Tata Steel

2013

IIM Lucknow

FM PROJECT REPORT

Submitted to: Prof. Madhumita Chakraborty

Group - 12

Sahil Bansal-ABM09047 Niraj Agarwal-PGP28385 Ashutosh Tripathi-PGP28389 Prashant Kajaria-PGP28405

Contents

Findings of the project ... 3

Introduction ... 4

TATA STEEL ... 4

SAIL ... 4

Bhushan Steel ... 5

Sesa Goa Limited ... 5

Cost of Capital ... 6

Tax Rate ... 6

Cost of Equity ... 6

Dividend Discount Model ... 6

Earnings Capitalisation Model ... 7

CAPM Model ... 7 Market Beta ... 8 Tata Steel ... 8 Bhushan Steel... 9 SesaGoa ... 10 SAIL ... 11 Cost of Debt ... 12

Weighted Average Cost of Capital ... 13

TEST OF WEAK FORM OF EMH FOR TATA STEEL ... 13

WEAK FORM OF EMH ... 13

Autocorrelation ... 13

Method ... 13

Observation and Analysis ... 13

RUNS Method (Non Parametric Test) ... 13

Autocorrelation Method: ... 14

Conclusion ... 18

References: ... 19

Appendix ... 19

Findings of the project

1. Kd less than Rf- In certain cases Kd is coming out to be less than Rf. The analysis suggests that these anomalies arise because of certain possible reasons like

FCCBs- The cost of foreign currency convertible bonds is comparatively less than the debt rate in the domestic market. This is basically due to low rate of interest in the foreign markets like U.S., Japan etc.

2. Ke variation in EDM and DDM –There is high variation in the calculation of the cost of the equity using the earning discount model and the dividend discount model due to the assumption that the industry growth rate is applicable to all the companies. Moreover the variation in the earning rate over the years due to business cycles and the variations in the rate of dividend have further accentuated the variation.

3. Beta is calculated using monthly data- Beta calculation has been done using monthly data. While using the daily or weekly data it is the high volatility will be set off over such a long period of time making beta to be very small in value. This very small beta will not be a true reflection of the sensitivity of the stock.

4. Kd calculation is approximated- For the calculation of the cost of debt, the formula used is Interest/Total debt. This formula is approximate and not exact. Thus the cost of debt is somewhat tainted. The reason for the acceptance of such cost of debt is the unavailability of the interest rate on debt in the financial statement and the notes of accounts of the

companies.

5. Weight should be Market value- The weight that should be used to calculate the cost of capital should include the market value of the equity. The calculations involve the use of both the book value as well as the market value to ascertain the anomalies. The data clearly shows that the book value is not a proper measure as it grossly underestimates the value of equity leading to much lower cost of capital.

Introduction

TATA STEEL

Backed by 100 glorious years of experience in steel making, Tata Steel Ltd is the world's 12th largest steel company with an annual crude steel capacity of 28 million tonnes. Established in the year 1907 as Tata Iron & Steel Company Ltd., the company is the first integrated plant in Asia and diversified steel producer with major operations in India, Europe and South East Asia. They have manufacturing units in 26 countries and a presence in 50 European and Asian markets. The company together with their subsidiaries, engages in the manufacture and sale of steel products in India and internationally.

The company is executing their plan to increase their crude steel capacity from 6.8 million

tonnes per annum to 9.7 million tonnes per annum at their Jamshedpur Works by 2012-13

and it has set a target of achieving an annual production capacity of 100 million tons by

2015. The company is also has major on-going capital projects which include capacity

augmentation of the Jamshedpur plant. The preliminary work on the 6 million tonne per

annum capacity Greenfield steel plant at Kalinganagar, Orissa is in progress.

Tata Steel is also India's second-largest and second-most profitable company in private

sector with

turnover of US$ 26.13 billion in FY 2011- 2012, having over 81,000 employees

across five continents and is a Fortune 500 company.

SAIL

Steel Authority of India Limited (SAIL) is the leading steel-making company in India. It is a

fully integrated iron and steel maker, producing both basic and special steels for domestic

construction, engineering, power, railway, automotive and defence industries and for sale in

export markets. SAIL is also among the five Maharatnas of the country's Central Public

Sector Enterprises. With a turnover of 48,681 crore (US$8.9 billion), the company is among

the top five highest profit earning corporate of the country.

