# Corporate Financial Distress of Industry Level Listings in Vietnam

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Model Specifications

- $\mathrm{E}$: Market value of the firm’s equity
- $\mathrm{V}$: Market value of the firm’s assets
- $\mathrm{F}$: Face value of the firm’s debt
- $\mathrm{r}$: Risk-free rate
- $\mathrm{T}$: Time to maturity of the firm’s debt
- $\mathrm{N}$: Cumulative standard normal distribution function
- ${\mathsf{\sigma}}_{\mathrm{E}}$: Volatility of the firm’s equity

- (i)
- Drift term $(\mathsf{\mu}-\text{}0.5{\text{}\mathsf{\sigma}}_{\mathrm{v}\text{}}^{2})\mathrm{T}$ is small, or close to zero;
- (ii)
- N(d1) is assumed to be equal to unity;
- (iii)
- Book value of debt is assumed to be the accounting leverage ratio.

_{1}) = 1 into Equation (6). We have:

- (1)
- $\mathrm{EDF}(\%)\le 0.52\%:$ Safe zone, where firms have a healthy financial foundation, with no risk of bankruptcy;
- (2)
- $0.52\%<\mathrm{EDF}\text{}(\%)\le 6.94\%:$ Grey zone, or warning zone, where the financial exposure is at a low level of potential bankruptcy;
- (3)
- $\mathrm{EDF}\text{}(\%)6.94\%:$ Bankruptcy zone, or dangerous zone, where the default probability is at a high level.

## 4. Data, ROC Curve and Definition of Variables

#### 4.1. Data and ROC Curve

^{2}checks are used to examine the efficiency of the model. In particular, the Area Under the ROC Curve (AUC) is used in assessing alternative ranking methodologies.

^{2}and Nagelkerke’s R

^{2}, as discussed in Cox and Snell (1989) and Nagelkerke (1991), respectively. These checks are based on a similar concept to the calculation of R

^{2}for the linear regression model in measuring the goodness-of-fit of an empirical model.

#### 4.2. Dependent Variable

#### 4.3. Explanatory Variables

#### 4.3.1. Accounting Variables

- (i)
- the financial, defined as working capital/total assets (WC/TA), is frequently used as a measure of corporate liquidity, and provides strong evidence of existing corporate defaults;
- (ii)
- the financial ratio, defined as retained earnings over total assets (RE/TA), measures the cumulative profitability over time, with young firms usually possessing a low value of RE/TA as they have not yet had sufficient time to accumulate substantial returns;
- (iii)
- the financial ratio, defined as earnings before interest and taxes over the total asset (EBIT/TA), indicates the true productivity of a firm’s assets;
- (iv)
- the ability to meet financial obligations is based on the financial ratio, defined as the book value of equity over the total liability (BVE/TL).

#### 4.3.2. Market Variables

#### 4.3.3. Macroeconomic Variables

- $Y\left(Classify\right)$: Binary variable denoting non-default (Y = 0) and default (Y = 1)
- $\frac{WC}{TA}$: Working Capital to Total Assets
- $\frac{RE}{TA}\text{}$: Retained Earnings to Total Assets
- $\frac{EBIT}{TA}$: Earnings Before Interest and Taxes (operating profit) to Total Assets
- $\frac{BVE}{TL}$: Book Value of Equity to Total Liabilities
- $MVE$: Market Value of Equity
- $LEVERAGE$: Leverage ratio
- ${\mathsf{\sigma}}_{\mathrm{E}}\text{}$: Volatility of Equity
- $PRICE$: Stock Price
- $TreasuryBill$: Short-term Treasury Bill one-year rate
- $Inflation$: Inflation rate
- $\epsilon $: Random error term.

