Is it necessary to buy a car for 750 thousand rubles, despite the fact that you drive 18 times a month, or is it cheaper to use a taxi? If you work in the back seat or listen to music - how does this change the assessment? What is the best way to buy an apartment - at what point is it optimal to finish saving on a deposit and make a down payment on a mortgage? Or even a trivial question: is it more profitable to deposit money at 6% with a monthly capitalization or at 6,2% with an annual capitalization? Most people do not even try to make such calculations and do not even want to collect detailed information about their money. Instead of calculations, feelings and emotions are connected. Or they make some narrow estimate, for example, calculate the annual cost of owning a car in detail, while all these expenses may be only 5% of total expenses (and spending on other aspects of life is not calculated). The human brain is subject to cognitive distortions. For example, it is difficult to quit, despite the non-repayment, a business in which a lot of time and money have been invested. People are usually overly optimistic and underestimate the risks, as well as easily suggestible and can buy an expensive trinket or invest in a financial pyramid.
Of course, in the case of a bank, emotional evaluation does not work. Therefore, I want to first talk about how an ordinary individual evaluates money (including me), and how a bank does it. Below there will be some financial educational program and a lot about data analytics in Sberbank for the entire bank as a whole.
The conclusions obtained are given only as an example and cannot be regarded as recommendations for private investors, since they do not take into account many factors that remain outside the scope of this article.
For example, any "black swan" event in macroeconomics, in the corporate governance of any company, etc., can lead to dramatic changes.
Suppose you have already paid off your mortgage and you have savings. This article may be useful to you if you:
- it doesnβt matter how much property you have accumulated and how to keep track of it
- wondering how to make your property bring you additional income
- I want to understand which of the ways to invest money is the best: real estate, deposits or stocks
- curious what the analysis of Sberbank data will advise on this issue
Often people make financial decisions without full information about the dynamics of their own income and expenses, without an assessment of the value of their own property, without taking into account inflation, etc. in their calculations.
Sometimes people make mistakes, like taking out a loan thinking they can pay it back and then failing. At the same time, the answer to the question of whether a person will be able to service a loan is often known in advance. You just need to know how much you earn, how much you spend, what is the dynamics of changes in these indicators.
Or, for example, a person receives some kind of salary at work, it is periodically increased, presenting it as an assessment of merit. But in reality, compared to inflation, this personβs earnings may fall, and he may not realize this if he does not keep records of income.
Some people cannot assess which choice is more profitable in their current situation: renting an apartment or taking out a mortgage at such and such a rate.
And instead of calculating what the costs will be in this and that case, somehow monetizing non-financial indicators in the calculations (βI estimate the benefit from Moscow registration at M rubles a month, I estimate the convenience of living in an apartment rented near work at N rubles per monthβ), people run to the Internet to discuss with interlocutors who may have a different financial situation and other priorities in assessing non-financial indicators.
I am for responsible financial planning. First of all, it is proposed to collect the following data on your own financial situation:
- accounting and valuation of all available property
- accounting for income and expenses, as well as the difference between income and expenses, i.e. property accumulation dynamics
Accounting and valuation of all available property
To begin with, here is a picture that probably misinterprets the financial situation of people. The picture shows only the monetary components of the property that the depicted people have. In reality, after all, people who give alms probably have some property besides loans, as a result of which their balance of money is negative, but the total value of their property is still greater than that of a beggar.
Assess what you have:
- real estate
- land
- vehicles
- bank deposits
- credit obligations (with a minus)
- investments (stocks, bonds, β¦)
- cost of own business
- other property
Among the property, one can note a liquid share, which can be quickly withdrawn and converted into other forms. For example, a share in an apartment that you own together with relatives living in it can be classified as illiquid property. Long-term investments in deposits or shares that cannot be withdrawn without loss can also be considered illiquid. In turn, real estate that you own but do not live in, vehicles, short-term and revocable deposits can be classified as liquid property. For example, if you need money for urgent treatment, then the benefits of some tools are approximately zero, so the liquidity share is more valuable.
