Федеральное государственное образовательное бюджетное учреждение
высшего образования
«ФИНАНСОВЫЙ УНИВЕРСИТЕТ ПРИ ПРАВИТЕЛЬСТВЕ
РОССИЙСКОЙ ФЕДЕРАЦИИ»
(Финансовый университет)
Международный Финансовый Факультет
Департамент анализа данных, принятия решений и финансовых
технологий
Направление подготовки 38.03.01 «Экономика»
Профиль «Международные финансы (на английском языке)»
РАСЧЕТНО – АНАЛИТИЧЕСКАЯ РАБОТА
ПО ДИСЦИПЛИНЕ «АНАЛИЗ ДАННЫХ»
Студента группы МФФ 18-3k
Фам Вьет Тунг
Научный руководитель:
_к.ф.-м.н., доцент,
доцент ДАД,ПРиФТ
Пыркина Ольга Евгеньевна
Москва 2020
FEDERAL STATE-FUNDED EDUCATIONAL INSTITUTION OF HIGHER
EDUCATION
FINANCIAL UNIVERSITY UNDER THE GOVERNMENT OF THE
RUSSIAN FEDERATION
International Finance Faculty
Department of Data Analysis, Decision Making and Finance Technologies
Computation and Analytics Work
In the discipline “Data Analysis”
Prepared by:
Student of the baccalaureate of
the International Finance
Faculty, Group IFF18-3k
Pham Viet Tung
Academic advisor:
O.E.Pyrkina
Moscow 2020
2
Contents
INTRODUCTION ..................................................................................................................................... 4
STRUCTURE OF TRADING DATA FOR THREE SHARE EMITTERS ............................................. 5
INTERVAL ESTIMATION AND PROGNOSTICATION .................................................................... 14
VALUE AT RISK (VAR) ........................................................................................................................ 14
RETURN AND LOG RETURN .............................................................................................................. 14
POSSIBILITY OF APPLICATION OF NORMAL DISTRIBUTION .................................................. 15
CONCLUSION ........................................................................................................................................ 15
3
INTRODUCTION
My work below will analyze changes in the share prices of three companies: Sberbank, Lukoil,
AFK Sistema. Companies’ databases at weekly intervals for the period from the beginning of
2011 to the end of April 2020 are included in my work.
The PJSC Sberbank is a state-owned Russian banking and financial services company
headquartered in Moscow. The company was known as "Sberbank of Russia" until 2015.
Sberbank has operations in several European and post-Soviet countries. As of 2014 it was the
largest bank in Russia and Eastern Europe, and the third largest in Europe, ranked 33rd in the
world and first in central and Eastern Europe in The Banker's Top 1000 World Banks ranking.
The PJSC Lukoil Oil Company is a Russian multinational energy corporation headquartered
in Moscow, specializing in the business of extraction, production, transport, and sale
of petroleum, natural gas, and petroleum products. It was formed in 1991 when three state-run,
western Siberian companies named after the respective town in Khanty–Mansi Autonomous
Okrug that each was based in, Langepasneftegaz, Urayneftegaz, and Kogalymneftegaz, merged.
Its name is the combination of the acronym LUK (initials of the oil-producing cities of Langepas,
Uray, Kogalym) and the English word "oil". Lukoil is the second largest company in
Russia after Gazprom, and the largest non-state enterprise in the nation in terms of revenue
(₽4,744 billion). Internationally, it is one of the largest global producers of crude oil. In 2012, the
company produced 89.856 million tons of oil (1.813 million barrels) per day. The company has
operations and subsidiaries in more than 40 countries around the world.
AFK Sistema is a Russian public financial corporation formed in 1993. Sistema’s investment
portfolio is primarily composed of Russian companies in various sectors of the economy. The
main assets of AFK Sistema are controlling stakes in MTS, the medical holding Medsi, and the
engineering holding RTI-Sistema. The company’s assets also include the banking sector (JSCB
MTS Bank), retail trade (PJSC Detsky Mir), and the tourism business (VAO Intourist). The
chairman of the board of directors of AFK Sistema is Vladimir Yevtushenkov. He owns 64.19%
of the shares. Another 19% of the shares are traded on the London Stock Exchange in the form
of GDR. One GDR is equal to 20 ordinary shares. The remaining shares are owned by
individuals, including members of the Board of Directors of Sistema.
