Elloumi F. & Gueylè JP (2001) consider firms that have experienced long-run negative earnings per share financially distressed. Earnings Per Share (EPS) is the portion of a company's profit allocated to each outstanding share of common stock. It serves as an indicator of a company's profitability. It is calculated as: (Net Income - Dividends on preferred stock)/average outstanding shares. Dependent variable used is the financial distress status of the firm. Financially distressed firms are coded 1 and healthy firms are coded 0.
According to Deng and Wang (2006), the definition of financial distress is in terms of "abnormality of financial situation" defined in the article 9.2.1 of 2001 Shanghai Stock Exchange…. Rule. In 1998 China securities Regulatory Commission (CSRC) firstly executed "special treatment" (ST) on the listed companies that suffer "abnormality of financial situation" or "other situation abnormality", ordaining that the daily stock-price fluctuation of "ST" companies should be confined within 5%. The Stock Listing Rule specified that if one of the situations below occurs, the listed company should be recognized to encounter "abnormality of financial situation”: 1) The net profits in the last two years were negative. 2) The value of the net worth per share was less than the face value of the stock in the last year. 3) The auditor presented an adverse opinion or a disclaimer opinion on the financial report of last year. 4) The value of the equity ownership recognized by the auditor and the departments concerned was less than the value of registered capital in the last year. 5) Other financial situation abnormality judged by CSRC, or SHSE and SZSE.
Could someone help me? thanks
Altman did some work on this in the 1980s based on indicators of impending corporate distress. I've linked one of his papers below. His approach was quantitative using ratio analysis. But as your question points out, there may also be useful qualitative indicators of financial distress.
Another source of data would be the cash flow statement under IFRS which would show the elements of all cash inflows/outflows. Analysis of the cash flow statement could show the historic trend of net cash flows, whether dividends are being funded by cash flows or borrowings, and allow cross-checks against the income statement and statement of financial position.
http://pages.stern.nyu.edu/~ealtman/PredFnclDistr.pdf
ISSUE: Which method to use to assess financial distress?
REGULATOR’S APPROACH: The factors you cited from Wang & Deng (2006) are factors used by market regulators. The legislative intent of these rules is to (i) protect investors against misrepresentation of investment vehicles or securities, (ii) maintaining the market as a source of capital; companies availing themselves to this source of capital must have good financial standing. Since the regulatory factors are specific for the market in China, it might not be on point for analyzing Italian firms. However, Wang & Deng would be a good comparison study. It might also be helpful to look into “special treatment” or labeling by regulator in other European markets, i.e. France and Germany for comparison.
LONG-RUN NEGATIVE EARNING: Although the criteria offered by Elloumi & Gueyle is straight forward by using the “long-run” negative earning as the indicator. Here, you would have to consider the term “long-run.” How many quarters or years? In defining financial distress for Italian firms, I would suggest looking into the listing regulation. You may find some useful information at Borsa Italiana's website. See link below.
ARTICLE: There is a good article (Tutino et. al. (2013) on Italian firms that are delisted from the exchange. This goes beyond “special treatment” by regulators; it deals with the ultimate outcome of financial distress of listed firms. This article has a good literature review on the subject. See attached PDF.
http://www.borsaitaliana.it/borsaitaliana/regolamenti/regolamentoborsa-istruzionialregolamento.en.htm
Take in mind self-selection when you deal with financial distress. I believe several papers ignore it..
There are many techniques to use. Bankruptcy prediction goes back almost half a century. You can use univariate or multivariate analysis to predict it as the most simple approaches. Check things like Beaver's failure ratios, Altman's z-score, Ohlson model, Beneish model, etc. However, the most easy techniques are usually unreliable and can lead to both type-1 and type-2 errors.
At empirical level there have been more than +100 techniques used. I provide a detailed-list immediately below that I compile a couple of years ago from reading the literature. Each technique has weaknesses and advantages so be very familiar with it before ou apply it.