## 5. Empirical Results and Analysis

- (1)
- Model 1: all explanatory accounting variables;
- (2)
- Model 2: all market variables;
- (3)
- Model 3: all accounting variables plus inflation;
- (4)
- Model 4: all accounting variables plus the short-term Treasury Bill one-year rate;
- (5)
- Model 5: all market variables plus inflation;
- (6)
- Model 6: all market variables plus the short-term Treasury Bill one-year rate;
- (7)
- Model 7: all accounting and market variables plus inflation;
- (8)
- Model 8: all accounting and market variables plus the short-term Treasury Bill one-year rate.

^{2}and Cox and Snell’s R

^{2}, confirm that the best empirical model in both periods is Model 3.

## 6. Concluding Remarks and Policy Implications

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

Variable | WCTA | RETA | EBITTA | BVETL | PRICE | MVE | VOL_MVE | LEVERAGE | INFLATION | SHTBRDEF |
---|---|---|---|---|---|---|---|---|---|---|

WCTA | 1 | |||||||||

RETA | 0.179 *** | 1 | ||||||||

EBITTA | 0.1327 *** | 0.4286 *** | 1 | |||||||

BVETL | 0.311 *** | 0.3292 *** | 0.2256 *** | 1 | ||||||

PRICE | 0.0252 | 0.2473 *** | 0.0409 * | 0.0009 | 1 | |||||

MVE | −0.0676 *** | 0.1801 *** | 0.0614 ** | 0.0153 | 0.4606 *** | 1 | ||||

VOL_MVE | −0.0319 | 0.0265 | 0.0532 ** | 0.0352 | 0.0018 | 0.1522 *** | 1 | |||

LEVERAGE | 0.1501 *** | 0.3271 *** | 0.1655 *** | 0.4815 *** | 0.4028 *** | 0.5012 *** | 0.1654 *** | 1 | ||

INFLATION | 0.0403 | −0.067 *** | 0.0587 ** | 0.0207 | −0.2242 *** | −0.1264 *** | 0.0153 | −0.0939 *** | 1 | |

SHTBRDEF | −0.0093 | −0.0541 ** | 0.0252 | 0.0014 | −0.3355 *** | −0.1446 *** | 0.0197 | −0.1874 *** | 0.8445 *** | 1 |

**Notes:**WCTA (working capital over total assets), RETA (retained earnings over total assets), EBITTA (earnings before interest and taxes [operating profit] to total assets), BVETA (book value of equity to total liabilities), MVE (market value of equity), PRICE (stock price), VOL_MVE (volatility of market value of equity), LEVERAGE (leverage ratio), INFLATION (inflation), and SHTBRDEF (short-term one-year rate), *** p < 0.01, ** p < 0.05, * p < 0.1.

## Appendix B

Variable | WCTA | RETA | EBITTA | BVETL | PRICE | MVE | VOL_MVE | LEVERAGE | INFLATION | SHTBRDEF |
---|---|---|---|---|---|---|---|---|---|---|

WCTA | 1 | |||||||||

RETA | 0.4308 *** | 1 | ||||||||

EBITTA | 0.1529 *** | 0.2529 *** | 1 | |||||||

BVETL | 0.5473 *** | 0.385 *** | 0.1602 *** | 1 | ||||||

PRICE | 0.1909 *** | 0.3431 *** | 0.184 *** | 0.1787 *** | 1 | |||||

MVE | 0.0679 *** | 0.326 *** | 0.1105 *** | 0.0951 *** | 0.4618 *** | 1 | ||||

VOL_MVE | −0.0117 | 0.0167 | 0.0124 | 0.0223 | 0.0624 *** | 0.2053 *** | 1 | |||

LEVERAGE | 0.3091 *** | 0.2879 *** | 0.1927 *** | 0.5721 *** | 0.3882 *** | 0.3435 *** | 0.2126 *** | 1 | ||

INFLATION | 0.0197 | 0.0948 *** | 0.0256 * | 0.0175 | 0.1063 *** | 0.083 *** | −0.0007 | −0.0387 | 1 | |

SHTBRDEF | 0.0316 * | 0.119 *** | 0.0494 *** | 0.029 ** | 0.0648 *** | 0.0576 *** | 0.0007 | 0.0015 | 0.91 | 1 |

**Notes:**WCTA (working capital over total assets), RETA (retained earnings over total assets), EBITTA (earnings before interest and taxes [operating profit] to total assets), BVETA (book value of equity to total liabilities), MVE (market value of equity), PRICE (stock price), VOL_MVE (volatility of market value of equity), LEVERAGE (leverage ratio), INFLATION (inflation), and SHTBRDEF (short-term one-year rate), *** p < 0.01, ** p < 0.05, * p < 0.1.