Further, among the property can be distinguished unprofitable and profitable. For example, real estate that is not rented out, as well as vehicles, can be regarded as unprofitable. And leased real estate, deposits and shares invested at a rate above inflation are profitable property.
You will get, for example, such a picture (the data is randomly generated):
For many people, this picture looks very skewed. For example, a poor grandmother can live in an expensive apartment in Moscow that does not bring profit, while living hand to mouth from pension to pension, not thinking about restructuring her property. It would be wise for her to exchange apartments with her grandson for a fee. On the contrary, an investor may become so engrossed in investing in stocks that he does not have other types of assets for a rainy day, which can be risky. You can draw such a picture of your property and wonder if it is not wise to shift the property in a more profitable way.
Accounting for income, expenses and dynamics of property accumulation
It is suggested that you regularly record your income and expenses electronically. In the era of internet banking, it doesn't take much effort. At the same time, income and expenses can be divided into categories. Further, aggregating them by years, one can draw conclusions about their dynamics. It is important to take into account inflation in order to have an idea of ββwhat the amounts for past years look like in today's prices. Everyone has their own consumer basket. Gasoline and food rise in price at different rates. But calculating your personal inflation is quite difficult. Therefore, with some error, it is possible to use data on the official inflation rate.
Monthly inflation data is available from many open sources, including those uploaded to the Sberbank data lake.
An example of visualizing the dynamics of income-expenses (the data is randomly generated, the dynamics of inflation is real):
Having such a complete picture, you can draw conclusions about your real growth / decrease in income and real growth / decrease in savings, analyze the dynamics of expenses by category and make informed financial decisions.
Which way of investing free cash beats inflation and brings the most passive income?
The Sberbank data lake has valuable data on this topic:
- dynamics of the cost per square meter in Moscow
- database of proposals for the sale and rental of real estate in Moscow and Moscow suburbs
- dynamics of the average annual interest rate on deposits
- ruble inflation dynamics
- Dynamics of the Moscow Exchange Gross Total Return Index (MCFTR)
- Moscow exchange stock quotes and data on dividends paid
This data will allow us to compare the returns and risks of investing in rental property, bank deposits and the equity market. Let's not forget to factor in inflation.
I must say right away that in this post we are engaged exclusively in data analysis and do not resort to the use of any economic theories. Let's just see what our data says - which way to preserve and grow savings in Russia has produced the best results in recent years.
We will briefly describe how the data used in this article and other data in Sberbank are collected and analyzed. There is a layer of source replicas that are stored in parquet format on hadoop. Both internal sources (various AS of the bank) and external sources are used. Source replicas are collected in different ways. There is a stork product based on spark, and the second product, Ab Initio AIR, is gaining momentum. Source replicas are uploaded to different Cloudera-managed hadoop clusters, and can be linked from one cluster to another. Clusters are divided mainly by business blocks, there are also Data Lab clusters. Based on source replicas, various data marts are built that are available to business users and data scientists. Various spark applications, hive queries, data analysis applications, and visualization of results in SVG graphics format were used to write this article.
Historical analysis of the real estate market
The analysis shows that real estate in the long run grows in proportion to inflation, i.e. in real prices neither increases nor decreases. Here are graphs of the dynamics of prices for residential real estate in Moscow, demonstrating the available initial data.
Price chart in rubles excluding inflation:
Price chart in rubles, taking into account inflation (in modern prices):
We see that historically the price fluctuated around 200 rubles/sq.m. in modern prices and volatility was quite low.
What percentage per annum above inflation is brought by investments in residential real estate? How does the yield depend on the number of rooms in the apartment? Let's analyze Sberbank's database of ads for the sale and rental of apartments in Moscow and the Moscow suburbs.
