managerial economics project 2010

8/3/2019 Managerial Economics Project 2010

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Comparative Analysis of Variation in BSE Sensex

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Comparative Analysis of Variation in BSE Sensex w.r.t.its Top 10 Listed Companies, NASDAQ & Nikkei and

Gold

Group:

1. Ajay Kumar Jha 10FN-0062. Ankit Rungta 10FN-0163. Arjun Malhotra 10HR-0094. Gargi Jalan 10DM-1885. Aditi Pandey 10DM-0066. Ankur Shah 10DM-018


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1. Summary:

1.1. Topic:

Comparative Analysis of Variation in BSE Sensex with respect to its Top 10 Listed

Companies, NASDAQ & Nikkei and Gold.

1.2. Objective:

Our objective is to find out how the BSE Sensex varies according to variation in its top 10

listed companies (according to market capitalization). We will also find out whether there is

any similarity in the variation of SENSEX and the price of GOLD.

Moreover we will also find out, how the percentage change in SENSEX is dependent on that

of NASDAQ and Nikki.

1.3. Methodology:

First we have collected data from BSE, NASDAQ, Nikkei, and Yahoo Finance. Then we have

used Regression Analysis using Microsoft Excel2007.

1.4. Result:

From the Regression Analysis we have got the result that variation in BSE Sensex doesnt

depend much on NASDAQ & Nikkei or Gold, but its variation is highly dependent upon the

variation of its top 10 listed companies.

2. Introduction:

Our objective is to find out the amount of dependence SENSEX has on its top 15 companiesin terms of market capitalization. We will also find out whether there is any similarity in the

variation of SENSEX and the price of GOLD. Moreover we will also find out, how the

percentage change in SENSEX is dependent on that of Nasdaq and Nikki.

2.1. SENSEX:

Bombay stock exchange or BSE is the largest stock exchange in India in terms of number of

listed companies in the exchange and the market capitalization of the listed companies. Theprime index of the Bombay Stock Exchange is the BSE 30 that is popularly known as the

Sensex. The Sensex is made with highly liquid stocks of 30 largest companies in terms of

market capitalization. The Sensex was first constructed in the 1986 on 1st of January withjust 30 stocks. Over the years of course these stocks have changed time and again

according to the condition of the market and economy of the country. The selection of thestocks is made on the basis of market capitalization and liquidity of stocks. The BSE indexcommittee decides on which stock to include in the Sensex and which stock should beremoved from the Sensex. This committee is made up of highly placed experts and

professionals from the field finance and industry that are well aware of the Indian stock

market scenario.


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2.2. NASDAQ:

The NASDAQ Stock Market, also known as the NASDAQ, is an American stock exchange.

"NASDAQ" originally stood for "National Association of Securities Dealers Automated

Quotations," but the exchange's official stance is that the acronym is obsolete.] It is the

largest electronic screen-based equity securities trading market in the United States andfourth largest by market capitalization in the world. With approximately 3,700 companies

and corporations, it has more trading volume than any other stock exchange in the world.

It was founded in 1971 by the National Association of Securities Dealers (NASD), who

divested themselves of it in a series of sales in 2000 and 2001. It is owned and operated by

the NASDAQ OMX Group, the stock of which was listed on its own stock exchange beginning

July 2, 2002, under the ticker symbol NASDAQ: NDAQ. It is regulated by the Securities and

Exchange Commission.

2.3. Nikkei 225

It is a stock market index for the Tokyo Stock Exchange (TSE). It has been calculated dailyby the Nikkei newspaper since 1950. It is a price-weighted average, and the componentsare reviewed once a year. Currently, the Nikkei is the most widely quoted average of

Japanese equities, similar to the Dow Jones Industrial Average. In fact, it was known as the"Nikkei Dow Jones Stock Average" from 1975 to 198.

The Nikkei 225 began to be calculated on September 7, 1950, retroactively calculated backto May 16, 1949.Currently, the index is updated every 15 seconds during trading sessions.

The Nikkei 225 Futures, introduced at Singapore Exchange (SGX) in 1986, the OsakaSecurities Exchange (OSE) in 1988, Chicago Mercantile Exchange (CME) in 1990, is now an

internationally recognized futures index.

