Financial Distress Comparison Across Three Global Regions
Abstract
:I. INTRODUCTION
II. LITERATURE REVIEW
- Evidence of layoffs, restructurings, or missed dividend payments, used by Lau (1987).
- A low interest coverage ratio, used by Asquith, Gertner and Scharfstein (1994).
- Cash flow less than current maturities of long-term debt, used by Whitaker (1999).
- The change in equity price or a negative EBIT, used by John, Lang, and Netter (1992).
- Negative net income before special items, used by Hofer (1980).
- ▪
- Negative EBITDA interest coverage (similar to Asquith, Gertner and Scharfstein (1994)).
- ▪
- Negative EBIT (similar to John, Lang, and Netter (1992)).
- ▪
- Negative net income before special items (similar to Hofer (1980)).
III. METHODOLOGY
Data
Dependent Variable: Financial Distress Defined
- ▪
- Negative EBITDA interest coverage (similar to Asquith, Gertner and Scharfstein (1994)).
- ▪
- Negative EBIT (similar to John, Lang, and Netter (1992)).
- ▪
- Negative net income before special items (similar to Hofer (1980)).
Independent Variables
Model Development, Specification and Comparison
- Model 1: y = X1ß1 + ε
- Model 2: y = X1ß1 + X2ß2 + ε
- Pi = probability of financial distress of the ith firm,
- Xij = jth variable of the ith firm, and
- Bj = estimated coefficient for the jth variable.
IV. RESULTS
Extending the Model to Other Regions
Comparing the Global Model with Regional Indicators to Three Distinct Regional Models
Model Robustness
V. CONCLUSION
IndustrySIC Code | Industry Name | US | Europe | Asia | Total | ||||
---|---|---|---|---|---|---|---|---|---|
FD | FD | FD | FD | ||||||
2200 | Textile Mill Products | 4 | 4 | 11 | 19 | ||||
7 | 1 | 3 | 11 | ||||||
5 | 1 | 2 | 8 | ||||||
14 | 20 | 10 | 44 | ||||||
4 | 0 | 4 | 8 | ||||||
9 | 3 | 6 | 18 | ||||||
2 | 1 | 2 | 5 | ||||||
2 | 2 | 12 | 16 | ||||||
12 | 1 | 7 | 20 | ||||||
6 | 4 | 8 | 18 | ||||||
83 | 22 | 21 | 126 | ||||||
46 | 33 | 18 | 97 | ||||||
27 | 4 | 6 | 37 | ||||||
137 | 22 | 4 | 163 | ||||||
358 | 118 | 114 | 590 |
Industry SIC | Financial Status | EBITDA Interest Coverage (00)3 | EBITDA Interest Coverage (01) | EBIT (00) | EBIT (01) | Net Income before Special Items (00)4 | Net Income before Special Items (01) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | Median | Mean | Median | Mean | Median | Mean | Median | Mean | Median | Mean | Median | ||
2800 | NFD | 986.21 | 144.75 | 1130.42 | 184.20 | 784.99 | 112.58 | 929.64 | 154.54 | 754.35 | 95.20 | 921.19 | 102.57 |
2800 | FD | -23.28 | -10.56 | -26.89 | -14.60 | -23.88 | -10.96 | -27.62 | -14.51 | -25.34 | -10.99 | -28.98 | -15.16 |
3500 | NFD | 391.24 | 58.07 | 494.30 | 85.49 | 285.93 | 40.26 | 393.14 | 67.13 | 264.32 | 30.36 | 377.36 | 52.29 |
3500 | FD | -8.43 | -3.36 | -5.36 | -2.89 | -12.31 | -3.49 | -8.03 | -3.07 | -12.70 | -3.77 | -7.88 | -3.42 |
3600 | NFD | 406.21 | 33.01 | 489.15 | 44.80 | 283.21 | 23.06 | 353.61 | 31.19 | 318.21 | 18.79 | 415.46 | 29.09 |
3600 | FD | -18.88 | -5.38 | -16.16 | -6.41 | -23.21 | -6.36 | -19.56 | -6.87 | -24.70 | -7.11 | -20.42 | -7.71 |
Europe | |||||||||||||
2800 | NFD | 862.81 | 90.50 | 1019.17 | 85.90 | 763.04 | 55.20 | 1447.45 | 436.80 | 581.90 | 48.54 | 756.61 | 52.02 |
2800 | FD | -53.68 | -15.95 | -50.59 | -11.92 | -54.16 | -14.11 | -55.14 | -15.10 | -65.94 | -14.71 | -56.35 | -11.98 |
