Data Analysis and Relationship Modeling in R. Data Analysis in R Environment Data Analysis in R Examples

Zazimko Valentina Lentievna Ph.D., Art. Lecturer of the Department of Economic Analysis of the Federal State Budgetary Educational Institution of Higher Professional Education "Kuban GAU"

The traditional approach to the analysis of financial position is based on the general concept of "equilibrium of systems", borrowed from countries with market economies (Figure 1).

Figure 1 - Methodology for the analysis of financial condition that meets the Western concept of "equilibrium" of the system

Meanwhile, such problems as: 1) inconsistency of some methodological approaches to the conditions of the Russian specifics of doing business; 2) failure to take into account the social nature of the agricultural sector of the economy in Russia (when differentiating approaches to assessment depending on the industry affiliation of organizations); 3) analysis of the main factors affecting business performance using statistical analysis; 4) structuring the methodology for analyzing financial condition (at least in order to restore linguistic justice); 5) the correspondence of financial analysis to the modern needs of economic entities and an ambiguous interpretation of borrowed economic categories - investigated with insufficient completeness.

The main direction of improving the methodology for analyzing the financial condition of an organization should be taking into account:

The existing political climate and state approach in assessing economic phenomena, processes, and economic results;

Features of the legislative regulation of the preparation of financial statements (in particular, it concerns the revision of approaches to assessing the solvency of an organization);

Sectoral structure of the property of an economic entity;

Modern parameters for assessing the effectiveness of doing business.

The purpose of the analysis of the financial condition of the organization is an objective assessment of the financial situation and the prospects for its development, taking into account the existing situation in the industry in a specific time interval corresponding to the general political and economic strategy in relation to the object of research.

The agrarian transformations of the modern era in the history of Russia are profound and significant: since the second half of 2005, the Government of the Russian Federation has significantly intensified its interest in agriculture, initiating, among others, the national project "Development of the agro-industrial complex"; at the end of 2006, the Federal Law "On the Development of Agriculture" was adopted. The state policy of supporting agriculture provides for stimulating the attraction of loans on the basis of subsidized interest payments. The weakening of the financial independence of the Companies, as a result of the measures taken, according to generally accepted approaches to determining the financial condition, is assessed negatively. According to the estimates of domestic economists, who recognize the shortcomings of the existing methodology for calculating indicators of the financial condition of agricultural producers, which is used, including by arbitration courts (Table 1), there would not be so many bankrupt farms in the country.

Table 1. Fragment of the calculation of coefficients for classifying agricultural producers to the groups of financial stability of the debtor

Odds:	Groups
Odds:
financial independence		0.56≤K<0,6	0.5≤K<0,56	0.44≤K<0,5
financial independence in relation to the formation of stocks and costs				0.65≤K<0,8
provision with own circulating assets

The study of the financial condition of the organization must meet the concept of consistency. At the same time, the methodology for analyzing the financial condition of an organization appears in the form of an agreed sequence, which makes it possible to state the fact of restoring the linguistic fairness of the term “methodology”. It consists of six main stages, the general block diagram is shown in Figure 2.

Figure 2 - Block diagram of the implementation of the stages of analysis of the financial condition of agricultural organizations

Gathering information consists in compiling a list of questions and obtaining relevant data from the surveyed organization and from other sources. The study of the conditions for the functioning of systems should be a preliminary stage of analysis, which is due to the task of an indispensable synthesis of internal and external factors, which arises taking into account the peculiarities of the evolution of economic analysis in Russia, described above. So, for agricultural organizations, the study of the geographical and weather-climatic conditions of the economy of the analyzed subject is specific. The structuring of the initial information should involve the compilation of data slices that should be included in the information base for analyzing the financial condition of an organization with its main characteristics: industry affiliation, business scale, and others.

At the next stage, it is necessary to select the indicators that are the most important criteria for the effectiveness of activities in the formed array of information. Above other indicators, many scientists - analysts, both foreign and Russian, put profitability indicators. Thus, E. Altman in his well-known five-factor "Z-model" for determining the likelihood of potential bankruptcy, two factors out of five presented indicators of profitability. The importance of profitability indicators is also reflected in the “Golden Rule of Economics”, which states that the growth rate of balance sheet profit should exceed the growth rate of revenue from product sales, and the growth rate of sales should exceed the growth rate of assets.

The criterion for the selection of phases in the graph of the traditional life cycle is also the profitability indicator (the ordinate axis in Figure 3).

Figure 3 - Organizational life cycle

Together with the absolute financial performance indicators, the key indicators of the activity of an agricultural organization are: gross production at current selling prices, revenue and profit (loss) from the sale of products (works, services), profit (loss) of the reporting year, net profit (loss) , the ratio of the operating capital turnover, the return on equity, the return on the operating capital.

The system of indicators proposed for the purpose of analyzing the financial condition of economic entities in the agricultural sector of the economy was tested on the example of actual data of CJSC Agrofirm Kavkaz, Krasnodar Territory. The organization ranks far from the last place in the rating of the three hundred largest and most efficient agricultural companies based on the results of 2003-2007, which are members of the Agro-300 club.

Figure 4 - Dynamics of financial performance indicators of CJSC "Agrofirm" Kavkaz "

Analysis of absolute financial performance indicators indicates the development and growth of the company (Figure 4). Thus, steady dynamics in the indicated direction is characteristic of the indicators of gross production (+ 39%), proceeds from product sales (+ 43.9%), as well as the final financial result of activities (+ 16.8%). Among the factors that positively influenced the dynamics of indicators, one can name an increase in the volume of produced and marketable crop production - first of all, grain (by 3.4%), sugar beet (13.9%), sunflower (47.9%) and milk (9 ,nine %). The indicator of return on operating capital for the reporting period compared to the baseline increased, which proves the high efficiency of the joint-stock company.

