1. The Master's research paper on the theme: “Discriminant analysis and its application in the prediction of bankruptcy of the enterprise ” (adapted from Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf) Student: Anastasia Bobrova, FC 10-M Supervisor: Associate Professor, Ph.D. in Economics, Victoria Varenik

2. SLIDE 2. CONTENTS INTRODUCTION SECTION 1. THEORETICAL ASPECTS OF DISCRIMINANT ANALYSIS AND ITS APPLICATION IN THE PREDICTION OF BANKRUPTCY OF THE ENTERPRISE 1.1. The essence of discriminant analysis and its application in predicting bankruptcy of enterprises 1.2. Forecasting of bankruptcy of the enterprise on the basis of discriminant analysis 1.3. Analysis and evaluation of the use of discriminant analysis in predicting bankruptcy of enterprises in Ukraine SECTION 2. ASSESSMENT OF THE APPLICATION OF DISCRIMINANT ANALYSIS IN PREDICTING BANKRUPTCY IN DNIPROPETROVSK TRAINING AND PRODUCTION ENTERPRISE OF UKRAINIAN SOCIETY OF THE DEAF 2.1. Organizational and economic characteristics of Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf 2.2. Practice in the application of discriminant analysis in predicting of bankruptcy of Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf 2.3. Analysis of bankruptcy of Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf SECTION 3. IMPROVING THE APPLICATION OF DISCRIMINANT ANALYSIS IN PREDICTING BANKRUPTCY IN DNIPROPETROVSK TRAINING AND PRODUCTION ENTERPRISE OF UKRAINIAN SOCIETY OF THE DEAF 3.1. Problems and prospects of application of discriminant analysis in predicting of bankruptcy in Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf 3.2. Using the method of fuzzy sets for the diagnosis of risk of bankruptcy in Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf 3.3. Development of models of diagnostics of bankruptcy with the help of discriminant analysis and building of the position identification matrix of Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf on the choice of the system of anti-crisis financial management CONCLUSIONS AND SUGGESTIONS REFERENCES

3. SLIDE 3. The purpose of the research is the theoretical and methodological synthesis and development of practical recommendations to improve the application of discriminant analysis in predicting the probability of bankruptcy. The object of the research is discriminant analysis. Subject of the research is discriminant analysis and its application in predicting bankruptcy of enterprises. The research base is Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf that is engaged in the production of working clothes.

4. SLIDE 4. THE ADVANTAGES AND DISADVANTAGES OF FOREIGN MODELS FOR DETERMINING THE PROBABILITY OF BANKRUPTCY AdvantagesDisadvantages 1. Low complexity of use while ensuring a sufficiently high accuracy of the results. 2. There is a possibility to compare the status of different objects. 3. Information for the calculation of all indicators is available and contained in the main reporting forms. 4. There is the opportunity not only to predict bankruptcy, but the evaluation of risk zones in which the enterprise is located. 5. High probability of evaluation and effectiveness in practice. 6. It can be used to confirm the results both individually and in the aggregate. 7. Taffler’s and Springate’s models are the most adapted to Ukrainian practice. 1. The specifics of individual countries are not taken into account. 2. The characteristics of the industry, the status of suppliers and competitors, income and consumer spending are not taken into account. 3. The balance sheet and the statement of financial performance are considered only. 4. There are various important indicators, which are due to differences in accounting for certain indicators, the impact of inflation on their formation, the mismatch between book value and market value of certain assets and other objective reasons. 5. Using different techniques is the risk of getting the opposite conclusions. 6. There may be situations where the companies with the worst performance of the coating and autonomy are fully functional and make a profit. 7. The models do not take into account specificity of the company activity depending on the industry. 8. There are differences in view of importance of individual indicators in the models. 9. The lack of Ukrainian statistics of bankrupt enterprises, which could confirm or refute the reliability of the model.