3500 | NFD | 309.87 | 32.59 | 373.57 | 43.85 | 283.94 | 36.28 | 572.03 | 54.64 | 190.24 | 18.96 | 266.02 | 26.16 |
3500 | FD | -27.17 | -13.85 | -26.57 | -10.09 | -27.77 | -11.75 | -37.88 | -35.01 | -28.53 | -14.86 | -27.76 | -12.66 |
3600 | NFD | 336.85 | 32.05 | 351.76 | 34.52 | 246.05 | 24.02 | 340.59 | 27.85 | 222.05 | 17.99 | 248.35 | 23.68 |
3600 | FD | -31.07 | -14.05 | -18.96 | -8.51 | -24.41 | -9.89 | -16.11 | -6.32 | -40.71 | -19.61 | -26.97 | -10.12 |
Asia | |||||||||||||
2800 | NFD | 203.68 | 24.94 | 346.33 | 27.66 | 155.74 | 16.44 | 289.62 | 19.67 | 179.97 | 13.74 | 343.15 | 14.88 |
2800 | FD | -64.00 | -15.32 | -80.73 | -11.67 | -65.50 | -9.69 | -76.90 | -13.77 | -82.27 | -13.85 | -159.13 | -12.58 |
3500 | NFD | 408.49 | 9.12 | 507.74 | 12.22 | 322.80 | 7.17 | 392.07 | 9.08 | 386.23 | 6.24 | 424.16 | 7.87 |
3500 | FD | -37.77 | -2.80 | -53.87 | -3.68 | -36.18 | -3.51 | -52.06 | -3.97 | -63.96 | -3.23 | -67.59 | -4.31 |
3600 | NFD | 909.13 | 21.23 | 1256.42 | 23.18 | 445.39 | 14.24 | 722.10 | 17.12 | 443.61 | 12.58 | 736.53 | 13.46 |
3600 | FD | -67.60 | -5.58 | -79.50 | -5.65 | -169.74 | -7.45 | -168.85 | -6.23 | -241.55 | -12.55 | -228.68 | -6.64 |
Individual Financial Items | Financial Ratios | |||
---|---|---|---|---|
Status | Inventories (Inv) | Profit Margin | Liquidity | Operating Efficiency |
Net Sales (S) | Inv (-1) | EBITDA/S | CA/CL | COGS/Inv |
S (-1)5 | Current Assets (CA) | NI/S | (CA-Inv)/CL | S/AR |
COGS | CA (-1) | CF/S | WC/TA | S/TA |
COGS (-1) | Net Fixed Assets (NFA) | Profitability | CA/TA | AR/TA |
Deprec+Amort (DA) | NFA (-1) | EBITDA/TA | NFA/TA | S/WC |
DA (-1) | Total Assets (TA) | NI/TA | Cash Position | S/Inv |
SGA | TA (-1) | EBIT/TA | Cash/CL | AR/Inv |
SGA (-1) | Accounts Payable (AP) | CF/TA | Cash/DA | (AR+Inv)/TA |
EBIT | AP (-1) | NI/EQ | Cash/TA | COGS/S |
EBIT (-1) | Notes Payable (NP) | Financial Leverage | Growth | SGA/S |
Interest Expense (Int) | NP (-1) | TL/TA | S-Growth % | (COGS+SGA)/S |
Int (-1) | Current Liabilities (CL) | CL/TA | NI/TA-Growth % | DA/S |
Net Income (NI) | CL (-1) | CL/TL | CF-Growth % | DA/EBIT |
NI (-1) | Long-term Debt (LTD) | NP/TA | Miscellaneous | S/CA |
Cash | LTD (-1) | NP/TL | EBIT/Int | |
Cash (-1) | Total Liabilities (TL) | LTD/TA | Int/S | |
Accounts Receivable (AR) | TL (-1) | Current LTD/TA | LTD/S | |
AR (-1) | Share Equity (EQ) | EQ/TA | CF/Int | |
EQ (-1) | LTD/EQ | CF/TL | ||
TD/TA | ||||
Calculated Items | ||||
EBITDA = EBIT + DA | ||||
EBITDA(-1) = EBIT (-1) + DA (-1) | ||||
CF = NI + DA | ||||
WC = CA - CL |
US | Europe | Asia | ||||
NFD7 | FD | NFD | FD | NFD | FD | |
CF/Sales | ||||||
2800 | 0.087 | -0.954 | 0.118 | -6.510 | 0.097 | -0.674 |
3500 | 0.063 | -1.856 | 0.083 | -4.444 | 0.068 | -0.297 |
3600 | 0.192 | -0.433 | 0.104 | -2.001 | 0.103 | -0.266 |
EBITDA/TA | ||||||
2800 | 0.120 | -0.448 | 0.138 | -0.372 | 0.109 | -0.075 |
3500 | 0.116 | -0.828 | 0.126 | -0.100 | 0.086 | -0.026 |
3600 | 0.132 | -0.696 | 0.160 | -0.350 | 0.104 | -0.110 |
TD/TA | ||||||
2800 | 0.324 | 0.411 | 0.205 | 0.165 | 0.245 | 0.152 |
3500 | 0.240 | 0.356 | 0.208 | 0.196 | 0.227 | 0.383 |
3600 | 0.235 | 0.445 | 0.180 | 0.172 | 0.221 | 0.325 |
Variables | Estimated Coefficient | p-value (two-tail) |
---|---|---|
CF/Sales | -0.141 | .001** |
EBITDA/TA | -2.129 | .000** |
CA/CL | 0.390 | .000** |
Sales/WC | -0.022 | .028* |
DA/EBIT | 0.004 | .447 |
NP/TA | 0.043 | .042* |
TD/TA | 0.471 | .000** |
Constant | -2.440 | .000** |
