Modern problems of science and education. Problems and prospects of financial forecasting in the Russian Federation

. Financial forecasting- research into specific prospects for the development of finances of business entities and government entities in the future, a scientifically based assumption about the volumes and directions of use of financial resources in the future.

Financial forecasting-the basis for financial planning in an enterprise (i.e., drawing up strategic, current and operational plans) and for financial budgeting (i.e., drawing up general, financial and operational budgets).

Types of financial forecasts:

1. Short-term forecasting (composite stock indices, exchange rates, weighted average returns, rates, quotes of futures contracts, etc.). In short-term forecasting, a forecast is drawn up based on trading data from the last working day of the week.

2. Medium-term forecasting (with a depth of one year) is based on data from the same indicators and indicators of financial markets. The form of medium-term financial forecasts can be very different, depending on the indicators used.

3. Long-term forecasting of financial markets (more than a year in depth) is based not only on specific data from fundamental and technical analysis, but also on the assessment of certain quantities that inform about the most expected development of events in financial markets and possible emergence new trends or strengthening of old ones.

Task financial forecasting at the enterprise level - obtaining the information necessary to anticipate, understand and timely adapt the goals and capabilities of the enterprise to current circumstances. In addition, forecasting is aimed at:

To identify objectively emerging economic trends;

Analysis of the company's potential;

Identification of development alternatives;

Identification of problems that require solutions during the forecast period;

Determining the level of resources (material, labor, financial, intellectual, etc.) that will be necessary for the company to achieve the goals of its activities.

18. Financial planning. Balance sheet method of financial planning, financial plans. Characteristics of individual financial plans. Development of territorial financial planning.

Financial planning is activities to draw up plans for the formation, distribution and further use of financial resources at the required level of individual business entities, that is, their associations, industry structures, territorial administrative units, the country as a whole.

Object of financial planning are financial resources, the formation of which occurs in the process of financial distribution and redistribution of GDP, and the result is various types of financial plans and forecasts.

Purpose of financial planning- determination of possible volumes of financial resources, capital and reserves based on forecasting the value of financial indicators: profit, working capital, depreciation, taxes, etc.

The essence of the balance sheet method of financial planning The point is that by building balances, a link is achieved between the available financial resources and the actual need for them. Now this method is of particular importance, since all expenses of enterprises depend on previously earned funds; enterprises have become completely independent, independent and must rely only on their own income, and in no case on assistance from the state or ministry.

Financial plan is a plan for the formation and use of financial resources.

All financial plans are divided into two large groups- consolidated and individual plans. And consolidated financial plans, in turn, are divided into national plans for individual economic associations (industrial and financial groups or associations of concerns, associations, etc.) and territorial ones. Individual plans- these are financial plans of individual business structures.

According to the duration of action they are divided into:

Long-term financial plans (calculated for a period of more than one year);

Current (for one year);

Operational (for a quarter or a month).

Basic financial plans At the national and territorial levels there are the budget (federal, regional, local) and the budgets of state extra-budgetary funds.

The budget as a planning document is a list of income and expenses of government bodies or local government. Compiled in the form of a balance of funds intended for financial security tasks and functions of the state and local government. Compiled by the executive authority for one calendar year and approved in the form of a law by representative authorities.

The budgets of state extra-budgetary funds (Pension Fund of the Russian Federation, Social Insurance Fund of the Russian Federation, federal and territorial compulsory health insurance funds) are formed in the form of a balance of income and expenses of state extra-budgetary funds, ensuring the implementation of the constitutional rights of citizens to social Security, health care and receiving free medical care.

Financial plans drawn up by business entities include a balance of income and expenses, a consolidated budget, an estimate of income and expenses, and a business plan (this is a plan for the implementation of a specific project or agreement).

Territorial planning- planning for the development of territories, including the establishment of functional zones, zones of planned placement of capital construction projects for state or municipal needs, zones with special conditions for the use of territories.

One of the important goals of territorial financial planning is the development of programs that involve combining the efforts of territorial authorities and enterprises located on their territories to develop social infrastructure.

In this regard, there is a need for information on the territorial budget, the balance of cash income and expenses of the population, etc., reflecting individual aspects and stages of the distribution and redistribution of national income created and used in a given territory.

Introduction
Chapter 1. Concepts and essence of financial forecasting
1.1. The concept and tasks of financial forecasting
1.2. System of financial forecasting methods
1.3. Problems of financial forecasting at macro and micro levels
Chapter 2. Prospects for financial forecasting in the Russian Federation
2.1. Prospects for social economic development
2.2. Main priorities of socio-economic development
Conclusion
List of sources used

Introduction

Nowadays, every day society is faced with a huge variety of financial relations, including quite diverse daily financial transactions. It is absolutely clear that for the growth and stability of the country’s economy, for the successful functioning and development of the entire market economy, it is necessary to have a reliable financial system, which is a set of interconnected areas and links of financial relations.

Thus, the creation of a reliable financial system is one of the important tasks of the state. It serves as the basis for the entire existing economy and undoubtedly plays a very important role. Currently, in the economic literature there are many different controversial statements regarding the composition and structure of the financial system. But different terms, views and controversial opinions on some individual issues do not cancel general idea about the main subjects of the financial system. There are long-established, traditional ideas about the composition of the financial system and terminology, which are defined by the regulations of the Russian Federation and are traditionally used in our country.

In addition to the main components of the financial system, each developed country has its own characteristics that have allowed it to reach certain heights at the present stage. Therefore, the study of the financial system of developed countries is of interest to Russian specialists in the field of economics and finance. Consideration of the features of the financial systems of foreign countries is advisable, from the point of view possible use some of its aspects in the structure of the Russian financial system.

Financial forecasting is one of the most important stages of financial planning. The purpose of financial forecasting is to link material-material and financial-cost proportions in the economy in the future; assessment of the expected volume of financial resources; determination of financial support options; identification possible deviations from accepted designs.

Chapter 1. Concepts and essence of financial forecasting

1.1. The concept and tasks of financial forecasting

Financial forecasting is a study of specific prospects for the development of finances of business entities and government entities in the future, a scientifically based assumption about the volumes and directions of use of financial resources in the future.

Financial forecasting reveals the expected future picture of the state of financial resources and the need for them, possible options for carrying out financial activities and represents a prerequisite for financial planning. The main goal of financial forecasting, carried out to scientifically substantiate the indicators of financial plans and contribute to the development of a concept for financial development for the forecast period, includes assessing the expected volume of financial resources and determining the preferred options for financial support for the activities of business entities, state authorities and local self-government.

The objectives of financial forecasting are:

– linking material and financial-cost proportions at the macro and micro levels for the future;

– determination of the sources of formation and volume of financial resources of business entities and government entities for the forecast period;

– justification of directions for the use of financial resources by business entities and government entities for the forecast period based on an analysis of trends and dynamics of financial indicators, taking into account the internal and external factors affecting them;

– determination and assessment of the financial consequences of decisions made by state authorities and local governments, business entities.

Financial forecasting is carried out by developing various options for the development of an organization, a separate administrative-territorial unit, or the country as a whole, their analysis and justification, assessing the possible degree of achievement of certain goals depending on the nature of the actions of the planning subjects. This is achieved by two different methodological approaches:

1) within the first approach, forecasting is carried out from the present to the future based on established cause-and-effect relationships;

2) in the second approach, forecasting consists in determining the future goal and guidelines for movement from the future to the present, when the chain is unfolded and studied possible events and measures that need to be taken to achieve a given result in the future based on the existing level of development of the organization, administrative unit and the country as a whole.