With an annual production of 13.5

million metric tons, SAIL is the 14th largest steel producer in the world. Major plants owned

by SAIL are located at Bhilai, Bokaro, Durgapur, Rourkela, Burnpur and Salem and is

investing Rs 21000 crore in West Bengal, to set up a wagon factory. The company has the

distinction of being India’s second largest producer of iron ore and of having the country’s

second largest mines network.

Currently, SAIL, is in the process of modernizing and expanding its production units, raw

material resources and other facilities to maintain its dominant position in the Indian steel

market. The objective is to achieve a production capacity of 26.2 MTPA of Hot Metal from

the base level production of 14.6 MTPA (2006-07 – Actual).

Bhushan Steel

Bhushan Steel Ltd formerly known as Bhushan Steel & Strips Ltd is a globally renowned and one of the leading players in the steel industry. Backed by more than two decades, of experience in steel making, Bhushan steel is now India’s 3rd largest Secondary Steel Producer Company with an existing steel production capacity of 2 million tonnes per annum. Bhushan Power and Steel Limited has seven plants at four locations – Chandigarh, Derabassi in Punjab, Bangihatti, near Dankuni in West Bengal, and Thelkoloi in Orissa

BSL uses advanced technology and replenishes the same as and when required. This has led to the Khopoli plant has given a tremendous boost of 425000 MT per annum to BSL’s total production capacity. This led to gross sales increasing 8-fold over a period of six years.

Its biggest expansion is in Orissa – it has signed an agreement with the Government of Orissa for setting up of a three million tonnes capacity steel plant at Meramandali in Dhenkanal district, and as part of its total integration of the steel value chain, Bhushan Steel is in the process of setting up a power plant and an advanced hot rolling plant on 1,618 acres (6.55 km2) at Meramandali in

Dhenkanal district near Angul, at a cost of 5,200crore and its subsequent backward integration and expansion to 4 million tonnes.

Sesa Goa Limited

Sesa Goa Limited is multinational iron-ore producer and exporter with operations in the states of Goa and Karnataka in India and in Liberia, West Africa. It is India's largest producer and exporter of iron ore in the private sector, with production of above 21 million tonnes of iron ore in fiscal year 2010.

In 2007, it became a majority-owned subsidiary of Vedanta Resources Plc, listed on the London Stock Exchange, when Vedanta acquired 51% controlling stake from Mitsui & Co., Ltd. In June 2009, Sesa Goa Limited acquired VS Dempo & Co. Private Limited (now Sesa Resources Limited) along with its fully owned subsidiary Dempo Mining Corporation (now Sesa Mining Corporation Limited) and 50% equity in Goa Maritime Private Limited. In 2011, Sesa acquired 51% stake in Western Cluster Limited, Liberia.

China is the biggest client accounting for 80% of the iron ore sales. Codli is the largest iron ore producing mine of Sesa Goa with a current production capacity of more than 7.0 mtpa. The Sonshi mine has a capacity of more than 3.0 mtpa. Approximately 65% of total production of metallurgical coke is consumed by Sesa group, for its pig iron production. The remainder is sold to customers located in India. Sesa Goa has patented a technology that provides high quality output and produces power.

Cost of Capital

The cost of capital is the rate of return that capital could be expected to earn in an

alternative investment of equivalent risk. It is used to evaluate new projects of a

company as it is the minimum return that investors expect for providing capital to the

company, thus setting a benchmark that a new project has to meet.

The Cost of Capital comprises of the cost of debt and cost of equity. The overall cost of

capital of a company may be calculated as the weighted average of these two costs.

Tata steel’s capital structure comprises both debt and equity. Hence, WACC would be an

ideal method to calculate its cost of capital. The WACC is calculated as:

WACC= K

d

*(1-T)*W

d

+ K

e

*W

e

Where:

K

d

= cost of debt

K

e

= cost of equity

W

d

= weight of debt

W

e

= weight of equity

T= tax rate

Tax Rate

Flat tax rate for Indian companies is 30%. For Indian companies, income is taxed at a flat

rate of 30%. Another way to derive the tax rate is to find out the taxes paid by the firm as a

percentage of its revenue. However this method is not recommended and hence we use the

statutory tax rate for WACC calculations.