Group Classified | Percent Classified Correctly |
---|---|
Non-financially distressed companies | 96.4% |
Financially Distressed companies | 82.1% |
All companies | 94.5% |
Global ModelModel 1 | Global Model with Regional IndicatorsModel 2 | |||
---|---|---|---|---|
Variable | Estimated Coefficient | p-value | Estimated Coefficient | p-value |
CF/Sales | -0.141 | .001*** | -0.096 | .012** |
EBITDA/TA | -2.129 | .000*** | -1.992 | .000*** |
CA/CL | 0.390 | .000*** | 0.273 | .013** |
Sales/WC | -0.022 | .028** | -0.003 | .860 |
DA/EBIT | 0.004 | .447 | -0.018 | .061* |
NP/TA | 0.043 | .042** | 0.031 | .173 |
TD/TA | 0.471 | .000*** | 0.402 | .002*** |
Dummy Europe (E) | -0.481 | .414 | ||
Dummy Asia (A) | -0.571 | .255 | ||
CF/Sales E | -0.636 | .007*** | ||
EBITDA/TA E | 0.670 | .081* | ||
CA/CL E | 0.633 | .027** | ||
Sales/WC E | -0.211 | .027** | ||
DA/EBIT E | 0.059 | .001*** | ||
NP/TA E | 0.321 | .050** | ||
TD/TA E | -0.387 | .244 | ||
CF/Sales A | -0.105 | .363 | ||
EBITDA/TA A | -0.396 | .265 | ||
CA/CL A | 0.170 | .462 | ||
Sales/WC A | -0.035 | .196 | ||
DA/EBIT A | 0.010 | .543 | ||
NP/TA A | -0.003 | .983 | ||
TD/TA A | 0.257 | .324 | ||
Constant | -2.440 | .000*** | -2.245 | .000*** |
Nagelkerke R2 | .702 | .716 |
Variables | US | Europe | Asia |
---|---|---|---|
CF/Sales | -0.128*** | -1.090** | -0.714*** |
EBITDA/TA | -2.484*** | -3.974*** | -2.256*** |
Debt/TA | 0.123*** (Current LTD/TA) | 0.632** (NP/TA) | 0.634* (TD/TA) |
Interest Coverage Before Tax | -0.084 | ||
Liquidity Ratio | 0.269** ([CA-Inv]/CL) | 1.820*** (CA/CL) | |
Sales Turnover | -0.356* (S/WC) | -1.918** (S/TA) | |
DA/EBIT | 0.068*** | -0.338*** | |
% Change in Sales | -0.964*** | ||
% Change in Cash Flow | -0.082*** | -0.010# | |
Japan Dummy | 1.002 | ||
Singapore Dummy | 2.384** | ||
Japan x EBITDATA | -9.138*** | ||
Constant | -4.298*** | -4.436*** | -2.566*** |
Nagelkerke R2 | 0.726 | 0.689 | 0.565 |
Group Classified | Asia | ||
---|---|---|---|
Non-financially distressed Companies | 94.8% n = 1,127 | 97.0% n = 908 | 95.4% n = 1,056 |
Financially Distressed Companies | 87.0% n = 276 | 81.2% n = 101 | 81.3% n = 80 |
All Companies | 93.2% n = 1,403 | 95.4% n = 1,009 | 94.4% n = 1,136 |
Model | Source of Data | Accuracy of Financial Distress Classification | Accuracy of Non-financially distressed Classification |
---|---|---|---|
Asian | Europe | 60% | 100% |
Asian | U.S. | 100% | 80% |
European | Asia | 50% | 100% |
European | U.S. | 80% | 80% |
U.S. | Asia | 10% | 100% |
U.S. | Europe | 60% | 100% |
VI. REFERENCES
- E.I. Altman. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” Journal of Finance Vol. 23 (1968): 589–609. [Google Scholar]
- E.I. Altman, and H. Izan. “Identifying Corporate Distress in Australia: An Industry Relative Analysis.” New York University, 1984, Working Paper. [Google Scholar]
- G. Marco, and F. Varetto. “Corporate Distress Diagnosis: Comparisons using Linear Discriminant Analysis and Neural Networks (the Italian Experience).” Journal of Banking and Finance Vol. 18 (1994): 505–529. [Google Scholar]
- P. Asquith, R. Gertne, and D. Scharfstein. “Anatomy of Financial Distress: An Examination of Junk-bond Issuers.” Quarterly Journal of Economics Vol. 109, No. 3 (1994): 1189–1222. [Google Scholar]
- W. Beaver. “Financial Ratios as Predictors of Failures.” Journal of Accounting Research Vol. 4 (1966): 71–102. [Google Scholar]
