Test for the level of attention development online. Attention test. Simple exercises to develop good attention and memory

Professor of the Omsk Humanitarian Institute

Sergienko O.V.,
Associate Professor, Omsk State Agrarian University

The insolvency of Russian agricultural enterprises during the general crisis is due to unfavorable macroeconomic conditions: disruption of traditional economic ties, decline in production, sudden, difficult to predict changes in government economic policy, inflation, political instability, imbalance in the financial market. However, monitoring of a bankrupt enterprise usually shows that bankruptcy occurs gradually. Constant analytical work aimed at identifying and neutralizing hidden negative trends prevents the onset of a crisis situation. Diagnosis of bankruptcy, along with the obvious external reasons also reveals mistakes of agricultural producers that aggravated negative impact external environment.

Commercial organizations in their development are subject to various types of crises (economic, financial, managerial), and their bankruptcy should be considered as an acute manifestation of the crisis. Throughout the world, bankruptcy is commonly understood as a financial crisis, that is, the inability of a company to fulfill its current obligations. In addition, a company may experience an economic crisis (a situation where the company’s material resources are used ineffectively) and a management crisis (ineffective use human resources, which often also means low management competence and, consequently, inadequacy of management decisions to environmental requirements).

Well-known methods for predicting bankruptcy reveal different kinds crises, so the estimates obtained with their help vary greatly. But any of these types of crises can lead to the liquidation of the organization. In practice, there is no universal method for predicting bankruptcy due to “specialization” in any one type of crisis. Therefore, it is advisable to track the dynamics of the resulting indicators for several of them. The choice of specific methods should be dictated by the characteristics of the industry in which the enterprise operates.

Despite the presence large quantity various models and techniques (R. Lis, D. Fulmer, G. Springate, R. Taffler, J. Conan and M. Golder, W. Beaver, D. Duran, L.V. Dontsova, A.V. Grachev, E S. Stoyanova, M.A. Fedotova, R.S. Sayfulina, P.A. Fomina, O.P. Zaitseva, V.V. Kovaleva, G.V. Savitskaya, T.B. Berdnikova), allowing to predict the onset of bankruptcy of a company with varying degrees of probability, in this area there are many problems in predicting bankruptcy, especially in such a specific industry as Agriculture. The imperfection of the institution of bankruptcy in our country makes it difficult to implement developments based on the realities of our economy and aimed at reliably predicting the possible bankruptcy of enterprises.

The use of foreign and domestic methods for diagnosing bankruptcy is not without its drawbacks, in particular, the weighting coefficients used in official methods require adjustment in relation to the domestic, regional and sectoral conditions of the functioning of business entities; existing statistics do not fully reflect information about the work of successful and weak enterprises, about the dynamics and structure of equity and borrowed capital, working capital, assessing the liquidity of the enterprise’s balance sheet, i.e., there is difficulty in collecting the necessary financial information that characterizes the financial position of an agricultural organization from the inside .

Currently, the problem of predicting bankruptcy for an individual enterprise consists, on the one hand, in the absence of generally accepted operating methods for predicting bankruptcy and the solvency of entities entrepreneurial activity, on the other hand, these methods are mainly focused on establishing the fact of insolvency when signs of bankruptcy of a commercial organization are already evident. Predicting the risk of bankruptcy also poses the risk of “insolvency of agricultural organizations that has or is becoming sustainable,” the cessation of budget revenues, and sometimes additional budget expenses. Therefore, the financial services of the region, as well as organizations, are interested in anticipating trends in the financial condition of agricultural producers in order to predict possible changes in their income base. For the local budget, this means the risk of direct financial losses, including the risk of bankruptcy of taxpayers. Timely analysis of trends in current solvency and diagnosis of bankruptcy of agricultural organizations will make it possible to calculate the amount of financial risk and the possible timing of income losses; develop health measures to prevent crisis situations, before the introduction of bankruptcy proceedings, when the financial losses of the business entity have already occurred, the sources of income for the budget are practically lost, and it is inappropriate to calculate the risks.

In modern economic science in Lately Numerous developments have appeared in the field of analysis and forecasting of the activities of commercial organizations, including methods for calculating the degree of distance of firms from bankruptcy and the degree of their reliability. Based on the results of financial analysis, a system of standard indicators (liquidity, financial stability, efficiency of resources used, return on assets) allows us to identify weak spots in the economy of a commercial organization, characterize the state of financial economic activity agricultural enterprises. At the same time, taking into account regulatory framework, some indicators may be in the critical zone, while others may be quite satisfactory. General indicators used as a criterion for the insolvency of an agricultural enterprise often do not take into account regional and industry specifics. Many indicators of liquidity and financial stability complement and replace each other, in regulatory documents analysis of the financial condition of the organization there is no clear definition of established industry standards, and more often these standards are absent altogether. Therefore, based on such an analysis, it is difficult to make an unambiguous conclusion that this agricultural enterprise will definitely go bankrupt in the near future or, conversely, will survive, because agribusiness enterprises have different organizational and technical specifics, their own unique market niches, strategies and goals, phases life cycle. In this case, for general analysis it is necessary to use the optimal number of coefficients, taking into account the specifics, size of the organization and the main goals and objectives of the analysis. The reliability of the analysis of the probability of bankruptcy increases significantly when comparing the indicators of a given enterprise and similar enterprises that have gone bankrupt or avoided bankruptcy. However, in Russia, for the agricultural sector of the economy, it is very problematic to find a suitable analogue for comparison, or there is no such analogue at all. There is a need to use tools that increase the reliability of the conclusions for predicting the probability of bankruptcy. One of these methods is the discriminant analysis method, which is used to solve classification problems, that is, dividing the set of analyzed objects into groups by constructing a classifying function in the form of a correlation model.