Mathematical modeling allows you to take into account many interrelated factors that influence the indicators of the financial forecast, and select from several options for the forecast project that is most consistent with the accepted concept of industrial, socio-economic development and financial policy goals.

Econometric forecasting is based on the principles of economic theory and statistics: the calculation of forecast indicators is carried out on the basis of statistical estimation coefficients for one or more economic variables acting as forecast factors; allows you to consider the simultaneous change of several variables that affect the financial forecast indicators. Econometric models describe, with a certain degree of probability, the dynamics of indicators depending on changes in factors influencing financial processes. When constructing econometric models, the mathematical apparatus of regression analysis is used, which gives quantitative estimates of the average relationships and proportions that have developed in the economy during the base period. To obtain the most reliable results, economic and mathematical methods are supplemented with expert assessments.

The method of expert assessments involves generalization and mathematical processing of the assessments of specialist experts on a specific issue. The effectiveness of this method depends on the professionalism and competence of the experts. Such forecasting can be quite accurate, but expert assessments are subjective, depend on the expert’s “feelings” and are not always amenable to rational explanation.

The trend method, which assumes the dependence of some groups of income and expenses only on the time factor, is based on constant rates of change (constant growth rate trend) or constant absolute changes (linear time trend). The disadvantage of this method is that it ignores economic, demographic and other factors.

The development of scenarios is not always based on science and objectivity; they are always influenced by political preferences, the preferences of individual officials, investors, owners, but this allows us to assess the consequences of the implementation of certain political promises.

Stochastic methods assume the probabilistic nature of both the forecast and the relationship between the data used and the forecast financial indicators. The likelihood of calculating an accurate financial forecast is determined by the amount of empirical data used in the forecast.

Thus, financial forecasting methods differ in costs and volumes of final information provided: the more complex the forecasting method, the greater the costs associated with it and the volumes of information obtained with its help.

The result of financial forecasting is the preparation of a financial forecast, which is a system of scientifically based assumptions about possible directions of future development and the state of the financial system, its individual areas and subjects of financial relations. Forecasts make it possible to consider various options for the development of finance, for example, under favorable, average and worst-case scenarios for the development of the economy, business entity, market conditions, etc. Financial forecasts can be short-term (up to 3 years), medium-term (5-7 years) and long-term (up to 10-15 years).

At the national and territorial levels, financial forecasts are compiled in the form of a long-term financial plan and balance of financial resources (country, region, municipality) (Articles 172, 174, 175 of the Budget Code of the Russian Federation).

1.2. System of financial forecasting methods

In world practice, more than two hundred forecasting methods are used, in domestic science - no more than twenty. The introduction stated that financial forecasting methods that have become widespread in developed foreign countries will be considered.

Thus, depending on the type of model used, all forecasting methods can be divided into three large groups:

Methods of expert assessments, which involve a multi-stage survey of experts according to special schemes and processing of the results obtained using economic statistics tools. The application of these methods in practice usually involves using the experience and knowledge of trade, financial, and production managers of an enterprise or government agency. The disadvantage is the reduction or complete absence personal responsibility for the forecast made. Expert assessments are used not only to predict the values ​​of indicators, but also in analytical work, for example, to develop weighting coefficients, threshold values ​​of controlled indicators, etc.

Stochastic methods that assume the probabilistic nature of both the forecast and the relationship between the indicators being studied. The likelihood of obtaining an accurate forecast increases with the number of empirical data. These methods occupy a leading position in terms of formalized forecasting and vary significantly in the complexity of the algorithms used. The simplest example is the study of trends in sales volumes by analyzing the growth rates of sales indicators. Forecasting results obtained by statistical methods are subject to the influence of random fluctuations in data, which can sometimes lead to serious miscalculations.

Stochastic methods can be divided into three typical groups, which will be named below. The choice of a method for forecasting a particular group depends on many factors, including the available source data.

The first situation - the presence of a time series - occurs most often in practice: a financial manager or analyst has at his disposal data on the dynamics of an indicator, on the basis of which it is necessary to build an acceptable forecast. In other words, we are talking about identifying a trend. This can be done in various ways, the main ones being simple dynamic analysis and analysis using autoregressive dependencies.

The second situation - the presence of a spatial aggregate - occurs if for some reason there is no statistical data on the indicator or there is reason to believe that its value is determined by the influence of certain factors. In this case, multivariate regression analysis can be used, which is an extension of simple dynamic analysis to a multivariate case.

The third situation - the presence of a spatio-temporal set - occurs in the case when: a) the time series are not long enough to construct statistically significant forecasts; b) the analyst intends to take into account in the forecast the influence of factors that differ in economic nature and their dynamics. The initial data are matrixes of indicators, each of which represents the values ​​of the same indicators for different periods or for different consecutive dates.

Deterministic methods that assume the presence of functional or strictly determined connections, when each value of a factor characteristic corresponds to a well-defined non-random value of the resultant characteristic. As an example, we can cite the dependencies implemented within the framework of the well-known factor analysis model of the DuPont company. Using this model and substituting into it the forecast values ​​of various factors, such as sales revenue, asset turnover, degree of financial dependence and others, you can calculate the forecast value of one of the main performance indicators - the return on equity ratio.

Others are very a clear example serves as a profit and loss statement form, which is a tabular implementation of a strictly determined factor model that connects the resultant attribute (profit) with factors (sales income, level of costs, level of tax rates, etc.). And at the level of state financial forecasting, the factor model is the relationship between the volume of government revenues and the tax base or interest rates.

Here we cannot fail to mention another group of methods for financial forecasting at the micro level, based on the construction of dynamic enterprise simulation models. Such models include data on planned purchases of materials and components, production and sales volumes, cost structure, investment activity of the enterprise, tax environment, etc. Processing this information within the framework of a unified financial model allows us to assess the projected financial condition of the company with a very high degree of accuracy. In reality, this kind of model can only be built using personal computers, which allow one to quickly perform a huge amount of necessary calculations.

1.3. Problems of financial forecasting at macro and micro levels

As such, there was no financial forecasting system at the macro level in the Russian Federation. This is objectively explained by the following factors: statistical concepts were not adapted to the changes associated with the transition from a planned system to a market economy, a small database of macroeconomic empirical parameters, a lack of qualified specialists, and a lack of government funding for the creation of a financial forecasting institute. These and perhaps many other factors prevented the creation of a state forecasting institute. Therefore, the initiative to create a forecasting institute had to come from outside, which ultimately happened.

The initiator of the creation of the Institute of Social and Economic Forecasting was the European Union program - TACIS "Long-term scientific forecast of economic and social development of the Russian Federation." This project started on April 8, 1998 and was completed on August 12, 2000.

As part of this TACIS project on the “Long-term scientific forecast of economic and social development of the Russian Federation,” the Ministry of Finance and the Center for Economic and Social Reforms formed a Policy Assessment and Planning Group (PAG). The OPP Group is designed to deal with long-term issues of the Kyrgyz economy and develop foresight regarding the development of the economy and society. In accordance with the Terms of Reference, the PPR team received an analytical framework to assist in the construction of scenarios and economic policy analysis, or in other words, a system designed for long-term forecasting.

