1.Steel Report Revised Final

8/9/2019 1.Steel Report Revised Final

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ICICI Bank Summer

Internship Programme2010 Statistical Analysisand Identification of LeadIndicators of core sectorsof the Indian Economy

Steel IndustrySubmitted by : Aayush Kamani,

NMIMS,Roll no. 101.


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Objective

Issues of growth in the iron and steel sector as well as in other energy intensive industriesin India have been discussed from various perspectives. Historical estimates vary fromindicating an improvement to a decline in the sectors growth. The variation depends

mainly on the time period considered, the source of data, the type of indices andeconometric specifications used for reporting productivity and growth.

India's iron and steel industry contributes about 2% of gross domestic product, or aboutUSD 20 billion to the country's USD 1 trillion economy. This report will analyze andidentify the lead indicators that are useful in forecasting the future outlook of the steelindustry. Additionally, this report will statistically prove a relation between theseforecasting factors i.e. lead indicators of the steel industry and the output of the steelindustry in India over the years.

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Index

Objective 2

Methodology.. 4

The Indian Steel Industry............. 5

Forecasting factors... 7

Results... 9

Interpretation of results... 12

Conclusion.. 14

Acknowledgements 15

Bibliography.. 16

10. Annexure. 17

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Methodology

The study was conducted and presented to understand and highlight the main quantitativelead parameters that can be used to forecast and predict the state of the industry in thefuture.

A lead indicator is a measurable parameter or metric, which will help to predict or giveprior information about the occurrence of some related critical events.

Study and review of several expert industry reports and analysis

Interview with the industry expert at ICICI Bank, GCRMG department.

Identifying the lead indicators

Extraction of data from various databases and research libraries.

Devise the hypothesis for testing and analysis of the results to verify and concludewhether to accept or reject the hypothesis.

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The Indian Steel Industry

India has now emerged as the fifth largest producer of steel in the world with aproduction capacity of 63MT, while on the other hand some Asian countries like Japan,south Korea saw significant decline in steel production. Almost all varieties of steel is

now produced in India.

The growth of the steel industry in India is dependant, to a large extent, on the level ofconsumption of steel in the domestic market. Steel consumption is significant in housing,auto manufacturing and infrastructure.

In recent years the surge in housing industry of India has led to increase in the domesticdemand for steel. It is estimated that by 2011 the demand for houses would grow toaround 400 million units. This will necessitate a minimum outlay of US$ 890 billion. InIndia, demand is being driven by infrastructure projects, like construction of roads,railways and ports. With salaries rising, consumers are becoming more discerning with

regard to their cars, houses, which will boost steel demand.

The private sector contribution in the total output has been increasing in India. During thelast decade more than 12mt of capacity has been added in the steel industry, this is mostlyin the private sector. With increasing need for large investments in the industry privatesectors role would be crucial in the development of the steel industry. 5-6 major playerssuch as SAIL, TATA, JSW and ESSAR to name a few dominate the industry. Significantcapacity expansion to the tune of 50MT is expected in the next decade.

Growth in Future:

Tata Steel ranks 5th in the world steel production and the company have plans ofexpanding its capacity from 6.8mt to 10mt by the year 2011

SAIL, India's biggest producer of steel has plans of increasing the production to24.98 million tons annually

The acquisition of the Corus, the Anglo-Dutch steel manufacturer by the TataSteel

The Algoma Steel, Canada was acquired by Essar Global for US$ 1.63 billion JSW steel has plans of expanding capacity from 7.8mt to 11mt by 2011

Source: Business Maps of India

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India has a potential for exponential growth in steel consumption

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Auto Sales - Auto and truck Sales measure the monthly sales of all domesticallyproduced vehicles. They are considered an important indicator of consumer demand,accounting for roughly 25% of total retail sales. Demand for big ticket items such asautos and trucks tend to be interest rate sensitive, making the motor vehicle sector aleading indicator of business cycles.

The automobile industry in India happens to be the ninth largest in the world. FollowingJapan, South Korea and Thailand, in 2009, India emerged as the fourth largest exporter ofautomobiles. Several Indian automobile manufacturers have spread their operationsglobally as well, asking for more investments in the Indian automobile sector by theMNCs.

autosales.xls

(refer Annexure I)

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Results

Steel Production Auto sales and Gross Fixed Capital Formation (GFCF)

Regression output:

Regression equation:

Graphically presented regression equation:

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Testing for Normality in data sets:

As can be seen from the regression output, we have used only 10 data sets. This is due tolimitations in finding and grouping common year data to get the appropriate output. Totest whether the existing data set tends towards a normal distribution we conducted thefollowing test.

Establishing the underlying distribution of a data set (or random variable) is crucial forthe correct implementation of some statistical procedures. For example, both the smallsample t test and ANOVA, require that the distribution of the underlying data be normal.Therefore, we first need to establish whether the normal applies before we can correctlyimplement these statistical procedures.

We used the Anderson-Darling Normality test to prove that the data used does follow anormal distribution. For the normality test, the hypotheses are,

H0 (null hypothesis): data follows normal distribution vs. H1 (alternate hypothesis):data do not follow a normal distribution

The Anderson-Darling methodology test's the p-value. If the p-value that is generatedfrom this test is greater than the value of 0.05 then we will accept the null hypothesis(H0) and safely say that the data follows a normal distribution.

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0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

1 2 3 4 5 6 7 8 9 10 11SteelProduction('000MetricTons)

Actual Steel

Production

Predicted Steel

Production
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As can be seen from the above graphs, the p-value of GFCF obtained from conductingthe Anderson-Darling test is 0.103 which greater than the value of 0.05. Similarly, the

p-value of auto sales is 0.273 which is greater than the value of 0.05. Hence, we willaccept the null hypothesis of this test and can safely say that this data set follows normaldistribution.