In order to identify significant factors affecting the level of business efficiency, a correlation and regression analysis of the business efficiency of 46 agricultural organizations in the central zone of the Krasnodar Territory was carried out. The level of return on equity (in percent), calculated as the ratio of the net profit (loss) of the reporting year and the average annual balance of equity capital, was taken as the effective indicator (y). The choice of this particular indicator is explained by its excessive demand by external users of financial statements as an indicator characterizing not only the efficiency of a business, but also its riskiness, strategic prospects of solvency and the quality of business management. For the analysis, key indicators-factors were selected that potentially affect the degree of return on equity; search and calculation of these factors can be carried out on the basis of public financial statements. These are: x 1 - the share of equity in the balance sheet currency,%; x 2 - the ratio between the borrowed and own capital (the ratio of financial leverage); x 3 - the share of liquid assets in assets,%; x 4 - asset turnover ratio (resource productivity).

The analysis of paired correlation coefficients showed that there is a direct and rather close relationship between the return on equity and the ratio of debt to equity capital, according to the Chaddock scale, which confirms the assertion that the search for a rational ratio between borrowed and own sources of financing is an obvious way to increase the effectiveness of the latter. The inverse average relationship between the performance indicator and the share of equity in the balance sheet currency (Tables 2 and 3) indicates that the return on equity in modern conditions grows in the event of a decrease in its share in the total capital. At the same time, there is a direct average relationship between the return on equity and the share of liquid assets in assets and a direct weak one - between it (profitability) and the return on assets.

Table 2. Matrix of paired correlation coefficients of a four-factor multiple regression equation

Analysis of β-ratios indicates that the weakest effect on the change in the return on equity is the share of equity in the balance sheet total, and the strongest is the ratio between borrowed and equity capital. At the same time, it is on the second basis that the studied set of agricultural organizations is extremely heterogeneous. In addition, this aggregate is heterogeneous in terms of the return on equity, the share of equity in the balance sheet currency and the share of liquid assets in assets, which indicates a different level of organization of production and financial activities and its efficiency in farms.

Table 3. General characteristics of the return on equity and selected factors, 2006

Sign	Mean	Paired Odds
Sign	Mean	correlations
y - return on equity,%
x 1 - the share of equity in the balance sheet currency,%
x 2 - the ratio of the ratio between debt and equity capital
х 3 - share of liquid funds in assets,%
х 4 - asset turnover ratio (resource productivity)

The multiple regression equation obtained as a result of the solution is:

y = -12.454-0.164x 1 + 0.688x 2 + 0.905x 3 + 39.335x 4. (1)

The positive value of the coefficient at x 2 is evidence that with rational agricultural practices and a normal ratio of return on assets and interest on the payment of interest on borrowed funding sources, the profitability of own resources should increase.

Table 4. General results of the assessment of the four-factor regression model

The relationship between the return on equity and all factors included in the model is close (multiple correlation coefficient R = 0.901) and statistically significant (Table 4). At the same time, the linear equation explains 81.2% of the variation in the return on equity. The rest are accounted for by random unaccounted for factors.

Practically for calculating the level of business efficiency of agricultural producers and ways to improve it, the main factors and the degree of their influence on the effective indicator are determined in the work. It was determined that the return on equity of the studied set of agricultural organizations: decreases with an increase in the share of equity in the structure of funding sources (the return on equity increases only up to a certain level of equity capital and begins to decline with further growth of its share in the structure of the balance sheet); increases with an increase in the financial leverage ratio, reflecting the ratio of borrowed and equity capital and characterizing the dependence of profit on the structure of funding sources, which is possible with a preferential tax burden and support of farms from the Government of the Russian Federation; has a growing dynamics with an increase in the share of liquid assets in the structure of the organization's property, which is logical in light of the implementation of the settlement and payment discipline, and is a consequence of the growth of the organization's business activity, manifested in an increase in income (proceeds) from the sale of agricultural products and other activities (priority of the marketing strategy activities of the organization); increases with an increase in the level of use of its own assets by the organization (a priority task of the organization's financial management).

Hence, it becomes possible to form the correct vector for increasing the efficiency of the business of agricultural organizations through the use of clear mechanisms that contribute to its growth. In the most general form, these mechanisms are: 1) a reasonable determination of the sources of financing for the organization's activities; 2) increasing the efficiency of using the organization's resources based on the stabilization of mutual settlements and the system of settlement and payment discipline; 3) improvement of the production management system.

The study of the dynamics of the profitability of equity capital of agricultural organizations, depending on the actual level of the share of equity capital in the structure of financing sources, showed that the highest value of the indicator of the efficiency of using equity capital was recorded at the level of equity capital in the range from 44 to 58%. With further growth of equity capital in the structure of sources, a decrease in profitability is observed (Figure 5).

Figure 5 - Dynamics of the return on equity depending on the share of equity in the capital structure

The study of the impact of the financial strategy of the organization in relation to the use of borrowed funds continues the described sequence.

An acceptable place here is acquired by the developed methodology for determining the rational ratio of borrowed and own funds in conjunction with the profitability of equity capital and concessional lending to agricultural organizations.

From the whole set of relative indicators of financial stability, we propose to calculate the ratio of financial independence (Equity to Total Assets), which characterizes the ongoing policy in the field of financing and reflects the share of equity capital in the structure of sources of property, and the ratio of debt to equity capital (financial leverage ratio, or "Leverage"), which characterizes the degree of risk of the organization.

