The higher the period of the moving average, be it SMA 20, EMA 60 or EMA 100, the more long-term and stable the trend this indicator will show you; however, the accuracy of the signals will slightly decrease, so you should not choose heavy moving averages, for example, EMA 500.

Use two or three easy moving averages, for example, EMA 8, EMA 13, EMA 21, to trade as efficiently as possible. You will quickly respond to all changes in the market trend, which in 90-95% of cases is predicted by the Moving Average absolutely correctly.

Today there are a lot of indicators without redrawing, for example, AutoTradeForecaster.

Many indicators, especially arrow ones, are prone to redrawing, but your task is to use those that quite accurately show the turning point of the price trend. It's quite simple. The red line is a downward trend, and the blue line is an upward trend. The signals are very reliable and accurate, so take this indicator into your service.

The fundamental possibility of studying and forecasting the market situation is based on the natural (deterministic) nature of changes in various indicators and on the inertia of technical and economic processes.

Inertia in market development manifests itself in two ways:

· as the inertia of relationships, i.e. as preservation in the main features of the mechanism of formation of phenomena and market processes (inertia of the first kind);

· as inertia in the development of individual aspects of processes, i.e. as a certain degree of preservation of their character (tempo, direction, fluctuation of the main quantitative indicators market conditions) over relatively long chronological periods (inertia of the second kind).

The degree of inertia depends on factors such as the size or scale of the process being studied. If we consider the organizational and production system operating in the market, we can conclude that the lower the level in the hierarchy “enterprise - industry - national economy”, the less inertial the corresponding characteristics are.

The latter circumstance can be explained by the fact that the influence of a single factor (for example, the introduction of innovations) at the grassroots level often turns out to be dominant. At the macro level, indicators are more stable, since their value is influenced by a much larger number of factors. Changing the action of a number of them (sometimes having the opposite effect) leads to a lesser loss of inertia than at the micro level.

Experience shows that the “younger” the system being studied (economic structure, market phenomenon, process) and, accordingly, the less time there was for the formation of more or less stable relationships and main trends in its development, the less inertia it has.

The presence of inertia does not mean that the economic system in its development will strictly follow the already emerging trend. Various factors will influence the system to a greater or lesser extent, leading to deviations from the established trend.

Forecasting inertial systems, such as the market, is carried out through an analysis of the area of ​​the possible, that is, what is possible in the future. Forecasting theory views the concept of possibility as a form of determination. There are two types of determination:

· internal determination, characteristic of holistic complex systems that have an internal source of self-development ( social systems);

· external determination, which involves the identification of stable, relatively unchanging relationships, when the system under study is considered as something permanent and stable. This is a simpler form of determination.

The principle of external determination involves testing the system under study for stability. This means that not any combination of properties and states of the elements that form an integral market system is possible in the future, but only one that forms a certain stable form that reflects the essence of this system.

The sustainability criterion allows us to select only those future options that can actually exist.

To determine the type of inertia of an economic system operating in the market, it is necessary to find out whether there is a trend in the time series of technical and economic indicators ( trend). Determining the type of inertia allows you to subsequently select an adequate forecasting method (for example, with inertia of the first kind, these can be regression models of a stationary nature, and with inertia of the second kind, extrapolation models or autoregression).

The main task of time series analysis is to isolate the deterministic component (trend) and the random component, as well as to evaluate their characteristics.

In general, a time series can be represented as

Where y t– values ​​of time series indicators;

– deterministic component;

xt– values ​​of deterministic factors influencing the deterministic component at a point in time;

e t– random component;

T– length of the time series.

In economics, the role of a deterministic component is often played by a resulting indicator, for example, the volume of trade turnover, determined by the general trend of economic growth, the pace and volume of innovation, and resource costs. For this result, except economic factors, some natural factors can also have a long-term effect. The random component accumulates the influence of many factors not included in the deterministic component, each of which individually has an insignificant impact on the result.

Many researchers, when analyzing time series, identify the following four main components:

· long-term, evolutionarily changing component, which is the result of the action of factors leading to a gradual change in a given economic indicator. So, as a result of scientific and technological progress, improvement of organization and management of production, relative indicators of productivity and efficiency of production are increasing, and specific resource consumption per unit of useful effect is decreasing;

· long-term cyclical fluctuations manifest themselves over a long period of time as a result of the action of factors that have large consequences or change cyclically over time (crises of overproduction, periodic natural phenomena);

· short-term cyclical fluctuations (seasonal component) show fluctuations in factors depending on the seasons (agricultural productivity, seasonal fluctuations in retail turnover);

· random component is formed as a result of the superposition of a large number of external factors that do not participate in the formation of the deterministic component and have an insignificant effect on changes in the values ​​of indicators.