T = 30%

Cost of Equity

The Cost of Equity may be calculated using the following methods

`

Dividend Discount Model

The cost of equity can be measured by the dividend discount model.

This model may not provide us the correct cost of equity as:

1. The dividend for all the companies has not been stable and has varied in the past few

years.

2. The dividend for the companies is not expected to follow any constant growth rate in

the future years as well.

Company Growth Rate Ke (DPS)

Tata Steel 0.035 6.148162

Bhushan Steel 0.035 4.665006

SesaGoa 0.035 5.5592

Sail 0.035 5.626316

Earnings Capitalisation Model

Ke = E

1

/ P

0

Company

E

1

P

0

K

e

(EPS)

Tata Steel 67.07 453.1 14.80247 Bhushan Steel 47.7 409.44 11.65006 SesaGoa 19.24 194.25 9.904762 Sail 8.25 94.05 8.77193

The limitations are:

The firm employs debt and the dividend pay-out is not 100 percent. The earnings are not

stable, and consequently the future earnings are not equal to the current earnings. Hence, the

growth rate is not zero.

Secondly, we cannot be sure whether the investment opportunities available to it are expected

to earn a rate of return equal to the cost of equity.

Hence this method will would not give us the right model for evaluation of cost of equity

.

CAPM Model

Since CAPM is the superior method of determining Ke, We use the value determined by this

method rather than DDM and ECM. The problem with the formulas used in DDM and ECM

is that they have an underlying assumption of regular future growth which is at best an

approximation. Second, errors inevitably creep into the estimate of g.

To estimate the cost of equity using the CAPM method, we need the following variables

1. Risk Free Rate: In 2010, the Indian government did not have a bond whose maturity

date was 2030. Hence we take the risk free rate as the current yield on the 30-year

Indian Government bonds which is 7.8%. This is chosen because this is the closest

approximation of Tata Steel’s valuation period which is we have taken as 20 years.

2. Market Return Rate: We consider the market return rate as the average return on the

BSE Sensex over the last 10 years is 2002 to 2012 (optimum time period).

Rm=12%

Market Beta

Beta analyzes the market volatility of Tata Steel’s stocks. To have a correct measurement of

Beta, we take the market returns and Tata steel returns for last six years.

Beta is calculated using the following formula:

Beta= covariance (Market Returns, Firm Returns)/Variance (Market returns)

Bhushan Steel y = 1.7021x - 0.006 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.15 -0.1 -0.05 0 0.05 0.1 0.15

Tata Steel

tata steel Linear (tata steel)

SesaGoa ` y = 1.4407x + 0.0159 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 -0.2 -0.1 0 0.1 0.2

Bhushan Steel

Bhushan Steel Linear (Bhushan Steel)

SAIL y = 1.3735x + 0.0072 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 -0.15 -0.1 -0.05 0 0.05 0.1 0.15

Sesagoa

Sesagoa Linear (Sesagoa)

We calculate the Cost of Equity as

Ke=R

f

+ β*(R

m

-R

f

)

Company

R

m

R

f

β

K

e

Tata Steel

12%

7.8%

1.702 0.14948

Bhushan Steel

12%

7.8%

1.44 0.13848

SesaGoa

12%

7.8%

1.373 0.13567

Sail

12%

7.8%

1.398 0.13672

Cost of Debt

Company

Total Debt Interest Kd

Tata Steel

59796.67 1925.42 0.032199452

Bhushan Steel

21350.97 1046.27 0.049003394

SesaGoa

3741.34 420 0.06789

Sail

17360.59 983.99 0.056679525 y = 1.3985x - 0.0033 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 -0.15 -0.1 -0.05 0 0.05 0.1 0.15

SAIL

SAIL Linear (SAIL) Linear (SAIL)

Weighted Average Cost of Capital

CAPM BV CAPM MV EDM DDM

Tata Steel 0.081300213 0.084648823 0.069151676 0.028169019 Bhusan Steel 0.07242991 0.076925351 0.039444663 0.015394001 Sesagoa 0.12222066 0.133758415 0.085179677 0.046790545 Sail 0.112607202 0.109658631 0.064337419 0.040397932

TEST OF WEAK FORM OF EMH FOR TATA STEEL

WEAK FORM OF EMH

The weak form of the EMH says that past prices, volume, and other market statistics provide

noinformation that can be used to predict future prices.If stock price changes are random,

then past prices cannot be used to forecast future prices.Price changes should be random

because it is information that drives these changes, andinformation arrives randomly.This

form of the EMH, if correct, repudiates technical analysis.

