collinearity

Statistics

B

Std.

Error Beta

Lower

Bound

Upper

Bound

Zero-

order Partial Part Tolerance VIF

1 (Constant) 10.597 37.977 .279 .794 -94.845 116.039

NP .007 .134 .024 .055 .959 -.363 .378 .863 .028 .008 .117 8.517

DPS-2.749 1.779 -.461

-

1.546.197 -7.687 2.189 .050 -.611

-

.231.253 3.960

ROE-6.389 7.251 -.365 -.881 .428 -26.521 13.743 -.221 -.403

-

.132.131 7.647

PE 3.228 1.514 1.166 2.132 .100 -.975 7.431 .774 .729 .319 .075 13.339

EPS 19.392 14.799 .751 1.310 .260 -21.697 60.481 -.094 .548 .196 .068 14.670

a. Dependent Variable:

S.PRICE

CoefficientsCo-efficient and constant of regression:

i. Regression equation

Y= 10.597+ .007x1-2.749x2-6.389X3+3.228X4+19.392X5

ii. Constant a= 10.597indicates that share price is equal to 10.597 tk irrespective of the

independent variables.

iii. b1=.007 suggests that for each million tk increase in net income share price is

increased by tk .007 m.

iv. b2=-2.749 suggests that for each additional % increase in DPS share price is

decreased by 2.749%.

v. b3=-6.389 suggests that for each % increase in ROE share price is decreased by

6.389%.

vi. b4=3.228 suggests that for each time increase in P/E ratio share price is increased

by 3.228 times.vii. b5=19.392 suggests that for each additional % increase in EPS share price is

increased by 19.392%

1=.024, 2=-0.461, 3=-.365, 4=1.166 and 5=.756 Beta measures strength of influence of the independent

variable over the dependent variable. Among the Betas 4 is the highest so that we can say upon the share

price influence of P/E ratio is the strongest.


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ANOVAb


1 Regression 11026.735 5 2205.347 8.120 .032a

Residual 1086.315 4 271.579

Total 12113.051 9

a. Predictors: (Constant), EPS, DPS, NP, ROE, PE


With df=5 and df=4 table value of F=6.26 and calculated value of F=8.120 where F>F table value means

all regression co-efficient are not zero that is to say for Beximo Pharmaceuticals share price some of the

independent variable do have the ability to explain the variation in the dependent variable.


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CON CLU S I ON

The aim of the report was to establish and define the association between the share prices of a

company and the performance indicators.

Throughout the analysis of the report it was possible to successfully establish such a relation

between them and to define them to significant precision.

Although the analysis is based on only the past records it, nevertheless, would help us to

predict the future trend of share prices in response to the performance indicators of the

particular companies.

The analysis is also a helpful in understanding of the effectiveness of the company performance

on its share prices. It not only relates to the only four companies we have dealt with, but the

overall capital market.


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BI BLI OGR APH Y

1. Text Books

i) Statistical Techniques in Business & Economics by Douglas A. Lind,William G. Marchal, Samuel A. Wathen.

ii) Principles of Statistics

2. Websites

i) National Bank Ltd (www.nblbd.com)ii) AB Bank Ltd. (www.abbank.com.bd)iii)Renata Pharmaceuticals (www.renata.com)iv)Beximco Pharmaceuticals Ltd. (www.beximco.com)

applications of statistics on banking and pharmaceutical companies

Documents