A forecasting system designed for the Russian Federation cannot fully take into account all the determining factors postulated by theories. Issues related to data availability need to be taken into account. A working model cannot be too complex.

In any economy there are strong links between productivity, income levels and the level and structure of demand, which are changed through fiscal and economic policies.

Thus, the DESP system should cover both supply and demand side factors. Particular attention should be paid to modeling investment in business and infrastructure (transport, communications and energy networks, education and training institutions). The main factor determining investment opportunities is the legal and regulatory framework of the economy.

There are limitations in creating a model. The data base for the Russian Federation still has gaps (for example, fixed capital). Due to the fact that statistical concepts were adapted to changes associated with the transition from a centrally controlled economy to a market system only after 1993, the length of time lags is currently a maximum of six years. These limitations indicate that this model cannot be an econometric model. classic type. To forecast horizons up to 20 years, estimates of model parameters must be based on at least 50-100 years of empirical data. Therefore, the problem of inaccurate forecasts arises.

In this regard, the parameters of the DESP system can only be based on the “experience” of other countries. The parameter values ​​were brought into line with the structures and relationships of the Russian Federation.

At the micro level, the main problem may be the inaccuracy of forecasts with all the ensuing consequences, which can take very threatening forms for the enterprise, due to the waste of time and time catching up on lost moments, while competing enterprises are progressing at a new level. It must be taken into account that the accuracy of forecasts is influenced by the human factor, since the competence of financial managers includes drawing up the most probable financial forecasts and plans. Therefore, the degree of accuracy of forecasting depends on the qualifications of the financial manager, the choice of financial forecasting method and the implementation of strict financial control.

Chapter 2. Prospects for financial forecasting in the Russian Federation

2.1. Prospects for socio-economic forecasting in the Russian Federation

The forecast of socio-economic development of the Russian Federation for 2017 and for the planning period of 2018 and 2019 (hereinafter referred to as the forecast) was developed on the basis of scenario conditions approved by the Government of the Russian Federation and the main parameters of the forecast and is based on the goals and priorities defined in the strategic planning documents.

The forecast was developed as part of three main options – basic, “basic+” and target. The basic option considers the development of the Russian economy in the context of maintaining conservative trends in changes in external factors, taking into account the possible deterioration of foreign economic and other conditions and is characterized by maintaining a restrained budget policy. The option reflects a conservative development scenario, has the status of a conservative forecast option and does not imply a fundamental change in the economic growth model.

The base case was developed based on a fairly low price trajectory for Urals oil: at the level of 41 US dollars per barrel in 2016 and stabilization at the level of 40 US dollars per barrel throughout the entire forecast period. Such an assessment of the oil price level is conservative, since it is significantly lower than the current consensus forecast for oil prices. A significant increase in oil export volumes is expected - almost 21 million tons by 2019 compared to 2016, while simultaneously increasing the share of non-resource exports in total exports to 34.8% in 2015 prices.

In conditions of limited financial opportunities and slow economic recovery, the main social parameters will be

characterized by restrained dynamics, while providing for the mandatory fulfillment of the state’s minimum social obligations.

Under these conditions, turnover retail will also recover at a moderate pace – up to 1.8% in 2019. While maintaining a moderately tight monetary policy, inflation will drop to 5.8% in 2016 (on an annualized basis), and at the end of 2017 it will reach 4.0% and will remain at this level until the end of the forecast period.

By mid-2017, investment activity is expected to stabilize. Investment growth will resume in 2018. The average annual increase in investment in 2018–2019 will be 1.3% and will be determined by the possibility of increasing private investment against the backdrop of a reduction in public investment spending.

Net capital outflow will increase from US$18 billion in 2016 to US$25 billion by the end of the forecast period.

Approaches to fiscal policy are generally conservative and do not differ in forecast options. The federal budget, according to the Russian Ministry of Economic Development, will be in deficit throughout the entire period 2016–2019 in all forecast options. In order to finance the deficit, it will be necessary to use budgetary funds, attract internal and external borrowings, and privatize state property.

In 2016, the decline in GDP will slow to 0.6%, and by the end of the year the economy is expected to transition from stagnation to restoration of economic growth. In 2017, the GDP growth rate will move into the positive area and amount to 0.6%, in 2018 the GDP growth rate will increase to 1.7%, and in 2019 – to 2.1 percent.

The “basic+” option considers the development of the Russian economy in more favorable external economic conditions and is based on a trajectory of moderate growth in Urals oil prices to US$48 per barrel in 2017, US$52 per barrel in 2018 and US$55 per barrel in 2019.

IN social sphere This version of the forecast provides for an increase in the standard of living of the population based on a moderate increase in the social obligations of the state and business. Consumer demand will recover as income growth accelerates and consumer lending expands. In 2019, the growth of retail trade turnover will increase to 3.5%, the volume of paid services to the population - to 2.8 percent.

Against the backdrop of a more active recovery in consumer demand, the slowdown in inflation will be moderate: at the end of 2017, inflation will drop to 4.5%, and in 2018–2019 to 4.3% and 4.1%, respectively. Net capital outflow from the private sector will decline and by 2019 will amount to US$15 billion.

Against the backdrop of rising oil prices, strengthening of the ruble exchange rate and more favorable external conditions, investments in fixed assets will recover at a faster pace. The average annual increase in investment in 2017–2019 will be 2.9% per year, with outpacing investment growth in the infrastructure sector and private investment.

Due to the higher oil price in the “base+” option, the amount of oil and gas revenues of the federal budget will be noticeably higher, which will ensure the achievement of a balanced federal budget.

Economic recovery under the “basic+” option will be characterized by a higher rate: 1.1% in 2017, 1.8% in 2018, 2.4% in 2019.

The target option focuses on achieving target indicators of socio-economic development and solving strategic planning problems. In the medium term, it is expected that the Russian economy will enter a trajectory of sustainable growth at rates no lower than the world average, while simultaneously ensuring macroeconomic balance. As a result, retail trade turnover, after moderate growth of 1.5-2.3% in 2017–2018, will accelerate to 5.3% in 2019.

Inflation will reach 3.9% at the end of 2018. In 2019, inflation will remain at the 2018 level amid increased consumer demand.

External conditions remain at the level of the “basic+” option, but in order to achieve the intended target parameters, it will be necessary to transition the economy to an investment model of development. This assumes curbing the growth of consumption expenditures and reducing various types costs for business.

Exports of goods will increase at a higher rate than in the base cases; the growth rate of non-commodity, non-energy exports will exceed the growth rate of exports as a whole and will average 4.9% in 2017–2019 in real terms. The volume of non-resource, non-energy exports in value terms will increase annually by 9 percent. The share of investment goods in the structure of imports will increase, while investment imports will grow at a faster pace. The gradual revival of the economy during the forecast period will contribute to an improvement in the business climate, which will be manifested in a reduction in net capital outflow until its complete cessation by 2019.

The new economic model assumes an active investment policy. Creating an investment resource and conditions for transforming savings into investments, increasing the propensity to invest through the implementation of macroeconomic and regulatory measures aimed at increasing the level of business confidence and improving the business environment will lead to an increase in the growth rate of investment in fixed capital in 2017 - 2019 on average up to 5.2% per year with accelerated growth of private investment and investment in the infrastructure sector.