To identify the type of inertia in market indicators, it is necessary to check their dependence on the time factor. For this purpose, in particular, we can recommend the method developed by F. Foster and A. Stewart, who proposed using the data of the series under study to determine the values u t And l t by sequentially comparing the levels of a series of dynamics:

The summation in formulas (3.4) and (3.5) is performed over all terms of the series. Received indicators s And d are used to test the hypothesis that there is no trend ( s– in the middle, d– in dispersion) in the dynamics of the economic indicator under study. The hypothesis is tested using t- Student's criterion, that is, determining

Where m– mathematical expectation of the value s;

s– mean square changes of quantities s And d.

Values m, s 1 And s 2 tabulated. The corresponding tables can be found in Appendix 1 (Table A.1.1). If , then the hypothesis about the presence of a trend is accepted, t cr found from tables of critical points of the Student distribution depending on the significance level of the hypothesis a(usually selected at the level of 0.05) and the number of degrees of freedom k:

Where n– number of levels of the series.

If , then the hypothesis is rejected, and the object under study is characterized by inertia of the second kind. This method is quite simple and can easily be used in practical developments. An example of using the Foster-Stewart method and the series criterion is presented in Figure 1.1.

Fig.1.1. An example of using the series criterion and the Foster-Stewart method

After checking the type of inertia of the market system (object), it is necessary to proceed to the selection of an adequate forecasting method, as well as parametric models in accordance with the algorithm shown for quantitative calculations and verification of results.

To analyze trends in patterns of change market parameters Various indicators and methods can be used, differing in complexity and labor intensity of calculations. Some of them are presented below.

Average growth rate (τ avg) - geometric average of a series of successive (chain) growth rates

Chain growth rate - the ratio of any level of a time series to the previous level (expressed in % or shares)

(1.10)

Disadvantages of average tempo as a general indicator

1. The average rate is completely determined by the two extreme levels of the series, while the choice of period for calculating the average rate significantly determines its value. Shifting the period even by one step can lead to a significant change in the tempo value.

2. Application of the average growth rate assumes that the development trajectory approaches an exponential curve, i.e. process in general case geometric progression.

3. The average pace hides the nature of the dynamics of the period under study, because does not take into account intermediate terms of the series, hence information essential for analysis is lost

Moving averages Smoothing (mechanical leveling of a time series) involves replacing the actual levels of a time series with calculated ones, which have significantly less variability than the original data.

One of the simplest smoothing techniques is to calculate sliding, or, moving averages

(1.11)

Moving average value for moment t (t=1,…,n);

y i – actual level value at time i;

i – serial number of the level in the smoothing interval.

With a large number of levels

(1.12)

, (1.13)

U 1 =y 1

U 2 = U 1 + y 2 etc.

Weighted moving averages. Each level within the smoothing interval is assigned a weight depending on the distance measured from this level to the middle of the smoothing interval (Table 1.2).

The central point of assessing and analyzing market conditions is the study of trends and features of market development, as well as its stability. To determine market development trends, time series of market activity indicators, including business activity, are constructed. The basic chain and average growth rates for the period are calculated.

Trends in the development of R are most often expressed graphically for clarity. The levels and rates plotted on the graph most often reflect the unevenness of development: acceleration, deceleration, surges of rise and fall, etc. You can detect a clear trend in these processes using the following techniques:

By the method of technical alignment (when a resultant line, straight or curved, is drawn visually (by eye) on the graph, reflecting the development trend in the researcher’s opinion;

The mechanical smoothing method (moving average method) is the calculation of moving averages of 3 - 5 or more levels reflecting the trend and cyclical development;

Analytical alignment method identified thus. the line characterizes the main trend in the development of R and can be used to forecast (extrapolate) the current trend for the future period.

Example.

The table shows actual and forecast data on the company's product turnover for 8 years.

1. Give a forecast estimate of the company’s product turnover for 2013. To do this, use the following analysis methods:

Graphic;

Method for smoothing the average value of the argument;

Least square method.

(400+460+470):3=443,3

(460+470+500):3=476,6

(470+500+530):3=500

(500+530+570):3=533,3

(530+570+600):3=566,6

(570+600+625):3=598,3

Forecast:

% completion rate of change in dynamics for each year quarter or month.

Percentage of plan completion or rate of decline or increase in the analyzed indicator for the period (over 8 years) n – number of years, months, etc. in the studied period (8). The standard deviation allows us to reflect fluctuations in the development of the period under study. Using the coefficient of variation (unevenness) V k per, you can analyze the unevenness of changes in the studied indicator. Thus, the uniformity of development R can be studied by calculating the standard deviation and the coefficient of variation.

Based on the technical trend model, it is possible to identify and, in the latter case, measure the stability of market development over time. The stability of dynamic market development is manifested in the nature of deviations of actual levels of development from the main trend, i.e. from the trend. For operational purposes, along with the already mentioned methods (3 which use tasks), it is also recommended to use the opportunistic testing method in the form trend poll, which is a variant of expert research.