In our database, there were quite a few apartment buildings in which there are ads for the sale of apartments and ads for renting apartments at the same time, and the number of rooms in the apartments for sale and for rent is the same. We compared such cases, grouping them by houses and the number of rooms in the apartment. If there were several offers in such a group, the average price was calculated. If the area of ββthe sold and rented apartments differed, then the offer price was proportionally changed so that the areas of the compared apartments corresponded. As a result, the proposals were put on the schedule. Each circle is actually an apartment that is offered to be purchased and rented at the same time. On the horizontal axis we see the cost of acquiring an apartment, and on the vertical axis - the cost of renting the same apartment. The number of rooms in the apartment is clear from the color of the circle, and the larger the area of ββββthe apartment, the greater the radius of the circle. Taking into account the super-expensive offers, the schedule turned out like this:
If you remove expensive offers, you can see the prices in the economy segment in more detail:
Correlation analysis shows that the relationship between the cost of renting an apartment and the cost of buying it is close to linear.
It turned out the following ratio between the cost of an annual apartment rental and the cost of acquiring an apartment (let's not forget that the annual cost is 12 monthly):
Number of rooms:
The ratio of the cost of the annual rental of an apartment to the cost of acquiring an apartment:
1-room
Present in several = 5,11%
2-room
Present in several = 4,80%
3-room
Present in several = 4,94%
Total
Present in several = 4,93%
Received an average rating of 4,93% per annum yield from renting an apartment in excess of inflation. It is also interesting that cheap 1-room apartments are a bit more profitable to rent. We compared the offer price, which in both cases (rental and purchase) is slightly overpriced, so no adjustment is required. However, other adjustments are required: rented apartments sometimes need to be at least cosmetically repaired, it takes some time to find a tenant and the apartments are empty, sometimes utility payments are not included in the rental price in part or in full, and there is also an extremely slight depreciation of apartments over the years.
Taking into account adjustments, from renting out residential real estate, you can have an income of up to 4,5% per annum (beyond the fact that the property itself does not depreciate). If such a yield is impressive, Sberbank has many offers on DomClick.
Historical analysis of deposit rates
Ruble deposits in Russia over the past few years have largely outpaced inflation. But not by 4,5%, like real estate for rent, but, on average, by 2%.
In the chart below, we see the dynamics of comparing deposit rates and inflation.
I will note such a moment that income from deposits beats inflation somewhat stronger than in the above chart for the following reasons:
- You can fix the rate on replenished deposits at a favorable time for several months in advance
- Monthly capitalization, characteristic of many contributions included in these averaged data, adds profit due to compound interest
- Above were taken into account the rates for the top 10 banks according to information from the Bank of Russia, outside the top 10 you can find rates slightly higher
As for deposits in dollars and euros, I will say that they beat inflation in dollars and euros, respectively, weaker than the ruble beats ruble inflation.
Historical analysis of the stock market
Now let's look at the more diverse and risky market for Russian stocks. The return on investment in stocks is not fixed and can vary greatly. However, if you diversify assets and invest for a long period, you can trace the average annual interest rate that characterizes the success of investing in a stock portfolio.
For readers who are far from the topic, I will say a few words about stock indices. In Russia, there is the Moscow Exchange index, which shows the dynamics of the ruble value of a portfolio consisting of the 50 largest Russian stocks. The composition of the index and the share of shares of each company depends on the volume of trading operations, the volume of business, the number of shares in circulation. The chart below shows how the Moscow Exchange index (i.e. such an average portfolio) has grown in recent years.
The owners of most stocks are periodically paid dividends that can be reinvested in the same stocks that generated income. Tax is payable on dividends received. The Moscow Exchange Index does not take into account the dividend yield.
Therefore, we will be more interested in the Moscow Exchange Gross Total Return Index (MCFTR), which takes into account dividends received and the tax deducted from these dividends. Let's show on the chart below how this index has changed in recent years. In addition, we take into account inflation and see how this index grew in modern prices:
The green graph is the real value of the portfolio in modern prices, if you invest in the Moscow Exchange index, regularly reinvest dividends and pay taxes.
Let's see what was the growth rate of the MCFTR index over the past 1,2,3,β¦,11 years. Those. What would our return be if we bought shares in proportion to this index and regularly reinvested the dividends received in the same shares:
Years old
Home
the end
MCFTR
early With
taking into account
infl.
MCFTR
con. With
taking into account
infl.