## Appendix C

Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
---|---|---|---|---|---|---|---|---|

WCTA | −0.000075 | −0.000346 | −0.000329 | −0.000414 | −0.000432 | |||

RETA | −0.009701 | −0.008298 | −0.007984 | −0.006677 | −0.006901 | |||

EBITTA | −0.248119 | −0.215074 | −0.228503 | −0.211114 | −0.222159 | |||

BVETL | 0.000428 | 0.000332 | 0.000370 | 0.000389 | 0.000408 | |||

PRICE | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | |||

MVE | 0.005573 | 0.005721 | 0.004832 | −0.000115 | −0.000143 | |||

VOL_MVE | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | |||

LEVERAGE | −0.009482 | −0.008831 | −0.007280 | −0.000057 | −0.000025 | |||

INFLATION | 0.000084 | 0.001244 | 0.000076 | |||||

SHTBRDEF | 0.000220 | 0.004788 | 0.000208 |

## Appendix D

Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
---|---|---|---|---|---|---|---|---|

WCTA | −0.018123 | −0.016384 | −0.016199 | −0.016074 | −0.015689 | |||

RETA | −0.052113 | −0.056582 | −0.057523 | −0.052396 | −0.051922 | |||

EBITTA | −3.608555 | −3.439809 | −3.431021 | −3.432932 | −3.394622 | |||

BVETL | −0.012977 | −0.012338 | −0.012458 | −0.018062 | −0.017669 | |||

PRICE | −0.000004 | −0.000004 | −0.000004 | 0.000000 | 0.000000 | |||

MVE | −0.036388 | −0.037685 | −0.038076 | −0.000942 | −0.000941 | |||

VOL_MVE | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | |||

LEVERAGE | −0.022332 | −0.022296 | −0.021023 | 0.003504 | 0.003213 | |||

INFLATION | 0.000997 | −0.002463 | 0.000980 | |||||

SHTBRDEF | 0.001754 | −0.005968 | 0.001714 |

## References

- Agarwal, Vineet, and Richard Taffler. 2008. Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance 32: 1541–51. [Google Scholar]
- Agrawal, Khushbu, Yogesh Maheshwari, S. Khilji, and L. Swinkels. 2016. Predicting financial distress: Revisiting the option-based model. South Asian Journal of Global Business Research 5: 268–84. [Google Scholar] [CrossRef]
- Ali, Asghar, and Kevin Daly. 2010. Macroeconomic determinants of credit risk: Recent evidence from a cross-country study. International Review of Financial Analysis 19: 165–71. [Google Scholar] [CrossRef]
- Allen, David Edmund, and Robert Powell. 2012. The fluctuating default risk of Australian banks. Australian Journal of Management 37: 297–325. [Google Scholar] [CrossRef]
- Allen, David Edmund, Ray Boffey, and Robert J. Powell. 2011. Peas in a Pod: Canadian and Australian Banks before and during a Global Financial Crisis. Available online: https://ssrn.com/abstract=1884084 (accessed on 13 August 2019).
- Altman, Edward I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23: 589–609. [Google Scholar] [CrossRef]
- Altman, Edward I. 2000. Predicting Financial Distress of Companies: Revisiting the Z-Score and ZETA Models. New York: Stern School of Business, New York University. [Google Scholar]
- Altman, Edward I. 2005. An emerging market credit scoring system for corporate bonds. Emerging Markets Review 6: 311–23. [Google Scholar] [CrossRef]
- Altman, Edward I., Robert G. Haldeman, and Paul Narayanan. 1977. Zeta to analyse a new model to identify bankruptcy risk of corporations. Journal of Banking & Finance 1: 29–54. [Google Scholar]
- Asquith, Paul, Robert Gertner, and David Scharfstein. 1994. Anatomy of financial distress: An examination of junk-bond issuers. Quarterly Journal of Economics 109: 625–58. [Google Scholar] [CrossRef]
- Badar, Munib, and Yasmin Javid. 2013. Impact of macroeconomic forces on nonperforming loans: An empirical study of commercial banks in Pakistan. WSEAS Transactions on Business and Economics 10: 40–48. [Google Scholar]
- Beaver, William H. 1966. Financial ratios as predictors of failure. Journal of Accounting Research 4: 71–111. [Google Scholar] [CrossRef]