The method of discriminant indicators first appeared in the USA (1960), when scientists tried to formulate models for predicting bankruptcy. There are several multifactor forecast models with the help of which business entities can be divided into potential bankrupts and non-bankrupts. The most basic research in foreign practice financial organizations published 1968 in Journal of Finance E.I. Altman<1>and was the starting point of numerous subsequent studies conducted in the field of bankruptcy diagnostics. This method is a step-by-step analysis, which, based on a number of coefficients, allows us to assess the financial situation of the company from the point of view of its viability and continuity of economic activity in the short term.

<1>Altman, Edward I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, Journal of Finance (September 1968), pp. 589-609.

The essence of the study is as follows.
— Comparative analysis for the same period based on a series of coefficients in two samples, including firms with payment difficulties and “healthy” firms.
— Selection, using various statistical tests, of coefficients to determine the best firm represented in one of the samples.
— Development, using the techniques of discriminant analysis, of a linear combination of Z from the determining coefficients, which will make it possible to distinguish between the insolvent and the healthy commercial organizations and can serve as a predictive tool.

Altman model was constructed using multiple discriminant analysis ( Multiple discriminant analysis - MDA). The author examined 22 different financial ratios, on the basis of which a step-by-step discriminant analysis was carried out on 66 companies (33 of which operated successfully and 33 failed between 1946 and 1965), which made it possible to estimate the weights of individual calculated indicators. As a result, only five main financial ratios remained in the model, each of which was assigned a certain weight established by statistical methods. With the help of your analytical method Altman derived the following reliability equation ( "Z score model"):

Z = 1.2 X1 + 1.4 X2 + 3.3 X3 + 0.6 X4 + 1.0 X5,

where X1 is the share of net working capital in assets;
X2 - the ratio of accumulated profit to assets;
X3 - return on assets;
X4 - the ratio of the market value of all ordinary and preferred shares enterprises to borrowed funds;
X5 - asset turnover.

In the model under consideration, the first factor represents the share of assets covered by its own working capital and characterizes the solvency of the enterprise; the second and fourth reflect the capital structure; third - return on assets, calculated on the basis of balance sheet profit; fifth - capital turnover.

An enterprise is assigned to a certain reliability class based on the following Z index values:
Z<1,81, то вероятность банкротства очень велика;
1,81 < Z < 2,675, то вероятность банкротства средняя;
Z = 2.675, then the probability of bankruptcy is 0.5;
2,675 < Z < 2,99, то вероятность банкротства невелика;
Z > 2.99, then the probability of bankruptcy is negligible.

In 1977<2>E.I. Altman, R.G. Holdman and P. Narayan developed a more accurate model (ZETA model), which allows predicting bankruptcy over a five-year horizon with an accuracy of 70%<3>. It showed greater accuracy than Z score model, especially when forecasting over long time horizons. Initially, the model used 27 financial ratios, of which only seven were subsequently selected:
X1 - return on assets: the ratio of earnings before interest and taxes to total assets;
X2 - profit stability, estimated over the last 5-10 years;
X3 - interest coverage ratio: the ratio of profit before interest and taxes to total amount interest payments;
X4 - total profitability: the ratio of retained earnings to the amount of assets;
X5 - current liquidity ratio: the ratio of working capital to short-term accounts payable of the company;
X6 - the ratio of market capitalization to the book value of capital, which is estimated on average over the last 5 years;
X7 is the size of the company, estimated as the logarithm of the company's total assets.

<2>This model is often dated back to 1978. See: Agaptsov S.A., Mordvintsev A.I., Fomin P.A., Shakhovskaya L.S. Indicative planning as the basis for the strategic development of an industrial enterprise: Monograph. - M.: graduate School, 2002.
<3>Altman E.I., Haldeman R.G., Narayanan P. Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporation, Journal of Banking and Finance, June 1977.

In 1983 E. Altman<4>received a modified version of his formula for companies whose shares were not quoted on the market:

Z = 0.717 X1 + 0.84 X2 + 3.107 X3 + 0.42 X4 + 0.995 X5,

where K4 is the book value equity in relation to borrowed capital. At Z< 1,23 Альтман диагностирует высокую вероятность банкротства.