Coeff.
growth
Annual
coeff.
growth
1
30.07.2019
30.07.2020
4697,47
5095,54
1,084741
1,084741
2
30.07.2018
30.07.2020
3835,52
5095,54
1,328513
1,152612
3
30.07.2017
30.07.2020
3113,38
5095,54
1,636659
1,178472
4
30.07.2016
30.07.2020
3115,30
5095,54
1,635650
1,130896
5
30.07.2015
30.07.2020
2682,35
5095,54
1,899655
1,136933
6
30.07.2014
30.07.2020
2488,07
5095,54
2,047989
1,126907
7
30.07.2013
30.07.2020
2497,47
5095,54
2,040281
1,107239
8
30.07.2012
30.07.2020
2634,99
5095,54
1,933799
1,085929
9
30.07.2011
30.07.2020
3245,76
5095,54
1,569907
1,051390
10
30.07.2010
30.07.2020
2847,81
5095,54
1,789284
1,059907
11
30.07.2009
30.07.2020
2223,17
5095,54
2,292015
1,078318
We see that, having invested any number of years ago, we would have got a victory over inflation of 5-18% annually, depending on the success of the entry point.
Let's make one more table - not profitability for every last N years, but profitability for each of the last N one-year periods:
Year
Home
the end
MCFTR
early With
taking into account
infl.
MCFTR
con. With
taking into account
infl.
Annual
coeff.
growth
1
30.07.2019
30.07.2020
4697,47
5095,54
1,084741
2
30.07.2018
30.07.2019
3835,52
4697,47
1,224728
3
30.07.2017
30.07.2018
3113,38
3835,52
1,231947
4
30.07.2016
30.07.2017
3115,30
3113,38
0,999384
5
30.07.2015
30.07.2016
2682,35
3115,30
1,161407
6
30.07.2014
30.07.2015
2488,07
2682,35
1,078085
7
30.07.2013
30.07.2014
2497,47
2488,07
0,996236
8
30.07.2012
30.07.2013
2634,99
2497,47
0,947810
9
30.07.2011
30.07.2012
3245,76
2634,99
0,811825
10
30.07.2010
30.07.2011
2847,81
3245,76
1,139739
11
30.07.2009
30.07.2010
2223,17
2847,81
1,280968
We see that not every one of the years was successful, but unsuccessful years were followed by successful years, which βfixed everythingβ.
Now, for a better understanding, let's abstract from this index and look at the example of a specific stock, what would be the result if you invested in this stock 15 years ago, reinvested dividends and paid taxes. Let's see the result taking into account inflation, i.e. at current prices. Below is an example of an ordinary share of Sberbank. The green graph shows the dynamics of the value of the portfolio, which initially consisted of one share of Sberbank at current prices, taking into account the reinvestment of dividends. For 15 years, inflation has depreciated the ruble by 3.014135 times. The share of Sberbank over the years has risen in price from 21.861 rubles. up to 218.15 rubles, i.e. the price increased by 9.978958 times excluding inflation. During these years, the owner of one share was paid at different times dividends, net of taxes, in the amount of 40.811613 rubles. The amounts of dividends paid are shown on the chart as red vertical sticks and do not refer to the chart itself, in which dividends and their reinvestment are also taken into account. If every time these dividends were used to buy shares of Sberbank again, then at the end of the period the shareholder already owned not one, but 1.309361 shares. Taking into account the reinvestment of dividends and inflation, the original portfolio has risen in price by 4.334927 times over 15 years, i.Π΅. rose in price by 1.102721 times annually. In total, an ordinary share of Sberbank brought the owner an average of 10,27% per annum above inflation each of the last 15 years:
As another example, let's take a similar picture with the dynamics of the preferred shares of Sberbank. A preferred share of Sberbank brought the owner even more on average, 13,59% per annum over inflation each of the last 15 years:
These results will be slightly lower in practice, because when buying shares you need to pay a small brokerage commission. At the same time, the result can be further improved if you use the Individual Investment Account, which allows you to receive a tax deduction from the state in a certain limited amount. If you have not heard of this, it is suggested to search for the abbreviation "IIS". Let's also not forget to mention that IIS can be opened at Sberbank.
