Regression Model for Samsung Stock Price: A Statistical Analysis

This report presents a statistical analysis of the historical quarterly closing prices of Samsung, using regression analysis to model its stock price. The analysis considers the GDP growth of South Korea, along with the stock prices of Google and Apple, as independent variables. The regression model expl...Read More

Running head: STATISTICS ASSIGNMENT
STATISTICS ASSIGNMENT
Name of Student
Name of University
Author Note

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Table of Contents
Data..................................................................................................................................................2
Calculations/Interpretations.............................................................................................................2
Graphs/Charts..................................................................................................................................4
Summary/Conclusion/Findings.......................................................................................................5
Sources.............................................................................................................................................6
Appendix..........................................................................................................................................7

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Data
The study analysis includes historical monthly closing prices of Samsung, Google and
Apple. Furthermore it includes the quarterly historical GDP growth of South Korea. The data of
closing prices were obtained from yahoo finance and the GDP of South Korea was obtained from
the OECD website (OECD, 2018). It is of interest to model the closing price of Samsung. The
factors considered are the GDP growth of South Korea, since Samsung is a South Korean
company and stock prices are believed to be positively associated with macro-economic statistics
(Gay, 2016). Aside from the software and hardware relationship that exists between Google and
Samsung , they share ad revenue and have in agreements for patent co-licensing as well as
collaboration in projects, setting standards of mobile security and computing as per Kristina
Zuchhi (2018) on her article in Investopedia. Hence their performance are anticipated to be
related. Additionally Apple being one of the primary competitors of Samsung, their performance
in the market is also expected to have an explanatory impact on the price of Samsung. The data
converts monthly prices to quarterly and spans from May of 2013 to February of 2018. The data
was then used to fit a regression model with Samsung closing prices as dependent and closing
price of Google and Apple as well as GDP of South Korea as independent variable.
Calculations/Interpretations
The goodness of fit statistic of the regression analysis on the data shows that model has
R-squared vale 0.518, that is model explains 51.8% of the variation in the quarterly closing price
of Samsung. The factor adjusted R-squared value was found to be 0.4277 that is for. This value

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increase when a predictor which is useful for the model in included in the model and decreases
otherwise (Anderson et al., 2016).
Regression Statistics
Multiple R
0.71976
9
R Square
0.51806
7
Adjusted R
Square
0.42770
4
Standard Error
144372.
7
Observations 20
Table 1: Regression Statistics
The coefficient of the variable GDP which is the GDP growth of South Korea is seen to
have a value 315562.4 meaning unit increase in GDP corresponds with increase in 315562.4
units of closing price of Samsung. The p-value for the test of significance was 0.00601 which is
less than 0.05 and hence the variable was significant at 5% level. The variable Apple price had
coefficient 2691.276 which means that unit increase of 2691.276 units in price of Apple
corresponds to almost little to no increase in price of Samsung share price. It had p-value
0.20515 and hence is insignificant at 5% level. The variable Google Price which is the share
price of Google had coefficient 159.542 meaning that unit increase in share price of Google
corresponds with 159.542 unit increase in share price of Samsung. It had p-value 0.63411 and
hence is insignificant at 5% level.
Coefficien
ts
Standar
d Error t Stat P-value
Intercept -575137
162442.
7
-
3.54056 0.00272
GDP
Value 315562.4
99720.2
9
3.16447
5 0.00601
Apple
Price 2691.276
2037.62
4
1.32079
1
0.20515
2

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Google
Price 159.5422
328.817
6 0.4852
0.63411
1
Table 2: Regression Summary
Graphs/Charts
The following charts show the relationship between the dependent variable with reach of the
independent variables.
-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
0
200000
400000
600000
800000
1000000
R² = 0.183993188754421
GDP growth
S.Korea quarterly GDP growth
Samsung Close price
Figure 1: GDP growth of S.Korea against Samsung share price