Interpretation of results

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A. The regression output has three components:

- Regression statistics table- ANOVA table- Regression co-efficient table

1. Regression statistics table:

Output Explanation Multiple Regression

Multiple R R = Square root of R 2 0.9895

R Square R 2 0.9792

Adjusted R Square Adjusted R 2 if more than onevariable

Not Required

Observations Observations used in theregression

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The above gives the overall goodness of fit measures. The goodness of fit of astatistical model describes how well it fits a set of observations. Measures of goodness offit typically summarize the discrepancy between observed values and the values expectedunder the model in question.

R2 is the most important measure that signifies how much of the variation in the steelproduction is explained by the independent variables i.e. both, auto sales and GFCFcombined explain 97% of the variation of steel production.

2. ANOVA Table:

The ANOVA table gives the answer for testing the claim that there is no significantrelationship between the steel production (dependent variable) and GFCF and auto sales(independent variables). At 95% confidence level if the Significance F value is lessthan value (0.05) i.e. 100% - 95% = 5%, then we can reject the claim that there is nosignificant relationship between the dependent and independent variables.

Significance F value

Multiple Regression 0

3. Regression co-efficient table:

The P-value for individual independent variables is given. This too tests the hypothesisthat there is no relationship between the independent variable and dependent variable.Thus, the claim is rejected that there is no relationship between the independent anddependent variable if the P-value < value.

Multiple Regression P-value

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GFCF Steel Production 0.016

Auto Sales Steel Production 0.040

B. Regression Equation and graph:

When the focus is on the relationship between a dependent variable and one or moreindependent variables. More specifically, regression analysis helps us understand how thetypical value of the dependent variable changes when any one of the independentvariables is varied, while the other independent variables are held fixed.

From the above equation and graph we can see the relationship between the dependentvariable (steel production) and independent variables (GFCF and auto sales). The valueof y in the equation gives us the value of steel production in thousand metric tons.0.016511(x1) tells us how much of an impact GFCF has on determining the quantity ofsteel production. Similarly, 0.106159(x2) tells us how much of an impact auto sales has

on determining the quantity of steel production.

The graph is a pictorial relationship depicting the accuracy of the regression equation.The predicted values have been generated using the equation and have been compared tothe actual values of steel production in India. As can be seen, the graph depicts a closeapproximation to the real values and is a good indicator.

Actual steel production:

steel_output.xls

(refer Annexure I)

Conclusion

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India is the fifth largest producer of steel in the world. India steel industry has grown onaccount of strong domestic demand driven by investment in infrastructure and growth inthe auto sector. The scope of steel industry is huge and industry estimates indicate thatthe steel industry will continue to grow substantially in the coming years.

By analyzing the above-mentioned factors we can conclude that auto sales and GFCFplay an important role in determining the future outlook of the Indian Steel Industry.GFCF is a more focused approach than GDP as it covers investments made forinfrastructure development. The growth in auto sales helps in predicting the futuredemand of steel as a major component for automobile manufacturing is steel.

Some of the important indicators to emphasize the robustness of the steel industry:

During 11th plan (2007-08 to 2011-12), the projected investment towardsinfrastructure is likely to be Rs. 2027000 crores, an increase of 180% over 10th

plan. Per capita steel consumption at 35 kg low as compared to world average of 150

kg. and 300kg for china. India's growing status as a global small-car hub is drawing global steel makers,

especially Japanese firms, to the country. World No. 2 steel maker Nippon Steel isin talks with Tata Steel for an automotive steel joint venture, JFE Steel has tied-upwith India's JSW Steel, while Sumitomo Metal Industries Ltd is considering a JVwith Bhushan Steel.

The automobile industry in India happens to be the ninth largest in the world.Following Japan, South Korea and Thailand, in 2009, India emerged as the fourthlargest exporter of automobiles.

The output of the Indias automotive sector will be $145 billion, contributing tomore than 10% of Indias Gross Domestic Product (GDP) and providingemployment to 25 million persons additionally by 2016 by employing bothdirectly in manufacturing and indirectly with service providers, according to TheAutomotive Mission Plan 2006-16.

Acknowledgements

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I thank ICICI Bank for providing me with this opportunity to understand and analyze theSteel Industry in India. Steel being one of the basic industries is of significant importancefor the overall performance of the country and economy. By conducting this study I havegained a lot of understanding into the functioning and parameters that affect the steel

industry in India.

I express my heartfelt gratitude to Mr. Vivek Sharma of the Global Risk ManagementGroup who has been kind enough to guide me through the whole process and withoutwhose adept suggestions this project would not have been possible.

I also take this opportunity to thank Mr. Deepak Kenkre of the Global Risk ManagementGroup whose insight into the industry was most incisive and whose timely feedbackshaped the project the way it is.

This acknowledgement would not be complete without mentioning my faculty project

guide Mr. Amit Srivastava who was instrumental in directing me through the course ofthe project by sharing with me the intricacies of the industry.

Lastly, I would like to thank my family and friends who have extended their utmostsupport during the course of this project.

Bibliography

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1. Ministry of Finance Website www.finmin.nic.in2. Filliben, J. J. (February 1975). "The Probability Plot Correlation Coefficient Test

for Normality"3. Bloomberg

4. The Associated Chambers of Commerce and Industry of India (ASSOCHAM)5. India Statistics www.indiastat.com6. CRISIL Report7. ICICI Securities Report8. World Steel

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Annexure

Annexure I

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1.Steel Report Revised Final

Documents

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