So, we have previously received that it is historically more profitable to invest in stocks than in real estate and deposits. For fun, here is a hit parade of the top 20 stocks that have been trading on the market for more than 10 years, obtained as a result of data analysis. In the last column, we see how many times the portfolio of stocks grew on average every year, taking into account inflation and the reinvestment of dividends. We see that many stocks beat inflation by more than 10%:
Promotion
Home
the end
Coeff. inflation
Beginning price
Con. price
Growth
number
shares
due to
reinve-
stations
divi-
dendov,
time
final
medium-
annual
growth, times
Lenzoloto
30.07.2010
30.07.2020
1,872601
1267,02
17290
2,307198
1,326066
NKNKH ap
30.07.2010
30.07.2020
1,872601
5,99
79,18
2,319298
1,322544
MGTS-4ap
30.07.2010
30.07.2020
1,872601
339,99
1980
3,188323
1,257858
Tatnft 3ap
30.07.2010
30.07.2020
1,872601
72,77
538,8
2,037894
1,232030
MGTS-5ao
30.07.2010
30.07.2020
1,872601
380,7
2275
2,487047
1,230166
Akron
30.07.2010
30.07.2020
1,872601
809,88
5800
2,015074
1,226550
Lenzol. up
30.07.2010
30.07.2020
1,872601
845
5260
2,214068
1,220921
NKNKh JSC
30.07.2010
30.07.2020
1,872601
14,117
92,45
1,896548
1,208282
Lenenerg-p
30.07.2010
30.07.2020
1,872601
25,253
149,5
1,904568
1,196652
GMKNorNik
30.07.2010
30.07.2020
1,872601
4970
19620
2,134809
1,162320
Surgnfgz-p
30.07.2010
30.07.2020
1,872601
13,799
37,49
2,480427
1,136619
IRKUT-3
30.07.2010
30.07.2020
1,872601
8,127
35,08
1,543182
1,135299
Tatnft 3ao
30.07.2010
30.07.2020
1,872601
146,94
558,4
1,612350
1,125854
Novatek jsc
30.07.2010
30.07.2020
1,872601
218,5
1080,8
1,195976
1,121908
SevSt-jsc
30.07.2010
30.07.2020
1,872601
358
908,4
2,163834
1,113569
Krasesb ao
30.07.2010
30.07.2020
1,872601
3,25
7,07
2,255269
1,101105
CHTPZ JSC
30.07.2010
30.07.2020
1,872601
55,7
209,5
1,304175
1,101088
Sberbank-p
30.07.2010
30.07.2020
1,872601
56,85
203,33
1,368277
1,100829
PIK JSC
30.07.2010
30.07.2020
1,872601
108,26
489,5
1,079537
1,100545
LUKOIL
30.07.2010
30.07.2020
1,872601
1720
5115
1,639864
1,100444
Now, having downloaded data, we will solve several problems on the topic of what exactly is worth investing in, if we believe that long-term trends in the value of certain shares will continue. It is clear that it is not entirely justified to predict the future price according to the previous chart, but we will look for winners in investing for past periods in several categories.
Task. Find the stock that consistently outperforms real estate (CAGR 1.045 over inflation) the maximum number of times in each of the last 10 one-year periods the stock traded.
In this and the following tasks, we mean the above model with the reinvestment of dividends and accounting for inflation.