The Nikkei average hit its all-time high on December 29, 1989, during the peak of the

Japanese asset price bubble, when it reached an intra-day high of 38,957.44 before closingat 38,915.87. Its high for the 21st century stands just above 18,300 points. In January

2010, it was 72.9% below its peak.

2.4. GOLD:Gold has been widely used throughout the world as a vehicle for monetary exchange, eitherby issuance and recognition of gold coins or other bare metal quantities, or through gold-

convertible paper instruments by establishing gold standards in which the total value of

issued money is represented in a store of gold reserves.However, the amount of gold in the world is finite and production has not grown in relation

to the world's economies. Today, gold mining output is declining.With the sharp growth of

economies in the 20th century, and increasing foreign exchange, the world's gold reservesand their trading market have become a small fraction of all markets and fixed exchangerates of currencies to gold were no longer sustained

Many holders of gold store it in form of bullion coins or bars as a hedge against inflation orother economic disruptions. However, some economists do not believe gold serves as ahedge against inflation or currency depreciation.


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3. Objectives: Our objective is to find out how the BSE Sensex varies according to variation in its

top 10 listed companies (according to market capitalization).

To find out whether there is any similarity in the variation of SENSEX and the price ofGOLD.

To find out, how the percentage change in SENSEX is dependent on that of NASDAQand Nikkei.

4. Methodology:

4.1. Regression analysis:

In statistics, regression analysis includes any techniques for modeling and analyzing several

variables, when the focus is on the relationship between a dependent variable and one or

more independent variables. More specifically, regression analysis helps us understand howthe typical value of the dependent variable changes when any one of the independent

variables is varied, while the other independent variables are held fixed. Most commonly,

regression analysis estimates the conditional expectation of the dependent variable given

the independent variables that is, the average value of the dependent variable when the

independent variables are held fixed. Less commonly, the focus is on a quantile, or other

location parameter of the conditional distribution of the dependent variable given the

independent variables. In all cases, the estimation target is a function of the independent

variables called the regression function. In regression analysis, it is also of interest to

characterize the variation of the dependent variable around the regression function, which

can be described by a probability distribution.

Regression analysis is widely used for prediction and forecasting, where its use has

substantial overlap with the field of machine learning. Regression analysis is also used to

understand which among the independent variables are related to the dependent variable,

and to explore the forms of these relationships. In restricted circumstances, regression

analysis can be used to infer causal relationships between the independent and dependent

variables.

Tools Used

Microsoft Excel 2007Assumption

1. The populations follow normal distribution.2. The populations are independent.


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5. Data Analysis:

5.1. Regression Analysis of BSE and top 10 Companies:

The regression output has three components:1. Regression statistics table2. ANOVA table3. Regression coefficients table.

Overall equation would be:

BSE = 0.117662433*RIL + 0.069490297*ONGC + 0.149307239*SBI +

0.05761936*TCS + 0.065782683*NTPC + 0.166616347*Infosys +

0.166305861*BHEL + 0.049933532*Bharti Airtel + 0.107791553*ITC +

0.102230324*L&T - 0.000135078

The above result shows that variation in BSE index is highly dependent upon variations of

its 10 major shares.


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Interpret Regression Statistics Table

This is the following output. Of greatest interest is Adjusted R Square.

Explanation

Multiple R 0.938169167 square root of R2

R Square 0.880161386 R2

Adjusted R

Square 0.871082703Adjusted R2 used if more than one x variable

Standard Error 0.003725963This is the sample estimate of the standard deviation of theerror u

Observations 143 Number of observations used in the regression (n)

R Square: This is the most important number of the output. R Square tells how well theregression line approximates the real data. This number tells you how much of the

output variables variance is explained by the input variables variance. Here R2 = 0.88.

Adjusted R Square: This is quoted most often when explaining the accuracy of theregression equation. Adjusted R Square is more conservative the R Square because it isalways less than R Square. Another reason that Adjusted R Square is quoted more oftenis that when new input variables are added to the Regression analysis, Adjusted RSquare increases only when the new input variable makes the Regression equation more

accurate (improves the Regression equations ability to predict the output). R Squarealways goes up when a new variable is added, whether or not the new input variableimproves the Regression equations accuracy. Adjusted R2 = 0.871 means that 87.1% of

the variation of BSE around its mean is explained by the regressors (top 10 companies).

Standard Error: The standard error here refers to the estimated standard deviation ofthe error term u. It is sometimes called the standard error of the regression. It is not tobe confused with the standard error of y itself or with the standard errors of theregression coefficients given below.