<4>Altman E.I. Corporate Financial Distress. - New York, John Wiley, 1983; Altman E.I. Further Empirical Investigation of the Bankruptcy Cost Question, Journal of Finance, September 1984, pp. 1067 - 1089.

At enterprises whose shares are not quoted on the market, and also due to the insufficient development of the Russian stock market and the lack of information in Russia on the market value of shares, as an exception, you can use the option proposed by economist Yu.V. Adamov, who, when calculating the K4 coefficient, replaces the market value of shares with the amount of authorized and additional capital, since an increase in the value of the enterprise’s assets leads either to an increase in its authorized capital(increase in denomination or additional release shares), or to an increase in additional capital (an increase in the market value of shares due to an increase in their reliability).

Bankruptcy forecasting models developed in the West correspond to the conditions of a developed market economy. For Russian economy the application of the Altman model has a number of limitations due to different methods for reflecting inflationary factors, different capital structures, due to differences in information and legislative framework. For example, the coverage ratio based on the market value of equity capital when used in calculations receives a very high estimate, which does not correspond to Russian reality due to the imperfection of the current methodology for revaluation of fixed capital. The share of equity capital is unreasonably inflated due to the revaluation fund (additional capital). As a result, a high level of the Altman Z-model indicator is achieved even for chronically unprofitable organizations due to the fact that they have a small share of borrowed capital in its total amount, and, accordingly, the indicator under consideration distorts the actual financial position of the company.

With the development of the bankruptcy system and the emergence of a sufficient number of bankrupts in various sectors of economic activity, it becomes possible to adapt E. Altman’s forecasting model to the realities of the Russian economy. The relevance of this task increases as the market develops and a healthy competitive environment is created. Until now in financial analysis Both original Altman models and criteria are actively used. For example, the Northwestern Society of Appraisers' business valuation methods include testing firms using Altman models. The ideas of Altman’s formulas are embedded in the “Audit Expert” program (Audit Expert 3.71). The description of the application of the “Audit Expert” program indicates the scope of application of the “Altman Z-score” methodology:
— Commercial enterprises - to analyze the likelihood of the threat of possible bankruptcy (loss of solvency).
— Auditing companies - to draw up an opinion on the financial condition (prospects for bankruptcy) of the customer enterprise.
— State-owned enterprises - for presentation financial indicators economic activities to higher structures.
— Potential counterparties and shareholders of the enterprise - to assess its future solvency and make strategic decisions in relation to this enterprise.

Users can guess only by indirect signs that in software the 1968 model is based (constructed using the apparatus of multiplicative discriminant analysis; the final bankruptcy probability coefficient Z is calculated using five indicators; depending on the value of the Z-indicator, the probability of bankruptcy within two years is assessed on a certain scale).

Scientists of the Irkutsk State economic academy proposed its own four-factor model for predicting the risk of bankruptcy of trading enterprises (R-account model), similar to the model of E.I. Altman 1968:

R = 8.38 K1 + K2 + 0.054 K3 + 0.63K4,

K1 - net working capital/total assets;
K2 - reserve capital + retained earnings/total assets;
K3 - profit (loss) + interest payable/total assets;
K4 - capital and reserves/general liabilities;
K5 - proceeds from sale/total assets.

To assess the values ​​of the R-score model, a scale of 5 intervals is used, by which one can judge the threat of bankruptcy:
R< 0 — вероятность банкротства максимальная (90—100%);
R< 0 < 0,18 — вероятность банкротства высокая (60—80%);
0,18 < R < 0,32 — вероятность банкротства средняя (35—50%);
0,32 < R < 0,42 — вероятность банкротства низкая (15—20%);
R > 0.42 - the probability of bankruptcy is minimal (up to 10%).

Taking into account world experience crisis management for diagnosing the bankruptcy of domestic enterprises by a team of scientists from the St. Petersburg state university under the guidance of Dr. Econ. Sciences, Professor S.V. Valdaitsev was empirically formulated Altman test<5>.

<5>Methodology for assessing the reliability of Russian enterprises based on official data from the consolidated balance sheet and other indirect information: http://nwsa.ru/pub/7/61_1.php.

This criterion is a discriminant function of four financial ratios calculated from the balance sheet and profit and loss account of the enterprise. Differences in accepted different countries systems accounting in this criterion are leveled by the fact that the discriminant function of the Altman criterion involves the multiplication of several relative indicators, in each of which the profit values ​​most dependent on the accounting system are normalized to the amount of the residual book value of the assets or liabilities of the enterprise.
The Altman criterion (Z) is calculated as follows:

Z = 6.56X1 + 3.26X2 + 6.72X3 + 1.05X4,

where X1 is the ratio of working capital to the sum of the value of all assets;
X2 is the ratio of book profit to the sum of the value of all assets;
X3 is the ratio of profit before interest and tax to the sum of the value of all assets;
X4 is the ratio of the book value of equity (net assets) to the total value of all the company’s liabilities.