The capital structure ratios characterize the degree of protection of creditors and investors from possible non-payment of debts and practically do not provide information about the economic potential of the organization. The described problem is "solved" by the indicator characterizing the dependence of profit on costs associated with the structure of sources of financing for the organization's activities - the "effect of financial leverage".

EFR = (1-Nesx) (CHRa-PK) x (ZK / SK), (2)

where EFR is the effect of financial leverage, which consists in an increase in the return on equity ratio,%; Nesx - the rate of the single agricultural tax, expressed as a decimal fraction; CHRa - coefficient of gross return on assets,%; PC - the average amount of interest on a loan paid by the organization for the use of borrowed capital,%; ЗК - the average amount of borrowed capital used by the organization; SK is the average amount of the organization's equity capital.

Formula (2) was obtained by taking into account the peculiarities of the formation of data in the financial statements of Russian organizations, as well as taxation of agricultural producers: 1) instead of the entire amount of capital used, in our opinion, it follows from its value to subtract the amount of the organization's accounts payable; 2) "the amount of gross profit without taking into account the cost of paying interest on a loan" is replaced by the indicator "profit from the sale of products (works, services)"; 3) income tax, which is paid under the general taxation regime, is not considered by the author as a factor influencing the size of the effect: in accordance with the current legislation, agricultural producers pay a single agricultural tax, which was introduced into the formula.

Table 5. Dynamics of financial stability indicators of CJSC Agrofirma Kavkaz

So, the share of borrowed capital in relation to equity in CJSC Agrofirm Kavkaz at the end of 2006, according to Table 5, amounted to 52.8%, which is 42.1 percentage points. more than the base year level. An increase in the share of borrowed capital in the structure of the balance sheet liabilities indicates a transition from a conservative to a moderate financial policy; and although this is due to the weakening of the autonomy of the economic entity, under certain conditions, this can lead to an increase in the return on equity. It should be noted that the degree of business activity of agricultural producers is not so high for the implementation of such a financing policy in the future, which means that one should carefully study the consequences of the changes being made and make a rational decision.

The results of the calculations performed to determine the effect of financial leverage for CJSC Agrofirma Kavkaz (Table 6) indicate its positive dynamics: the value in 2006 was 2.5%, which is 3.3 percentage points. more than the base year level. Consequently, CJSC Agrofirm Kavkaz, having formed its assets by 65% at its own expense and by 35% at the expense of borrowed capital, increased its return on equity by 2.5%, all other things being equal, due to the fact that for credit resources it pays taking into account the policy of concessional lending to agricultural producers pursued by the Government of the Russian Federation, and the return on total capital is 16.2%. The factor analysis of the model of the effect of financial leverage showed that in the current conditions it is profitable to use borrowed funds in the turnover of the organization, since the result is an increase in the efficiency of using equity capital. This means that by attracting borrowed resources, the analyzed organization can increase its own capital, provided that the return on invested capital exceeds the price of attracted resources.

Table 6. The mechanism of formation of the effect of financial leverage

Index	2004 r.	2005 year	2006 year	Change over the period (+, -)
Profit from the sale of products, works, services, thousand rubles
Interest payable, thousand rubles
The amount of profit from the sale of products, works, services, taking into account the cost of paying interest on a loan, thousand rubles
Average annual value of capital (assets) used minus accounts payable, thousand rubles
Financial Leverage Ratio
Return on total capital,%
Weighted average nominal price of borrowed resources,%
Financial leverage effect,%
Deviation of the effect of financial leverage in total,%
including due to:
Return on assets,%
Loan interest rates,%
Financial leverage ratio,%

To determine the boundaries of the growth of financial leverage, one should apply the model developed by the French scientists J. Conan and M. Golder. The explanation for this is the composition of the criteria, which is most adapted to the requirements of the construction of domestic financial statements. The lower the value of the estimate, the lower the likelihood of delayed payments by the firm. The actual values of the criteria, calculated according to the data of CJSC Agrofirma Kavkaz, are presented in Table 7.

Table 7. Estimation of the probability of delay in payments of CJSC "Agrofirma" Kavkaz "

Index	2004 r.	2005 year	2006 year
The ratio of the amount of cash and receivables to assets (U1)
The ratio of the amount of equity capital and long-term liabilities to the sources of property coverage (U2)
The ratio of finance costs to proceeds from sales (U3)
The ratio of personnel service costs to value added (U4)
The ratio of profit before interest and taxes to debt capital (Y5)
Estimation of the likelihood of delayed payments: Q = -0.16xU1-0.22xU2 + 0.87xU3 + 0.10xU4-0.24xU5

Calculations show that the probability of delay in payments by the company is very small, however, the dynamics of the integral indicator tends to zero, which means that the level of solvency in the future is under threat. That the wave is justified against the background of the growth in the amount of borrowed funds and debt service costs. In order to prevent possible difficulties, it is necessary to promptly monitor the settlement and payment discipline.

In order to synchronize positive and negative cash flows, operational management of solvency is necessary. The authors of the study are categorically against the use of liquidity ratios as indicators of solvency due to contradiction with the requirement of accounting for continuous activity. The degree of solvency, in our opinion, depends on the filling of financial performance indicators with real money. The use of offset transactions in calculations, the replacement of cash with receivables creates a threat to the organization's ability to meet its current obligations.

Currently, not enough attention is paid to the analysis of cash flows (cash-flow). Meanwhile, this is the most non-controversial method that allows you to trace the degree of sufficiency of funds to cover short-term liabilities. D.A. Endovitsky offers to compare net cash flow from current activities with profit from sales. The negative value of the net cash flow, while, in the presence of profit from sales, will indicate that the formation of working capital requires large financial injections. This situation can lead to insolvency. Reasons: low profitability of sales, high costs for the formation of working capital.