Starting from 2017, subject to a gradual reduction in interest rates, which will have a positive impact on business lending, taking into account the implementation and start of implementation of new large investment projects and economic policy measures aimed at activating economic growth factors and improving economic efficiency, GDP growth rates will reach 4 .4% in 2019, which is 2.3 percentage points higher compared to the base case.

Main indicators of the forecast of socio-economic development of the Russian Federation for 2017-2019.

Table 1 – Main indicators of the forecast of socio-economic development of the Russian Federation for 2017-2019

Indicator	2016	2017	2018	2019
Urals oil prices, dollars per barrel
Consumer price index at the end of the year, %
Gross domestic product,%
Industry, %
Real disposable cash income of the population, %
Real wages of employees of organizations,%
Export - total, billion US dollars
Import - total, billion US dollars

The GDP growth rate increases in 2017 to 1.8%, in 2018 – to 3 percent. To achieve the target parameters of socio-economic development, it is necessary to carry out significant structural reforms within the framework of budget policy, implying, along with optimization and increased efficiency, an increase in productive expenses that ensure the macroeconomic efficiency of budget expenditures.

2.2. Main priorities of socio-economic development of the Russian Federation

By 2019, it is necessary to create conditions for the implementation of the main elements of the new model of economic development; the domestic economy must achieve stable growth based on accelerated growth private investment using modern technological solutions and strategic planning mechanisms. In accordance with the Federal Law of June 28, 2014 No. 172-FZ “On Strategic Planning in the Russian Federation”, by the end of 2018 the stage of forming a system of strategic planning documents within the framework of a single cycle of strategic planning of the Russian Federation will be completed.

A set of scientific and methodological studies and organizational measures will be carried out to improve the regulatory framework for strategic planning, to optimize the reporting system of federal executive authorities on the progress of implementation of strategic planning documents and the effectiveness of implemented measures government regulation in the field of socio-economic development.

The socio-economic policy of the Government of the Russian Federation for the medium term (2017 – 2019) is based on the priorities formulated in Decrees of the President of the Russian Federation dated May 7, 2012 No. 596-606 and in the Main Directions of Activities of the Government of the Russian Federation for the period until 2018 .

The main priorities of economic policy during the forecast period are:

– increasing the investment attractiveness of the Russian Federation,

Improving the business climate and creating a favorable business environment;

An increase in the share of productive expenses in the structure of budgets of the budget system of the Russian Federation;

Import substitution;

Improving the quality of life and increasing investment in human capital;

Balanced regional development;

Improving the quality of functioning of government institutions;

Development of information technologies and support of high-tech sectors of the economy.

In terms of increasing the investment attractiveness of the Russian Federation, improving the business climate and creating a favorable business environment in 2017–2018, the main attention will be paid to the implementation of new initiatives of the business community, law enforcement practices and communicating information about the results of the implementation of road maps to business representatives.

In order to preserve potentially solvent participants in economic turnover, changes will be made to the bankruptcy legislation aimed at improving the mechanisms for their financial recovery.

An increase in the share of productive expenditures in the structure of budgets of the budget system of the Russian Federation will be ensured through the use of the mechanism of state programs of the Russian Federation in the budget process against the backdrop of budgetary consolidation, which involves, first of all, measures to optimize and increase the efficiency of budget expenditures that do not provide significant macroeconomic effects.

The implementation of the Federal Law of July 13, 2015 No. 246-FZ “On Amendments to the Federal Law “On the Protection of the Rights of Legal Entities and Individual Entrepreneurs in the Exercise of State Control (Supervision) and Municipal Control” is aimed at maintaining the stability of tax conditions, which provides for the establishment 3-year “supervisory holidays” in relation to scheduled inspections for enterprises that have not had serious violations of established requirements for conducting business activities for three years.

As part of the import substitution process, starting in 2017, measures will be implemented to create a preferential lending mechanism for simplified access of agricultural producers to credit funds. In terms of improving the quality of life and investing in human capital, a set of measures is planned:

Maintaining a zero VAT rate in 2017 for services for the transportation of passengers by rail in suburban transport;

Construction of engineering, social and transport infrastructure facilities, including 92 children’s facilities, within the framework of the “Housing for the Russian Family” program preschool institutions and 44 secondary schools;

Creating conditions for citizens of the Russian Federation to improve their living conditions at least once every 15 years.

It is expected that by 2018, the average interest rate on residential mortgage loans (in rubles) will exceed the consumer price index by no more than 2.2 percentage points.

Regional, national and industry championships are held in the field of education professional excellence, All-Russian Olympiads and competitions in professions and specialties of secondary vocational education to increase the public prestige of blue-collar professions and secondary vocational education.

A program has been approved to promote the creation in the constituent entities of the Russian Federation (based on the projected need) of new places in general education organizations for 2016 – 2025, equal conditions for access to financing through budgetary allocations of state, municipal and private organizations for additional education of children have been established. In addition, a network of resource educational and methodological centers for training people with disabilities has been created on the basis of educational organizations of higher education.

In the healthcare sector, key areas public policy until 2018 are:

Ensuring that additional measures are taken to improve the medical and economic efficiency of the healthcare system based on evidence-based analysis;

Development of a methodology for accounting for costs in medical organizations for the provision of medical care and calculating the cost of the program of state guarantees of free provision of medical care to citizens;

Adoption of a “road map” for the development of nuclear medicine and diagnostic centers.

In the field of professional development, over the next two years, in order to develop an effective and flexible skilled labor market, work will continue to develop professional qualifications, including by updating the requirements for the competencies and qualifications of employees, as well as creating a system for independent assessment of their professional level. In terms of developing information technologies and supporting high-tech sectors of the economy, work continues to eliminate the “digital divide” through the development of broadband access to the Internet information and telecommunications network, the launch of digital broadcasting throughout Russia, and ensuring widespread availability of television, taking into account new technological capabilities. In 2017–2018, support for import substitution of products in the field of information technology and export promotion will continue software.

In 2018, the Spatial Development Strategy of the Russian Federation will be developed, within the framework of which the share of overdue accounts payable in the expenditures of the consolidated budgets of the constituent entities of the Russian Federation should be reduced from 0.22% in 2015 to 0.1% in 2020.

In terms of improving the quality of functioning of government institutions important goal structural socio-economic transformations is to improve the quality of public administration.

To achieve this goal, it is planned to introduce an institute for assessing the actual impact of adopted legislative acts, creating a network multifunctional centers provision of state and municipal services.

The tasks of increasing the efficiency of federal property management, privatization and the formation of integrated structures, as well as improving the mechanisms for managing federally owned shares and real estate, including land plots, remain relevant. At the same time, in 2017–2018 the course of consistent reduction will be continued. public sector economy.

Chapter 3. Improving financial forecasting in the Russian Federation

The transition of the Russian economy to an innovative path of development in the context of globalization and ever deeper integration of the country into world economic relations, increasing economic openness, is an imperative for maintaining sustainable rates of economic growth in the medium and long term. In the era of globalization of the world economy, the basis for the successful positioning of a country, region, or industry lies in constant innovative renewal aimed at achieving maximum productivity, competitiveness, and development of human capital. According to existing estimates, in developed countries from 50% to 90% of GDP growth is determined by innovation and technological progress; innovation is becoming a prerequisite and the main “engine” for the development of all sectors of industry and the service sector.

Financial forecasting is a study of specific prospects for the development of finances of business entities and government entities in the future, a scientifically based assumption about the volumes and directions of use of financial resources in the future. On the one hand, financial forecasting precedes financial planning, and on the other hand, it is its integral part, since the development of financial plans is based on indicators of financial forecasts.