Based on surveys, opportunistic tests are developed, which are arithmetic averages of three possible assessments of the current market development trend: growth or rise; stability, reduction, each of which is assigned a corresponding score; As a result, a general assessment of the market development trend is given.

Market stability can also be assessed using groupings of firms (enterprises, regions) according to the main indicators of the state and development of the market. For example, groupings of competing firms by volume or rate of sales, by price level.

In the development of the market, a certain repeatability and cyclicality may appear:

Intra-annual cyclicity is usually seasonal in nature; it is detected using seasonality indices and mechanical smoothing methods. Due to seasonal cyclicity, coefficients of variation can also be measured. The cyclical nature of the market is also related to the life cycle of goods.

Changing cycles is an integral property of a market economy, when growth leads to saturation, a sales crisis, alternating depression, which in turn turns into recovery and the cycle is repeated many times. The classification of cycles and their sequence and duration do not always coincide in different economic schools. Cycles are distinguished by duration:

· 20 year cycles, caused by shifts in the reproductive structure of the production sector and shifts economic policy;

· Jungler cycles(+ – 10 years). Manifests itself as a result of the interaction of various monetary factors;

· Katchina cycles(3-5 years) are based on the dynamics of inventory turnover;

· private business cycles(from 1 to 12 years) due to fluctuations in investment activity;

· classical cycles of the Harvard School look like this:

Depression - the activity of the commodity cycle weakens, prices tend to decline;

Revival - in commodity production and trade begins to grow;

Rise – the pace of business activity at commodity market rise, prices continue to rise;

Inflation - the growth of production and trade is suspended, commodity prices slow down;

Crisis - industrial and commercial activity freezes, 50% of contracts are not implemented, deliveries are not paid, inventories increase, prices fall.

In the 20th century, especially in its second half, the interaction between market participants radically changed.

For a long time, the authors of economics textbooks depicted the relationship between sellers and buyers as a market perfect competition, which consists of too small enterprises that are not able to influence the market price. Now, according to P. Samuelson and V. Nordhaus, quite large enterprises:

“have a certain influence on the market price. Most markets in modern economy are actually influenced by several large companies, most often two or three. Welcome to the real world - the world of imperfect competition."

What is this new world? It can be understood if we identify the trends that are characteristic of the development of monopoly and competition in the second half of the 20th century.

Increased market monopolization

The first trend is towards increased monopolization of the market. The scientific and technological revolution caused a transition to much more high level consolidation of the economy. The centralization of production led to the formation of powerful natural ones, covering the national economic space(main pipelines for transporting oil and gas, routes for transmitting electrical and thermal energy, etc.). In addition, a public sector that was monopolistic in nature emerged in the national economy.

Increasing competition

The second tendency is towards exacerbation. The scientific and technological revolution has enormously accelerated the improvement of the technical base of production. In this regard, in countries with new economy Rivalry has sharply intensified, especially in the field of introducing the latest achievements of technology and technology. Such competition has become truly global, covering all countries with open economies.

Consequences of current trends

Thus, at the end of the 20th century, a paradoxical situation arose: directly opposite types of interaction between business entities simultaneously strengthen their positions on the market. The question arises: what consequences does the collision of these two trends entail?

• First, the economic role of many monopolies is changing radically. For the end of the 19th - first half of the 20th century. traditional was the desire of monopolies to slow down the use of scientific and technical innovations in order to prevent a reduction in market prices. But now large business associations are becoming legal monopolies and, for a certain period of time, using patent law to update technology.
• Secondly, the implementation of the achievements of the scientific and technological revolution requires large material costs. As a rule, small enterprises typical of free competition are not able to carry out such expenses. In contrast, monopolies with their high incomes can achieve primacy in scientific and technological progress in their industry. By doing this, they strengthen their dominant position in the market.
• Thirdly, in recent years, the participation of large business associations in global competition aimed at conquering the world market space has become important. In many countries, the state provides assistance to national monopolies in mastering the latest scientific and technological achievements, in training scientific and engineering personnel, and also provides them with other economic advantages.

The guideline in this regard is the determination of the rating in global competition. Thus, according to surveys conducted by the International Economic Forum (the indicators of 46 countries of the world are assessed), in the 1990s. The USA was recognized as the most competitive country. Closest to it were Singapore, Hong Kong and small European countries. Meanwhile, from 1992 to 1997 in this ranking, Japan dropped from 2nd to 10th place, Germany - from 5th to 14th, France - from 15th to 19th. Russia consistently occupied 46th place.

So, it is obvious that in modern period characterized by an unusual synthesis (from the Greek synthesis - connection, combination) of competition and monopoly.