Transcript Discriminant analysis and its application in the prediction of

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
INTRODUCTION
SLIDE 2.
CONTENTS
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
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.
SLIDE 4.
THE ADVANTAGES AND DISADVANTAGES OF FOREIGN MODELS FOR DETERMINING
THE PROBABILITY OF BANKRUPTCY
Advantages
Disadvantages
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.
SLIDE 5.
The share of unprofitable enterprises in the economy of Ukraine for 20052014
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)
Enterprises
Impact factor
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
+
Dynamics of assets of Dnipropetrovsk UTOG for 2011-2014
1764
1640
1800
1600
1400
1709
1554
1418
1730
1456
1224
1000 UAH
1200
1000
800
600
400
200
0
2011 year
non-current assets
2012 year
current assets
2013 year
2014 year
Slide 8.
SLIDE 9.
The calculation of the probability of bankruptcy Dnipropetrovsk UTOG-based discriminant analysis (20112014)
(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).
Estimation of probability of bankruptcy
THE DISCRIMINANT ANALYSIS MODEL
2011
2012
2013
2014
1. Z – criterion E. Altman
М
М
М
М
2. Y – criterion R. Taffler and G. Tishow
М
М
М
М
3. R – criterion Davydova–Belikova
М
М
М
М
4. Z − criterion. Hidaka and D. Stos
M/NSF
M/NSF
M/NSF
M/NSF
5.1. Biver Ratio
SPS
SPS
SPS
SPS
5.2. The coefficient of total liquidit
SPS
SPS
SPS
SPS
5.3. Return on equity net profit margin
HPS
SPS
B5
B5
5.4. The concentration ratio of borrowed capital
SPS
SPS
SPS
SPS
5.5. The coverage ratio of own current assets capital
B5
B5
B5
B5
6. Z − criterion R. Liz
М
М
М
М
7. Z − criterion K. Springate
В
С
С
С
8. N – criterion J. Fulmer
М
М
М
М
9. Z – criterion K. Berman
М
М
М
М
10. Z – criterion of Conan and Holder
М
М
М
М
НФС
SPS
SPS
SPS
М
М
М
М
5. Model Of Beaver
11. R – rating the number Saifullin - Kadykova
12. Z is the universal criterion of discriminant functions
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.
Index
2012
2013
2014
1). The average cost of non-current assets
1702
1597
1505
2). The average amount of current inventory
733
733
642
3). The average amount of equity
1752
1856
1942
4). The average amount of long-term bank
loans
0
0
0
5). The average amount of short-term Bank
loans
0
0
0
2435
2330
2147
2435 > 1752
2330 > 1856
2147 > 1942
The
probability of
bankruptcy is
very high
The probability
of bankruptcy
is very high
The probability
of bankruptcy is
very high
Р. 1 + Р. 2
The obtained inequality
Interpretation of bankruptcy probabilities
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.
recommendations.
Formulation
of
conclusions
and
SLIDE 12.
Estimation of probability of bankruptcy of Dnipropetrovsk UTOG
on the results of applying the method of fuzzy sets for 2011-2014
Calculated values Хi
Indicator name Хi
2011
2012
2013
2014
0,56627
0,5925
0,5823
0,6224
-0,0177
0,2058
0,3531
0,5474
-0,0588
0,1213
0,2025
0,3046
0,0650
0,0315
0,0190
0,0984
Asset turnover
1,2999
1,5114
1,6353
1,5684
The profitability of the entire capital
-0,0214
0,0392
0,027
0,0261
Level of marketing
-0,4706
0,8000
0,6617
0,5061
0,0028
-0,0014
0,0076
0,0056
0,41919
0,27889
0,36391
0,20704
medium risk of
bankruptcy
low risk of
bankruptcy
medium risk of
bankruptcy
low risk of
bankruptcy
The autonomy factor
The ratio of current assets equity
The quick ratio
The absolute liquidity ratio
The level of technological renovation
The degree of risk
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