Autocorrelation

The Serial Correlation Coefficient measures the relationship between the values of a random

variable at time t and its value in the previous period. Autocorrelations are reliable measures

for testing of dependence/independence of random variables in a series. If no autocorrelations

arefound in a series then the series is considered random. The study used return to investors

in BSESensex derived from the log transformation of the price ratio to convert the data

intocontinuously compounded rates than using discrete compounding. It is given by R=

ln(Pt/Pt-1)The closing rates of the past 1250 trading days are selected for the study.

Method

If the random walk theorem to prevail, the stock prices should vary around a constant mean

with constant variance and should be probabilistically independent. The independence can be

tested using Auto Correlation Function which shows the pattern of auto correlations present

in the time series as well as the extent to which current values of the series are related to

various lags of the past data.

In an efficient market, the testing hypothesis is defined as

H

0

= Auto correlation is Zero against an alternate hypothesis of

H

1

= Auto correlation is non- zero

Observation and Analysis

RUNS Method (Non Parametric Test)

consecutive positive and negative returns is tabulated and compared against its sampling

distribution under the random walk hypothesis. A run is defined as the repeated occurrence of the same value or category of a variable. It is indexed by two parameters, which are the type of the run and the length. Stock price runs can be positive, negative, or have no change. The length is how often a run type occurs in succession.

In an efficient market, the testing hypothesis is defined as: H0 = Auto correlation is Zero against an alternate hypothesis of H1= Auto correlation is non- zero

.

Runs Test

tatasteel bhusansteel sesagoa sail nifty

Test Valuea .0015 .0223 .0133 .0028 .0044

Cases < Test Value 41 41 42 38 38

Cases >= Test Value 38 38 37 41 41

Total Cases 79 79 79 79 79

Number of Runs 44 39 42 40 50

Z .807 -.327 .377 -.100 2.168

Asymp. Sig. (2-tailed) .420 .743 .706 .920 .030

Here it is seen that at 95% Confidence Interval, the observed Z value lies in the range of (-1.65, 1.65). Hence, we do not reject H0. This means that all the companies are efficient in the weak form of market. This implies that the past or historical data doesn’t aid in predicting the future or current market outcome.

Autocorrelation Method:

From the above table we that the autocorrelation values are almost 0 and the consistency is maintained in the lags. Additionally, the test results lead us to accept the null hypothesis H0 that autocorrelation doesn’t exist. This further implies that the market is efficient in the weak form.

Value df Sig.b Partial Autocorrel ation Std. Error 1 0.119 0.11 1.156 1 0.282 0.119 0.113 2 0.043 0.11 1.313 2 0.519 0.03 0.113 3 0.108 0.109 2.295 3 0.514 0.101 0.113 4 0.107 0.108 3.268 4 0.514 0.084 0.113 5 -0.029 0.108 3.34 5 0.648 -0.058 0.113 6 -0.247 0.107 8.683 6 0.192 -0.262 0.113 7 -0.041 0.106 8.835 7 0.265 -0.008 0.113 8 -0.115 0.105 10.035 8 0.263 -0.102 0.113 9 -0.064 0.105 10.408 9 0.318 0.023 0.113 10 -0.056 0.104 10.694 10 0.382 0.014 0.113 11 -0.191 0.103 14.132 11 0.226 -0.191 0.113 12 0.005 0.102 14.134 12 0.292 0.003 0.113 13 -0.13 0.102 15.769 13 0.262 -0.149 0.113 14 -0.154 0.101 18.113 14 0.202 -0.168 0.113 15 0.044 0.1 18.306 15 0.247 0.121 0.113 16 -0.012 0.099 18.321 16 0.305 -0.037 0.113 Lag Autocorrel ation Std. Error a Box-Ljung Statistic

Bhushan Steel:

Lag Autocorrelation Std.