4
STRUCTURE OF TRADING DATA FOR THREE SHARE EMITTERS
First of all, I will describe the Sberbank indicators. With the sheet Values without outliers, we
can see that the maximum value of the yield indicator in this company was 8.09% and the
minimum is -8.21%.
Sberbank’s histograms below will inform you about the distribution of values for all four
characteristics. The histograms of returns, log-returns, and LN volume mostly are bell-shaped.
Those graphs tell us about the normal distribution of the values of these characteristics.
Figure 1: Return of Sberbank
Figure 2: Log Return of Sberbank
5
Figure 3: LN Price of Sberbank
Figure 4: LN Volume of Sberbank
In the time-plot for return and log return, there are no features that you should be aware of in
one of the companies. These charts only show the daily change from negative to positive
values. The Time-plot of LN price in Sberbank is positive. Trend line shows that indicator
increases. The volumes sold were stable, and the LN volume value varied on average from 18
to 21.
6
LN Price
6
5
4
3
2
1
1
15
29
43
57
71
85
99
113
127
141
155
169
183
197
211
225
239
253
267
281
295
309
323
337
351
365
379
393
0
Figure 5: LN Price of Sberbank (Time-plot)
LN Volume
21.5
21
20.5
20
19.5
19
18.5
18
17.5
17
1
16
31
46
61
76
91
106
121
136
151
166
181
196
211
226
241
256
271
286
301
316
331
346
361
376
391
16.5
Figure 6: LN Volume of Sberbank (Time-plot)
The second company is Lukoil, we can see that Lukoil's share price is the highest. The return
and log return show that we change the share price of the company. In other words, if these
indicators are positive, that means the company's stock price is higher on a particular day than
the previous day. If less than 0 is the opposite. Lukoil, like other companies, does not have a
clear trend of changes in share prices. They often show both positive and negative changes. If
we set the exceptions aside, the maximum return in Lukoil is 7.04% and the minimum is 7.21%.
Lukoil histograms below are similar to the previous company (return and log return). Here are
histograms of four characteristics of Lukoil Company.
7
Figure 7: Return of Lukoil
Figure 8: Log Return of Lukoil
Figure 9: LN Price of Lukoil
8
Figure 10: LN Volume of Lukoil
According the LN price change schedule, we see a positive trend and it’s similar to Sberbank
trend. It’s mean that the price of the company's shares falls over time. But the LN volume chart
shows that the volume of shares sold changes regularly, but in general there are no sharp jumps
and falls. On average, this value varied from 14 to 17 over the entire period.
LN Price
9
8.5
8
7.5
7
1
15
29
43
57
71
85
99
113
127
141
155
169
183
197
211
225
239
253
267
281
295
309
323
337
351
365
379
6.5
Figure 11: LN Price of Lukoil (Time-plot)
9
LN Volume
16.5
16
15.5
15
14.5
14
13.5
1
15
29
43
57
71
85
99
113
127
141
155
169
183
197
211
225
239
253
267
281
295
309
323
337
351
365
379
13
Figure 12: LN Volume of Lukoil (Time-plot)
Next one is the AFK Sistema company. AFK Sistema data is different from both Sberbank and
Lukoil. For the return indicator, the maximum for the period was 10.39% and the minimum
was -10.19%.
Below are histograms of AFK Sistema company characteristics. In this company, only some
graphs are similar to bell-shaped.
Figure 7: Return of AFK Sistema
10
Figure 8: Log Return of AFK Sistema
Figure 9: LN Price of AFK Sistema
Figure 10: LN Volume of AFK Sistema
11
Time-plots in this company are not stable. We should pay attention to the LN Price chart once
sank significantly, and then recovered again. The trend line shows a positive trend but it’s very
low. LN Volume is similar to Lukoil company. In average, it’s from 14 to 18. The charts are
shown below.
LN Price
4.5
4
3.5
3
2.5
2
1.5
1
0.5
1
15
29
43
57
71
85
99
113
127
141
155
169
183
197
211
225
239
253
267
281
295
309
323
337
351
365
379
0
Figure 11: LN Price of AFK Sistema (Time-plot)
LN Volume
20
18
16
14
12
10
8
6
4
2
1
15
29
43
57
71
85
99
113
127
141
155
169
183
197
211
225
239
253
267
281
295
309
323
337
351
365
379
0
Figure 12: LN Volume of AFK Sistema (Time-plot)
Scatterplots show strong correlation levels of different indicators. In the case of all three
companies, of course there is only a direct relationship between profit and profit, because the
second index is taken from the first. The rest of the scatter charts show that the data is not
correlated with each other.