Table 8. Ratio of net cash flow and profit from sales, thousand rubles.

The net cash flow from current activities in CJSC Agrofirma Kavkaz is positive, however, in more detail the sufficiency of cash receipts for financing working capital will be demonstrated by factor analysis (formula 3):

, (3)

where Дптд - cash flow from current activities, thousand rubles, OK - working capital, thousand rubles; Dotd - outflow of funds for current activities, thousand rubles. The effective indicator ( Kdost1) in a given relationship characterizes the organization's ability to finance working capital, shows the sufficiency of cash inflows to cover the costs associated with financing working capital. The recommended value of the indicator should be at least 1.

1. The impact of changes in the net cash inflow ratio for current activities:. (4)

2. The impact of changes in the outflow of funds attributable to one ruble of working capital:. (5)

Table 9. Data for factor analysis of the coefficient of sufficiency of cash receipts for financing working capital, thousand rubles.

Index	Years	Deviations
Index		Deviations
Cash inflow from current activities, thousand rubles
Outflow from current activities, thousand rubles
Total cash outflow for all types of activities, RUB thous.
Adequacy ratio of cash receipts for financing working capital
Net cash flow ratio for current activities
The share of cash outflow from current activities of the total cash outflow for all types of activities, thousand rubles.
Cash outflow from current activities, attributable to 1 rub. working capital
Net cash flow from all types of activities, thousand rubles
Adequacy ratio of net cash flow to cover short-term liabilities
Net cash flow per RUB 1 proceeds
Sales proceeds for 1 rub. short-term liabilities, rub.
The ratio of net cash flow to net profit
The ratio of the ratio of the growth rate of accounts receivable and sales volume

Thus, the positive change in the ratio of the adequacy of cash receipts for the analyzed period (+0.148) is due to an increase in cash outflow from current activities to cover working capital. The negative impact on the coefficient was exerted by the outstripping growth rate of cash outflow over the growth rate of their inflow.

According to CJSC Agrofirma Kavkaz, the ratio of cash inflow and outflow for current activities in the reporting period was 1.018, while the dynamics of the ratio is negative - a decrease of 0.076. However, this does not at all mean a lack of funds to cover short-term liabilities. The ratio of the adequacy of cash flows to cover short-term liabilities is very acceptable both in the previous and in the reporting periods (0.966, 4.216 and 2.780, respectively).

Regular monitoring of the current state of funds

Figure 6 - Stages of the analysis of the solvency of an agricultural organization

The next step is to assess the quality of profit (formula 4):

, (4)

where NPP- net cash flow for all types of activities, thousand rubles, PE - net profit, thousand rubles.

If, according to the results of operations, the organization has a persistent negative net cash flow, this can lead to financial insolvency caused by an actual decrease in resources and a decrease in the economic potential of the organization. In the analyzed situation, as can be seen from Table 9, the organization received a net profit, while for each ruble of profit there are 3 rubles of the balanced result of comparing the inflow and outflow of funds. The study of the possibilities for assessing the solvency of an agricultural organization made it possible to formulate an analysis plan presented in Figure 7.

The results of the study are based in their entirety on the realities of the work of agricultural organizations. This solves the problem of the lack of industry specificity in the existing methods of financial analysis. The practical significance of the study lies in the fact that on the basis of the developed methodology for agricultural organizations, the foundations of the formation of a rational financial policy in the transforming economic situation of the rural sector are proposed. The use of the recommended methodology will make it possible to more accurately measure the level of financial risk and develop a more effective mechanism for managing it in order to increase the effectiveness of entrepreneurial activity.

R-analysis, or the acceptability of criteria approaches in assessing the financial condition of agricultural organizations

In the current economic conditions, the main emphasis in the activities of financial services of commercial enterprises is focused on the operational tracking of indicators of the financial condition of the organization. In this case, priority is given to relative indicators that characterize the ratio of reporting data that carries this or that information. In terminological terms, the method of analyzing the company's activities based on the described approach is called R-analysis, or analysis of financial ratios.

The set of coefficients within an individual business entity depends on the strategy and goals that he wants to achieve. At the same time, the coefficients that should be calculated are identified, and their standard values are established. This work is usually performed as part of a management accounting, budgeting, or balanced scorecard project. “If we take a set of indicators from a textbook on finance,” say practicing analysts, “such a financial analysis will not bring any benefit to the company” / 10 /.

Meanwhile, certain indicators concerning the aspects of financing the organization of its activities have developed traditionally and are included in all methodological algorithms, including those legally regulated.

These are the following indicators:

I. Liquidity Ratios - Liquidity Ratios

Liquidity indicators characterize the company's ability to satisfy the claims of holders of short-term debt obligations.

1. Absolute liquidity ratio

Shows what proportion of short-term debt liabilities can be covered by cash and cash equivalents in the form of marketable securities and deposits, that is, almost absolutely liquid assets.

2. Quick ratio (Acid test ratio, Quick ratio)

The ratio of the most liquid part of working capital (cash, accounts receivable, short-term financial investments) to short-term liabilities. It is usually recommended that the value of this indicator be more than 1. However, the real values for Russian enterprises rarely exceed 0.7 - 0.8, which is recognized as acceptable.

3. Current Ratio

It is calculated as a quotient of dividing current assets by short-term liabilities and shows whether the company has enough funds that can be used to pay off short-term liabilities. According to international practice, the values of the liquidity ratio should be in the range from one to two (sometimes up to three). The lower limit is due to the fact that there should be at least enough working capital to pay off short-term liabilities, otherwise the company will be under the threat of bankruptcy. The excess of current assets over short-term liabilities by more than three times is also undesirable, since it may indicate an irrational structure of assets.