Errora

Box-Ljung Statistic

Value df Sig.b Partial

Autocorrelation Std. Error 1 0.149 0.11 1.813 1 0.178 0.149 0.113 2 0.051 0.11 2.025 2 0.363 0.029 0.113 3 0.036 0.109 2.134 3 0.545 0.025 0.113 4 -0.225 0.108 6.46 4 0.167 -0.241 0.113 5 -0.037 0.108 6.581 5 0.254 0.03 0.113 6 0.029 0.107 6.656 6 0.354 0.052 0.113 7 0.003 0.106 6.657 7 0.465 0.014 0.113 8 0.022 0.105 6.701 8 0.569 -0.042 0.113 9 -0.069 0.105 7.138 9 0.623 -0.082 0.113 10 -0.03 0.104 7.219 10 0.705 0.014 0.113 11 -0.131 0.103 8.835 11 0.637 -0.125 0.113 12 -0.07 0.102 9.306 12 0.677 -0.03 0.113 13 0.096 0.102 10.191 13 0.678 0.096 0.113 14 -0.096 0.101 11.103 14 0.678 -0.125 0.113 15 -0.061 0.1 11.473 15 0.718 -0.097 0.113 16 0.137 0.099 13.371 16 0.645 0.156 0.113

From the above table we that the autocorrelation values are almost 0 and the consistency is maintained in the lags. Moreover, the test results lead us to accept the null hypothesis H0 that autocorrelation doesn’t exist. This further implies that the market is efficient in the weak form. Hence, the past records are futile to predict the current prices.

Sesagoa:

Lag Autocorrelation Std.

Errora

Box-Ljung Statistic

Value df Sig.b Partial

Autocorrelation Std. Error 1 -0.01 0.11 0.008 1 0.927 -0.01 0.113 2 -0.103 0.11 0.889 2 0.641 -0.103 0.113 3 0.015 0.109 0.908 3 0.824 0.013 0.113 4 0.204 0.108 4.448 4 0.349 0.196 0.113 5 -0.106 0.108 5.414 5 0.367 -0.103 0.113 6 -0.021 0.107 5.452 6 0.487 0.016 0.113 7 -0.042 0.106 5.609 7 0.586 -0.069 0.113 8 -0.024 0.105 5.662 8 0.685 -0.065 0.113 9 0.071 0.105 6.119 9 0.728 0.111 0.113 10 0.148 0.104 8.137 10 0.615 0.14 0.113 11 0.002 0.103 8.137 11 0.701 0.043 0.113 12 -0.214 0.102 12.525 12 0.404 -0.207 0.113 13 -0.028 0.102 12.598 13 0.479 -0.092 0.113 14 -0.076 0.101 13.166 14 0.514 -0.175 0.113 15 0.013 0.1 13.184 15 0.588 0.045 0.113 16 -0.023 0.099 13.237 16 0.655 0.095 0.113

From the above table we that the autocorrelation values are almost 0 and the consistency is maintained in the lags. Moreover, the test results lead us to accept the null hypothesis H0 that autocorrelation doesn’t exist. This further implies that the market is efficient in the weak form. Hence, the past records are futile to predict the current prices.

SAIL:

Lag Autocorrelation Std.

Errora

Box-Ljung Statistic

Value df Sig.b Partial

Autocorrelation Std. Error 1 0.071 0.11 0.415 1 0.52 0.071 0.113 2 0.122 0.11 1.643 2 0.44 0.117 0.113 3 0.02 0.109 1.677 3 0.642 0.004 0.113 4 0.116 0.108 2.828 4 0.587 0.102 0.113 5 -0.094 0.108 3.586 5 0.61 -0.113 0.113 6 -0.025 0.107 3.642 6 0.725 -0.037 0.113 7 0.056 0.106 3.923 7 0.789 0.084 0.113 8 -0.006 0.105 3.926 8 0.864 -0.019 0.113 9 0.039 0.105 4.062 9 0.907 0.051 0.113 10 0.081 0.104 4.665 10 0.912 0.076 0.113 11 -0.118 0.103 5.969 11 0.875 -0.172 0.113 12 -0.15 0.102 8.106 12 0.777 -0.142 0.113 13 -0.171 0.102 10.942 13 0.616 -0.141 0.113 14 -0.315 0.101 20.703 14 0.109 -0.316 0.113 15 -0.006 0.1 20.706 15 0.146 0.133 0.113 16 -0.073 0.099 21.246 16 0.169 -0.001 0.113