It is believed that if the Z score is less than 1.10, then there is a threat of insolvency of the enterprise.

If this indicator is more than 2.90, then it can be argued that there is no threat of insolvency of the enterprise.

Enterprises for which the Altman criterion score is between 1.10 and 2.90 qualify as being in the “gray zone”. This means that nothing definite can be said about their prospects for maintaining solvency.

The authors tested the hypothesis of the possibility and limitations (impossibility) of applying Altman models to agricultural enterprises. Our research confirms the acceptability of using the Altman criterion in domestic business conditions for diagnosing the insolvency of agricultural enterprises.

The testing methodology we carried out was to diagnose the bankruptcy of agricultural enterprises that are systematically included in the rating of the best farms in the region according to the indicator highest level profitability from product sales based on Altman models, as well as the adapted Altman criterion. A fragment of the constructed analytical table 1 demonstrates the acceptability of use various models Altman in domestic agribusiness conditions. The “+” sign in the table indicates the coincidence of the results of the model forecast and the actual positive financial condition.

Table 1

Acceptability of using the Altman criterion in domestic business conditions for diagnosing the insolvency of agricultural enterprises

Calculations of the probability of bankruptcy using the Altman criterion showed the reliability of the positive financial condition of agricultural enterprises at 91%, while 9% (Fig. 1.) of organizations are in the “gray zone”. Based on the results of economic activities of agricultural enterprises in the Omsk region for 2004-2005, published in the collection of the Omsk Regional Statistics Committee, this group enterprises confirm their stable financial position. Using Altman's (1968, 1983) models to determine the probability of bankruptcy of the same agricultural organizations gives conflicting results (Fig. 2, 3). For example, 58% of the best farms in the region were classified by the 1983 model into the group with a high and very high probability of bankruptcy; while the 1968 model was more accurate: 45%.

Rice. 1. Degree of bankruptcy probability according to the Altman criterion

Rice. 2. The degree of probability of bankruptcy according to Altman’s method (1983)

Rice. 3. The degree of probability of bankruptcy according to Altman’s method (1968)

Thus, the presence of numerous approaches to assessing bankruptcy confirms the feasibility of developing this topic. After making adjustments to the methodology proposed by Altman, most financial experts agreed that his forecasts are highly efficient and statistically reliable, i.e. Using these models, you can most accurately identify enterprises that have a high probability of financial “failures.” The closer bankruptcy is, the more obvious the results shown by both the Altman model and any other method. The advantage of methods similar to the Altman model is the high probability with which bankruptcy is predicted approximately two years before the actual announcement of the competition; the disadvantage is a decrease in the statistical reliability of the results when making forecasts regarding the distant future.

Also on this topic.

Altman ratio (creditworthiness index). This method was proposed in 1968 by the famous Western economist Edward I. Altman. The creditworthiness index was constructed using the apparatus of multiplicative discriminant analysis (MDA) and allows, as a first approximation, to divide business entities into potential bankrupts and non-bankrupts.

In constructing the index, Altman examined 66 businesses, half of which failed between 1946 and 1965 and half of which were successful, and examined 22 analytical ratios that could be useful in predicting possible bankruptcy. From these indicators, he selected the five most significant and built a multivariate regression equation. Thus, the Altman index is a function of certain indicators characterizing the economic potential of an enterprise and the results of its work over the past period. IN general view The creditworthiness index (Z-score) has the form:

Z = 1.3 X1 + 1.4 X2 + 3.3 X3 + 0.6 X4 +1.0X5 (1)

Where X1 is working capital/amount of assets;

X2 – retained earnings/amount of assets;

X3 – operating profit/total assets;

X4 – market value of shares/debt;

X5 – revenue/amount of assets.

The results of numerous calculations using the Altman model have shown that the general indicator Z can take values ​​within the range of [-14, +22], while enterprises for which Z>2.99 are among the financially stable, enterprises for which Z<1,81 являются безусловно-несостоятельными, а интервал составляет зону неопределенности.

Table 1

The degree of probability of bankruptcy according to E. Altman

The Z-factor has a common serious drawback - essentially it can only be used in relation to large companies that list their shares on stock exchanges. It is for such companies that one can obtain an objective market assessment of equity capital.

Practice shows high accuracy of assessments and bankruptcy forecasts using z-scores for large and medium-sized companies.

In 1983, Altman developed a modified version of his formula for unlisted companies:

Z = 0.717X1 + 0.847X2 + 3.107 X3 + 0.42 X4 + 0.995X5 (2)

(here X4 is the book value, not the market value of the shares). The “borderline” value here is 1.23.