In the process of forecasting economic development, it is necessary to optimally take into account the effect of a whole complex of contradictory factors determined by the requirements for solving current and future problems of economic development. Today, in terms of forecasting the Russian economy, it is relevant to choose a development option that

would make it possible to overcome the general negative trend of development towards intensification, improving the quality indicators of the functioning of economic systems, such as increasing the efficiency of use of all types of resources, accelerating the growth rate of national income both absolutely and relatively per capita, increasing the share of accumulation to scientifically justified values .

Using the example of the modern Russian economy, we can identify the following imbalances that negatively affect economic development:

Discrepancy between the volume and structure of capital investments and the requirements for ensuring normal reproduction of fixed capital

Imbalance in the structure and volume of fixed capital and labor resources in regions, industries and enterprises

Unjustifiably high prices for basic types of fuel, energy and raw materials

Sharp property differentiation of the population

Imperfection of the tax system

Imbalance of payment turnover, means of payment and the need for them by enterprises and organizations.

The listed and other indicators of the imbalance of various aspects of the functioning of market economic systems are due to both objective factors and subjective factors, which are manifested in the absence and weak development of a forecasting system for the Russian economy, in an insufficiently balanced and politically determined approach to solving a number of complex problems.

Currently, the question of the need for a nationwide system of plans and forecasts for the development and functioning of all types of economic systems with an optimal balance between state regulation and self-regulation of market relations is relevant. As such, there was no financial forecasting system at the macroeconomic level in the Russian Federation. This was largely due to the fact that there was a transition from a planned system to a market economy, there were no qualified specialists in this field, and no funds were allocated for these purposes.

In order to change the current situation and ensure the competitiveness of the national economy in the long term, it is necessary to organize the process of forming a coherent vision of the technological future of Russia among all participants in this process: the state, business, science, civil society and jointly try to realize the set goals. Key role in organizing this process belongs to the state not only as its initiator, but also as a guarantor of the implementation of the agreements reached.

The most adequate tool for achieving this task is Foresight, used in almost all developed and many developing countries.

What is foresight?

Foresight is a tool for forming priorities and mobilization large quantity participants to achieve qualitatively new results in the field of science and technology, economics, state and society.

The difference between Foresight and forecasting is a much more comprehensive approach.

Firstly, forecasts, as a rule, are formed by a narrow circle of experts and in most cases are associated with predictions of poorly controllable events (forecast of stock prices, weather, sports results, etc.). Within the framework of foresight, we are talking about assessing possible prospects for innovative development related to the progress of science and technology, outlining possible technological horizons that can be achieved by investing certain funds and organizing systematic work, as well as the likely effects on the economy and society.

Secondly, Foresight always implies the participation (often through intensive mutual discussions) of many experts from all fields of activity, to one degree or another related to the topic of a particular foresight project, and sometimes conducting surveys of certain groups of the population (residents of the region, youth and etc.) directly interested in solving problems discussed within the project.

The third main difference between Foresight and traditional forecasts is the focus on developing practical measures to bring closer selected strategic guidelines

Foresight methodology differs from traditional forecasting, futurology (study of the future) and strategic planning and is not limited to prediction: it is a methodology for organizing a process aimed at creating a common vision of the future among participants, which all stakeholders strive to support with their current actions. Thus, this methodology is not associated with predicting the future, but rather with its formation, which allows us to consider Foresight a specific tool for managing technological development, based on the infrastructure created within its framework.

The concept of modern Foresight is based on: the interest of participants in anticipating their future; their readiness to cooperate; their understanding of the need to focus on the long term; desire to combine efforts and resources; creating a coordinating structure to help reach consensus.

The forecasting system cannot fully take into account all factors. The model cannot be too complex, but must cover the factors and take into account the main relationships:

Due to the fact that the Russian Federation is a less open economy, it is necessary to take into account interactions with the global economy and the impact of international competitiveness;

Due to the fact that the Russian Federation is a developing country, capital and innovation are the main constraint on growth and productivity improvements;

Particular attention must be paid to modeling investment in business and infrastructure (transport, communications and energy networks, education and training institutions). The main factor determining investment opportunities is the legal and regulatory framework of the economy.

Ways to solve problems of financial forecasting and improve it lie in eliminating problems, i.e., first of all, these are the following actions:

Creation of special research centers on the development of forecasts in the Russian Federation at the level of government agencies;

Training of highly qualified specialists in this field;

The use of methodological foundations of financial forecasting based on scientific developments developed countries and methods of financial forecasting;

Attention should be paid to the poor quality of the projections of the long-term financial plan, which is a valid document, as well as the complete freedom and independence of executive bodies from federal laws that approve the parameters of the federal budget in relation to indicators of the federal budget surplus and contributions to the stabilization fund;

In financial and economic policy, it is necessary to develop a mechanism for assessing the quality and reliability of forecast and planned (budget) projections by economic departments, develop a political and criminal system responsibility of officials for gross miscalculations and deviations of forecasts and budget plans in comparison with the approved initial federal laws, as well as in comparison with real, actual indicators and real, completely predictable dynamics.

Conclusion

Financial forecasting is carried out by developing various options for the development of an organization, a separate administrative-territorial unit, or the country as a whole, their analysis and justification, assessing the possible degree of achievement of certain goals depending on the nature of the actions of the planning subjects.

In the process of financial forecasting, specific methods such as mathematical modeling, econometric forecasting, expert assessments, trend building and scenario development, and stochastic methods are used to calculate financial indicators.

Today, in the management of the Russian economy, an inertial forecast of the economy plays a very serious role, on the basis of which, according to legislation, a concept of long-term economic development must be built, detailing the forecast itself.

The inertial forecast, of course, is multivariate, considers various scenarios and depends mainly on a set of important indicators: oil price, inflation, demography, etc. Economic growth rates are derived from resources and other factors, and the product created in the future is “divided” into different goals: social and others. But not everything is so simple; as a rule, some socio-economic standards are set that need to be reached in the future, but still they mainly depend on the dynamics of the volumes and productivity of economic resources, and not vice versa.

A conceptual and predictive solution to specific socio-economic problems at the level precisely proves the real social orientation of the state, brings the necessary stability to public opinion and is an important factor in ensuring confidence in the future (especially if plans are systematically implemented). And the population’s confidence in the future, interest in the future is a powerful socio-psychological factor in increasing labor activity and economic growth.

The planning and forecasting system must be clearly focused on the specific benefit of each individual person, and not the individual’s efforts should be directed towards the abstract general goals of the state.

Moreover, the process of future forecasting itself should be more democratic in nature - if desired, not only the relevant government agencies, but also independent organizations and experts should freely participate in it; it is possible to carry out widespread surveys of the population about the shape of the desired future, wishes and fears, about development goals and ways to achieve them.

Future forecasting will be focused on general conceptual qualitative goals, build appropriate strategies, analyze multivariate development scenarios, take into account the probabilistic nature of development (which has recently been sharply increasing) and the system of risks (especially military-political factors and the increased catastrophic nature of world development), the unpredictability of many events . Specific solutions to problems are detailed in major national economic projects and programs. In the future, as planning and forecasting develop, real programming and open planning of turning on/off economic regulators is possible, depending on the state of the system of established development indicators - in this situation, the population and business will feel more and more confident and not fear the future. In many ways, the forecasting and planning system will move closer to public policy. Regulators of a market economy, as it develops, will be of an increasingly economic, indirect, non-rigid nature, replacing direct administration; the legislative system is stabilizing; the institutional environment will approach world standards.