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In addition, I created Pareto charts for price and volume. These charts show the cumulative
distribution of prices and volume by percentage. In general, these charts are similar for all
three companies. They simply represent the Pareto principle, saying that the distribution is
uneven. These charts are located, just like the rest, in the Excel file.
It is much more interesting to look at the Box-and-Whisker charts that I have compiled for
price, volume and profit indicators. Despite the fact that in all the companies I have cleared
outliers, these charts still show single deviations. In the case of price and volume, the situation
is clearer, since the cleaning is done in relation to the characteristics. Prices and volumes show
their natural logarithms, still showing slightly different values. But the situation with
productivity indicators is slightly different. Although I cleared the time period of the
exceptions, they reappeared. The problem is that I took the original normal time period as an
example and didn't change it, but when I deleted the data, it changed. This can be seen in charts
in my Excel file.
Next, I created a Portfolio of investments according to the data from the beginning of 2011 to
the end of 2017. The formula for the value of an investment portfolio is
P = 0,333333333900848x + 0,333333333355386y + 0,333333332743766z
Сбербанк
x
ЛУКОЙЛ
y
Система ао
x
Applied this formula to calculating the change in the value of the portfolio in 2019, we have
the price trajectory is described in the chart below.
TRAJECTORIES OF THE PORTFOLIO
1.06
1.04
1.02
1
0.98
0.96
0.94
0.92
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Figure 13: Trajectories of the Portfolio (2019)
13
INTERVAL ESTIMATION AND PROGNOSTICATION
The next step in the analysis is to calculate the vehicle and standard deviation intervals. For
this we take the means / standard deviation values, standard errors, number of elements in the
sample - all of this can be found in “Descriptive statistics” that we have done before. Our p
value is 0.95, because we are required to perform a test at 95%. For the expected values, we
calculate the left and right boundaries using the confidence level function, when for the
standard deviation function of Chisq.inv.rt (ХИ2.ОБР) and Chisq.inv (ХИ2.ОБР.ПХ).
VALUE AT RISK (VAR)
In this task, I calculated how much we could lose if we created a portfolio by investing 100
thousand rubles in each company. Here I have worked with all the original data, because we
need to get a specific actual number.
In total, I have received that the total VaR will be equal to -7907.94 rubles. In other words,
VaR is the amount of losses, with a probability equal to 95% will not be exceeded. Therefore,
in 5% of cases, losses will be higher than VaR. Therefore, we receive the risk value, use the
profit, calculate the profit / loss from our assets and the total portfolio. As a result, with a 95%
probability, we can lose up to 7907.94 rubles from the entire portfolio and 5% - more than this
value, and there are about 100 000 rubles when investing in each asset. production. Most of
all, we can lose on Mechel company (-5272.69 rubles).
RETURN AND LOG RETURN
Here is the test of hypothesis. We have Hypothesis H0 (expected value of return is equal to
zero) and we will test it against the right-handed alternative – H1 (E(X) > 0). We need sample
size, mean and standard deviation for the calculation.
First, we need to find empirical, observed value which we are going to compare later. We may
start hypothesis testing by implying there are two levels – 5% and 1%. The results are included
in my Excel file and we can see that on both levels of significance, we reject hypotheses for
all companies.
14
POSSIBILITY OF APPLICATION OF NORMAL DISTRIBUTION
This point is to estimate the results of normality testing for all the tickers. We can see that by
the shape of histograms that particularly all characteristics are far from normal distribution of
values. We need to calculate B-Statistics, and values at chisq 5%. If B-Statistics is greater than
this value, we reject out hypothesis and vice versa.
According to calculations, we get the situation:
-
We accept Returns and Log-Return for all companies
We accept LN Price for AFK Sistema and reject for Sberbank and Lukoil
We accept LN Volume for Sberbank and reject for Lukoil and AFK Sistema
CONCLUSION
Therefore, having conducted all the necessary studies, it can be argued that the relative
characteristics in all three companies are more or less the same, of course, in the presence of
some differences. In fact, no company is attractive to invest. All three companies had total
negative profits for the first 7 years. However, it is worth considering investing in a shorter
time. In the case of VaR calculation, we can see that Lukoil had the lowest value of money at
risk – about 4524.33 rubles. And also, Lukoil shows a good positive trend in changing the
price of its shares. In any case, when making a portfolio, different time periods should be
considered and a larger number of companies recruited.
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