BAFN205: Economic Analysis of Samsung in India - Semester 1, 2019

This project presents an economic analysis of Samsung's performance within the Indian market. The study investigates the sensitivity of financial institutions to interest rate fluctuations, focusing on the Bank of Queensland as a case study. The methodology employs the Stone two-index model to assess the impact of changes in the 10-year Commonwealth government securities yield and the S&P/ASX return on the bank's stock returns. Findings from the OLS regression reveal statistically significant relationships between these variables and the bank's stock returns. The analysis calculates the 10-day equity Value at Risk (VaR) for the financial institution as of December 30, 2018. The project concludes that financial institutions' stock returns are sensitive to interest rate changes and market returns, supporting prior research and providing insights into the financial dynamics of Samsung in India. The analysis is done using the provided data and regression outputs to calculate and analyze the various financial aspects of Samsung in India.

Running head: ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
Economic Analysis of Samsung in India
Name of the Student:
Name of the University:
Author’s Note:

1ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
Table of Contents
Introduction.................................................................................................................................................2
Literature review.........................................................................................................................................3
Methodology...............................................................................................................................................3
Findings.......................................................................................................................................................4
Conclusion...................................................................................................................................................6
Reference....................................................................................................................................................7

2ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
Introduction
Interest rate are more influencing and volatile in nature than any other factors of
monetary policy. It has a major impact on institutes and companies especially financial
companies. Interest rate can influence the profits of institutes and the values of the companies by
changing the value of their bonds, effecting interest rate expenses on the loans that the company
has already taken and also changing the behavior in spending pattern of the society. This has a
direct effect on the aggregate demand of the companies’ services, goods and shares. Banks and
other financial institutes might be the most sensitive to the interest rates than other kind of
companies as there is a difference in the balance sheet of a bank in comparison of other
companies. This makes them more opened to interest rate risk compared to other companies. The
influence of interest rate variation is a concern for the bank officials in the form of the risk
exposure management. However, for the investors who wants to have the understanding of how
the financial markets going to react to interest rates variations for the portfolio selection. This
kind of influences of change in interest rate is a great concern for the banks in the terms of risk
management. This is also a concern for the investors that wants to gain some understanding
about the reaction of financial market to the change in interest rate for the selection of portfolio.
Studies and researches on the dissimilarity in magnitude of the influence of interest rate
across various studies differ due to the use of different procedures, methods and choices of
specific interest rates within the literature (Di Iorio, Faff, and Sander 2013). Two index model is
also used in the study of stock returns of bank and sensitivity of interest rate. This study focuses
on the returns of the stocks of the financial institutes that is sensitive to the interest rate on bank
stock returns. The linear regression estimation is used to estimate the relationship and the
impacts of interest rate on the returns of financial institutes.

3ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
The organization of this paper is as follows. Section 2 discusses the literature of the
researches, studies and papers related to the interest rate sensitivity. Section 3 describes the
model that is used in this paper and a description of data is there. Section 4 presents the findings
from the analysis using the model. Section 5 concludes the paper.
Literature review
There are too many various studies that investigated the sensitivity of bank stock returns
to interest rates in both the senses volatility and level. The finding of the research where interest
rates and the large banks stocks’ market return were orthogonalizing, the research used different
process and methodologies to test that if there exist any dependency on the method choices. The
result was different estimates for the long-term and short-term returns (Di Iorio, Faff and Sander
2013). Stone has introduced the two-index model to measure the systematic risk of interest rate
(Sahlins 2017). Argue was that the two-index model is more preferable than the single-index
model because of the asset expansion. The author has included the expansion in model to add
more information. The empirical researches on the stones two-index model have shown some
mixed results of different interest rate in long-term and short-term (Shivaani, Yadav and Jain
2015). There is also some studies that empirically justified that the two-index model does not
support that stocks of banks are sensitive to the interest rate (Narayan, Narayan and Singh 2014).
Again, the holding period returns on bonds are negatively correlated with the changes in interest
rate, a positive coefficient indicates that the market value of the firm decreases with the rise in
interest rate (Ferrando, Ferrer and Jareño 2017). The results are also dependent on the model,
how the models are created and arranged. The outcomes of the paper are in line with the results
from the multi-index model (Amarasinghe, 2015), the expanding the two-index model including
debt return indices (Ferrando, Ferrer and Jareño 2017). Moreover, unexpected changes in T-

4ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
bonds yields have important influence on the financial institutions’ portfolio yield. Another
argument that some researches have some limitations, with the use of individual stocks and not a
portfolio of banks (Priti, 2016). It also use a two-index model and reinvestigates the sensitivity of
the return of bank and bank holding companies’ securities to interest rate changes (Mouna and
Anis 2013). The re-examination was done by considering the anticipated and unanticipated
interest rate changes, the interest rate sensitivity of common bank stock returns (Bengtsson and
Persson 2018). The estimates are found to have a negative effect on bank stock returns and these
results are in line with (Mouna and Anis 2013) and (Amarasinghe 2015) but in disagreement
with other paper (Narayan, Narayan, and Singh 2014)
Methodology
For this study the chosen financial institute is Bank of Queensland and so the return of
Australian Security Exchange stock market plays a role as the Bank of Queensland is listed in the
ASX market. The motive of this paper is to investigate the sensitivity of the stock return of the
financial institution to the change in the long-term interest rate. This paper investigates
sensitivity of the stock return of the Bank of Queensland to the change in 10-year
Commonwealth government securities yield and to do so Stone two-index model is used. The
model is mentioned below:
ri , t=α0 +α 1 rm ,t +δi Δ I t +εi ,t
Where ri,t presents the monthly BOQ.AX return, rm,t is the return on the S&P/ASX, ΔIt
presents the change in 10-year Commonwealth government securities yield and εi,t is the random
error at month t. To proceed with the analyses, the data is collected on the variable yield on 10-
year commonwealth government securities and the monthly adjusted closing stock prices for the

5ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
chosen financial institution and the all ordinary index from January 1995 to December 2018. The
data sources are mentioned below:
http://www.rba.gov.au/statistics/tables/index.html#interest_rates
http://au.finance.yahoo.com
From the data, necessary variables like 10-year government security yield, monthly market
return and the monthly BOQ.AX return is extracted to use in the model. After collection of data,
the change in 10-year Commonwealth government securities yield is calculated from the yield
percentage of the 10-year Commonwealth government securities return. The new generated
variables are used in the model and with the OLS regression technique the fitness of the model
and the efficiency of the analysis is checked by interpreting the SE and R square from the
regression result. The effect of the yield change on the return of a financial institute is estimated
that is shown by the estimated coefficient of the variable and the respective SE and p-value
shows the efficiency and the significance level of the variable.
Findings
The OLS model is used here to estimate the coefficients of the change in yield and
monthly market return. The table 1, presents the regression statistics of the model on which the
regression is run. The adjusted R square is 0.135 which is very low. However, the SE is 0.088
which is very low and shows that the estimated value of the dependent variable that is the
estimated monthly stock return consists a maximum of ±0.088 amount of error. The low amount
of error states that the model is a good fit.
Table 1: Regression Statistics

6ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
Multiple R 0.376
R Square 0.141
Adjusted R Square 0.135
Standard Error 0.088
Observations 288
The below table is the regression result that contains the estimated coefficients of the
independent variables: change in yields and monthly market return.
Table 2: Regression result
Coefficients Standard Error t Stat P-value
Intercept 0.004 0.005 0.768 0.443
Change in Yield 6.230 2.414 2.581 0.010
Market Return (Monthly) 0.851 0.145 5.850 0.000
The estimated intercept term has very less amount of standard error but the high p-value
states that there is not enough evidence to reject the null hypothesis that the intercept term is
equal to zero. The estimated coefficient of the change in yield is 6.230 and the estimated
coefficient of the monthly market return is 0.851. The SE of the change in yield is 2.414 and the
SE of the monthly market return is 0.145 this means that the error in the respective variables is
±2.414 and ±0.145. The p-value of the estimated coefficient of change in yield is 0.01 which is
less than 0.05 which implies that the change in yield has statistically significant effect at 5%
significance level. The p-value of the estimated coefficient of monthly market return is 0.00
which is less than 0.01 which implies that the monthly market return has statistically significant
effect at 1% significance level. From the above regression result, the estimated model is as
follows:

7ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
^ri , t= ^α1 rm ,t + ^δi Δ It
The above estimated model states that there is no constant influence on the return from
the stocks of Bank of Queensland as there is no intercept term. The return from BOQ’s stock is
dependent on the change in 10-year Commonwealth government securities yield and return on
the S&P/ASX. The above regression the result shows the coefficient of the change in 10-year
Commonwealth government securities yield which is very high. The result states that the one
unit of change in the change in 10-year Commonwealth government securities yield will change
the return from BOQ’s stock by 6.230 units.
Conclusion
The focus of the study is to investigate the interest rate sensitivity of stock returns of
financial institutes. The study uses the OLS method to estimate the linear model to estimate the
change in yield and the monthly S&P/ASX stock return’s effect. Both the coefficients of the
variables are significant. The change in yield coefficient is significant at 5% level of significance
and the monthly S&P/ASX return coefficient is significant at 1% level of significance. This case
study contributes towards the literature of the interest rate sensitivity of financial institutes and
complement with the result of sensitive monthly stock returns of Bank of Queensland to the
change in 10-year Commonwealth government securities yield to change. This case study shows
and supports the results of the literatures that claims that the returns of the financial institutes are
sensitive to the interest rates. The paper also shows that the monthly stock returns of the Bank of
Queensland is also dependent on the returns of the S&P/ASX. However, this does not influence
the monthly return of the stock of BOQ as much as the 10-year Commonwealth government
securities yield. The return from BOQ’s stock is linearly dependent and estimated by the
coefficients of the variables on the change in 10-year Commonwealth government securities

8ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
yield and return on the S&P/ASX. The above regression provides the coefficient of the change in
10-year Commonwealth government securities yield which is very high. This indicates the
dependency and the direction of changes in the BOQ’s return. The result states that there will be
a 6.230 units of increase due to increase of one unit in the change in 10-year Commonwealth
government securities yield.

9ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
Reference
Ferrando, L., Ferrer, R. and Jareño, F., 2017. Interest Rate Sensitivity of S panish Industries: A
Quantile Regression Approach. The Manchester School, 85(2), pp.212-242.
Di Iorio, A., Faff, R. and Sander, H., 2013. An investigation of the interest rate risk and
exchange rate risk of the European financial sector: Euro zone versus non-Euro zone
countries. Accounting and Management Information Systems, 12(2), pp.319-344.
Sahlins, M., 2017. Stone age economics. Routledge.
Shivaani, M.V., Yadav, S.S. and Jain, P.K., 2015. Market Risk Exposure: Evidence from Indian
Banking Industry. IUP Journal of Applied Finance, 21(3).
Narayan, P.K., Narayan, S. and Singh, H., 2014. The determinants of stock prices: new evidence
from the Indian banking sector. Emerging Markets Finance and Trade, 50(2), pp.5-15.
Ferrando, L., Ferrer, R. and Jareño, F., 2017. Interest Rate Sensitivity of S panish Industries: A
Quantile Regression Approach. The Manchester School, 85(2), pp.212-242.
Amarasinghe, A., 2015. Dynamic relationship between interest rate and stock price: Empirical
evidence from colombo stock exchange. International Journal of Business and Social
Science, 6(4).
Priti, V., 2016. The Impact of Exchange Rates and Interest Rates on Bank Stock Returns:
Evidence from US Banks. Studies in Business and Economics, 11(1), pp.124-139.
Mouna, A. and Anis, M.J., 2013. The Impact of Interest Rate and Exchange Rate Volatility on
Bank's Returns and Volatility: Evidence from Tunisian. The Journal of Commerce, 5(3), p.1.

10ECONOMIC ANALYSIS OF SAMSUNG IN INDIA
Bengtsson, F. and Persson, A., 2018. Bank stock return sensitivity to changes in interest rate
level and volatility.
Mouna, A. and Anis, M.J., 2013. The impact of interest rate and exchange rate volatility on
bank’sreturnsandvolatility: Evidencefrom Tunisian. Business and Management, 5(4).
Amarasinghe, A., 2015. Dynamic relationship between interest rate and stock price: Empirical
evidence from colombo stock exchange. International Journal of Business and Social
Science, 6(4).
Narayan, P.K., Narayan, S. and Singh, H., 2014. The determinants of stock prices: new evidence
from the Indian banking sector. Emerging Markets Finance and Trade, 50(2), pp.5-15.
Di Iorio, A., Faff, R. and Sander, H., 2013. An investigation of the interest rate risk and
exchange rate risk of the European financial sector: Euro zone versus non-Euro zone
countries. Accounting and Management Information Systems, 12(2), pp.319-344.