As a means of achieving the set goals, first of all, not potentially possible resource investments will be considered, but the development of fundamental and applied science, innovative processes, the formation of new principles of trust, public-private partnerships, partnerships in society, the consolidation of society on the basis of common socio-political and cultural values.

The economic bloc must adapt to these goals and objectives, gradually ceasing to dominate in public consciousness as self-worth.

The development of the economic bloc is clearly linked in forecasts to human capital, managerial capital, and social capital (relationship capital). All types of investments in the human factor (and the effect of these investments) become the most important ways of achieving the goals set by society.

A system of lifelong education in conjunction with the improvement and expanded implementation of publicly available information and communication technologies, the Internet, electronic libraries and databases, etc. is becoming a necessary infrastructure for the development of the human factor in the economy.

Another qualitatively important block of the future forecasting and planning system is long-term technology foresight, anticipating revolutionary technological breakthroughs, planning transitions to higher technological levels in order to solve socio-economic problems. In principle, in the future, the knowledge intensity of products, works, services, and any type of activity will steadily increase and indirectly indicate the successful development of the economy.

List of sources used

1. Finance./ Ed. S.A. Bedozerov, S.S. Gorbushena M.: TK Welby, 2004
2. Finance./Ed. Eatwell J., Milgate M., Newman P. M.: State University Higher School of Economics, 2008
3. Finance, money circulation and credit. / Ed. Romanovsky M.V., Vrublevskoy O.V. M.: Yurayt, 2010
4. Finance. /Ed. Kovaleva A.M. M.: Finance and Statistics, 2006
5. Finance. /Ed. Rodionova V.M. M.: Finance and Statistics, 2005
6. G.B. Polyakov Financial management: Textbook for universities / Edited by G.B. Polyakova. – M.: Finance, UNITY, 2013 – 124 p.
7. Polyak G.B. Financial and budget planning: Textbook - M.: University textbook, 2010 - 149
8. Lukasevich I.Ya. Financial management / 2nd ed., revised. and additional – M.: Eksmo, 2010 – 128 p. With.
9. Financial management: textbook. / ed. E.I. Shokhina. – 3rd ed., erased. – M.: KNORUS, 2011. – 480 p.
10. Cheremushkin, S.V. Methodology of economic forecasting: simple, but not simpler than necessary / S.V. Cheremushkin // Financial management. – 2011. – No. 2. – pp. 14-17.
11. Melnikov, E.N. Comparative analysis existing models of cash flow management / E.N. Melnikov // Audit and financial analysis. – 2011. – No. 4. – pp. 174-178.
12. Agibalov A.V., Orekhov A.A. Concept of optimization of financial resources in integrated structures of the agricultural and industrial complex / No. 13 2014-P. 47-54.
13. Brusov, P.N. Financial management. Financial planning: textbook. allowance / P.N. Brusov, T.V. Filatova. – 2nd ed., erased. – M.: KNORUS, 2013. – 232 p.
14. Voronchenko, T.V. Forecasting and analysis of cash flows / T.V. Voronchenko // Economic analysis: theory and practice. – 2014. – No. 4. – P. 46-51.
15. Balabanov, A.I. Finance: textbook / Ed. A.I. Balabanova, I.T. Balabanov. – St. Petersburg: Publishing House “Peter”, 2011. – 192 p.
16. Gryaznova, A. G. Finance: Textbook / Ed. A.G. Gryaznova, E.V. Markina – M.: Finance and Statistics, 2011. – 148 p.
17. Komarov, I.I. The essence and types of basic financial forecasts / I.I. Komarov, V.A. Strukov // Vestn. Voronezh. un-ta. Ser. Problems of higher education. – 2012. – No. 24. – P. 55-61.

“Financial market” - Stock and currency exchanges. The main problems of the functioning of the financial market in the Russian Federation until 2007. Depositories. Professional market participants securities. Changes in the Dow Jones Industrial Average over the past 3 months. The main anti-crisis measures at the end of 2008 and the beginning of 2009. Management Fund (UIF), spec. mutual fund depository.

“Accounting for fixed assets” - A method of writing off value by the sum of the numbers of years of useful life. 5. Task 2. Calculate depreciation for 3 years 4 months (item 7 in the journal entry). The railway tariff for transporting the machine is 6,000 rubles. 3. Accounting for depreciation of fixed assets. Receipt of fixed assets. Task 2. Revaluation of the OS.

“Financial resources” - The situation with savings and investments in various groups and subgroups of countries, 2006. Financial capital can also be called financial resources. Structure - official (public) external debt (about $1 trillion) - private external debt (more than $2 trillion). O. Savings and investments as the main elements of the capital formation process (end).

"Capital Market" - Factors influencing the assessment of a firm's profitability. Shows the future value of the deposit. Ruble. Directing money to purchase additional capital. Period of accumulation of funds. Cash: Formation of market price. Equity capital market. Money market. Loan capital market. Capital is borrowed.

“Financial system” - 27. 4. Objectives of monetary policy. Functions of the Central Bank. Balance sheet of the Central Bank. Central Bank. Refinancing rate. Broad money is usually under some influence of the central bank's monetary policy. 8. Federal Reserve System - US Central Bank, Washington. 6. Monetary policy instruments.

“Financial system of Russia” - 5. The paradox of the banking concept 2005-2008. Banking market. Requirements for the financial system. False targets. The Russian financial sector is extremely small compared to the size of the economy. 9. 6. 7.

There are a total of 16 presentations in the topic

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. 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.

To summarize modern research in the field of theories of pricing 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 outlook will be much more accurate than that 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 one.

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.

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 similar systems 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, heteroskedasticity, 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.

1

The activities of any enterprise must be managed in such a way as to ensure that the threat of financial crises (the extreme manifestation of which is bankruptcy) is minimized. At the same time, despite the awareness of the importance of this problem, in fact, little attention is paid to it in practice. The article discusses various approaches to forecasting financial crises in the activities of an enterprise. More than 20 different models and their modifications were analyzed. It is shown that main drawback Many methods consist of fixing attention on individual performance indicators, to the detriment of comprehensive analysis. The practice of financial management at a number of the largest enterprises in Russia belonging to various industries allows us to draw an important methodological conclusion: for almost any enterprise, a risk of any kind can ultimately be expressed as a sum of money that can be underreceived and/or overpaid by the enterprise. A methodology for forecasting financial crises is proposed, based on modeling and analysis of the current and future cash flows of an enterprise, as well as its modification in the form of a probabilistic model.

financial model

financial crisis

forecasting

comprehensive analysis

cash flow

probability

bankruptcy

1. Ermasova N.B. Risk management of an organization: a textbook. - M.: Alfa-Press, 2005. - ISBN 5942801398.

2. Ivliev S.V., Kokosh A.M. Estimation of the probability of bankruptcy of closed companies based on data financial statements// Banks and risks. - 2005. - No. 1 [Electronic resource]. - URL: http://www.ifel.ru/br1/15.pdf.

