Thanks to the rapid development of information technology, it has become possible to analyze large amounts of information, build complex mathematical models, and solve multicriteria optimization problems in a matter of seconds. Scientists interested in cyclical economic development began to develop theories, believing that tracking trends in a number of economic variables would help clarify and predict periods of boom and bust. The stock market was chosen as one of the objects for study. Repeated attempts have been made to build a mathematical model that would successfully solve the problem of predicting the increase in stock prices. In particular, “technical analysis” has become widespread.

Technical analysis(technical analysis) is a set of methods for studying market dynamics, most often through charts, in order to predict the future direction of price movements. Today, this analytical method is one of the most popular. But can we consider those. Is the analysis suitable for generating profit? First, let's look at the theories of pricing in the stock market.

One of the basic concepts since the 1960s. counts efficient market hypothesis(effective market hypothesis, EMH), according to which information on prices and sales volumes for the past period is publicly available. Consequently, any data that could ever be extracted from the analysis of past quotes has already found its way into the stock price. As traders compete to make better use of this public knowledge, they necessarily drive prices to levels at which expected rates of return are entirely consistent with the risk. At these levels it is impossible to say whether buying a stock is a good or bad deal, i.e. the current price is objective, which means that you cannot expect to receive a return above the market. Thus, in an efficient market, asset prices reflect their true values, and the conduct of those. analysis loses all meaning.

But it should be noted that today none of the existing stock markets in the world can be called completely informationally efficient. Moreover, taking into account modern empirical research, we can conclude that the theory of an efficient market is rather a utopia, because is not able to fully rationally explain the real processes occurring in financial markets.

In particular, Yale University professor Robert Shiller discovered a phenomenon that he later called excessive volatility in stock asset prices. The essence of the phenomenon lies in the frequent changes in quotes, which defy rational explanation, namely, there is no possibility of interpreting this phenomenon with corresponding changes in fundamental factors.

At the end of the 1980s. the first steps were taken towards creating a model that, unlike the concept of an efficient market, would more accurately explain the actual behavior of stock markets. In 1986, Fisher Black introduced a new term in his publication - “noise trading”.

« Noise trade is trading on noise, perceived as if noise were information. People who trade on noise will trade even when objectively they should refrain from doing so. Perhaps they believe that the noise they trade on is information. Or maybe they just like to trade" Although F. Black does not indicate which operators should be classified as “noise traders,” descriptions of such market participants can be found in the work of De Long, Shleifer, Summers and Waldman. Noise traders mistakenly believe that they have unique information about future asset prices. The sources of such information may be false signals about non-existent trends given by technical indicators. analysis, rumors, recommendations of financial “gurus”. Noise traders greatly overestimate the value of available information and are willing to take on unreasonably large risks. Conducted empirical studies also indicate that noise traders should primarily include individual investors, i.e. individuals. Moreover, it is this group of traders who suffer systematic losses from trading due to the irrationality of their actions. For Western stock markets, empirical confirmation of this phenomenon can be found in the studies of Barber and Odin, and for operators of the Russian stock market - in the work of I.S. Nilova. The theory of noise trading also helps explain the phenomenon of R. Schiller. It is the irrational actions of traders that cause excessive price volatility.

Summarizing modern research in the field of pricing theories in the stock market, we can conclude that the use of technical analysis to make a profit is ineffective. Moreover, traders using tech. analysis attempts to identify repeating graphic patterns (from the English pattern - model, sample). The urge to find different price patterns is strong, and the human eye's ability to pick out obvious trends is amazing. However, the identified patterns may not exist at all. The chart shows simulated and actual data for the Dow Jones Industrial Average through 1956, taken from research by Harry Roberts.

Chart (B) is a classic head-and-shoulders pattern. Chart (A) also looks like a “typical” market behavior pattern. Which of the two graphs is based on actual stock index values ​​and which is based on simulated data? Graph (A) is based on actual data. Graph (B) is generated using the values ​​produced by the random number generator. The problem with identifying patterns where none actually exist is the lack of necessary data. By analyzing previous dynamics, you can always identify trading schemes and methods that could provide profit. In other words, there is a set of an infinite number of strategies based on those. analysis. Some strategies from the total population demonstrate a positive result on historical data, others – a negative one. But in the future, we cannot know which group of systems will allow us to consistently make a profit.

Also, one of the ways to determine the presence of patterns in time series is to measure serial correlation. The existence of serial correlation in quotes may indicate a certain relationship between past and current stock returns. A positive serial correlation means that positive rates of return are usually accompanied by positive rates (persistence property). Negative serial correlation means that positive rates of return are accompanied by negative rates (reversion property or “correction” property). Applying this method to stock quotes, Kendall and Roberts (1959) proved that no patterns could be detected.

Along with technical analysis, it has become quite widespread fundamental analysis. Its purpose is to analyze the value of a stock based on factors such as earnings and dividend prospects, expectations of future interest rates, and the risk of the firm. But, as with technical analysis, if all analysts rely on publicly available information about a company's earnings and industry position, it is difficult to expect that any one analyst's assessment of the prospects will be much more accurate than those of others. Such market research is carried out by many well-informed and generously funded firms. Given such fierce competition, it is difficult to find data that other analysts do not already have. Therefore, if information about a particular company is publicly available, then the rate of return that an investor can expect will be the most common.

In addition to the methods described above, they are trying to use neural networks, genetic algorithms, etc. to forecast the market. But an attempt to use predictive methods in relation to financial markets turns them into self-destructive models. For example, suppose one of the methods predicts the underlying growth trend of a market. If the theory is widely accepted, many investors will immediately begin buying shares in anticipation of rising prices. As a result, growth will be much sharper and more rapid than predicted. Or growth may not take place at all due to the fact that a large institutional participant, having discovered excessive liquidity, begins to sell off its assets.

The self-destruction of predictive models arises due to their use in a competitive environment, namely in an environment in which each agent tries to extract his own benefit by influencing the system as a whole in a certain way. The influence of an individual agent on the entire system is not significant (in a fairly developed market), however, the presence of a superposition effect provokes the self-destruction of a particular model. Those. if the trading algorithm is based on predictive methods, the strategy becomes unstable, and in the long term the model self-liquidates. If the strategy is parametric and predictively neutral, then this provides a competitive advantage compared to trading systems that use a forecast to make decisions. But it is worth considering that the search for strategies that satisfy such parameters as, for example, profit/risk occurs simultaneously with the search for similar systems by other traders and large financial companies based on the same historical data and practically the same criteria. This implies the need to use systems based not only on generally accepted basic parameters, but also on indicators such as reliability, stability, survivability, heteroscedasticity, etc. Of particular interest are trading strategies based on the so-called "additional information dimensions". They appear in other, usually related areas of activity and, for various reasons, are rarely used by a wide range of people in the stock market.

The above considerations allow us to draw the following conclusions:

1. The theory of noise trading, in contrast to the concept of an efficient market, allows us to more accurately explain the real behavior of stock assets.
2. There is no pattern in changes in quotes of trading instruments, i.e. It is impossible to predict the market.
3. The use of predictive methods, in particular technical analysis, leads to the inevitable ruin of the trader in the medium term.
4. To successfully trade on the stock market, it is necessary to use predictively neutral strategies based on “additional information dimensions.”