- Beaver, William H., Maureen F. McNichols, and Jung-Wu Rhie. 2005. Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies 10: 93–122. [Google Scholar] [CrossRef]
- Bharath, Sreedhar T., and Tyler Shumway. 2008. Forecasting default with the Merton distance to default model. Review of Financial Studies 21: 1339–69. [Google Scholar] [CrossRef]
- Black, Fischer, and Myron Scholes. 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81: 637–54. [Google Scholar] [CrossRef]
- Byström, Hans Ne. 2006. Merton unraveled: A flexible way of modeling default risk. Journal of Alternative Investments 8: 39–47. [Google Scholar]
- Byström, Hans, Lugkana Worasinchai, and Srisuda Chongsithipol. 2005. Default risk, systematic risk and Thai firms before, during and after the Asian crisis. Research in International Business and Finance 19: 95–110. [Google Scholar] [CrossRef]
- Chaibi, Hasna, and Zied Ftiti. 2015. Credit risk determinants: Evidence from a cross-country study. Research in International Business and Finance 33: 1–16. [Google Scholar] [CrossRef]
- Charitou, Andreas, Dionysia Dionysiou, Neophytos Lambertides, and Lenos Trigeorgis. 2013. Alternative bankruptcy prediction models using option-pricing theory. Journal of Banking & Finance 37: 2329–41. [Google Scholar]
- Chava, Sudheer, and Robert A. Jarrow. 2004. Bankruptcy prediction with industry effects. Review of Finance 8: 537–69. [Google Scholar] [CrossRef]
- Cox, David Roxbee, and Joyce J. Snell. 1989. The Analysis of Binary Data, 2nd ed. London: Chapman and Hall. [Google Scholar]
- Crosbie, Peter, and Jeffrey Bohn. 2003. Modelling Default Risk. Available online: http://www.moodyskmv.com/research/files/wp/ModelingDefaultRisk.pdf (accessed on 13 August 2019).
- Ćurak, Marijana, Sandra Pepur, and Klime Poposki. 2013. Determinants of non-performing loans–evidence from Southeastern banking systems. Banks & Bank System 8: 45–54. [Google Scholar]
- Delianedis, Gordon, and Robert L. Geske. 2003. Credit risk and risk-neutral default probabilities: Information about rating migrations and defaults. Paper presented at the EFA 2003 Annual Conference, Glasgow, UK, August 20–23. [Google Scholar]
- Demirgüç-Kunt, Asli, and Enrica Detragiache. 1998. The determinants of banking crises in developing and developed countries. Staff Papers 45: 81–109. [Google Scholar] [CrossRef]
- Hillegeist, Stephen A., Elizabeth K. Keating, Donald P. Cram, and Kyle G. Lundstedt. 2004. Assessing the probability of bankruptcy. Review of Accounting Studies 9: 5–34. [Google Scholar] [CrossRef]
- Koutsomanoli-Filippaki, Anastasia, and Emmanuel Mamatzakis. 2009. Performance and Merton-type default risk of listed banks in the EU: A panel VAR approach. Journal of Banking & Finance 33: 2050–61. [Google Scholar]
- Leland, Hayne. 2002. Predictions of expected default frequencies in structural models of debt. Paper presented at the Venice Conference on Credit Risk, Venice, Italy, September 8. [Google Scholar]
- Lopez, Jose A. 2004. The empirical relationship between average asset correlation, the firm probability of default, and asset size. Journal of Financial Intermediation 13: 265–83. [Google Scholar] [CrossRef]
- Mare, Davide Salvatore. 2015. Contribution of macroeconomic factors to the prediction of small bank failures. Journal of International Financial Markets, Institutions and Money 39: 25–39. [Google Scholar] [CrossRef] [Green Version]
- Merton, Robert C. 1974. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29: 449–70. [Google Scholar]
- Nagelkerke, Nico J. 1991. A note on a general definition of the coefficient of determination. Biometrika 78: 691–92. [Google Scholar] [CrossRef]