- D. T. Brown, C. M. James, and R. M. Mooradian. “The Information Content of Distressed Restructurings Involving Public and Private Debt Claims.” Journal of Financial Economics Vol. 33 (1992): 92–118. [Google Scholar]
- R. Davidson, and J. McKinnon. “Several Tests for Model Specification in the Presence of Alternative Hypotheses.” Econometrica Vol. 49 (1981): 781–793. [Google Scholar]
- W. J. Dixon, and F. J. Massey Jr. Introduction to Statistical Analysis, 3rd edition. New York: McGraw Hill, 1969. [Google Scholar]
- H. Frydman, E. I. Altman, and D. Kao. “Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress.” Journal of Finance Vol. 60, No. 1 (1985): 269–291. [Google Scholar]
- S.C. Gilson. “Management Turnover and Financial Distress.” Journal of Financial Economics Vol. 25 (1989): 241–262. [Google Scholar]
- K. John, and L. H. P. Lang. “Troubled Debt Restructurings: An Empirical Study of Private Reorganization of Firms in Default.” Journal of Financial Economics Vol. 27 (1990): 315–353. [Google Scholar]
- J. Hausman, H. Ichimura, W. Newey, and J. Powell. “Measurement Errors in Polynomial Regression Models.” Journal of Econometrics Vol. 50 (1991): 271–295. [Google Scholar]
- C. W. Hofer. “Turnaround Strategies.” Journal of Business Strategy Vol. Summer (1980): 19–31. [Google Scholar]
- N. T. Hill, S. E. Perry, and S. Andes. “Evaluating Firms in Financial Distress: An Event History Analysis.” Journal of Applied Business Research Vol. 12, No. 3 (1996): 60–71. [Google Scholar]
- K. John, L. H. D. Lang, and J. Netter. “The Voluntary Restructuring of Large Firms in Response to Performance Decline.” Journal of Finance Vol. 47 (1992): 891–917. [Google Scholar]
- J. Kmenta. Elements of Econometrics. New York: Macmillan Publishing Company, 1986. [Google Scholar]
- P. Lachenbruch. “An Almost Unbiased Method of Obtaining Confidence Intervals for the Probability of Misclassification in Discriminant Analysis.” Biometrics Vol. 23 (1967): 639–645. [Google Scholar]
- A. H. Lau. “A Five-State Financial Distress Prediction Model.” Journal of Accounting Research Vol. 25, No. 1 (1987): 127–138. [Google Scholar]
- B. Lev. “Industry Averages as Targets for Financial Ratios.” Journal of Accounting Research Vol. 7 (1969): 290–299. [Google Scholar]
- P. Lin, L. Ko, and E. Blocher. “Prediction of Corporate Financial Distress: An Application of the Composite Rule Induction System.” In presented at the 1999 Annual Meetings of the American Accounting Association, San Diego; 1999. [Google Scholar]
- A. Lo. “Logit versus Discriminant Analysis: A Specification Test and Application to Corporate Bankruptcy.” Journal of Econometrics Vol. 31 (1986): 151–178. [Google Scholar]
- Y. M. Mensah. “An Examination of the Stationarity of Multivariate Bankruptcy Prediction Models: A Methodological Study.” Journal of Accounting Research Vol. 22 (1984): 380–395. [Google Scholar]
- J.A. Ohlson. “Financial Ratios and the Probabilistic Prediction of Bankruptcy.” Journal of Accounting Research Vol. 19 (1980): 109–131. [Google Scholar]
- H. Platt, and M. B. Platt. “A Note on the Use of Industry-Relative Ratios in Bankruptcy Prediction.” Journal of Banking and Finance Vol. 15 (1991a): 1183–1194. [Google Scholar]
- “Predicting Corporate Financial Distress: Reflections on Choice-Based Sample Bias.” Journal of Economics and Finance Vol. 26, No. 2 (2002): 184–199.