List of used literature:

1. Shiller R. Irrational Exuberance. Princeton: Princeton University Press, 2000.
2. Black F. Noise // Journal of Finance. 1986. Vol. 41. R. 529-543.
3. De Long J. B., Shleifer A. M., Summers L. H., Waldmann R. J. Noise Trader Risk in Financial Markets // Journal of Political Economy. 1990. Vol. 98. R. 703-738.
4. Barber B. M., Odean T. Trading is hazardous to your wealth: The common stock investment performance of individual investors // Journal of Finance. 2000. Vol. 55. No. 2. P. 773-806.
5. Barber B. M., Odean T. Boys will be boys: Gender, overconfidence, and common stock investment // Quarterly Journal of Economics. 2001. Vol. 116. R. 261-292.
6. Odean T. Do investors trade too much? // American Economic Review. 1999. Vol. 89. R. 1279-1298.
7. Nilov I. S. Who loses their money when trading on the stock market? // Financial management. 2006. No. 4.
8. Nilov I. S. Noise trade. Modern empirical research // RCB. 2006. No. 24.
9. Harry Roberts. Stock Market Patterns and Financial Analysis: Methodological Suggestions // Journal of Finance. March 1959. P. 5-6.

Each country directly depends on the level of development of the national economy and events occurring at a given time in the global arena of the financial market. Due to the influence exerted by various political and economic conditions, since these markets are very sensitive to various political events, as well as taking into account the influence of external and internal economic factors, the stock market alternately rises and falls into the abyss of crisis. Considering the interconnection of all stock markets, which is expressed in issues of the international interweaving of various capitals, fluctuations that occur in the stock market of the country in question can cause significant changes in the stock markets of another country, and perhaps several. , like many other countries, is only at the stage of its development, despite the speed of development that has been gaining momentum in recent years. But sometimes there is an opinion that further development is simply not feasible, since there are a number of significant problems in the development of the stock market. One of the most important problems is the poor understanding among bidders of all potential bargaining opportunities. At the same time, they still have a rather poor understanding of the types of transactions available on the stock market. Another characteristic feature is the low level of investment culture, due to which the market is growing at a slower pace than it could be. In addition to the ignorance of market participants, legislation can be identified as a separate problem, which does not reflect all the provisions and difficulties of the daily work of traders and investors. It is an undeniable fact that the legislative framework is constantly being improved, but so far the very fact of speculation on the stock exchange has not been eradicated and periodically appears in all its glory. Another item on our list is the lack of financial risk management capabilities. European stock exchanges are distinguished by the fact that they have insurance companies on the stock market, thanks to which the risks associated with the transaction process are minimized. Another guarantee of the absence of risk is the use of a special mechanism for concluding transactions, which, thanks to the level of education of the people of European countries, is becoming more and more streamlined every year. However, the level of education of Russian investors sometimes becomes the cause of serious development problems in the stock market. One should not underestimate the abilities of the Russian stock market, since every year its role in attracting investments becomes more and more significant, but a number of unresolved problems continue to hinder the development of investment processes. The main one is the lack of long-term government policy regarding the securities market, which allows them to be converted into effective investments. In 2008, the first steps were taken thanks to research carried out by various specialists in investment and with the help of foreign consultants. The result of this study was a model for the further development of the Russian stock market in the near future, thanks to which the volume of capitalization of the Russian market in the world economy should move from 11th place to 4th. The second problem is a number of problems brought together - unfair pricing, lack of free access and procedures for protecting investors in the stock market. All this is manifested in the infringement of the rights of smaller investors by large shareholders, in the presence of high costs when purchasing and selling shares, as well as in the presence of a procedure for re-registration of rights, despite the small volume of transactions of illiquid shares. The third point on this list is the lack of a more or less long history of joint stock companies created during privatization. Because of this, most enterprises cannot boast of a clearly developed dividend policy. Many issuers either do not pay interest at all or are so small that they simply begin to lose their attractiveness in the eyes of investors. Shares are purchased not with the expectation of paying good dividends, but in the hope of an increase in their value. Therefore, the transactions are speculative and not investment. Regular dividend payments, coupled with a high dividend percentage, could return shares to their initial attractiveness, and enterprises could raise additional funds by issuing new blocks of shares. The degree of confidence of the country's population in the processes in the stock market is important in the development of the stock market. Experience gained in developed countries shows that how stable the stock market is, in many respects, depends on the availability of . The reality of the participation of small and medium-sized investors is ensured by the possibility of them investing their savings in mutual and joint-stock funds. However, if we compare the volume of investments in such funds in Russia and in developed countries, then statistics inexorably establishes the fact that our fellow citizens do not trust these funds, which is why the volume of investments is indecently small. The circle of people who invest their money in the stock market is also small. It is possible to discuss the topic - “Problems of the stock market” - in general or in Russia in particular, almost endlessly, and the list of problems given above is only a small part of a huge list, which includes a variety of reasons for the slowdown in the development of the stock market. If we nevertheless find a way to solve (even if not all at once, but through a gradual approach to the problem) the problems of the financial market, then we will ultimately get a strong Russia and a competitive stock market.

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INTRODUCTION

The securities market has become an important and integral part of the economic life of our country. Currently in Russia, in connection with its inclusion in the global financial market system, the assignment of an international credit rating to the country, the placement of a tranche of Eurobonds, and the quotation of American depositary receipts for Russian shares on foreign exchanges, there is an urgent need for a civilized approach to the research and study of price dynamics on various segments of the securities market. The development of the Russian securities market has now reached a level at which its participants are faced not only with the problem of planning the size and direction of their own investments, but also with determining the most optimal ways to analyze the market situation. Russian brokers do business in conditions of large fluctuations in quotes and growing competition. It should be noted that due to existing trends in operations on the Russian securities market, underdeveloped infrastructure and insufficient liquidity of this market, many investors will have to focus on developed world securities markets. To function successfully in this situation, you will have to use the tools and methods of analysis and forecasting used in these markets. Of primary importance in this matter is scientific and methodological support for the activities of stock market participants. The most important tool for such support is the use of economic and mathematical methods, which include statistical methods for analyzing and forecasting the state of the stock market. Methods have been developed and described in detail, which have been practically tested and have been successfully used in developed stock markets for decades. Most often, Russian analysts try to adjust the methods used in Western markets, taking into account Russian characteristics. However, in domestic practice, statistical methods for analyzing the securities market are not given due attention, as evidenced by the relatively small number of scientific publications.

All this determined the choice of the topic of the course work and its relevance.

The purpose of the course work is to forecast the Russian stock market.

To achieve this goal, it is necessary to solve a number of problems:

Consider the main approaches to forecasting the state of the stock market;

Justify the method for forecasting the state of the stock market in the Russian Federation;

Present the characteristics of the stock market as an object of research;

Conduct a retrospective analysis of the stock market in the Russian Federation;

Present scenarios for the development of the Russian stock market in the long term;

Describe a forecast model of the state of the Russian stock market;

Make a forecast for the Russian stock market;

Conduct verification of the Russian stock market forecast.