- Ohlson, James A. 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18: 109–31. [Google Scholar] [CrossRef]
- Patel, Kanak, and Prodromos Vlamis. 2006. An empirical estimation of default risk of the UK real estate companies. The Journal of Real Estate Finance and Economics 32: 21–40. [Google Scholar] [CrossRef]
- Pham, Binh, Trung Do, and Duc Vo. 2018. Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam. Economic Systems 2: 616–24. [Google Scholar] [CrossRef]
- Pindado, Julio, Luis Rodrigues, and Chabelade la Torre. 2008. Estimating financial distress likelihood. Journal of Business Research 61: 995–1003. [Google Scholar] [CrossRef]
- Rees, Bill. 1995. Financial Analysis. London: Prentice Hall. [Google Scholar]
- Rinaldi, Laura, and Alicia Sanchis-Arellano. 2006. Household Debt Sustainability: What Explains Household Non-Performing Loans? An Empirical Analysis. Rinaldi, Laura and Sanchis-Arellano, Alicia, Household Debt Sustainability: What Explains Household Non-Performing Loans? An Empirical Analysis (January 2006). ECB Working Paper No. 570. Available online: https://ssrn.com/abstract=872528 (accessed on 13 August 2019).
- Shumway, Tyler. 2001. Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business 74: 101–24. [Google Scholar] [CrossRef]
- Stanisic, Nemanja, Vule Mizdrakovic, and Goranka Knezevic. 2013. Corporate bankruptcy prediction in the Republic of Serbia. Industrija 41: 145–59. [Google Scholar]
- Taffler, Richard J. 1984. Empirical models for the monitoring of UK corporations. Journal of Banking & Finance 8: 199–227. [Google Scholar]
- Theodossiou, Panayiotis. 1991. Alternative models for assessing the financial condition of business in Greece. Journal of Business Finance & Accounting 18: 697–720. [Google Scholar]
- Tinoco, Mario Hernandez, and Nick Wilson. 2013. Financial distress and bankruptcy prediction among listed companies using accounting, market, and macroeconomic variables. International Review of Financial Analysis 30: 394–419. [Google Scholar] [CrossRef]
- Trujillo-Ponce, Antonio, Reyes Samaniego-Medina, and Clara Cardone-Riportella. 2014. Examining what best explains corporate credit risk: Accounting-based versus market-based models. Journal of Business Economics and Management 15: 253–76. [Google Scholar] [CrossRef]
- Uğurlu, Mine, and Hakan Aksoy. 2006. Prediction of corporate financial distress in an emerging market: The case of Turkey. Cross Cultural Management: An International Journal 13: 277–95. [Google Scholar] [CrossRef]
- Vasicek, Oldrich A. 1984. Credit Valuation: KMV Corporation. Available online: http://www.ressourcesactuarielles.net/EXT/ISFA/1226.nsf/0/c181fb77ee99d464c125757a00505078/$FILE/Credit_Valuation.pdf (accessed on 13 August 2019).
- Vassalou, Maria, and Yuhang Xing. 2004. Default risk in equity returns. Journal of Finance 59: 831–68. [Google Scholar] [CrossRef]
- Whitaker, Richard B. 1999. The early stages of financial distress. Journal of Economics and Finance 23: 123–32. [Google Scholar] [CrossRef]
- World Bank. 2016. GDP (Current US$). Available online: http://data.worldbank.org/indicator/NY.GDP.MKTP.CD/ (accessed on 13 August 2019).
- Wu, Yanhui, Clive Gaunt, and Stephen Gray. 2010. A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics 6: 34–45. [Google Scholar]
- Zhang, Benjamin Yibin, Hao Zhou, and Haibin Zhu. 2009. Explaining credit default swap spreads with the equity volatility and jump risks of individual firms. Review of Financial Studies 22: 5099–131. [Google Scholar] [CrossRef]
- Zmijewski, Mark E. 1984. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research 22: 59–82. [Google Scholar] [CrossRef]