- “Understanding Differences Between Financial Distress and Bankruptcy.” Review of Applied Economics Vol. 2, No. 2 (2006): 211–227.
- PricewaterhouseCoopers. “International Accounting Standards: Similarities and Differences.” 2001. [Google Scholar]
- K. Schipper. “Financial Distress in Private Colleges.” Journal of Accounting Research Vol. 15 (1977): 1–40. [Google Scholar]
- T. Shumway. “Forecasting Bankruptcy More Accurately: A Simple Hazard Model.” Journal of Business Vol. 74, No. 1 (2001): 101–124. [Google Scholar]
- P Thedodossious, E. Kayha, R. Saidi, and G. Philippatos. “Financial Distress and Corporate Acquisitions: Further Empirical Evidence.” Journal of Business Finance and Accounting Vol. 23, No. 5&6 (1996): 699–719. [Google Scholar]
- R. B. Whitaker. “The Early Stages of Financial Distress.” Journal of Economics and Finance Vol. 23, No. 2 (1999): 123–133. [Google Scholar]
- A. R. Yang, M. B. Platt, and H. D. Platt. “Probabilistic Neural Networks in Bankruptcy Prediction.” Journal of Business Research Vol. 44, No. 2 (1999): 67–74. [Google Scholar]
- M.E. Zmijewski. “Methodological Issues Related to the Estimation of Financial Distress Prediction Models.” Journal of Accounting Research Vol. 22 (1984): 59–82. [Google Scholar]
- 1Data from 1998 were also collected to allow measurement of growth rates from 1998 to 1999.
- 2This approach is analogous to the well-known paradigm used by many researchers to predict bankruptcy with prior year data. In our case, instead of bankrupt companies we use those that are severely financially distressed as defined by a two-year three-screen approach. That is, the technique looks for characteristics in prior year data that distinguishes between future severely financially distressed and non-financially distressed companies.
- 3EBITDA – Interest expense
- 4Net income + Special items (US); Net income before extraordinary items – Extraordinary items + Special items (Non US)
- 5Variable values specified as VARIABLE (-1) were collected in 1998. Otherwise, the variable value was collected in 1999. Thus, growth variables indicate growth rates from 1998 to 1999.
- 6SIC 2800 is the chemicals and allied products industry; SIC 3500 is the industrial machinery and equipment industry; SIC 3600 is the electrical and electronic equipment industry.
- 7NFD indicates companies that are non-financially distressed; FD indicates companies that are financially distressed.
Share and Cite
Platt, H.D.; Platt, M.B. Financial Distress Comparison Across Three Global Regions. J. Risk Financial Manag. 2008, 1, 129-162. https://doi.org/10.3390/jrfm1010129
Platt HD, Platt MB. Financial Distress Comparison Across Three Global Regions. Journal of Risk and Financial Management. 2008; 1(1):129-162. https://doi.org/10.3390/jrfm1010129
Chicago/Turabian StylePlatt, Harlan D., and Marjorie B. Platt. 2008. "Financial Distress Comparison Across Three Global Regions" Journal of Risk and Financial Management 1, no. 1: 129-162. https://doi.org/10.3390/jrfm1010129