Interpret ANOVA TableANOVA table for our case is given:

df SS MS F Significance F

Regression 10 0.013459114 0.001345911 96.94813643 8.5407E-56

Residual 132 0.001832529 1.38828E-05

Total 142 0.015291644

Significance of F: This indicates the probability that the Regression output could havebeen obtained by chance. A small Significance of F confirms the validity of the

Regression output. For example, if Significance of F = 8.5407E-56, there is only8.5407E-54 % chance that the Regression output was merely a chance occurrence. In

this case it is almost zero.


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Interpret Regression Coefficients Table

The regression output of most interest is the following table of coefficients and associated

output:

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 90.0% Upper 90.0%

Intercept -0.000135078 0.000317461 -0.425494878 0.671168857 -0.000763047 0.000492891 -0.000660945 0.000390789

RIL 0.117662433 0.020994147 5.604534972 1.16836E-07 0.076133935 0.159190931 0.082886063 0.152438803

ONGC 0.069490297 0.021038086 3.303071308 0.001230869 0.027874885 0.111105709 0.034641144 0.104339449

SBI 0.149307239 0.026566969 5.620032777 1.08687E-07 0.096755148 0.20185933 0.105299605 0.193314873

TCS 0.05761936 0.028625688 2.012855034 0.046163983 0.000994921 0.114243799 0.010201501 0.105037219

NTPC 0.065782683 0.034615542 1.90038001 0.059563972 -0.002690278 0.134255645 0.008442756 0.12312261

Infosys 0.166616347 0.034629308 4.811425852 4.03061E-06 0.098116154 0.23511654 0.109253616 0.223979078

BHEL 0.166305861 0.03038016 5.474160087 2.13689E-07 0.106210902 0.22640082 0.115981755 0.216629967

Bharti

Airtel 0.049933532 0.014443289 3.457213396 0.000734897 0.021363279 0.078503785 0.026008523 0.073858541

ITC 0.107791553 0.024548975 4.390877963 2.29397E-05 0.059231255 0.156351851 0.067126685 0.148456421

L&T 0.102230324 0.025236674 4.050863604 8.65757E-05 0.052309689 0.152150959 0.060426297 0.144034351

Coefficients: In simple or multiple linear regression, the size of the coefficient for eachindependent variable gives you the size of the effect that variable is having on your

dependent variable, and the sign on the coefficient (positive or negative) gives you thedirection of the effect. In regression with a single independent variable, the coefficient

tells you how much the dependent variable is expected to increase (if the coefficient is

positive) or decrease (if the coefficient is negative) when that independent variableincreases by one. In regression with multiple independent variables, the coefficient tells

you how much the dependent variable is expected to increase when that independentvariable increases by one, holding all the other independent variables constant.

Remember to keep in mind the units which your variables are measured in.

Standard Error: The standard error is an estimate of the standard deviation of thecoefficient, the amount it varies across cases. It can be thought of as a measure of the

precision with which the regression coefficient is measured. If a coefficient is largecompared to its standard error, then it is probably different from 0.

t Stat: The t statisticis the coefficientdivided by its standard error. P-value: The P-Values of each of these provide the likelihood that they are real results

and did not occur by chance. The lower the P-Value, the higher the likelihood that that

coefficient or Y-Intercept is valid. For example, a P-Value of 1.16836E-07 (in the case of

RIL) for a regression coefficient indicates that there is only 0.00001168% chance thatthe result occurred only as a result of chance.


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5.2. Regression Analysis of BSE and NASDAQ & Nikkei:


BSE = 0.009958446*NASDAQ + 0.315843341*Nikkei + 0.000499004

The above result shows that variation in BSE index is not dependent upon the variation in

NASDAQ &Nikkei indexes. Though, its the common perception that the BSE index varies

according to the variation in the NASDAQ & Nikkei indexes, but our results shows that the

perception is not true.

Interpret Regression Statistics TableThis is the following output:

Explanation

Multiple R 0.45197131 square root of R2

R Square 0.204278065 R2

Adjusted R

Square 0.19184491Adjusted R2 used if more than one x variable

Standard Error 0.009234468This is the sample estimate of the standard deviation of theerror u


Adjusted R2 = 0.1918 means that 19.18% of the variation of BSE around its mean isexplained by the regressors (NASDAQ & Nikkei). Which is very low.