CHAPTER 1. THEORETICAL ASPECTS OF FORECASTING THE STOCK MARKET OF THE RF

stock market forecasting

1.1 Basic approaches to forecasting the state of the stock market

Potential traders in stocks and derivatives are attracted by the appearance of easy money, but the stock market does not forgive signs of weakness, and it does not like lazy and absent-minded people. Any trader must constantly predict the stock market, be extremely careful and ready to make the right decision at any moment. Regular work on yourself is a prerequisite for those who are going to become a successful trader.

Trading is working under constant psychological pressure. And not every person can withstand this. The level of nervous tension in stock trading is constantly going off scale, and if you do not consciously deal with this phenomenon, you can easily lose your health and money. There are many ways to reduce the level of stress when working on the stock exchange or avoid it altogether. One of these methods is the use of a mechanical trading system Agapova T.A., Seregina S.F. Macroeconomics: Textbook - M.: MSU, 2009. - 563 pp..

The mechanical trading system allows you to remove the emotional component in the process of stock market forecasting, tactical and strategic decisions. Such a system will never doubt whether to complete a losing trade or not. Work on creating an automated system takes place in a calm, favorable environment, which promotes careful and leisurely elaboration of all the details. This approach allows the trader to save his health and the funds with which he operates on a real account. While the system is running, the trader’s main task is to initiate the launch of the system and ensure that it works without failures; the system will do the rest on its own. On the Internet you can find information about mechanical systems that can be used to work on the Russian stock market.

Forecasting methods on the stock exchange are individual for each trader. Players who trade intraday are called intraday traders. Such people can carry out about ten different transactions in one day. Medium-term speculators make 1-2 transactions per week, while the position is held from several days to several months. A special category of players - investors - make do with several transactions per year. All these people are divided into four main groups, and they are distinguished by different methods of forecasting the stock market.

Technical forecasting method. This includes people who rely on the tenets of technical market analysis. There are several main types of such analysis Burenin, A. N. Securities market and derivative financial instruments. - M.: Federative Book Trading Company, 2009.- 489 p.:

· technical indicators;

· wave and candlestick analysis;

· price charts;

· use of artificially created intelligence method.

The group of these traders is the largest. This is due to the huge amount of market analysis information and the ability to easily access it. Any beginner can learn the basics of such analysis on their own. The main principle of technical analysis is the study of the process of price fluctuations using special indicators - these are technical indicators; charting is also used. The widely accepted idea is that by carefully studying the behavior of the market in the past, one can predict how it will behave in the future - market movement is usually characterized by such terms as cyclicality and wave-like behavior.

If technical analysis worked, then every person who read and studied the basics could make money in the stock market. Practice shows that 90% of traders lose money on the stock exchange, therefore we can conclude that technical analysis does not work or does not work the way most speculators think about it.

Fundamental forecasting method. This group brings together fans of fundamental analysis. This type of analysis is considered the most complex on the market and requires analytical thinking abilities. To carry out such an analysis, you need to correctly process a very large amount of information - the trader here acts as an analyst. The concept of fundamental analysis is based on predicting the consequences of price behavior as a result of the influence of certain events in the world economy. Financial news, natural disasters and other similar events leave a definite imprint on the functioning of financial markets. A true fundamentalist must be an expert in the world economy to successfully use this method.

Most often, this method is used by conservative investors with a long investment horizon. Fundamental analysts evaluate a company's stock in terms of its business profitability and cash flow generation. Much attention is paid to quarterly and annual reports, before the release of which, as a rule, there are strong movements in shares Lyalin V.A. Russian securities market: main stages and development trends // Eurasian international scientific-analytical journal. - 2012. - No. 2. - pp. 182-186..

Gambling approach. This group usually includes gambling people who perceive the trading process as if they were playing in a casino; some of them use the theory of probability to make deals. Such people are simple players who use certain cunning principles when developing their own system for working on the stock exchange. They place stop orders using an increase in the bet size after they fix a losing trade, in the hope of inevitably receiving a win in the future.

As a rule, such guys do not stay on the market for long. A series of luck gives way to a series of failures and sooner or later, like casino players, they end up losing their account.

An intelligent method for forecasting the stock market. After several years of trading and studying various strategies and methods, a person’s subconscious mind merges it all into a single whole. At the same time, decisions to buy/sell shares are made intuitively. No analysis is used Blank, I. A. Investment management. -- Kyiv: MP "ITEM" LTD "United London Trade Limited", 2009. - 412 pp. They engage in trading, relying solely on their intuition. Often these are very successful traders, with many years of experience in the market, they only need to take a quick look at one or another price chart in order to give a correct assessment of the current situation and make a decision on how to make a profit. There are very few such traders. Their secret weapon is their vast experience of regular trading in the stock market. At first glance, it may seem that these people are predicting the stock market using the method of the third group of gamblers, but this is a mistaken opinion. Financial market gurus use technical and fundamental analysis on a subconscious level, thanks to their enormous experience - they say about such people “they feel the market.” The above division into groups is quite arbitrary. There are people who use the techniques of different groups. Many players have developed several trading systems and work with them.

1.2 Justification of the method for forecasting the state of the stock market in the Russian Federation

Correlation analysis is often used to assess the relationships between global stock markets. The development of research in this area is facilitated, on the one hand, by the presence of quantitative statistics characterizing the dynamics of market conditions in the form of stock indices, and on the other hand, by the high practical value of research results.

You can give examples of quite interesting works by scientists working under the auspices of the World Bank and the IMF http://www.naufor.ru/download/pdf/factbook/ru/RFR2012.pdf:

- Pritsker M.<Каналы распространения финансовой инфекции>(The channels for Financial Contagion).

In this work, Mat Pritsker examines the reasons for the interconnections of financial markets, expressed, among other things, through the correlation of stock indices.

- Baig T., Goldfajn I.<Российский дефолт и финансовая инфекция в Бразилию>(Russian default and contagion to Brazil).

Based on an analysis of statistical data on the financial markets of Brazil and Russia, the authors concluded that the financial crisis in Brazil was aggravated, but not caused by the Russian default. They also noted the existence of a significant correlation between the Russian and Brazilian financial markets, which is especially pronounced in the Eurobond market.

- Forbes K., Rigobon R.<Измерение финансовой инфекции. Концептуальные и эмпирические аспекты>(Measuring Contagion: Conceptual and Empirical Issues).

It is necessary to pay attention to certain features associated with measuring correlation between indices:

First, the correlation coefficient is measured not between stock indices, but between relative changes in stock indices: the longer the study period, the greater the distortion obtained when this rule is violated.

Secondly, the researcher must decide the issue of choosing a period for changing stock indices. You can calculate the correlation coefficient between daily, weekly, monthly returns of stock indices, and in each case the result of the study will be different; the shorter the period of profitability, the greater the likelihood that the correlation coefficient will not take into account real-life influences that appear with a certain lag; as the period lengthens, the number of observations decreases and, accordingly, the correlation coefficient becomes less significant.

Thirdly, when assessing the dynamics of the correlation coefficient, the problem of heteroscedasticity arises. The essence of the problem is that the assessment of correlation in certain periods is distorted due to changes in the amplitude of fluctuations in stock indices.