The Altman coefficient is one of the most common. However, a careful study of it shows that it is composed incorrectly: member X1 is associated with a management crisis, X4 characterizes the onset of a financial crisis, while the rest - an economic one. From the point of view of a systematic approach, this indicator has no right to exist.

In general, according to this formula, enterprises with profitability above a certain limit become completely “unsinkable”. In Russian conditions, the profitability of an individual enterprise is significantly exposed to the danger of external fluctuations. Apparently, this formula in our conditions should have lower parameters for different profitability indicators.

Other similar criteria are known. So the British scientist Taffler proposed in 1977. four-factor predictive model, which was developed using the following approach:

Using computer technology, at the first stage, 80 ratios are calculated based on data from bankrupt and solvent companies. Then, using a statistical technique known as multivariate discriminant analysis, a solvency model can be constructed by determining the partial ratios that best distinguish the two groups of companies and their ratios. This selective calculation of ratios is typical for determining some of the key dimensions of corporate performance, such as profitability, working capital adequacy, financial risk and liquidity. By combining these indicators and putting them together accordingly, the solvency model produces an accurate picture of the financial health of the corporation. A typical model for analyzing publicly traded companies takes the form:

Z = 0.53X1 + 0.13X2 + 0.18 X3 + 0.16 X4 (3)

x1=profit before tax/current liabilities;

x2=current assets/total liabilities;

x3=current liabilities/total assets;

x4=revenue / amount of assets.

If the Z-score value is greater than 0.3, this indicates that the company has good long-term prospects; if it is less than 0.2, then bankruptcy is more than likely.

In Russia, the system of criteria for determining the structure of the balance sheet of enterprises is unsatisfactory was approved by Decree of the Government of the Russian Federation dated June 20, 1994 N 498 “On some measures to implement legislation on the insolvency (bankruptcy) of enterprises”, adopted in pursuance of the Decree of the President of the Russian Federation dated December 22, 1993. No. 2264 “On measures to implement legislative acts on the insolvency (bankruptcy) of enterprises.” Based on the norms of civil law, the most important features of a legal entity are the presence of separate property and its independent property liability. Every enterprise, being a legal entity, is responsible for the results of economic activities with all its property.

Federal Law "On Insolvency (Bankruptcy)" No. 6-FZ dated January 8, 1998. establishes for all legal entities engaged in entrepreneurial activities uniform grounds for declaring them insolvent (bankrupt), as well as a uniform procedure for the liquidation of such legal entities. Thus, the legislation does not provide for any exceptions with respect to liability for the obligations of enterprises in various industries and areas of economic activity, and it is this approach that was implemented in the above-mentioned resolution of the Government of the Russian Federation.

It should be especially noted that declaring an enterprise insolvent and having an unsatisfactory balance sheet structure does not mean declaring the enterprise insolvent (bankrupt), does not change the legal status of the enterprise and does not lead to civil liability. This is only a state of financial instability of the enterprise recorded by a body authorized by the owner of the property, and such actions do not go beyond the authority of the owner of the enterprise to monitor the effective use of his property. Therefore, the normative values ​​of the system of criteria are established in such a way as to ensure timely control over the financial condition of the enterprise and the implementation of measures to prevent insolvency, stimulating enterprises to independently overcome the crisis.

Altman model is a formula proposed by American economist Edward Altman that predicts the probability of bankruptcy of an enterprise.

The Altman model is analyzed in the FinEkAnalysis program in the block Models for assessing the probability of bankruptcy of an enterprise.

Altman model - what it shows

The Altman model, showing the probability of bankruptcy, is based on a selection of 66 companies - 33 successful and 33 bankrupt. The model predicts accurately 95% of the time.

The simplest is the two-factor Altman Model. It uses two key indicators: the current liquidity indicator and the indicator of the share of borrowed funds in assets. They are multiplied by the corresponding constants - weight coefficients (a, b, y) determined by practical calculations.

Altman two-factor model formula

Since the two-factor model does not provide a comprehensive assessment of the financial position of the enterprise, analysts more often use the five-factor model (Z5) by Edward Altman. It is a linear discriminant function, the coefficients of which are calculated based on data from a study of a population of companies.

Altman Five Factor Model Formula

Z5= 1.2 * X1 + 1.4 * X2 + 3.3 * X3 + 0.6 * X4 + 0.999 * X5

• X1 - working capital / amount of assets of the enterprise,
• X2 - retained earnings / amount of assets of the enterprise,
• X3 - profit before tax / total asset value,
• X4 - market value of equity / accounting (book) value of all liabilities,
• X5 - sales volume / total assets of the enterprise.

As a result of calculating the Z-score, the following conclusion is made:

Later, a modified Altman five-factor model was obtained for companies whose shares are not listed on the stock exchange:

Formula of the modified Altman five-factor model

Z modified = 0.717 * X1 + 0.847 * X2 + 3.107 * X3 + 0.42 * X4 + 0.995 * X5

where X4 is the book value of equity / borrowed capital (liabilities).