5. SLIDE 5. The share of unprofitable enterprises in the economy of Ukraine for 2005- 2014

6. SLIDE 6. Evaluation of the influence factors on the prospects of development of the enterprises of Ukraine in 2015 (+ strengthening the influence of the factor; - reducing the impact factor) Impact factor Enterprises Agricultural Industri al Constr uction Trading Transp ort The service sector High fuel prices + Lack of working capital + ++++ Imperfect legislation + + High interest rates on loans + + Low solvent demand - +++ High taxes - -++ High tariffs of natural monopolies + Lack of funding + The lack of work orders + Competition from domestic enterprises --- Growth in the physical volume of trade for most groups of food products + The decrease in the physical volume of trade for most groups of non-food products + The slowdown in the reduction in the volume of orders for domestic goods + The decrease in the volume of orders for imported goods + The shortage of fuel and lubricants +

8. Slide 8.

9. SLIDE 9. The calculation of the probability of bankruptcy Dnipropetrovsk UTOG-based discriminant analysis (2011- 2014) (M is a minimal threat of bankruptcy; C – average threat of bankruptcy; – the high threat of bankruptcy; B5 – the probability of bankruptcy after 5 years; SPS – financially stable; NSF – precarious financial condition). THE DISCRIMINANT ANALYSIS MODEL Estimation of probability of bankruptcy 2011201220132014 1. Z – criterion E. Altman ММММ 2. Y – criterion R. Taffler and G. Tishow ММММ 3. R – criterion Davydova–Belikova ММММ 4. Z − criterion. Hidaka and D. Stos M/NSF 5. Model Of Beaver 5.1. Biver Ratio SPS 5.2. The coefficient of total liquidit SPS 5.3. Return on equity net profit margin HPSSPSB5 5.4. The concentration ratio of borrowed capital SPS 5.5. The coverage ratio of own current assets capital B5 6. Z − criterion R. Liz ММММ 7. Z − criterion K. SpringateВССС 8. N – criterion J. Fulmer ММММ 9. Z – criterion K. Berman ММММ 10. Z – criterion of Conan and Holder ММММ 11. R – rating the number Saifullin - KadykovaНФСSPS 12. Z is the universal criterion of discriminant functions ММММ

10. SLIDE 10. Assessment of the probability of bankruptcy using the coefficient of financing of difficult to liquid assets of Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf for 2012-2014, ths. Index201220132014 1). The average cost of non-current assets170215971505 2). The average amount of current inventory733 642 3). The average amount of equity175218561942 4). The average amount of long-term bank loans000 5). The average amount of short-term Bank loans000 Р. 1 + Р. 2243523302147 The obtained inequality2435 > 17522330 > 18562147 > 1942 Interpretation of bankruptcy probabilities The probability of bankruptcy is very high

11. SLIDE 11. Stages of application of model-based fuzzy logic methods in Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf 1 stage The definition of sets, subsets, and the selection of the list of indicators for the diagnosis of bankruptcy. 2 stage Assessing the significance of indicators based on the weight coefficients according to Fishburnes’s rule. 3 stage Classification of degree of risk and the values of selected indicators. 4 stage Assessment indicators: equity ratio; the ratio of current assets equity capital; the quick ratio absolute liquidity; asset turnover; return on equity; level of marketing; level of technical and technological renovation. 5 stage Classification of level of calculated indicators based on the selected criteria. 6 stage Risk assessments are based on formal arithmetic operations on assessing the risk of bankruptcy. 7 stage Linguistic recognition. Formulation of conclusions and recommendations.

12. SLIDE 12. Estimation of probability of bankruptcy of Dnipropetrovsk UTOG on the results of applying the method of fuzzy sets for 2011-2014 Indicator name Хi Calculated values Хi 2011201220132014 The autonomy factor 0,566270,59250,58230,6224 The ratio of current assets equity -0,01770,20580,35310,5474 The quick ratio -0,05880,12130,20250,3046 The absolute liquidity ratio 0,06500,03150,01900,0984 Asset turnover 1,29991,51141,63531,5684 The profitability of the entire capital -0,02140,03920,0270,0261 Level of marketing -0,47060,80000,66170,5061 The level of technological renovation 0,0028-0,00140,00760,0056 The degree of risk 0,419190,278890,363910,20704 medium risk of bankruptcy low risk of bankruptcy medium risk of bankruptcy low risk of bankruptcy

13. The SLIDE 13. The position identification matrix of Dnipropetrovsk training and production enterprise of Ukrainian Society of the Deaf for selecting the system of anti-crisis financial management