3. Kovalev V.V., Volkova O.N. Analysis economic activity enterprises. - M.: OOO "TK Velby", 2002. - 424 p.

4. Lyapunov A.M. A new form of the limit of probability theorem. Collected works. - M., 1954. - T. 1. - 157 p.

5. Nedosekin A.O. Business risk assessment based on fuzzy data [Electronic resource]. - URL: http://sedok.narod.ru/ - 100 p.

6. On insolvency (bankruptcy): Federal Law of October 26, 2002 No. 127-FZ // Collection of Legislation. - 2002. - No. 43. - Art. 4190.

7. Review of financial risk and treasury management practices in Russia. Website of KPMG CJSC, member of the KPMG International association [Electronic resource]. - URL: http://www.kpmg.ru/.

8. Fomin P.A. Methodology for the formation and planning of the financial potential of an enterprise within the framework of the economic growth strategy: monograph. - M.: Publishing and trading corporation "Dashkov and K", 2008. - 224 p.

9. Eitingon V.N., Anokhin S.A. Forecasting bankruptcy: basic techniques and problems [Electronic resource]. - URL: http://www.iteam.ru/publications/strategy/section_16/article_141/.

10. Valeriy Galasyuk, Viktor Galasyuk. Consideration of economic risks in a valuation practice: journey from the kingdom of tradition to the kingdom of common sense. // ICFAI Journal of Applied Finance - 2008. - Vol.14. - No. 6. - Рp. 18-33.

Introduction

It is obvious that the activities of any enterprise must be conducted in such a way as to minimize the threat of financial crises, the extreme manifestation of which is bankruptcy. In economic science, a significant number of approaches have been developed to assess the threat of bankruptcy in the activities of an enterprise, most of which are based on the analysis of profitability, liquidity and/or cost of capital indicators. However, in the context of a system-wide financial and economic crisis, various negative factors, aggravating and acting synergistically, can create a serious risk even for a potentially stable enterprise in all respects. Therefore, in a crisis, the issue of assessing the risk of bankruptcy becomes especially relevant for any business entity, and the criteria for assessing financial risks are often the main basis for decision-making in the financial management system.

At the same time, despite the awareness of the importance of this problem, in fact, little attention is paid to it at enterprises. A survey conducted by KPMG (ZAO KPMG, a member of the KPMG International association) during the period from September to December 2007 among managers of more than 100 largest Russian companies, showed that “... the vast majority of group subsidiaries and more than 40% of parent companies do not have a risk management system. Only 10% of parent companies and about 5% of subsidiaries have a fully implemented risk management system” (Fig. 1).

Rice. 1. Qualitative state of the risk management systemat Russian enterprises.

The reason for this, in our opinion, lies in the large gap between classical theory and recent practice, which has led to the discrediting of existing methods for predicting financial crises in the activities of an enterprise due to their obvious inadequacy to real life situations.

Analysis of existing approaches

For the first time, the task of predicting financial crises in the activities of an enterprise was posed in the United States after the end of World War II. This was facilitated by a sharp jump in the number of bankruptcies of enterprises, associated with a sharp reduction in military orders, as well as the clearly manifested uneven development of companies (the prosperity of some against the backdrop of the collapse of others). The desire of various authors to take into account the diversity of financial conditions of a modern enterprise that occur in practice has led to the development to date of a number of different models for assessing the likelihood of crises.

Rice. 2. Comparison of bankruptcy risk assessment using various models.

Integral methods (the so-called Z-models) have gained the greatest popularity in the West, which are based on the calculation of one integral indicator based on a set of coefficients characterizing the financial activities of the enterprise (for example, current liquidity, share of borrowed funds, etc.), which are then multiplied to the values ​​of the weighting coefficients found empirically and are summed up. Comparison of the calculated integral indicator with the established standard value allows us to draw a conclusion about the likelihood of bankruptcy of the enterprise. As a rule, these models bear the names of their creators: “Altman model”, “Liess model”, “Taffler model”, etc. . Russian scientists, based on accumulated statistical data on domestic enterprises, have also developed a number of their own models, such as the “Fedotova model”, “Zaitseva model”, “Saifullin-Kadykov model”, etc. . However, according to Professor V.N. Eitingon, “not one of them can claim to be used as universal.”

Conclusion V.N. Eitingon can be clearly illustrated by the following practical example: Figure 2 shows graphs of changes in the risk of bankruptcy of the same enterprise over several periods, obtained on the basis of calculations using eight different models (of which 4 are foreign, 4 are domestic).

In total, we analyzed more than 20 different models and their modifications. The analysis shows that, despite the fact that almost all methods more or less reliably characterize the qualitative dynamics of changes in the state of the enterprise, quantification bankruptcy risk obtained by different methods varies significantly. Thus, the same enterprise, depending on the chosen assessment methodology, can simultaneously be recognized as bankrupt, an enterprise in a pre-crisis state, and a stable business entity. From such a comparison, it becomes obvious that the approaches proposed by various authors do not satisfy the key requirement - resistance to variations in the source data. And therefore they can be effectively used only for enterprises of that group and for the economic situation of that period for which they were originally developed.

In addition to the integral models discussed above, a number of approaches are also known, which are based on methods of mathematical analysis and modeling. However, all of them, as a rule, are quite labor-intensive and are not entirely suitable for a practicing financial manager, since they require special mathematical training to understand them, and practical application- availability of specialized software. Whereas when making management decisions, the speed and ease of obtaining, as well as logical transparency (understanding “why is this so?”) of the required assessments play a key role.

Thus, in the current economic situation, an important task is to develop our own approach and methodology for assessing the risk of financial crises in the activities of an enterprise, eliminating the above-mentioned shortcomings.

Development of a methodology for forecasting financial crises

In the theory of risk management, the group “financial risks” is usually distinguished, which usually includes, first of all, risks associated with fluctuations in prices for goods and services, inflation dynamics and the refinancing rate of the Central Bank, bank interest rates on loans and deposits, currency fluctuations rates and quotes of government and corporate securities, as well as a number of other indicators.

At the same time, Academician of the Academy of Economic Sciences of Ukraine, Honored Appraiser of the Ukrainian Society of Appraisers V. Galasyuk proved that “no matter how diverse and numerous the risk factors of a particular project/business may be (for example, a jump in prices for raw materials, delays in the construction of a new workshop, violation of production technology , the emergence of a serious competitor on the market, the loss of a group of key specialists, a change political regime, weather disasters, etc.), all of them ultimately manifest themselves in only two forms: actual positive conditional cash flows (cash flows, income) will be less than expected and/or actual negative conditional cash flows (cash flows, expenses ) will be greater than expected." The fairness of this interpretation of the concept of risk, in relation to specific business entities, has been repeatedly confirmed by us in our practical activities, which allowed us to draw the following methodological conclusion: for any enterprise, a risk of any type can ultimately be expressed as a sum of money that may not be received and/ or overpaid by the business entity.

N.B. Ermasova, considering risks as the possibility of losses for an enterprise, identifies three possible degrees of danger: acceptable risk - the possibility of losses in the amount settlement amount profit; critical risk - the possibility of losses in the amount of the estimated amount of income (that is, the loss of the enterprise will be calculated by the amount of costs incurred by it); catastrophic risk - the possibility of loss in the amount of all equity capital or a significant part of it. At the same time, P.A. Fomin proposes to consider financial risks from three positions: 1 - as the danger of a potential, probable loss of financial resources (financial risk manifests itself as a direct loss); 2 - as the danger of not receiving expected income compared to the option that is designed for the rational use of all resources in a given field of activity (financial risk manifests itself as an indirect loss); 3 - as the probability of receiving additional profit associated with risk (financial risk manifests itself as additional income) .