• Z in the range from 1.23 to 2.89 the situation is uncertain,
• Z more than 2.9 is typical for stable and financially sound companies.

Synonyms

Altman Z-score

Was the page helpful?

More found about the Altman model

1. Financial diagnostics of Russian enterprises using the Altman model for developed and emerging markets
   Altman In Russia, Altman's discriminant models are well known and widely used in practice. However, their relevance is often questioned
2. Assessing the risk of bankruptcy probability using logit models
   In Russia, among MDA models for predicting bankruptcy risk, two- and five-factor Altman models are often used, which have many disadvantages. The operating conditions of Russian enterprises often differ from Western ones. Economic
3. Current issues and modern experience in analyzing the financial condition of organizations - part 5
   Therefore, foreign analysts use Altman’s five-factor model Z5 Z5 1.2X1 1.4X2 3.3X3 0.6X4 0.999X5, 41 where X1 is working capital to
4. Vector method for predicting the probability of enterprise bankruptcy
   E Altman's Z-score E Altman's five-factor model E Altman's seven-factor model Beaver's coefficient D Connan's model - M
5. Which model better predicts the bankruptcy of Russian enterprises?
   K V Shamshev said that according to the Altman model, signs of bankruptcy were identified in 40% of companies; 8 of the 20 largest companies have signs of bankruptcy
6. Balancing the solvency of an enterprise and the liquidity of its financial resources
   Economists from different countries, who tested many methods in practice, also tested Altman’s model, applying it to different periods of time. After making minor adjustments to the one proposed by Altman
7. Development of models for predicting bankruptcy of Russian enterprises for the construction and agriculture industries
   From 28-35. Let us analyze in detail the modeling results. The Altman model provides overall forecast accuracy in 64.1% of cases for construction enterprises and 62.7% -
8. Analysis of financial condition in order to determine the creditworthiness of the organization
   Kolyshkin Let's consider the methodology for determining the probability of bankruptcy using the Altman model as the example of the most famous Altman Z-model looks like this 4, with 131 Z5 1.2
9. The effectiveness of using the case method in assessing the risk of bankruptcy of an organization
   In particular, students came to the conclusion that the Altman Model is currently the most well-known, but the Z-coefficient has a common serious drawback -
10. Methods for predicting the likelihood of bankruptcy of an organization
    Return on investment in an enterprise 19.4 The Altman model is a five-factor model built according to data from successfully operating and bankrupt industrial enterprises
11. Diagnosis of bankruptcy of an agricultural enterprise taking into account international experience
    Saratov region according to Altman's five-factor model for 2013-2015 Coefficients 2013 2014 2015 Ratio 2015
12. Anti-crisis management as a tool for financial stabilization of an enterprise
    To build the model, Altman examined 66 enterprises, half of which went bankrupt between 1946 and 1965
13. Improving approaches and methods for analyzing the financial condition of an enterprise
    From this point of view, the predictive potential of existing methods is interesting, among which the most well-known and widely used, at least for educational purposes, are methods for predicting the probability of loss of solvency, the Altman five-factor model, the Beaver model, as well as the method of assessing financial condition used by arbitration managers. As shown
14. How to predict debtor bankruptcy
    Altman's two-factor model is one of the simplest ways to assess the probability of bankruptcy of a potential buyer. And most importantly
15. Analysis of the effectiveness of using the Altman Z-score in diagnosing the bankruptcy of Russian companies in the context of crisis phenomena in the economy
    To analyze the bankruptcy of an enterprise, we use the Altman model and analyze the company’s performance for 2013-2014. In Western practice of financial and economic activities for
16. Credit index
    The Altman model is analyzed in the FinEcAnalysis program in the block Models for assessing the probability of bankruptcy of an enterprise Results of numerous
17. Assessment of the probability of bankruptcy of the company Kryotherm LLC in the conditions of the modern economy
    Table 3 - Calculation of the probability of bankruptcy using the two-factor Altman model Indicator Code 2013 2014 Current liquidity ratio Ktl 1.76 1.58 Borrowed capital ZK
18. Bankruptcy probability assessment
    The FinEcAnalysis program assesses the probability of bankruptcy of enterprises using the following methods: Altman's five-factor model, Altman's modified five-factor model, Fulmer's model, Stringate's model, Lees and Taffler's model Additionally
19. Methodology for managing receivables of an enterprise taking into account risks
    Altman's two-factor model 1 2. Altman's five-factor model 2 3 Taffler's R model 2 4. Model
20. Methods for assessing the risk of bankruptcy of enterprises
    One of the most popular approaches is multiplicative discriminant analysis represented by the Altman model 8 whose features are as follows in relation to a specific country and interval

Let's build an Altman model based on Appendix 1 and Appendix 2. They show the balance sheet and profit and loss statement of an enterprise with the legal form of OAO Slavneft.