Here are the winners in this category according to our data analysis. The stocks at the top of the table consistently perform well year after year without dips. Here Year 1 is 30.07.2019/30.07.2020/2-30.07.2018/30.07.2019/XNUMX, Year XNUMX is XNUMX/XNUMX/XNUMX-XNUMX/XNUMX/XNUMX, etc.:
Promotion
Number
victories
over
real estate
press-
stu
behind
after-
days
10 years
Year 1
Year 2
Year 3
Year 4
Year 5
Year 6
Year 7
Year 8
Year 9
Year 10
Tatnft 3ap
8
0,8573
1,4934
1,9461
1,6092
1,0470
1,1035
1,2909
1,0705
1,0039
1,2540
MGTS-4ap
8
1,1020
1,0608
1,8637
1,5106
1,7244
0,9339
1,1632
0,9216
1,0655
1,6380
CHTPZ JSC
7
1,5532
1,2003
1,2495
1,5011
1,5453
1,2926
0,9477
0,9399
0,3081
1,3666
SevSt-jsc
7
0,9532
1,1056
1,3463
1,1089
1,1955
2,0003
1,2501
0,6734
0,6637
1,3948
NKNKh JSC
7
1,3285
1,5916
1,0821
0,8403
1,7407
1,3632
0,8729
0,8678
1,0716
1,7910
MGTS-5ao
7
1,1969
1,0688
1,8572
1,3789
2,0274
0,8394
1,1685
0,8364
1,0073
1,4460
Gazpromneft
7
0,8119
1,3200
1,6868
1,2051
1,1751
0,9197
1,1126
0,7484
1,1131
1,0641
Tatnft 3ao
7
0,7933
1,0807
1,9714
1,2109
1,0728
1,1725
1,0192
0,9815
1,0783
1,1785
Lenenerg-p
7
1,3941
1,1865
1,7697
2,4403
2,2441
0,6250
1,2045
0,7784
0,4562
1,4051
NKNKH ap
7
1,3057
2,4022
1,2896
0,8209
1,2356
1,6278
0,7508
0,8449
1,5820
2,4428
Surgnfgz-p
7
1,1897
1,0456
1,2413
0,8395
0,9643
1,4957
1,2140
1,1280
1,4013
1,0031
We see that even the leaders did not win real estate in terms of profitability every year. Strong jumps in the level of profitability in different years show that if you want stability, it is better to diversify assets, and ideally, invest in an index.
Now we formulate and solve such a problem for data analysis. Is it worth it to speculate a little, each time buying stocks M days before the dividend payout date and selling shares N days after the dividend payout date? Is it better to harvest dividends and βget out of the stockβ than to βsit in the stockβ all year round? Let's assume that there are no losses on the commission from such an entry-exit. And data analysis will help us find the boundaries of the M and N corridor, which has historically been most successful in harvesting dividends instead of holding shares for a long time.
Here is an anecdote from 2008.
John Smith, who jumped out of the window of the 75th floor on Wall Street, after hitting the ground, jumped 10 meters, which somewhat won back his morning fall.
So it is with dividends: we assume that in the market movement in the vicinity of the dividend payment date, too much market reflection is manifested, i.e. for psychological reasons, the market may fall or rise more than the dividend amount requires.
Task. Estimate the rate of recovery of shares after the payment of dividends. Is it better to enter on the eve of the dividend payment and exit some time later than to own the stock all year round? How many days before the dividend payment should I enter the stock and how many days after the dividend payment should I exit the stock in order to get the maximum profit?
Our model has calculated all the variations in the width of the neighborhood around the dividend payout dates throughout history. The following restrictions were adopted: M<=30, N>=20. The fact is that the date and amount of the payment are not always known in advance than 30 days before the payment of dividends. Also, dividends do not come to the account immediately, but with a delay. We believe that it takes at least 20 days to be guaranteed to receive dividends on the account and reinvest them. With these restrictions, the model produced the following response. The best time to buy shares is 34 days before the dividend payout date and sell them 25 days after the dividend payout date. Under this scenario, an average growth of 3,11% over this period was obtained, which gives 20,9% per annum. Those. with the considered investment model (with reinvestment of dividends and taking into account inflation), if you buy a share 34 days before the dividend payment date and sell it 25 days after the dividend payment date, then we have 20,9% per annum above the inflation rate. This is verified by averaging over all cases of dividend payments from our database.
For example, for a preferred share of Sberbank, such an entry-exit scenario would give 11,72% growth above the inflation rate for each entry-exit in the vicinity of the dividend payment date. This is as much as 98,6% per annum above the inflation rate. But this, of course, is an example of random luck.