Calculated by the formula:

II. Gearing ratios - Capital structure indicators (financial strength ratios)

Capital structure indicators reflect the ratio of equity and borrowed funds in the company's sources of financing, that is, they characterize the degree of financial independence of the company from creditors. This is an important characteristic of the sustainability of an enterprise. To assess the capital structure, the coefficient of financial independence (Equity to Total Assets) is most often used, which characterizes the dependence of the company on external loans. The lower the value of the coefficient, the more loans the company has, the higher the risk of insolvency. The low value of the coefficient also reflects the potential danger of the enterprise having a shortage of funds. The interpretation of this indicator depends on many factors: the average level of this ratio in other industries, the company's access to additional debt sources of financing, and the specifics of current production activities.

Calculated by the formula:

Other indicators, such as: Profitability ratios - Ratios of profitability, Activity ratios - Ratios of business activity, Investment ratios - Investment criteria, within this article, will not be given for reasons of disclosure of the issue raised in the context of the brevity of the material.

The main thing when conducting financial analysis is not the calculation of indicators, but the ability to interpret the results obtained. The conclusions, however, do not differ in breadth of scope: the main conceptual approach is based on a comparison of the data obtained with the standards that have developed within the framework of the traditional approach. The traditional approach, in this case, is understood as a set of methods, tools and technologies used to collect, process and interpret (interpret) data on the company's economic activities.

Although the main contribution to the theory and practice of financial analysis was made by economists of countries with developed market economies, it is necessary to recall the works of the Soviet economist N. Blatov of the 1920s, which set forth advanced concepts and methods of analysis for their time: comparative analytical balance, distribution coefficients, coordination coefficients, etc.

An interesting moment is borrowing and, to a certain extent, the interpretation of "extreme values" of analytical coefficients characterizing solvency and financial stability, with their comprehensive distribution.

So, in one of the sections of the work of Sokolov Ya.V., written jointly with Kovalev V.V., we find a description of the interpretation of Western accounting and analytical practice to Russian specifics. At the same time, information is provided on the financial condition of ten large joint-stock companies in Russia based on the results of 1907, 1908:

JSC Kavkaz and Mercury (shipping company), Bogorodsko-Glukhovskaya manufactory, Firm "Provodnik" (rubber and telegraph production), M.S. Kuznetsova (production of porcelain products), Russian Electric Company “Westinghouse”, AO of Russian electrical engineering plants “Siemens and Gallskoye”, company “Zinger”, AO Maltsovskiye Zavody, Bryansk Rail Rolling, Iron and Mechanical Plant (AO), Society of Putilov Plants "/ 2 , with. 280 /.

A limited list of coefficients is calculated (their list is given above). The average values of the coefficients calculated on the basis of the given sample (the criterion for grouping enterprises is not specified) are compared with the “world” standards. If their proximity was found, it was concluded that these values are acceptable in relation to the current situation in the country in the structure of assets and sources of their coverage / 11 /.

At the present time, there are a number of contradictions, which, in our opinion, means to keep silent about the main thing.

Let us refer to the instructions (recommendations) of the ministries and other federal executive bodies on the aspect of methodological approaches to the analysis of the financial condition in the context of the coefficients given in them. Among these, the most significant are the techniques presented in the following documents:

1. Methodological provisions for assessing the financial condition of enterprises and establishing an unsatisfactory balance sheet structure, approved by the order of the Federal Office for Insolvency (Bankruptcy) of Enterprises under the State Property of Russia dated August 12, 1994, No. 31-r.

3. The procedure for reporting the heads of federal state unitary enterprises and representatives of the Russian Federation in the governing bodies of open joint-stock companies, approved by the Government of the Russian Federation of October 4, 1999, No. 1116.

4. Guidelines for analyzing the financial condition of organizations, approved by order of the Federal Service of Russia for Financial Recovery and Bankruptcy (hereinafter - FSFR) dated January 23, 2001 No. 16.

5. Rules for conducting financial analysis by the arbitration manager. Approved by Decree of the Government of the Russian Federation No. 367 of June 25, 2003. These rules, in accordance with Federal Law No. 127 FZ of October 26, 2002 "On Insolvency (Bankruptcy)", determine the principles and conditions for conducting financial analysis by the arbitration manager, as well as the composition of information used in this.

6. Instructions on the procedure for drawing up and submitting financial statements, approved by order of the Ministry of Finance of Russia dated July 22, 2003 No. 67n.

7. Decree of the Government of the Russian Federation of January 30, 2003 No. 52 "On the implementation of the Federal Law" On the financial rehabilitation of agricultural producers ".

A review of these documents demonstrated the complete absence of any industry distinctions between the analyzed enterprises. Meanwhile, it should be remembered that the permissible values of indicators can differ significantly not only for different industries, but also for different enterprises of the same industry, and a complete picture of a company's financial condition can be obtained only by analyzing the entire set of financial indicators, taking into account the peculiarities of its activities. The approved values of the indicators are purely informational in nature and cannot be used as a guide to action. In this regard, it is required to develop a regulatory framework at the level of resolutions of the Government or ministries and departments in the sectoral context.

Distinctive features of modern agribusiness enterprises are the lack of working capital, low solvent discipline, an increase in the volume of barter transactions, and the high cost of credit resources. As a result of these and other factors, enterprises do not have the means to fulfill their payment obligations, including the payment of wages, payment for goods (works, services), and debts to the budget are growing.

At the same time, even in such difficult conditions, many enterprises remain “afloat”. Therefore, the "extreme" values of indicators characterizing the structure of the asset and liability of the balance sheet, the solvency and financial stability of organizations should take into account the specifics of the current situation and the boundaries within which the company's management is still able to develop strategic steps to overcome the crisis, without bringing the matter to bankruptcy procedures ...