Altman's two-factor model is one of the simplest and most visual methods for predicting the probability of bankruptcy, when using which it is necessary to calculate the influence of two indicators.

The Altman model formula takes the form:

Z = - 0.3877 - 1.0736 * K tl + 0.579 * K zs (1),

where Ktl is the current liquidity ratio;

Кзс - capitalization ratio.

The current ratio is calculated as the ratio of current assets to current liabilities.

The capitalization ratio is calculated as the ratio of the share of borrowed capital to the amount of liabilities.

To predict the risk of insolvency (bankruptcy) of the company, reporting for 2012-2013 was used.

Table 1 presents the results of the analysis included in Altman's two-factor Z-model.

Table 1. Altman's two-factor Z model

Let's substitute these coefficients into the formula of Altman's two-factor Z-model.

We get: Z 2013 = - 0.3877 - 1.0736 * 1.47 +0.579 * 0.65 = - 1.589542

Z 2012 = - 0.3877 - 1.0736 * 1.85 +0.579 * 0.89 = - 1.85855

Z< 0 - вероятность банкротства меньше 50 % и далее снижается по мере уменьшения Z.

Based on the obtained Z values, the following conclusions can be drawn:

The probability of bankruptcy in 2012-2013 is this enterprise was less than 50% and further decreases as Z decreases, since

Z 2013 = - 1.589542, Z 2012 = - 1.85855.

Altman's five-factor model for publicly traded companies. Altman’s most popular model, it was the one that was published by the scientist in 1968.

The formula for calculating the Altman five-factor model is:

Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + X5 (2),

where X1 = working capital to the amount of assets of the enterprise. The indicator evaluates the amount of a company's net liquid assets in relation to total assets;

X2 = undistributed profit to the total assets of the enterprise, reflects the level of financial leverage of the company;

X3 = profit before tax to total assets. The indicator reflects the efficiency of the company's operating activities.

X4 = market value of equity / accounting (book) value of all liabilities;

X5 = sales volume to the total assets of the enterprise characterizes the profitability of the enterprise's assets.

Z< 1,23 - вероятность банкротства высокая;

Z > 1.23 - the probability of bankruptcy is low.

The calculation of the indicators included in Altman’s five-factor model is presented in Table 2.

Table 2. Altman's five-factor Z-model

Let's substitute these coefficients into the formula of Altman's five-factor Z-model.

We get: Z 2013 = 1.2*0.38 + 1.4*0.32 + 3.3*0.25 + 0.6*0.54 + 0.15 = 2.203

Z 2012 = 1.2*0.39 + 1.4*0.08 + 3.3*0.06 + 0.6*0.13 + 0.18 =1.036

Thus, the probability of bankruptcy in 2013 at this enterprise was in the interval, which constitutes a zone of uncertainty (Z = 2, 203), that is, in this interval it is impossible to say anything definite about the possibility of bankruptcy.

The probability of bankruptcy in 2012 at this enterprise was Z< 1,81. Это говорит о том, что предприятие в этом году было несостоятельным.

Calculation part. Grade business activity companies

The financial manager of Voskhod OJSC drew attention to the fact that over the past twelve months the volume Money accounts have decreased significantly. Financial information on the activities of Voskhod OJSC is given in table. 2.1.

All purchases and sales were made on credit, the period was one month.

Required

1. Analyze the information provided and calculate the duration of the financial cycle, during which funds are diverted from the company’s turnover, for 2007 and 2008.

2. Compose an analytical note on the impact of changes during the periods under study (see paragraph 5 of the Instructions for solving the problem).

Additional Information:

1) for calculation it should be assumed that the year consists of 360 days;

2) all calculations should be performed accurate to the day.

Table 2.1. Financial information on the activities of JSC "Voskhod"

Instructions for solving problem option 2

1. Analyze the available information on the largest possible range of areas (Table 2.2).

Table 2.2. Dynamic profit analysis

Let's calculate gross profit enterprises:

Gross profit = Sales revenue - Cost of goods sold

for 2007: 477,500 - 350,000 = 127,500 rubles;

for 2008: 535,800 - 370,000 = 165,800 rubles;

Absolute change for 2008-2007: 58,300 - 20,000 = 38,300 rubles.

Relative change for 2008-2007: 12.21 - 5.7 = 6.51%

The cost of production in 2007 amounted to 73.3% of revenue, and in 2008 - 69% of revenue, which, together with an increase in sales revenue, influenced the growth of gross profit.

Revenue growth can be explained by the following reasons:

increase in physical sales volumes;

changing the product range in favor of more profitable types of products;

successful marketing activities;

Table 2.3. Working capital analysis

NOC = DZ + Inventories + Nes. production + GP - KZ (1)

In 2007: NOC = 80,900 + 18,700 + 24,100 + 58,300 - 19,900 = 162,100 rubles.

In 2008: NOC = 101,250 + 25,000 + 28,600 + 104,200 - 27,900 = 232,000 rubles.