The trend extrapolation method was created on the basis of static observation of the dynamics of a specific indicator, identifying development trends and maintaining this trend for subsequent periods. Otherwise, we can say that the methods of trend extrapolation allow the trends of the past development of the object under study to be transferred to the future period Gerasimenko V. Modern securities market // Russian Economic Journal. - 2011. - No. 9.- P. 53-75..

This method of trend extrapolation is used mainly for short-term forecasting, up to a year, in the case where the number of changes is equal to the minimum value. This method is implemented for each specific object separately and in stages for each subsequent moment of the current time. When it is necessary to make a forecast for a product or service based on extrapolation, the forecasting task involves analyzing demand and analyzing sales of a given product.

The results of forecasting a parabolic trend are applicable for all areas of intra-company planning, as well as for strategic, financial, marketing planning, production planning and inventory management, management of trade flows and operations. The following types of trend extrapolation methods are most often used for short-term forecasting: moving average methods and exponential smoothing methods. The moving average method is based on a simple assumption, which states that the subsequent indicator in a specific period of time is equal in value to the average. This extrapolation figure is calculated for the last 3 months.

And the exponential trend smoothing method can be characterized as a forecast of the current indicator for the upcoming period, which is presented as the total amount of the actual indicator for the current period and a short-term forecast for the current period, weighted using special indicators. In some cases, these trend methods are complemented by other parabolic trend correlation methods. This method involves studying the interaction of various trends in order to find their mutual influence and directly improve the quality of forecasts. Consequently, correlation analysis studies the relationship between two or more indicators, depending on this, this method is called pair or multiple correlation. These methods are used by both Russian and foreign enterprises, as they are the simplest, most traditional and effective.

CHAPTER 2. RESEARCH OF THE FORECAST STATE OF THE STOCK MARKET IN THE RF

2.1 Characteristics of the stock market as an object of research

The year was marked by a sharp reduction in the instrumental base of the Russian stock market: on the domestic organized market, the number of issuers decreased by 45 units - to 275 companies.

The capitalization of the domestic stock market changed slightly and amounted to $817 billion in foreign currency equivalent (2.3% more than a year earlier); in comparison with GDP, capitalization fell to 40%.

Structural imbalances in capitalization persisted. The reduction in the share of the ten most capitalized issuers stopped at 62%. The oil and gas industry accounted for 50% of capitalization at the end of the year. The shares of the metallurgy and electric power industries in capitalization have decreased; there has been an increase in the capitalization of the chemical industry, communications and trade companies.

The volume of transactions in shares on the domestic exchange market (excluding repo transactions) fell constantly throughout the year and totaled RUB 11.5 trillion. (41% less than a year earlier), turnover fell most significantly in the Classica sector of the Moscow Exchange group. The average daily turnover, accordingly, sharply decreased to 45.4 billion rubles Gerasimenko V. Modern securities market // Russian Economic Journal. - 2011. - No. 9.- P. 53-75..

The concentration of internal exchange turnover on shares of individual issuers remains high: the ten most liquid issuers of shares account for 85% of the total turnover, while about half of the turnover is the share of only two issuers - Gazprom OJSC and Sberbank of Russia OJSC. At the same time, the share of transactions with shares of Sberbank of Russia OJSC in the total turnover for the year decreased by 5 percentage points.

Earnings per share (P/E) declined throughout the year and stood at 5.3 at the end of the year.

Corporate bond market. The number of bond issuers in secondary circulation decreased over the year by 5.2% and became 292. The number of bond issues increased to 767 issues (10.8% more than a year earlier). The volume of placements at the end of the year reached 1.2 trillion rubles, which is the maximum value for the observed period.

The volume of the domestic corporate bond market reached RUB 4.2 trillion by the end of the year. at nominal value - 21% more than a year earlier. However, compared to GDP, this is slightly less than 7%.

The total volume of exchange and over-the-counter transactions (at par, excluding repo transactions) with corporate bonds for the year amounted to 6.7 trillion rubles. - 15% more than the previous year. Over 80% of secondary turnover is exchange transactions. Their volume (actual price, excluding repo transactions) increased by 3% compared to the previous year - to RUB 5.3 trillion. The share of the top ten issuers of corporate bonds in the total volume of exchange transactions in bonds accounts for 53% of the turnover, and this figure tends to grow.

The share of transactions with exchange-traded bonds at the end of the year increased to 30% http://www.naufor.ru/download/pdf/factbook/ru/RFR2012.pdf.

The problem of defaults on corporate bonds in 2012 lost its relevance; indicators of violations in the fulfillment of obligations by issuers on corporate bonds returned to pre-crisis levels.

Government bond market. The growth in the volume of government bond issues (GKO-OFZ) accelerated; at the end of the year, the volume of this market at par reached 3.3 trillion rubles. (17% more than a year earlier), in relation to GDP this is not much more than 5%. The volume of the secondary market (in the main trading mode and the negotiated transactions mode) increased 2.5 times over the year and amounted to 4.4 trillion rubles.

Market of subfederal and municipal bonds. Over the past years, this market has not shown positive trends. Its volume in comparison with other sectors of the debt securities market is small and at the end of the year amounted to 440 billion rubles. at par. The secondary market is characterized by great variability, however, there is a clear trend of a constant decline in turnover; at the end of the year, the total trading volume (exchange and over-the-counter transactions at par value, excluding repo transactions) decreased to 533 billion rubles. (8.5% less than a year earlier).

Composite stock indices were unable to recover the previous year's losses over the year. Positive dynamics of the indices were noted only in the first three months of the year, then there was a fall, which was replaced by a sideways trend.

The RTS and MICEX indices reached their maximum values ​​for the year almost simultaneously: the RTS Index on March 15, 2012 amounted to 1754, having increased by 22.4% since the beginning of the year, the MICEX Index on March 14, 2012 reached the level of 1631, having increased by 12.9% since the beginning of the year. . The indices reached their minimum value for the year in May-June: The RTS Index on June 1, 2012 was 1227, decreasing from the maximum value by 27.2%, the MICEX Index on May 23, 2012 fell to 1256, decreasing from the maximum value by 22.9% Lyalin V A. Russian securities market: main stages and development trends // Eurasian international scientific and analytical journal. - 2012. - No. 2. - pp. 182-186..

At the end of the year, the consolidated stock indices of shares showed a slight positive return; the greatest growth was demonstrated by the RTS Index (1526.98 points - plus 10.5% per annum).

Composite stock indices do not demonstrate the ability to overcome the historical highs reached in the first half of 2008, attempts to enter the long-term upward trend observed in 2010-2012. end unsuccessfully.

Among the industry indices, the electric power industry index showed the largest decline for the second year in a row. And the most profitable index was the consumer goods index. Capitalization indices were in the neutral zone at the end of the year. At the end of the year, the volatility of the main composite stock indices was at a fairly low level.

The average total income of investors in corporate bonds (MICEX CBI TR index) grew until mid-May, then there was a decline. Then, until the end of the year, this index resumed growth and at the end of the year increased by 8.6%. The weighted average yield to maturity of corporate bonds on the MICEX CBI TR index was subject to multidirectional changes and averaged 8.7%. It is characteristic that throughout the year the weighted average yield to maturity constantly exceeded the refinancing rate of the Bank of Russia, with the average spread amounting to 0.65 percentage points.