The criteria that apply to agricultural enterprises in the United States (since we have embarked on the path of borrowing the Anglo-American financial model) are also far from Russian specifics. This is primarily due to two reasons: first, the economic conditions of Russian agricultural production are very different from the economic conditions of the United States or Canada; Secondly, a distinctive feature of domestic politics and agriculture is the fact that - especially among small agricultural enterprises - economic difficulties are beginning to take on a social character. Thus, the principles of a market economy are violated.

In our opinion, the main attention in adapting the traditional approach should be focused on closing the existing gaps in financial analysis procedures.

The main proposals for the further development of the final financial analysis procedures are as follows:

Calculation of own standards or optimal levels of financial ratios for the analyzed company using well-known methodological techniques;

Allocation narrow (<индикаторной>) a sample of financial ratios, the composition of which may differ for different organizations;

Qualitative assessment and determination of weights of indicator indicators based on comparison with calculated optimal levels, trends of change, intercomparison and accepted logical rules;

Development of a standard format for a conclusion on the financial activities of the company, which not only states the problems of the analyzed company, but also indicates the factors of ongoing and future changes, as well as makes recommendations for overcoming, mitigating or strengthening them.

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9. Savitskaya, G.V. Analysis of the economic activity of enterprises in the agro-industrial complex: textbook. allowance / G.V. Savitskaya. - 5th ed., Rev. and additional - Minsk: New knowledge, 2005

10. Kubyshkin I. Using financial analysis for company management / Kubyshkin I. // Financial Director. - 2005. -№ 4

11. Sokolov Ya.V. Accounting from the beginnings to the present day / Sokolov Ya.V. - M .: Audit. UNITY. 1996.

12. Zimin N.E. Analysis and diagnostics of the financial and economic activities of the enterprise / N.E. Zimin, V.N. Solopov. M .: KolosS, 2005 -384 p.

13. Voitolovsky N.V. Economic Analysis: Foundations of Theory. Comprehensive analysis of the organization's economic activities: Textbook / Voitolovsky N.V., Kalinina A.P., Mazurova I.I. - M .: Higher education, 2005 .-- 509s

Target training "Data Analysis and Relationship Modeling in the R Package" - to study the basic capabilities of the R program - a free programming language for performing statistical calculations, as well as learn how to organize and manage data entry, conduct primary statistical analysis of data, present them in graphical form, and be able to find relationships in data. The training is designed for students with no experience in R or with basic knowledge of the package.

Trainees are encouraged to have programming skills and be familiar with the basics of statistical analysis.

Upon graduation, you will be able to:

Correctly sample data for analysis
Organize data entry and manage data
Perform descriptive statistical analysis
Examine relationship in contingency tables
Test statistical hypotheses about equality of means
Use graphics capabilities
Conduct correlation analysis
Perform regression analysis
Perform analysis of variance

Duration of training: 32 academic hours. or 4 days.

Training program:

Topic 1. Basic concepts of statistical data analysis - 2 academic hours.

Statistical research
Data acquisition methods
The difference between observation and experiment
General population and sample
Sampling data requirements
Understanding Point and Interval Statistical Estimation
Signs and variables
Measurement scales of variables
Areas of statistical data analysis
Descriptive and analytical statistics
The choice of methods of statistical analysis depending on the scales of measurement of variables
Statistical hypothesis
Types of statistical errors
Principles for Testing Statistical Hypotheses
Choosing a Significance Level for Hypothesis Testing

Topic 2. Introduction to work in the R environment - 2 academic hours.

Features of working with R
Program installation
Launching the program
Wednesday R
Command line interface and dialog boxes
Command rules
Creating a working directory
Packages
Graphical interfaces
R as a calculator
reference system

Topic 3. Fundamentals of programming in R - 2 academic hours.

Object types in R
Vectors
Lists
Matrices
Factors
Data tables
Expressions
Data Access Operators
Functions and arguments
Loops and conditional statements
Database Management in R
Operation vectorization
Debugging
Object Oriented Programming

Topic 4. Entering and organizing data in R - 2 academic hours.

Data loading methods
Direct data entry
Entering data in a table
Data import from MS Excel
Importing data from other statistical packages and databases
Saving Analysis Results
Quantitative data assignment
Setting ordinal and nominal data
Specifying Missing Values in Data
Identifying outliers and errors
Data transformation principles

Topic 5. Graphic capabilities of R - 2 academic hours.

Graphic functions
Graphics Devices
Graphic parameters
Interactive graphics
Composite images
Output devices

Topic 6. Descriptive statistical analysis in R - 4 academic hours.

Central trend statistics
Arithmetic mean
Modal meaning
Median value
Scatter statistics
Dispersion and standard deviation
The coefficient of variation
Percentiles
Histograms
Box charts
Z-transform
Normal distribution law
Asymmetry and kurtosis
Checking the distribution for normality
Some distribution laws
Binomial distribution
Poisson distribution
Even distribution
Exponential distribution
Lognormal distribution
Standard error and spacing for mean

Topic 7. Formation of data for analysis by the sampling method - 2 academic hours.

General and sample population
Sample characteristics
Features of the sampling method of research
Sample classification
Types and methods of probabilistic selection
Sampling methods
Simple random selection
Systematic random sampling
Cluster selection
One-stage cluster selection
Multistage cluster selection
Algorithm for conducting sample surveys
Determining the required sample size

Topic 8. Statistical tests for identifying differences in samples in R - 4 academic hours.

