# 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)).

_{0}

_{a}

_{b}

_{0}would allow for a single explanation of how firms succumb to financial distress in different locations. Not being able to reject H

_{a}, the first variation of the alternate explanation, would modify the single global model with differential regional intercepts and slopes as needed while seeking to maintain the maximum degree of similarity across regions. Finally, not rejecting H

_{b}would relax all constraints so that the explanation of financial distress on each region would have separate factors explaining that region’s financial distress process.

## 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**=**X**+_{1}ß_{1}**ε** - Model 2:
**y**=**X**+_{1}ß_{1}**X**+_{2}ß_{2}**ε**

_{1}is the set of factors contained in the global model and X

_{2}is the set of region dummies and the interaction terms which when added to the global model creates Model 2. The null hypothesis states that

**ß**; alternatively,

_{2}= 0**ß**. If the null hypothesis cannot be rejected, then the global model is the best specification regardless of global location. However, if the null hypothesis is rejected, then different specifications for each region is best.

_{2}≠ 0- P
_{i}= probability of financial distress of the i^{th}firm, - X
_{ij}= j^{th}variable of the i^{th}firm, and - B
_{j}= estimated coefficient for the j^{th}variable.

## IV. RESULTS

#### Extending the Model to Other Regions

**ß**where

_{2}= 0**ß**represents the additional variables found in Model 2. The particular equation for the F-statistic is shown in equation 3 below.

_{2}_{(23, 3901)}= 4.698, with SSE

_{1}= 154.35, SSE

_{2}= 150.19, n = 3931, K

_{1}= 7, and K

_{2}= 23. Thus, the null hypothesis that the regional location has no effect is rejected. Based on this result, the specific region does affect factors predicting financial distress.

#### Comparing the Global Model with Regional Indicators to Three Distinct Regional Models

_{1}

_{2}

**X**+ α

_{1}ß_{1}**X**+ ε

_{2}ß_{2}_{1}and H

_{2}above are indicated by their respective variables and coefficients. Thus, testing H

_{1}is basically testing whether or not α = 0. Because α is not identified, the J-test replaces

**β**with ${\stackrel{\mathbf{\u02c6}}{\mathbf{\beta}}}_{\mathbf{2}}$, where ${\stackrel{\mathbf{\u02c6}}{\mathbf{\beta}}}_{\mathbf{2}}$ is the simple least squares estimator defined as ${\stackrel{\mathbf{\u02c6}}{\mathbf{\beta}}}_{2}={\text{(}{\mathbf{\text{X}}}_{2}^{\xb4}{\mathbf{\text{X}}}_{2}\text{)}}^{-1}{\mathbf{\text{X}}}_{2}^{\xb4}\mathbf{\text{y}}$. When H

_{2}_{1}is true, α divided by its standard error is distributed N(0,1). A second test is also performed because of the asymmetry of H

_{1}and H

_{2}. That is, when we test H

_{1}, we use H

_{2}to challenge the validity of H

_{1}. However, when we reject H

_{1}, it may be some other model other than H

_{2}that has caused us to reject H

_{1}. To make a statement about H

_{2}, we conduct a second J-test to test α in the following equation:

_{2}against H

_{1}. Consistent inferences from the two tests would indicate which of the two models is preferred. Inconsistent results would indicate that neither model is useful to predict financial distress or that the data cannot discriminate between the models.

^{st}century, it makes sense that any improvement in a company’s profitability was a significant signal of financial health.

_{1}is the global model and M

_{2}are the separate regional models. The estimated α parameter was 8.475, with p-value of 0.000. Thus, the null hypothesis that α = 0 is rejected, indicating that M

_{2}, separate models, is best for predicting Y, financial distress. The second J-test was conducted to estimate α in the following equation:

_{2}or separate models is best for predicting Y, financial distress. Thus, both J-test results indicate that separate regional models are best for predicting financial distress.

#### 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 |

**Table 4.**Descriptive Statistics across Regions, SIC Code6 and Status

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 |

**Table 5.**Estimated Coefficients for the Global Model Dependent Variable is Categorical (1 if financially distressed and 0 otherwise)

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** |

^{2}= .702* Significant beyond the .05 level of significance** Significant beyond the .01 level of significanceWhere:CF/Sales = Net Cash Flow/SalesEBITDA/TA = Earnings before interest, taxes, depreciation and amoritization/Total AssetsCA/CL = Current Assets/Current LiabilitiesSales/WC = Sales/Working CapitalDA/EBIT = Depreciation and amortization/EBITNP/TA = Notes Payable/Total AssetsTD/TA = Total Debt/Total Assets

Group Classified | Percent Classified Correctly |
---|---|

Non-financially distressed companies | 96.4% |

Financially Distressed companies | 82.1% |

All companies | 94.5% |

**Table 6.**Comparison of the Global Model (Model 1) to the Global Model with Regional Indicators (Model 2)

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 R^{2} | .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 R^{2} | 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% |

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^{1}Data from 1998 were also collected to allow measurement of growth rates from 1998 to 1999.^{2}This 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.^{3}EBITDA – Interest expense^{4}Net income + Special items (US); Net income before extraordinary items – Extraordinary items + Special items (Non US)^{5}Variable 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.^{6}SIC 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.^{7}NFD indicates companies that are non-financially distressed; FD indicates companies that are financially distressed.

## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Platt, 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