The average total income of investors in government bonds (MICEX RGBI TR index) continued the trend formed at the end of the previous year and grew at an accelerated pace until the beginning of May, then there was a sharp decline, followed by accelerated growth in July - August. As a result, over the year the MICEX RGBI TR index increased by 14.7%. The behavior of the effective yield to maturity of government bonds RGBEY in the first half of the year was stable, but since May the effective yield to maturity began to grow. In May-June, the effective yield of government bonds exceeded the refinancing rate of the Bank of Russia; one-time excesses of the refinancing rate of up to 0.34 percentage points were recorded. Then the effective yield to maturity of government bonds decreased until the end of the year, on average at the end of the year it amounted to 7.4%.

At the end of the year, the volatility of stock indices of corporate and government bonds was at a fairly low level.

The Russian volatility index RTSVX is qualitatively almost completely identical to the international index VIX. At the same time, RTSVX significantly exceeds its international counterpart in its values. The behavior of the volatility index is clearly non-stationary. In the period January - April, the index showed stable behavior relative to the average value of 33%. However, in early May it began to grow, and in certain periods the index value exceeded 50%. In the second half, the volatility index decreased and by the end of the year fell to 20%, getting as close as possible to its international counterpart.

2.2 Retrospective analysis of the stock market in the Russian Federation

In table 1 shows summary data on shares that are offered on the stock market of the Moscow Exchange group, as well as an estimate by Standard&Poor"s1 of the number of Russian issuers whose shares are admitted to trading on the MICEX Stock Exchange, NYSE, NASDAQ and are included in the LSE-listed S&P EMDB Russia index.

Table 1. Organized stock market in 2011-2013.

			S&P Valuation
	Number of share issuers		Number of share issuers	Number of share issues (JSC, AP) in quotation lists
Moscow Exchange Group

According to data as of the end of June, there are 270 of them on the domestic market, 44 companies less than a year earlier, a significant decrease - by 16.3%.

According to foreign sources, 301 Russian companies issuing shares are represented on the domestic and foreign markets, which is 39 companies less than a year earlier (a decrease of 12.9%).

The number of issues of shares included in the quotation lists on the domestic market is also decreasing, but the rate of reduction is lower: since June 2012, over the twelve months it has decreased by six issues (5.6% in relative terms) Lyalin V. A. Russian securities market papers: main stages and development trends // Eurasian international scientific and analytical journal. - 2012. - No. 2. - pp. 182-186..

During the second half of 2012 - the first half of 2013, there was a change in the long-term trend associated with a gradual decrease in the number of “market” bond issuers: as of June 2013, their number increased to 306 companies against 288 a year earlier - by 6.3 % (Table 2).

Table 2. Number of issuers of corporate bonds and issues in 2012-2013

The growth in the number of “market” issues did not stop, and at the end of the first half of 2013, the number of such issues reached 834 (20% more than a year earlier). In Fig. Figure 3 shows a graph characterizing the volumes of the corporate bond market (placed “market” and “non-market” issues, at par value). In the first half of 2013, the growth in the volume of the corporate bond market continued and at the end of June in nominal terms the volume reached 4631 billion rubles, by 919 billion rubles. more than a year earlier (by 19.8% in relative terms). At the same time, it should be noted that the growth rate is gradually slowing down: if in the fourth quarter of 2012 compared to the previous quarter the increase was 9.0%, then in the second quarter of 2013 - 4.2%.

The placement of new corporate bond issues traditionally occurs unevenly. In table Table 3 shows data on the placement of new “market” and “non-market” issues of corporate bonds.

Table 3. Placements of new issues of corporate bonds in 2012-2013.

	Number of bond issuers, pcs.	Number of new issues, pcs.	Volume of placement, billion rubles.
	Market	Non-market		Market	Non-market		Market	Non-market

In the first half of 2013, there was high placement activity: 790 billion rubles were raised. against 475 billion rubles. a year earlier. At the same time, the share of “market” placements in this volume decreased from 92 to 87%.

Traditionally, at least 80% of secondary trading in corporate bonds is carried out on the organized market.

The number of corporate bond issuers represented on the Moscow Exchange Group Stock Market has begun to grow over the past two quarters.

According to data at the end of June 2013, there were 322 of them compared to 310 a year earlier. At the same time, the growth in the number of bond issues turned out to be even more intense - by 21% over the same period. At the same time, the number of issuers whose bonds are included in the quotation lists remained virtually unchanged - slightly more than 180 issuers (on average, about 56% of the total number of bond issuers).

2.3 Scenarios for the development of the Russian stock market in the long term

The Russian market still remains monopolistic; many industries (for example, oil and gas, construction) are closed to competition: pricing in these industries is far from market-based, and there is no competition in principle. Companies do not have the opportunity to enter these markets and compete for customers on equal terms. Although for the final formation of many markets, the development of the stock market is necessary. Securities markets // TRINFICO Investment Group / Electronic resource // http://www.trinfico.ru..

According to L.A. Chaldaev, to solve the previously identified problems it is necessary:

1 Improve legislation (special attention to tax legislation). In a number of cases, the tax legislation in force in Russia does not take into account the peculiarities of taxation of certain types of financial transactions;

2 Reduce administrative barriers and simplify procedures. Recently, the Federal Financial Markets Service of Russia has taken a number of steps to simplify the procedures for state registration of securities issues, which has become one of the most important conditions for the dynamic growth of the volume of transactions with securities on the Russian market;

3 Develop the derivatives market and collective investment market. Currently, the Federal Financial Markets Service of Russia has prepared a concept and draft of the corresponding legislative act.

4 Improve the quality of corporate governance. In recent years, much has been done in Russia to ensure the required quality of corporate governance. At the same time, a number of problems require solutions, both in the short and long term;

5 Improve mechanisms for attracting private investors and mechanisms for protecting their interests. Constant and meaningful participation of the population in the financial market is one of the signs not only of an increase in the standard of living in the country, but also an indicator of a certain maturity of the financial market, ensuring the transformation of individual savings into investments necessary for the economy.

Increasing the population's interest in the financial market and stimulating investment in the financial market of individual savings is ensured by a number of measures, including in the area of ​​improving taxation, as mentioned above.

Compensation and insurance schemes can become an important means of stimulating the participation of retail investors in the financial market;

6 Improve regulation in the financial market. Improving regulation in the financial market should be carried out in three main directions: firstly, increasing the role of SROs and establishing their closer connection with the state regulator of the financial market, secondly, unifying the rules and regulations of state regulation of activities in the financial market with a gradual concentration of government functions on regulation, control and supervision in the financial market in one government body, thirdly, the development of a system of prudential supervision;

Solving the stated problems would make it possible to create a reliable basis for the long-term growth of the Russian financial market and increase its role both within the national and global economy. As a result, the target indicators presented in Table 4 can be achieved.

Table 4. Prospects for the development of the securities market

Index
Capitalization of public companies, trillion. rub.
Ratio of capitalization to GDP, %
Exchange trade in shares, trillion. rub.
Value of corporate bonds in circulation, trillion. rub.
Assets of mutual investment funds, trillion. rub.
Annual volume of public offerings of shares on the domestic market, by market value, trillion. rub.
Number of retail investors in the securities market, million people
Share of foreign securities in the turnover of Russian exchanges, %

It is expected that the ruble to dollar exchange rate will be at 34-35 at the end of 2013. According to V.A. Lyalin, oil prices during this period will fluctuate in the range of 100 - 130 dollars per barrel.