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Interpret ANOVA Table

ANOVA table for our case is given:


Regression 2 0.002802168 0.001401084 16.43010658 4.45374E-07

Residual 128 0.010915252 8.52754E-05

Total 130 0.01371742

F = 4.45374E-07 means there is only 0.000045% chance that the Regression outputwas merely a chance occurrence. In this case it is almost zero.


The regression output of most interest is the following table of coefficients and associatedoutput:


Intercept 0.000499004 0.000810559 0.615629288 0.539232325 -0.001104825 0.002102832 -0.000843966 0.001841974

NASDAQ 0.009958446 0.071257129 0.139753674 0.889074281 -0.131035956 0.150952847 -0.108103588 0.128020479

Nikkei 0.315843341 0.068940107 4.581416432 1.08176E-05 0.179433562 0.45225312 0.201620253 0.430066429

P-Value of 0.889074281 (in the case of NASDAQ) for a regression coefficient indicatesthat there is only 88.9% chance that the result occurred only as a result of chance.

However in the case of Nikkei its 0.0000108%.


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5.3. Regression Analysis of BSE and Gold:


BSE = -0.202475256*NASDAQ + 0.000229806

The above result shows that variation in BSE index is not dependent upon the variation in

Gold. But the result is showing that there is some inverse relationship between the variation

in BSE index and that in Gold price. Its the common perception that the price of gold

increases when the market is going down and our result confirms that, but our result

explains only 2.06% of the variation.

Interpret Regression Statistics Table

This is the following output:

Explanation

Multiple R0.16585462

square root of R2

R Square 0.027507755 R2

Adjusted R

Square

0.020610647Adjusted R2 used if more than one x variable

Standard Error0.010269767 This is the sample estimate of the standard deviation of the

error u


Adjusted R2 = 0.0206 means that 2.06% of the variation of BSE around its mean isexplained by the regressors (Gold). Which is very low.


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Interpret ANOVA Table

ANOVA table for our case is given:


Regression 1 0.000420639 0.000420639 3.98830269 0.047741886

Residual 141 0.014871005 0.000105468

Total 142 0.015291644

F = 0.047741886 means there is only 4.77% chance that the Regression output wasmerely a chance occurrence.


The regression output of most interest is the following table of coefficients and associatedoutput:


Intercept 0.000229806 0.000859227 0.267457273 0.789507929 -0.001468826 0.001928439 -0.001192843 0.001652456

Gold -0.202475256 0.10138598 -1.997073531 0.047741886 -0.402908389 -0.002042124 -0.370343296 -0.034607216

P-Value of 0.047741886 (in the case of Gold) for a regression coefficient indicates thatthere is only 4.77% chance that the result occurred only as a result of chance.


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6. Conclusion:From the Regression Analysis we have got the result that variation in BSE Sensex doesnt

depend much on NASDAQ & Nikkei or Gold, but its variation is highly dependent upon the

variation of its top 10 listed companies.

7. Recommendation:As we have seen from the result that the variation in BSE Sensex is more dependent on the

top 10 companies. However the common perception is that it varies according to the

variation in NASDAQ and Nikkei. So based on our project report we would suggest the

common trader to trade into the share market by seeing the result of top 10 companies like

RIL, ONGC, TCS, Infosys rather than watching the Index of NASDAQ and Nikkei.

8. Bibliography:References

http://www.bseindia.com/

http://www.nasdaq.com/

http://e.nikkei.com/e/fr/marketlive.aspx

http://in.finance.yahoo.com

http://www.mcxindia.com

http://www.wikipedia.org/
http://www.bseindia.com/http://www.bseindia.com/http://www.nasdaq.com/http://www.nasdaq.com/http://e.nikkei.com/e/fr/marketlive.aspxhttp://e.nikkei.com/e/fr/marketlive.aspxhttp://in.finance.yahoo.com/http://in.finance.yahoo.com/http://www.mcxindia.com/http://www.mcxindia.com/http://www.wikipedia.org/http://www.wikipedia.org/http://www.wikipedia.org/http://www.mcxindia.com/http://in.finance.yahoo.com/http://e.nikkei.com/e/fr/marketlive.aspxhttp://www.nasdaq.com/http://www.bseindia.com/

managerial economics project 2010

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