From the above table we that the autocorrelation values are almost 0 and the consistency is maintained in the lags. Moreover, the test results lead us to accept the null hypothesis H0 that autocorrelation doesn’t exist. This further implies that the market is efficient in the weak form. Hence, the past records are futile to predict the current prices.

Conclusion

We have calculated cost of capital of four steel organisations viz. Tata Steel, Sail India, Bhushan Steel and SesaGoa of Vedanta. For cost of equity we have used DDM model, EDM model and CAPM model. Using EDM and DDM model the cost of equity is coming lesser than risk free interest rate

which is absurd so we have rejected these results and we accepted the results of CAPM model. For cost of debt we have used total debt value and interest. After getting cost of equity and cost of debt, we have calculated cost of capital using WACC in which we have used market value of equity. In efficiency test, auto correlation is coming to be zero so historical data doesn’t help in predicting the current prices so there is no scope of making excess money thus all the four organisations are efficient in weak form of market. Additionally this is also substantiated by runs test that null hypothesis is correct that autocorrelation is zero.

References:

1) Nseindia.com 2) Tatasteel.com 3) Bhushansteel.com 4) Sesagoa.com 5) Sail.co.in 6) Capitaline.com 7) Google.com/finance

8) Brealey, Myers, Allen, Mohanty (2007), Principles of Corporate Finance, McGrawHill Publications

Appendix

Tata Steel Bhusan Steel Sesagoa Sail

31-03-2013 31-03-2013 31-03-2013 31-03-2013 BOOK VALUE 43061 7572.72 15118.21 40273.16 MARKET VALUE 45685.62 8825.27 16882.27 38847.63 TOTAL DEBT 59796.67 21350.97 3741.34 17360.59 Beta 1.702 1.44 1.373 1.398 Rm 0.12 0.12 0.12 0.12 Rf 0.078 0.078 0.078 0.078 Risk premium 0.042 0.042 0.042 0.042 Ke 0.149484 0.13848 0.135666 0.136716 interest 1925.42 1046.27 420 983.99 Kd 0.03219945 0.049003394 0.06789 0.0566795 EPS 67.07 47.7 19.24 8.25 DPS 11.998823 4.77 3.999996 1.9998 g 0.035 0.035 0.035 0.035 MP per share 453.1 409.44 194.25 94.05 Ke (EPS) 14.8024719 11.65005862 9.904761905 8.7719298 Ke(DPS) 6.14816222 4.665005862 5.5592 5.6263158

CAPM BV 0.08130021 0.07242991 0.12222066 0.1126072 CAPM MV 0.08464882 0.076925351 0.133758415 0.1096586 EDM 0.06915168 0.039444663 0.085179677 0.0643374 DDM 0.02816902 0.015394001 0.046790545 0.0403979

Daily Data of TATA Steel:

Date TATA

STEEL

Log Returns Return Actual 01-04-2005 368.33 -0.016927234 -0.03823 04-04-2005 354.25 -0.012599945 -0.0286 05-04-2005 344.12 0.009104202 0.021184 06-04-2005 351.41 -0.005146849 -0.01178 07-04-2005 347.27 -0.00543646 -0.01244 ………. 16-01-2006 320.63 -0.006262102 -0.01244 18-01-2006 316.04 0.01135218 0.026484 19-01-2006 324.41 -0.004048361 -0.00928 23-01-2006 321.4 0.008841212 0.020566 24-01-2006 328.01 -0.00143231 -0.00329 ………. 26-10-2009 505.91 -0.030831117 -0.06853 27-10-2009 471.24 -0.017892382 -0.04036 28-10-2009 452.22 -0.012420532 -0.02819 29-10-2009 439.47 0.002306308 0.005325 30-10-2009 441.81 0 0 ……….