List of Bankruptcy Prediction Techniques
1. Univariate Discriminant Analysis (UDA)
2. Multivariate Discriminant Analysis (MDA)
3. Linear Discriminant Analysis (LDA)
4. Quadratic Discriminant Analysis (QDA)
5. Binomial Discriminant Analysis
6. Non -Parametric Discriminant Analysis Kernel Method
7. Step Wise Discriminant Analysis
8. Non Parametric Discriminant Analysis
9. Non-Para Linear Discriminant Analysis
10. Optimal Discriminant Plane
11. Z-Score
12. Probit
13. Random Effects Probit
14. Logit/ Logistic Regression
15. Binomial Logit Analysis
16. Multinomial Logit Analysis
17. Mixed Logit Analysis
18. O-Score
19. Least Squares Regression
20. Cusp Catastrophe Model
21. Recursive Partitioning Algorithm
22. Proportional Hazard
23. Linear Programming/ Linear Optimization
24. Inductive Dichotomizer
25. Interactive Dichotomizer 3
26. Cox Proportional Hazards
27. Backpropagation Learning Algorithm
28. Backpropagation Multi-Layer Perceptron
29. Single-Layer Perceptron
30. Multi-Layer Perceptron
31. Learning Vector Quantization
32. Counter-Propagation Neural Networks
33. Radial Basis Function Network
34. Neural Networks With Pascal Backpropagation Algorithm
35. General Purpose Nonlinear Optimizer
36. Optimal Estimation Neural Network
37. Self-Organizing Neural Network
38. Competitive Neural Network
39. Neural Network Generic Algorithm
40. Hybrid NN: MDA Assisted
41. Hybrid Nn: ID3 Assisted
42. Hybrid NN: Self-Organizing Feature Map MDA Assisted
43. Hybrid NN: Self-Organizing Feature Map ID3 Assisted
44. Probabilistic Nn
45. Cascade Correlation Neural Network
46. Neural Network Architectures
47. Probabilistic Neural Network Without Patterns Normalized
48. Athena Model
49. Feed-Forward Neural Networks
50. Self-Organizing Maps
51. Kohonen Self-Organizing Algorithm
52. Auto-Associative Neural Network
53. GenSoFNN-CRI(S)
54. Multiple Backpropagation Algorithm
55. Multiple Feed-Forward Networks
56. Learning Vector Quantiser
57. MVA
58. K-Nearest Neighbour Algorithm
59. Multivariate Cumulative Sum
60. Modified Algorithm MCM-1
61. Modified Algorithm MCM-2
62. Case-Based Reasoning Artificial Intelligence System
63. Chaos Theory
64. Case-Based Forecasting
65. Timeo-Series Cum Sums
66. Utilities Additives Discriminantes
67. Neuro-Fuzzy Models
68. Fuzzy Clustering
69. Generic Self-Organizing Fuzzy Neural Networks
70. Fuzzy Cerebellar Model Articulation Controller
71. Improved Fuzzy Classifier Functions Model
72. Fuzzy C-Means Clustering
73. Fuzzy Rule Based Model
74. Genetic Fuzzy Neural Network
75. Fuzzy Set Theory
76. Fuzzy Inductive Learning Method
77. Neuro-Fuzzy Classifier
78. Fuzzy Rough K-NN
79. Multivariate Adaptive Regression Splines
80. Genetic Algorithm
81. Trait Recognition
82. Hybrid Genetic Algorithms
83. Support Vectors Machines
84. Generalized Linear Model
85. Generalized Linear Mixed Model
86. Generalized Additive Models
87. Linear Probability Model
88. Method Of Maximum Likelihood
89. Cash Flow Model
90. Market Return Model
91. Market Return Variation Model
92. Random Effects Logit Model
93. Classification And Regression Trees
94. Chi-Squared Automatic Interaction Detection
95. Quest
96. C4.5
97. C5.0
98. Entropy Reduction Algorithm
99. Case-Based Reasoning (Based On A Distance Function)
100. Case-Based Reasoning (Based On A Combination Function)
101. Operations Research
102. Mathematical Programming
103. Non-Linear Programming
104. Linear Goal Programming
105. Integer Programming
106. Mixed Integer Programming
107. Data Envelopment Analysis
108. Soft Computing
109. Hybrid Financial Analysis
110. Hybrid Case-Based Reasoning
111. Rough Sets
112. Gaussian Processes
113. Isotonic Separation
114. Gambler'S Ruin Model
115. Option Pricing Theory
116. Risk Index Models
117. Self-Organizing Learning Array
118. Dynamic ModelingTechinques
119. Disqual Methodology
120. Survival Analysis
121. Machine Learning Techinques
122. Evolutionary Computation
123. Signal Extraction
124. Exchange Rate Pressure Model
125. Financial Ratio And Peer Group Analysis Systems
126. Comprenhesive Bank Risk Assessment System
127. Accounting-Based Measures
128. Black-Scholes-Merton Probability Of Bankruptcy/ Bsm Score
129. Discrete Hazard Model
130. Systemic Risk Model
131. Cumulative Sums
132. Leading Indicator Approach
133. Panel Data Analysis
134. Option-Based Model
135. Case-Based Reasoning
136. Principal Component Analysis
137. Graphics Processing Units
138. D-Score
139. Factor Analysis
140. Quadratic Programming
141. Rough Classifier
142. Hybrid System Integrating Information Retrieval
143. Quadratic Interval Regression Model
144. Delphi Method
145. Forecast By Analogy
Hope this helps.
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