Hypotheses about comparing averages
Z-test for comparing means
Z-test for comparison of shares
One-sample t-test
T-test for independent samples
T-test for dependent samples
Conditions for applying nonparametric criteria
One-sample Wilcoxon signed rank test
Mann-Whitney test
Sign test for related samples
Wilcoxon sign test for related samples
Nonparametric Kruskal-Wallis ANOVA
Friedman's test for dependent samples

Topic 9. Assessment of the relationship between variables in R - 4 academic hours.

Analyzing the relationship between categorical variables
Crosstabs
Expected frequencies and residuals in contingency tables
Chi-square test
Consent criterion
Classification of types of relationship between quantitative variables
Scatter plots
Prerequisites and conditions for conducting correlation analysis
Pearson's correlation coefficient
Rank correlation coefficients
Spearman's correlation coefficient
Testing the Significance of a Link
Interval estimates of correlation coefficients
Partial correlation coefficients

Topic 10. Modeling the form of communication using regression analysis in R– 4 academic hours.

Basic concepts of regression analysis
Paired and multiple linear regression models
Prerequisites for Linear Regression Analysis
Estimating Regression Coefficients
Validating the validity of the regression model
Significance of the regression equation
Significance of regression coefficients
Selecting Variables in Regression Analysis
Estimating the accuracy of the regression equation
Estimation of the statistical stability of the regression equation
Point and Interval Estimation of the Dependent Variable
Nonlinear Regression Models
Categorical explanatory variables in a regression model

Topic 11. Modeling the relationship using analysis of variance in R– 4 academic hours.

ANOVA Models
Prerequisites for the use of analysis of variance
Testing the hypothesis of equality of variances
One-way ANOVA model
One way ANOVA table
Assessment of the degree of influence of the factor
Post hoc tests for pairwise comparisons
ANOVA with two or more factors
Two-way ANOVA table with interaction
Graphic interpretation of the interaction of factors
Multivariate model analysis

Introduction

First of all, let's discuss the terminology. This is an area that is called Data Mining in Western literature, and is often translated into Russian as “data mining”. The term is not entirely successful, since the word "analysis" in mathematics is quite familiar, has an established meaning and is included in the title of many classical sections: mathematical analysis, functional analysis, convex analysis, non-standard analysis, multivariate complex analysis, discrete analysis, stochastic analysis, quantum analysis etc. In all of these areas of science, a mathematical apparatus is being studied, which is based on some fundamental results and allows solving problems from these areas. In data analysis, the situation is much more complicated. This is, first of all, applied science, in which there is no mathematical apparatus, in the sense that there is no finite set of basic facts from which it follows how to solve problems. Many problems are "individual", and now more and more classes of problems appear, for which it is necessary to develop a mathematical apparatus. An even greater role is played by the fact that data analysis is a relatively new direction in science.

Next, it is necessary to clarify what "data analysis" is. I called it the "area," but the area of what? This is where the fun begins, as this is not just a field of science. A true analyst primarily solves applied problems and is focused on practice. In addition, it is necessary to analyze data in economics, biology, sociology, psychology, etc. Solution

new problems, as I said, requires the invention of new techniques (these are not always theories, but also techniques, methods, etc.), so some say that data analysis is also an art and a craft.

V applied areas, the most important thing is practice! It is impossible to imagine a surgeon who has not performed a single operation. Actually, this is not a surgeon at all. Also, a data analyst cannot do without solving real applied problems. The more of these tasks you solve on your own, the more qualified you will become.

First, data analysis is practice, practice, and more practice. We need to solve real problems, many, from different areas. Because, for example, the classification of signals and texts are two completely different areas. Experts who can easily build an engine diagnostic algorithm based on sensor signals may not be able to make a simple spam filter for emails. But it is very desirable to get basic skills when working with different objects: signals, texts, images, graphs, feature descriptions, etc. It also allows you to choose the tasks you like.

Second, it is important to choose your training courses and mentors wisely.

V In principle, you can learn everything yourself. After all, we are not dealing with an area where there is some secrets passed by word of mouth. On the contrary, there are many competent training courses, source codes of programs and data. In addition, it is very useful when several people solve the same problem in parallel. The fact is that when solving such problems one has to deal with very specific programming. Let's say your algorithm is

gave 89% of correct answers. The question is: is it a lot or a little? If it’s not enough, then what’s the matter: did you program the algorithm incorrectly, chose the wrong algorithm parameters, or the algorithm itself is bad and not suitable for solving this problem? If the work is duplicated, then errors in the program and incorrect parameters can be quickly found. And if it is duplicated by a specialist, then the issues of evaluating the result and the acceptability of the model are also resolved quickly.

Third, it is helpful to remember that solving a data analysis task takes a long time.

Statistics

Analyzing data in R

1. Variables

V R, as in all other programming languages, there are variables. What is a variable? In fact, this is the address with which we can find some data that we store in memory.

Variables consist of left and right sides, separated by an assignment operator. In R, the assignment operator is “<-”, если название переменной находится слева, а значение, которое сохраняется в памяти - справа, и она аналогична “=” в других языках программирования. В отличии от других языков программирования, хранимое значение может находиться слева от оператора присваивания, а имя переменной - справа. В таком случае, как можно догадаться, оператор присваивания примет конструкцию следующего вида: “->”.

V Depending on the stored data, variables can be of different types: integer, real, string. For example:

my.var1<- 42 my.var2 <- 35.25

In this case, the variable my.var1 will be of type integer, and my.var2 will be of real type.