Promotion
Sign In
Dividend date
Log out
Coeff. growth
Sberbank-p
10.05.2019
13.06.2019
08.07.2019
1,112942978
Sberbank-p
23.05.2018
26.06.2018
21.07.2018
0,936437635
Sberbank-p
11.05.2017
14.06.2017
09.07.2017
1,017492563
Sberbank-p
11.05.2016
14.06.2016
09.07.2016
1,101864592
Sberbank-p
12.05.2015
15.06.2015
10.07.2015
0,995812419
Sberbank-p
14.05.2014
17.06.2014
12.07.2014
1,042997818
Sberbank-p
08.03.2013
11.04.2013
06.05.2013
0,997301095
Sberbank-p
09.03.2012
12.04.2012
07.05.2012
0,924053861
Sberbank-p
12.03.2011
15.04.2011
10.05.2011
1,010644958
Sberbank-p
13.03.2010
16.04.2010
11.05.2010
0,796937418
Sberbank-p
04.04.2009
08.05.2009
02.06.2009
2,893620094
Sberbank-p
04.04.2008
08.05.2008
02.06.2008
1,073578067
Sberbank-p
08.04.2007
12.05.2007
06.06.2007
0,877649005
Sberbank-p
25.03.2006
28.04.2006
23.05.2006
0,958642001
Sberbank-p
03.04.2005
07.05.2005
01.06.2005
1,059276282
Sberbank-p
28.03.2004
01.05.2004
26.05.2004
1,049810801
Sberbank-p
06.04.2003
10.05.2003
04.06.2003
1,161792898
Sberbank-p
02.04.2002
06.05.2002
31.05.2002
1,099316569
So, the above-described market reflection takes place, and in a fairly wide range of dividend payment dates, the yield has historically been slightly higher than from year-round holding of shares.
Let's set our model one more task for data analysis:
Task. Find the stock with the most regular entry-exit earnings opportunity around the dividend payout date. We will evaluate how many cases of dividend payouts have made it possible to earn more than 10% on an annualized basis above the inflation rate, if you enter the stock 34 days before and exit 25 days after the dividend payment date.
We will consider stocks for which there were at least 5 cases of dividend payments. The resulting hit parade is shown below. Note that the result is most likely of value only from the point of view of the problem of data analysis, but not as a practical guide to investing.
Promotion
Quantity
cases of winning
more than 10% per annum
above inflation
Quantity
cases
payments
dividends
Share
cases
victories
Average coefficient growth
Lenzoloto
5
5
1
1,320779017
IDGC SZ
6
7
0,8571
1,070324870
Rollman-p
6
7
0,8571
1,029644533
Rosseti ap
4
5
0,8
1,279877637
Kubanenr
4
5
0,8
1,248634960
LSR JSC
8
10
0,8
1,085474828
ALROSA JSC
8
10
0,8
1,042920287
FGC UES jsc
6
8
0,75
1,087420610
NCSP JSC
10
14
0,7143
1,166690777
KuzbTK JSC
5
7
0,7143
1,029743667
From the analysis of the stock market, we can draw the following conclusions:
- It has been verified that the return on shares declared in the materials of brokers, investment companies and other interested parties is higher than deposits and investment real estate.
- The volatility of the stock market is very high, but it is possible to invest for a long time with a significant diversification of the portfolio. For the sake of an additional 13% tax deduction when investing in IIS, it is quite advisable to open the stock market for yourself and this can be done, including at Sberbank.
- Based on the analysis of the results for previous periods, leaders were found in terms of stable high profitability and the profitability of entry-exit in the vicinity of the dividend payment date. However, the results are not so unambiguous and you should not be guided only by them in your investment. These were examples of data analysis tasks.
Total
It is useful to keep a record of your property, as well as income and expenses. It helps in financial planning. If you manage to save money, then there are opportunities to invest it at a rate higher than inflation. An analysis of data from the Sberbank data lake showed that deposits annually return 2%, rental apartments - 4,5%, and Russian shares - about 10% above inflation with significantly greater risks.
Author: Mikhail Grichik, expert of the professional community of Sberbank SberProfi DWH/BigData.
The SberProfi DWH/BigData professional community is responsible for developing competencies in such areas as the Hadoop ecosystem, Teradata, Oracle DB, GreenPlum, as well as BI tools Qlik, SAP BO, Tableau, etc.
Source: habr.com