Maksimova T.P. assumes that in terms of bonds rates are expected to stabilize at the level of 9-10% per annum. The possible range of fluctuations in these two years appears to be in the range of 8-12% per annum, depending on the rate of economic growth. At the same time, the maximum of 12% per annum may occur in mid-2012 with a further decrease to 9-10% in 2013. With the acceleration of economic recovery, inflationary processes are inevitable, which may lead to changes in rates in the money and bond markets. In general, no turmoil is expected in these markets and it is thought that there may be fluctuations in rates depending on the situation with US debt securities and LiBOR rates, movements in risk spreads (premiums) on borrowings, including bonds, shifts in international currency rates and uneven rates of development of regional economies. At the same time, a sharp increase in the yield of the interbank market of non-deliverable forwards on the US dollar is not expected and, therefore, the situation with bond yields is assessed as generally calm during this period.

Thus, increasing the efficiency of the functioning of the securities market in modern Russia can be achieved by improving legislation, reducing administrative barriers, developing the derivatives market and the collective investment market, improving the quality of corporate governance, improving the attraction of private investors and mechanisms for protecting their interests, improving regulation in the financial market , suppression and prevention of unfair activities, improvement of regulation, consolidation and formation of a positive image.

CHAPTER 3. STATE OF THE STOCK MARKET OF THE RF: FORECAST FOR 2014-2016.

3.1 Construction and description of a forecast model for the state of the Russian stock market

To determine the forecast value of the RTS index return, a three-factor model was developed using regression and correlation analysis tools to predict the return value of the RTS index.

The development of the model was carried out in several stages http://www.naufor.ru/download/pdf/factbook/ru/RFR2012.pdf.

1. Determination of possible exogenous factors that may influence the return of the stock index.

2. Collection of statistical information about past values ​​of factors.

3. Checking the probability distribution for its compliance with the normal law for each factor.

4. Calculation of the paired linear correlation coefficient of each factor (normally distributed) with the resulting indicator.

5. Adding to the explanatory variables a factor that has a significant correlation with the resulting indicator.

6. Final formation of the model.

7. Checking the model for significance.

8. Calculation of partial coefficients of pairwise correlation of all variables in order to prevent multicollinearity of factors.

In the process of building the model, the following macroeconomic factors were considered:

- growth rate of portfolio foreign investments in the economy;

- rate of growth of the money supply;

- rate of change in the refinancing rate of the Central Bank of the Russian Federation;

- GDP growth rate (in real terms);

- growth rate of oil prices (NYMEX);

- rate of increase in the price of gold;

- rate of change in the interest rate on deposits;

- rate of change in interest rates on loans.

Of the above factors, only three were selected that had a correlation with the resulting indicator that was significantly different from zero. Among them:

- growth rate of oil prices;

- growth rate of non-cash money supply;

- GDP growth rate.

3.2 Preparing a forecast for the Russian stock market

All growth rates used in the model were taken over the quarterly period from 2005 to 2013. The choice of a quarterly rather than a monthly interval is due to the lack of statistics for the monthly period for most macroeconomic indicators. The return on the RTS index was determined using the formula

y = ln(RTS1/RTS0)?4 , (1)

where y is the profitability of the RTS index, RTS1, RTS0 are the latest values ​​of the RTS indices for the considered and previous quarters, respectively.

As can be seen from formula (1), the return on the RTS index is calculated using a continuous percentage scheme.

The annual growth rate of oil prices was calculated using the formula

x1 = ln(P1/P0)?4 , (2)

where x1 is the annualized growth rate of oil prices, P1, P0 are the closing prices for Brent crude oil (NYMEX) for the current and previous quarters, respectively. The growth rate of oil prices (2) is calculated using a continuous percentage scheme.

The annual growth rate of non-cash money supply was calculated using the formula

x2 = ln(DM1/DM0)?4 , (3)

where x2 is the growth rate of the non-cash money supply on an annualized basis, DM1, DM0 are the volumes of the non-cash money supply in rubles on the 1st day of the quarter following and under consideration, respectively.

The growth rate of the non-cash money supply (3) was calculated using a continuous interest rate scheme.

The annual GDP growth rate was calculated using the formula

x3 = ln(TGDP1/TGDP0)?ty/tq , (4)

where x3 is the annualized GDP growth rate, TGDP1, TGDP0 are the growth rates of real GDP in the current and previous quarters, respectively, as a percentage of 1995 GDP, ty and tq are the number of working days in the year and quarter, respectively.

All three factors and the resulting indicator over time are random processes (X1(t), X2(t), X3(t), Y(t)). Let us introduce the assumption of their statistical stationarity. Then the values ​​x1i, x2i, x3i, yi in each quarter can be considered a random outcome of the test, and unbiased consistent estimates of the distribution parameters of random variables can be determined by the sampling method.

If random variables X1, X2, X3, Y are normally distributed, then there can only be a linear relationship between them.

Let us show that the random variable Y is distributed according to the normal law.

Unbiased consistent sample parameter estimates can be found from empirical data. To do this, the range of Y values ​​was divided into 6 equal intervals (the number of intervals was determined by the Sturges formula), after which the values ​​of the empirical frequencies were determined, and then the Y frequency. Based on the obtained values, the distribution of the random variable can be normal. The unbiased estimates of the distribution parameters are as follows:

E(Y) = 0.3558,

S2(Y) = 1.4004 .

We put forward the null hypothesis H0: the random variable Y has a normal distribution law with parameters a and y2 (Y ~ N(a; y2)).

To test the hypothesis at a significance level of 0.05, we use the criterion of goodness-of-fit h2 - the Pearson test. Statistical and hypothetical probabilities of Y falling into the interval are presented in table. 5.

Table 5. Statistical and hypothetical probabilities of Y falling into the interval

Since ch2

0.05;3 = 7.814728, therefore h2 Ј h2

Let us show that the random variable X1 is distributed according to the normal law.

Unbiased consistent sample parameter estimates can be found from empirical data. To do this, the range of X1 values ​​was divided into 6 equal intervals (the number of intervals was determined by the Sturges formula), after which the values ​​of the empirical frequencies and then the X1 frequencies were determined. Based on the obtained values, the distribution of the random variable may be normal. The unbiased estimates of the distribution parameters are as follows:

E(X1) = 0.1543 ,

S2(X1) = 0.6227 .

We put forward the null hypothesis H0: the random variable X1 has a normal distribution law with parameters a and y2 (X1 ~ N(a; y2)).

To test the hypothesis at a significance level of 0.05, we use the criterion of goodness-of-fit h2 - the Pearson test. Since the number of hits in the second interval is less than 5, it is advisable to combine the first interval with the second. Statistical and hypothetical probabilities of X1 falling into the interval are presented in Table. 6.

Table 6. Statistical and hypothetical probabilities of X1 falling into the interval

Since ch2

0.05;2 = 5.991465, and therefore h2 Ј h2

b;k, then the hypothesis H0 does not contradict the experimental data.