We believe that the methodology for assessing the risk of financial insolvency, when making strategic decisions in the field of financial security of an enterprise, should be based not on the forecast of individual indicators of financial condition, but on a detailed analysis and forecast of the enterprise’s cash flow and its dynamics as a result of changes in external and/or internal factors.

To form the model, we will proceed from the postulate put forward by J. Conan and M. Golder - “bankruptcy is the inability of an enterprise to timely repay its obligations to counterparties and creditors.” Thus, we propose to evaluate the probability of a financial crisis as the probability of an event occurring in which the enterprise will be unable to repay its obligations. Let us note that this principle is fully consistent with the definition of bankruptcy established by the Federal Law of Russia “On Insolvency (Bankruptcy)”.

Let's consider the activity of the enterprise at any point in time (in practice, for convenience, we select the moment that coincides with the moment of formation of the next quarterly or annual reporting). Let us record the current financial condition of the enterprise. Then, in the worst case, the enterprise at the next moment in time may not receive additional income from counterparties, but may receive payment requirements for its obligations.

The enterprise will repay the obligations presented for payment at the expense of the liquid assets at its disposal, which represent the sum of the accumulated cash flow, the required short-term financial investments, as well as the accounts receivable planned for repayment. At the same time, for correct accounting of receivables, it is necessary to take into account that not all the debt will be repaid in the next period, and also that the advances reflected in the receivables will be covered by the completion of work (delivery of products or provision of services).

Thus, the total amount of financial coverage available to the enterprise (CA - Cover Amount) can be determined as:

where: - the amount of accumulated cash flow; - the amount of short-term financial investments required; - the amount of short-term receivables that will be repaid during the forecast period (minus advances issued); - the amount of long-term accounts receivable (minus advances issued); - a coefficient showing the share of long-term receivables that will be repaid during the forecast period (with long-term planning, it can be calculated as the reciprocal of the average repayment period for long-term receivables for the enterprise).

The amount of obligations that will be presented to the enterprise for payment is calculated as the sum of payable loans and borrowings, interest on the use of loans and borrowings, as well as accounts payable planned for repayment. To correctly calculate the amount of payable loans, borrowings and accounts payable, it is necessary to take into account that not all of them will be repaid in the current period, and also that the received advances reflected in accounts payable will be covered by the performance of work (delivery of products or provision of services).

Thus, the total amount of possible obligations to be presented to the enterprise (OP - Obligations to Payment) can be determined as:

where: - the amount of short-term loans and borrowings; - the amount of long-term loans and borrowings that will be repaid during the forecast period; - a coefficient showing the share of long-term loans and borrowings that will be repaid during the reporting period (for long-term planning, it can be calculated as the reciprocal of the average repayment period for long-term loans and borrowings for the enterprise as a whole); - the amount of interest payable for the use of loans and borrowings; - the amount of accounts payable (minus advances received); - a coefficient showing the share of accounts payable that will be repaid during the forecast period (with long-term planning, it can be calculated as the reciprocal of the average period for repayment of accounts payable for the enterprise).

Then the probability of an enterprise’s financial insolvency (bankruptcy) can be assessed as the probability of an event occurring in which the amount of financial coverage available to the enterprise will be less than the amount of possible obligations to pay, that is, in relation to the case under consideration, the condition of the enterprise’s financial security can be written as:

Note that although the expression (3) obtained above is similar in form to that proposed by S.V. Ivliev and A.M. Kokosh, the content of the indicators included in it is different. The approach we propose, based on detailed modeling and analysis of an enterprise’s cash flows, turns out to be more reliable and flexible in practice. In addition, the proposed model makes it possible to implement the principle of mutual compensation of financial indicators: the deterioration of some indicators can be compensated by the improvement of others (for example, an increase in the amount of accounts payable can be compensated by an adequate increase in accounts receivable), which in general will not lead to a change in the likelihood of a financial crisis.

Probabilistic model for forecasting financial crises

It is obvious that economic processes are subject to the influence of a large number of independent random factors, therefore it seems necessary to introduce the concept of “probability” into the model being developed.

In the forecast period, the most favorable situation for the enterprise will be the case when the amount of future receipts is maximum and the amount of future payments is zero. On the contrary, in the most unfavorable situation, the amount of receipts will be zero and the amount of payments will be maximum. Then, taking into account that each future receipt and payment has a certain probabilistic assessment, it is possible to assess the risk of a financial crisis in the enterprise’s activities as the risk of a financing gap. That is, as the ratio of the value of the expected total outflow of funds, taking into account the probability of payments, to the total mass of all probable financial transactions (that is, the total amount of all received and paid funds, taking into account the corresponding probability, but without taking into account the sign):

,(4)

where: - predicted i-th arrival cash; - predicted j-th cash outflow; , - the probability of the i-th receipt and j-th outflow of funds, respectively.

Graphically, this ratio is illustrated in Figure 3 - the risk of financing gaps is numerically equal to the ratio of the area of ​​the rectangle to the total area of ​​both rectangles.

Rice. 3. On the issue of determining the likelihood of financing gaps.

In the case when it is not possible to estimate the probability of receipts and payments (for example, when forecasting financial flows for the long term), with some general conditions it can be assumed that the result of the influence of external factors on the financial condition of the enterprise has a distribution close to normal (although each of the terms separately may not obey the normal law of probability distribution). This position is mathematically substantiated in the theorem of A.M. Lyapunova. It can be assumed that the hypothesis of normal distribution is valid for all cases, except for cases of a progressive system-wide financial crisis, accompanied by a sharp jump in non-payments.

Then the probability of bankruptcy of an enterprise (PB - Probability of Default) can be estimated using probabilistic methods by applying the normal distribution function to equation (3) as:

, (5)

where: - the amount of financial coverage available to the enterprise; - the amount of possible payment obligations to be presented to the enterprise; - standard normal distribution function; - standard standard deviation of the random component of the indicator.

Conclusion

The basis for the use of the proposed methods and models is a comprehensive financial and economic model of the enterprise’s activities, which reveals in detail the inflow and outflow of funds during the forecast period. All larger number domestic companies come to the conclusion that to ensure further successful development they need to work out their strategic plans. And for large companies, the absence of a development strategy based on a financial model is already beginning to be perceived as bad form. Therefore, such a model in any case should be developed at the enterprise.

The proposed approaches were tested by us at a number of the largest Russian enterprises belonging to various industries, and showed positive results. Thus, we can recommend the use of the proposed methods for predicting the occurrence of financial crises in the system of financial and crisis management of an industrial enterprise.

Reviewers:

• Stroev V.V., Doctor of Economics, Vice-Rector for Research and Quality, Federal State Budgetary Institution of Higher Professional Education "Moscow state university food production", Moscow.
• Fomin P.A., Doctor of Economics, Professor. General Director of ZAO Business Effect, Moscow.

Bibliographic link

Kuznetsov N.V. THE PROBLEM OF DEVELOPING A METHOD FOR FORECASTING FINANCIAL CRISES IN ENTERPRISE OPERATIONS // Contemporary issues science and education. – 2011. – No. 6.;
URL: http://science-education.ru/ru/article/view?id=5079 (access date: 03/18/2019). We bring to your attention magazines published by the publishing house "Academy of Natural Sciences"