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400 500 600 700 800 900 1000 1100 1200
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
1000000
R² = 0.188871590173727
Google Price
Google Close Price
Samsung Close Price
Figure 2: Google share price against Samsung share price
0 2 4 6 8 10 12
0
2
4
6
8
10
12
R² = 0
Apple Price
Apple Close Price
Samsung Close Price
Figure 3: Apple share price against Samsung share price
Summary/Conclusion/Findings
The study found that GDP growth of South Korea, share price of Google and share price
of Apple explain 62% of the variation in the share prices of Samsung. However out of all these

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factors only GDP of South Korea has a significant impact on the share price of Samsung. The
study thus concludes that the share price of Samsung is closely associated with that of the GDP
of South Korea and a causal relationship exists.

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Sources
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J.
(2014). Essentials of statistics for business and economics. Cengage Learning.
Apple Inc. (AAPL). (2018). Historical Prices. Retrieved from
https://finance.yahoo.com/quote/AAPL/history?p=AAPL&.tsrc=fin-srch-v1
Gay, R. D. (2016). Effect of macroeconomic variables on stock market returns for four emerging
economies: Brazil, Russia, India, and China. The International Business & Economics
Research Journal (Online), 15(3), 119.
Google (GOOG). (2018). Historical Prices. Retrieved from
https://finance.yahoo.com/quote/GOOG/
OECD. (2018). Domestic product - Quarterly GDP - OECD Data. Retrieved from
https://data.oecd.org/gdp/quarterly-gdp.htm#indicator-chart
Samsung Electronics Co., Ltd. (005930.KS). (2018). Historical Prices. Retrieved from
https://finance.yahoo.com/quote/005930.KS/history?p=005930.KS&.tsrc=fin-srch-v1
Zucchi, K. (2018). Samsung and Google: A Beautiful Friendship?. Retrieved from
https://www.investopedia.com/articles/personal-finance/062515/samsung-and-google-
beautiful-friendship.asp

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Appendix
Significance test of model: Result
ANOVA
df SS MS F
Significanc
e F
Regressio
n 3
3.58E+1
1
1.19E+1
1
5.73320
6 0.007339
Residual 16
3.33E+1
1
2.08E+1
0
Total 19
6.92E+1
1
Cleaned Quarterly Data used for model
Date
Samsun
g
GDP
Value
Apple
Price
Google
Price
30-06-2013
27733.3
3
0.86166
8
60.4471
5
426.585
5
30-09-2013 28000
0.96569
8
67.4523
8
432.281
8
31-12-2013 27640
0.82819
2
78.0852
4
531.688
6
31-03-2014 26900
0.94595
3
74.4561
9
582.003
8
30-06-2014
27386.6
7
1.00614
4
89.2190
5
550.914
1
30-09-2014
24413.3
3
0.60433
2
99.6166
7
570.383
7
31-12-2014
26526.6
7
0.66472
2
112.436
7
539.470
4
31-03-2015
28053.3
3
0.47763
9 123.35
543.979
8
30-06-2015
25066.6
7
0.82867
7
126.953
3
529.986
7
30-09-2015
23966.6
7
0.36975
9 114.84 623.88
31-12-2015
24626.6
7
1.24574
2
114.353
3 737.43
31-03-2016 24900
0.76017
5
101.006
7
728.556
7
30-06-2016 28373.3 0.63141 96.4 706.943

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3 9 3
30-09-2016 32380
0.80848
9
107.786
7
771.043
3
31-12-2016
36806.6
7
0.39990
1
113.293
3
771.466
7
31-03-2017 41420
0.71117
6 134 816.52
30-06-2017
46813.3
3
0.97581
1 146.81
926.516
7
30-09-2017
50893.3
3
0.63236
5
155.616
7 942.98
31-12-2017
884253.
3
1.39508
8 170.04 1028.15
31-03-2018 49760 -0.2126 171.11
1102.15
3