Let us show that the random variable X2 is distributed according to the normal law. Unbiased consistent sample parameter estimates can be found from empirical data. To do this, the range of X2 values ​​was divided into 6 equal intervals (the number of intervals was determined by the Sturges formula), after which the values ​​of the empirical frequencies and then the X2 frequencies were determined. Based on the obtained values, the distribution of the random variable may be normal. The unbiased estimates of the distribution parameters are as follows:

E(X2) = 0.1543,

S2(X2) = 0.6227 .

We put forward the null hypothesis H0: the random variable X2 has a normal distribution law with parameters a and y2 (X2 ~ N(a; y2)).

To test the hypothesis at a significance level of 0.05, we use the criterion of goodness-of-fit h2 - the Pearson test. Since the number of hits in the second interval is less than 5, it is advisable to combine the first interval with the second. Statistical and hypothetical probabilities of X2 falling into the interval are presented in Table. 7.

Table 7. Statistical and hypothetical probabilities of X2 falling into the interval

Since ch2

0.05;3 = 7.814728, and therefore h2? ch2

b;k, then the hypothesis H0 does not contradict the experimental data.

Let us show that the random variable X3 is distributed according to the normal law.

In general, it is clear from empirical data that the time series is not strictly stationary. Obviously, the most significant GDP growth rates are observed in the third quarter of any year, and the most serious decline is in the first quarter.

Thus, even if the variances are equal, the mathematical expectation of the random variable depends on the quarter number. Let us bring the time series to a stationary form, leveling out the influence of the seasonal factor.

The seasonally adjusted levels of the series are presented in Table. 4.

They were calculated using the following algorithm:

Where is the average value of the GDP growth rate for the j quarter,

The GDP growth rate in the j quarter in year k, E(X3) is the average value of the random variable X3, calculated from the data of the grouped variation series.

The results of calculating seasonality indices are presented in table. 8.

	1st quarter	2nd quarter	3rd quarter	4th quarter

The values ​​of the adjusted series levels are obtained using the formula:

Unbiased consistent sample parameter estimates can be found from empirical data. To do this, the range of corrected X3 values ​​was divided into 6 equal intervals (the number of intervals was determined by the Sturges formula), after which the values ​​of the empirical frequencies and then the X3 frequencies were determined. Based on the obtained adjusted values, the distribution of the random variable may be normal. The unbiased estimates of the distribution parameters are as follows:

S2(X3) = 0.1074.

We put forward the null hypothesis H0: the random variable X3 has a normal distribution law with parameters a and y2 (X3 ~ N(a; y2)).

To test the hypothesis at a significance level of 0.05, we use the criterion of goodness-of-fit h2 - the Pearson test. Statistical and hypothetical probabilities of X3 falling into the interval are presented in Table. 9.

Table 9 Statistical and hypothetical probabilities of X3 falling into the interval

Hence, h2 = 4.533912.

Since h2 0.05;3 = 7.814728, and therefore h2 Ј h2 b;k, then the hypothesis H0 does not contradict the experimental data.

Since the random variables X1, X2, X3, Y have a normal distribution law, the relationship between them can only be linear, and the multiple linear regression model can be represented as:

yi = в0 + в1x1i + в2x2i + в3x3i + еi , (8)

where b0, b1, b2 and b3 are unknown parameters, ei is a disturbance (random error).

Or in matrix form:

The sample estimator of this model is the equation

yi = b0 + b1x1i + b2x2i + b3x3i + ei (9)

or in matrix form

Using elements of linear algebra and the least squares method, we determine the vector of unknown parameters b

b0 = -1.07296; b1 = 0.50645; b2 = 3.16038;

The results obtained are understandable from an economic point of view. As the price of oil rises, the probability of a high price in the future, calculated using hindsight, increases. This increases the expected income of Russian fuel and energy companies and, as a consequence, the fair value of Ramzaev E.P.’s fuel and energy business. The securities market is an integral part of the financial market // Economic Sciences. - 2011. - No. 77. - P. 47-50.. The market value of fuel and energy companies makes up a significant share of the capitalization of the RTS index. An increase in the non-cash money supply leads to an increase in investment activity by institutional investors and corporations and, of course, contributes to the growth of the stock market.

Thus, the mathematical expectation of Y for specific values ​​of uncertainty factors can be calculated using the formula

E (Y)i = -1.07296 + 0.50645 ? x1i + 3.16038 ? x2i - 0.0182 ? x3i. (10)

Multiple correlation coefficient R for this model

Determination coefficient R2 for this model

It can be concluded that the variability of the selected factors explains approximately 42% of the variability of the dependent variable.

Using the Fisher-Snedecor F test, we will check the significance of the regression equation at the significance level b = 0.05.

If the coefficient of determination R2 is known, then the criterion for the significance of the regression equation can be written as:

F = R2?(n - p - 1) / ((1 - R2) ?p) > Fb;k1;k2 , (11)

where k1 = p, k2 = n - p - 1; p - number of explanatory variables; n is the number of elements in the sample.

For this model:

F0..05;3;31= 2.9113.

Since F > Fb;k1;k2, the multiple regression equation is significant.

Since the model is multifactorial, to ensure the reliability of the forecast, it is necessary to check it for multicollinearity of factors. It is necessary that the explanatory variables be independent random variables. For this purpose, sample partial coefficients of pair correlation of factors were calculated, their values

rx1x2 = 0.0727; rx1x3 = -0.0314; rx2x3 = 0.1029.

As can be seen from the calculations, the sample coefficient of partial pair linear correlation is much less than 0.7 for all explanatory variables. It can be argued that there is no multicollinearity of factors.

The confidence interval for the mathematical expectation Ex0(Y) will take the form

yX0 - t1-b;n-p-1?sYx ? EX0 (Y) ? yX0 + t1-b;n-p-1?sYx, (12)

where sYx characterizes the variability of the dependent variable as a result of fluctuations in the explanatory variables.

The confidence interval for individual values ​​of the dependent variable y0 will take the form:

yX0 - t1-b;n-p-1?sY0 ? y0 ? yX0 + t1-b;n-p-1?sY0, (13)

where sY0 characterizes the variability of the dependent variable both as a result of fluctuations in the explanatory ones and under the influence of random factors not taken into account in the model.

Let's predict the value of the RTS index based on the values ​​of the factors included in the Forecast of the socio-economic development of the Russian Federation until 2010. This document determines the forecast values ​​of GDP growth rates and the price of 1 barrel of oil.

The GDP growth rate in 2014 will be 106.5%, in 2015 - 106.1%, in 2016 - 106.0%.

Hence, the logarithmic growth rate of GDP in 2014 will be 6.3%, in 2015 - 5.9%, in 2016 - 5.8%.

The price for 1 barrel of oil in 2014 will be 55 US dollars, in 2015 - 53 US dollars, in 2016 - 52 US dollars.

Thus, the annual logarithmic growth rate of oil prices in 2014 will be 10.35%, in 2015 - 3.70%, in 2016 - 1.9%.

We will determine the forecast growth rate of the non-cash money supply by analyzing the time series and finding the equation of the trend line.