Just like in other programming languages, you can perform various arithmetic operations on variables.

my.var1 + my.var2 - 12

my.var3<- my.var1^2 + my.var2^2

In addition to arithmetic operations, you can perform logical operations, that is, comparison operations.

my.var3> 200 my.var3> 3009 my.var1 == my.var2 my.var1! = my.var2 my.var3> = 200 my.var3<= 200

The result of a logical operation will be a true (TRUE) or false (FALSE) statement. You can also perform logical operations not only between a variable with some value, but also with another variable.

my.new.var<- my.var1 == my.var2

Random Forest is one of my favorite data mining algorithms. Firstly, it is incredibly versatile, it can be used to solve both regression and classification problems. Search for anomalies and select predictors. Secondly, this is an algorithm that is really difficult to apply incorrectly. Simply because, unlike other algorithms, it has few configurable parameters. It is also surprisingly simple at its core. And at the same time, it is remarkable for its precision.

What is the idea behind such a wonderful algorithm? The idea is simple: let's say we have some very weak algorithm, say. If we make a lot of different models using this weak algorithm and average the result of their predictions, then the final result will be much better. This is the so-called ensemble training in action. The Random Forest algorithm is therefore called the "Random Forest", for the data obtained it creates a set of decision trees and then averages the result of their predictions. The important point here is the element of randomness in the creation of each tree. After all, it is clear that if we create many identical trees, then the result of their averaging will have the accuracy of one tree.

How does he work? Suppose we have some input data. Each column corresponds to some parameter, each row corresponds to some data element.

We can randomly select a certain number of columns and rows from the entire dataset and build a decision tree based on them.

Thursday, May 10, 2012

Thursday, January 12, 2012

That's all. The 17-hour flight is over, Russia is overseas. And through the window of a cozy 2-bedroom apartment San Francisco, the famous Silicon Valley, California, USA is looking at us. Yes, this is the very reason why I practically did not write lately. We moved.

It all started back in April 2011, when I was doing a phone interview at Zynga. Then it all seemed like some kind of game that had nothing to do with reality, and I could not even imagine what it would result in. In June 2011, Zynga came to Moscow and conducted a series of interviews, about 60 candidates who passed telephone interviews were considered, and about 15 of them were selected (I don’t know the exact number, someone later changed his mind, someone immediately refused). The interview turned out to be surprisingly simple. No programming problems, no tricky questions about the shape of the hatches, mostly the ability to talk was tested. And knowledge, in my opinion, was assessed only superficially.

And then the gimmick began. First, we waited for the results, then the offer, then the LCA approval, then the approval of the petition for a visa, then the documents from the USA, then the queue at the embassy, then an additional check, then the visa. At times it seemed to me that I was ready to drop everything and score. At times I doubted whether we need this America, after all, it’s not bad in Russia either. The whole process took about six months, as a result, in mid-December we received visas and began to prepare for departure.

Monday was my first day on the job at a new place. The office has all the conditions for not only working but also living. Breakfasts, lunches and dinners from our own chefs, a bunch of varied food crammed all over the place, a gym, massage and even a hairdresser. All this is completely free of charge for employees. Many people get to work by bike and there are several rooms for storing vehicles. In general, I have never come across anything like this in Russia. Everything, however, has its own price, we were immediately warned that we would have to work a lot. What is "a lot", by their standards, is not very clear to me.

Hopefully, however, despite the amount of work, I will be able to resume blogging in the foreseeable future and maybe tell you something about American life and work as a programmer in America. Wait and see. In the meantime, I congratulate everyone on the coming New Year and Christmas and see you soon!

For an example of use, we will print out the dividend yield of Russian companies. As a base price, we take the closing price of a share on the day of the register closing. For some reason, this information is not on the site of the troika, and it is much more interesting than the absolute values of dividends.
Attention! The code takes a long time to execute, because for each promotion, you need to make a request to the finam servers and get its value.

Result<- NULL for(i in (1:length(divs[,1]))){ d <- divs if (d$Divs>0) (try ((quotes<- getSymbols(d$Symbol, src="Finam", from="2010-01-01", auto.assign=FALSE) if (!is.nan(quotes)){ price <- Cl(quotes) if (length(price)>0) (dd<- d$Divs result <- rbind(result, data.frame(d$Symbol, d$Name, d$RegistryDate, as.numeric(dd)/as.numeric(price), stringsAsFactors=FALSE)) } } }, silent=TRUE) } } colnames(result) <- c("Symbol", "Name", "RegistryDate", "Divs") result

Similarly, you can build statistics for past years.

Today I will talk a little about solving the classification problem using the R software package and its extensions. The classification task is perhaps one of the most common in data analysis. There are many methods for solving it using different mathematical techniques, but you and I, as apologists for R, can not but rejoice that at the same time you do not need to program anything from scratch - everything is there (and far from a single copy) in the R.

Classification problem

The classification task is a typical example of supervised learning. As a rule, we have data in the form of a table, where the columns contain the value of the feature sets for each case. Moreover, all the lines are pre-marked in such a way that one of the columns (let us assume that the last one) points to the class to which the given line belongs. A good example is the problem of classifying emails into spam and non-spam. In order to use machine learning algorithms, you first need to have labeled data - such for which the class value is known along with other features. Moreover, the data set must be substantial, especially if the number of features is large.

If we have enough data, we can start training the model. The general strategy with classifiers does not really depend on the model and includes the following steps:

selection of training and test sets;
training a model on a training set;
checking the model on a test set;
cross validation;
improvement of the model.

Accuracy and completeness

How to evaluate how well our classifier is performing? This is not an easy question. The fact is that different scenarios are possible, even if we have only two classes. Let's say we are solving a spam filtering problem. After checking the model on a test set, we get four values:

TP (true positive) - how many messages were correctly classified as spam,
TN (true negative) - how many messages were correctly classified as not spam,
FP (false positive) - how many messages were incorrectly classified as spam (that is, the messages were not spam, but the model classified these messages as spam),
FN (false negative) - how many messages were incorrectly classified as not spam, but in fact it was after all the American English Center.

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