Thus, the absolute change in net working capital in 2007-2008 was:

20,350 + 6,300 + 4,500 + 45,900 - 7,150 = 69,900 rubles, and the relative change in NER: 25.15 + 33.69 + 18.67 + 78.73 - 35.93 = 120.32%

The coverage coefficient is calculated using the formula:

This ratio shows the possibility of repaying short-term obligations using working capital. The result obtained is compared with 1. In our case, we get the ratio 9:

1. A ratio of 3:1 or more reflects a high degree of liquidity and favorable conditions for lenders and suppliers. But it can also mean that a business has more funds at its disposal than it can effectively use. As a rule, this leads to a deterioration in profitability and turnover.

A ratio of 2:1 is theoretically considered normal and means that by selling its current assets for just half their value (1/2=0.5 or 50%), the company will be able to fully pay off its short-term liabilities. For different areas of business, it can range from 1.2 to 3 or higher and strongly depends on both industry characteristics and the chosen working capital management strategy.

Absolute change in coverage ratio:

3. The financial cycle and the duration of one turnover of working capital elements should be determined according to the data in Table. 2.4.

Table 2.4. Analysis of working capital turnover

Turnover indicators (business activity) play important role in financial analysis, since they characterize the speed of transformation of various company resources into cash and have a direct impact on its liquidity, solvency and profitability. They also serve as a measure of the efficiency and intensity of use of the assets owned by the enterprise.

Inventory turnover characterizes the average time required for a company to sell its products and services. Another interpretation of this indicator is the period of supply with inventories for a given average daily sales volume. The shorter the turnover period, the more efficient the production and sales of products are, and it is also necessary to take into account industry average values.

Accounts receivable turnover in days characterizes middle period the time during which funds from customers arrive in the company’s bank accounts. How less value this indicator, especially favorable conditions the enterprise is located. Too much short period repayments should not always be regarded as positive factor, as this can mean strict payment terms leading to the loss of potential customers. In our problem, the accounts receivable turnover in 2008 became equal to 68 days, which is 7 days more compared to 2007.

Accounts payable turnover in days characterizes the average number of days during which the company pays its bills. The turnover of accounts payable in 2007 was equal to 40 days, in 2008 it increased and was equal to 44 days.

Comparing the amounts of receivables and payables allows you to compare the conditions for receiving funds from an enterprise from its customers with the terms of payment for products and services to its suppliers. It is considered preferable when payment terms to suppliers are better than those offered to customers. Then the company generates sources of additional financing due to the time difference between payments. The ratio is not in favor of the enterprise (as in our problem, the turnover of accounts receivable is 3 days higher than the turnover of accounts payable) may mean problems with the sale of products, i.e. implementation under any conditions.

Based on turnover indicators, the duration of the operating and financial (cash) cycle of the enterprise is calculated.

Operating cycle period (OCP) is the period of turnover of all current assets from the moment of purchasing raw materials and materials until receiving money for goods and services sold.

OCP 2007 = 44.88 + 23.93 + 59.97 + 60.99 = 190 days,

OCP 2008 = 49.15 + 23.94 + 101.38+ 68.03 = 242 days.

Absolute change in operating cycle: 242 - 190 = 52 days

The duration of cash turnover is calculated as follows: Inventory + Nec. production + Reserves GP + DZ - KZ

In 2007, the duration of cash turnover was: 44.88 + 23.93 + 59.97 + 60.99 - 40 = 150 days;

In 2008, the duration of cash turnover was: 49.15 + 23.94 + 101.38 + 68.03 - 44.3 = 198 days;

Absolute change in the duration of cash turnover for 2007-2008. = 198 - 150 = 48 days.

4. The volume of products sold in 2008 was 12.21% higher than in 2007, and the cost of products sold was 5.7% higher. Investments in inventories and accounts receivable less accounts payable increased from 162.1 thousand rubles. up to 232 thousand rubles, or 70%, which is disproportionate to the growth in sales volume and indicates unsatisfactory control of working capital turnover periods. Increase in net working capital by 69.9 thousand rubles. means that net income from profit in 2008 is 26.65 thousand rubles. is greater than what would have been achieved had there been no increase in inventory and accounts receivable (less accounts payable) during 2008, so the company may have to take out an unnecessary bank loan. At the same time, a significant increase in net working capital means that too much long-term funds are invested in current assets.

A total liquidity ratio excluding cash and short-term loans greater than 9:1 (explain this ratio) can be considered too (high/low). Reasons for the growth of net working capital:

1) growth in sales volume;

2) increase in turnover periods.

The second of these reasons is more significant. The turnaround time for raw materials and supplies increased from 45 to 49 days, although this increase was offset by an extension of the credit period provided by suppliers from 40 to 44 days. The loan period for debtors increased from 61 to 68 days. This period seems too long. However, the most significant change was the increase in turnover time finished products from 60 to 101 days, and the exact reason for this is not possible to establish.