The dynamics of the non-cash money supply, as well as the trend function graph, are presented in Fig. 1.

Rice. 1. Dynamics of non-cash money supply

Rice. 2. Expected return of the RTS index in 2014-2010.

3.3 Verification of the Russian stock market forecast

The equation for the trend line can be written as

Y =195.98?e0.0922x,

where Y is the value of the non-cash money supply; x - quarter number at the beginning of the calculation from the second quarter of 2005.

The quality of approximation is very high (R2 = 0.9945).

Thus, we determine the value of the non-cash money supply as of January 1, 2014 in the amount of 8,588.79 billion rubles, as of January 1, 2015 in the amount of 12,419.37 billion rubles, as of January 1, 2016 in the amount of 17,958.39 billion. rub., as of January 1, 2010 in the amount of 25,967.8 billion rubles.

The logarithmic annual growth rate of the non-cash money supply in 2014 is 32.42%, in 2015-2010. equal to 36.88% annually.

As a result of applying the forecast model, we calculate the forecast (sample) return of the RTS index for 2014, it is equal to 10.19%.

The expected return with probability 0.6 lies in the range from -28.08% to 7.69%. With a probability of 0.6, the return on the RTS index in 2014 will lie in the range from -71.76% to 51.37%.

As a result of applying the forecast model, we calculate the forecast (sample) return of the RTS index for 2015, it is equal to 7.28%.

The expected return with probability 0.6 lies in the range from -10.1% to 24.65%. With a probability of 0.6, the return on the RTS index in 2015 will lie in the range from -54.23% to 68.79%.

As a result of applying the forecast model, we calculate the forecast (sample) return of the RTS index for 2016, it is equal to 8.19%. The expected return with probability 0.6 lies in the range from -9.07% to 25.45%.

CONCLUSION

The modern stock market, as one of the most powerful tools for attracting investment funds, requires its participants to have a high level of professionalism in asset management. As modern theory and practice show, effective work in the stock market is impossible without a powerful apparatus for analyzing and forecasting stock prices, currencies and other inventory assets traded on the market.

Modern financial theory, together with mathematics and statistics, has several well-known models that describe the behavior of prices for financial assets. Currently, investment analysis exists in three forms: technical analysis, fundamental analysis and “academic” analysis (stock market econometrics).

Technical analysis, due to its simplicity and ease of use, has been one of the most popular decision-making methods in the stock market for almost a hundred years. However, his methods, while often very effective, are not strictly scientific and require a lot of experience and a bit of intuition to apply effectively.

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Stock market

Forecasting refers to scientifically based judgments of experts and ordinary investors about the future development of an object or its individual elements, as well as judgments about methods, timing and alternative ways to achieve a certain state of a given object. Securities market forecasts are necessary to determine the prospects for investing in a company's shares, conditions and priorities of a particular industry.

There are several theories about the possibility. One of them is the efficient market hypothesis, according to which all information is already taken into account in the price of a security and there is no point in making forecasts. This hypothesis is continued by the theory of random walks, according to which information is divided into two main categories - known (predictable) and unexpected (new). If predictable information is already included in the price, then there is no new information in the price yet. One of the properties of unpredictable information is its randomness, as well as the randomness of future price changes. It is the arrival of new unexpected information that explains price changes in the efficient market hypothesis. The theory of random walks only complements this with the assumption that price fluctuations are random.

Methods for forecasting the securities market

Today, professional participants use different methods stock market forecasting, the main ones are:

1. Expert methods. Here we should highlight the Delphi method, the essence of which is to collect assessments and opinions of different experts for the purpose of their subsequent generalization into a single assessment. When forecasting the market using this method, it is necessary to select a group of experts who are well versed in the subject area (banks, professional investors, analysts, etc.), then conduct a survey or questionnaire and summarize the information received about the current situation in the stock market.
2. Economic and mathematical methods based on the formation of models of the object under study. This model is a specific scheme that reflects the development path of the stock market under given conditions. Optimization models are also very significant - systems of equations that include various types of restrictions, as well as an equation called the optimality criterion (functional). With its help, the best solution for a specific indicator is found.
3. Methods of logical modeling based on searching for patterns in the securities market in the long term. This group includes: the scenario method (describing the sequence of results of an event, creating a database), stock market forecasting according to the model, as well as the method of analogies.
4. Statistical methods. Based on the construction of various indices (mixed, diffuse), mathematical expectation, calculation of dispersion values, interpolation, covariance, extrapolation.
5. Fundamental analysis. A method for predicting the stock market value of securities, based on an analysis of the company's production and financial performance indicators. Using fundamental analysis, experts determine the state of current affairs in the company and the profitability of its activities. Financial indicators such as net profit, revenue, net worth of the company, EBIDA, cash flow, liabilities, production indicators and the amount of dividends paid are analyzed.

Technical analysis. Stock Market Forecasting Method, based on an analysis of past price changes. Technical analysis includes a large number of methods and tools, all of them are based on one assumption: by highlighting trends and analyzing time series, one can make a forecast of price behavior. Information on trade volumes and other statistics is also used. Most often, technical analysis methods are used to analyze freely changing exchange prices.

The price of securities on the stock exchange is made up of two factors: the real cost of the company's capital (its prospects) and the ratio of supply and demand. On the one hand, the better the issuing organization is doing, the higher the profitability of its securities and the lower the risk, which causes an increase in the exchange rate. On the other hand, a simple market law applies: the higher the demand for securities, the more expensive they are.

These factors can have different directions. Thus, a company can prosper, but its shares become cheaper due to too high supply and low demand.

The first factor, which takes into account the current and future financial condition of the company and industry, underlies fundamental analysis stock market. The second, in which only the movement of securities prices is assessed, is used when technical analysis. These stock market forecasting methods predict the movement of stock prices in the short or long term.

Technical analysis of the stock market

Those. stock market analysis appeared in the 18th-19th centuries (the so-called “Japanese candlesticks”). In the past, when investors did not have access to information about the financial position of the company, they were left to focus on external indicators (primarily, the dynamics of the exchange rate). The method allows you to predict significant increases and decreases in prices in the short term, but does not cover all factors that can affect the rate.

The analyst has only asset price charts at his disposal. Programs for technical analysis of the stock market also work on their basis. The most popular program is MetaStock. Some brokers and platforms develop their own software.

Technical analysis of the stock market in Russia and the world is based on three principles:

1. The current price consists of all factors that can influence it (the state of the company, industry, market, etc.), which means that the trader does not need to study them.

3. Everything repeats itself. The price is influenced by psychological factors, as in the distant past, and the market may be dominated by either bullish, bearish, or neutral trends.

Features of fundamental analysis of the stock market

The spread of fundamental analysis is due to two prerequisites. The first is the lack of accuracy among those. stock market analysis. When assessing quotes, a trader does not always correctly track the trend, and in relation to new issuing companies this is completely impossible: the shares do not have dynamics.

The second prerequisite is the emergence of new rules on stock exchanges. Recently, issuers of securities are required to publish financial statements. This is what investors analyze when making decisions in favor of certain stocks.

The investor's task is to determine the real price of assets and predict its movement in the future. The analyst assesses the condition of the company itself in the context of the industry and the entire market and identifies undervalued and overvalued securities.