Classify | Frequency | % | Cumulative |
---|---|---|---|

0 | 1367 | 86.96 | 86.96 |

1 | 205 | 13.04 | 100 |

Total | 1572 | 100 |

Classify | Frequency | % | Cumulative |
---|---|---|---|

0 | 3718 | 73.23 | 73.23 |

1 | 1359 | 26.77 | 100 |

Total | 5077 | 100 |

Variable | Obs. | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|

Working capital/total asset | 1572 | 0.252 | 0.224 | −0.499 | 0.998 |

Retained earnings/asset | 1572 | 0.061 | 0.067 | −0.437 | 0.422 |

EBIT/Total assets | 1572 | 0.060 | 0.074 | −0.129 | 0.664 |

Book value of equity/Total liabilities | 1572 | 0.712 | 0.284 | 0.001 | 1.000 |

Price | 1572 | 30594 | 33927 | 317 | 304820 |

Ln (Market value of equity) | 1572 | 12.221 | 2.025 | 6.994 | 22.284 |

Volatility of equity | 1572 | 4940 | 10,900 | 20.123 | 336,000 |

Leverage | 1572 | 1.413 | 1.313 | 0.003 | 12.551 |

Inflation | 1572 | 12.9 | 7.2 | 7.3 | 23.1 |

Treasury Bill | 1572 | 8.1 | 3.3 | 4.2 | 12.1 |

Variable | Obs. | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|

Working capital/total asset | 5077 | 0.198 | 0.243 | −1.362 | 1.000 |

Retained earnings/asset | 5077 | 0.032 | 0.148 | −2.760 | 0.529 |

EBIT/Total assets | 5077 | 0.024 | 0.071 | −2.290 | 0.981 |

Book value of equity/Total liabilities | 5077 | 0.649 | 0.313 | −0.568 | 1.000 |

Price | 5077 | 17,300 | 18,740 | 400 | 202,000 |

Ln (Market value of equity) | 5077 | 12.129 | 1.872 | 6.947 | 21.136 |

Volatility of equity | 5077 | 6836 | 98,955 | 18.4 | 530,000 |

Leverage | 5077 | 0.886 | 1.012 | 0.004 | 11.315 |

Inflation | 5077 | 7.4 | 5.4 | 0.9 | 18.6 |

Treasury Bill | 5077 | 7.5 | 3.1 | 4.0 | 12.4 |

Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
---|---|---|---|---|---|---|---|---|

WCTA | −0.03 | −0.161 | −0.142 | −0.198 | −0.194 | |||

(0.05) | (0.28) | (0.25) | (0.34) | (0.34) | ||||

RETA | −3.813 | −3.867 | −3.446 | −3.193 | −3.093 | |||

(1.49) | (1.46) | (1.33) | (1.17) | (1.15) | ||||

EBITTA | −97.532 | −100.221 | −98.611 | −100.968 | −99.567 | |||

(10.70) *** | (10.36) *** | (10.50) *** | (10.26) *** | (10.38) *** | ||||

BVETL | 0.168 | 0.155 | 0.16 | 0.186 | 0.183 | |||

(0.41) | (0.36) | (0.38) | (0.37) | (0.37) | ||||

PRICE | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |||

(1.94) * | −1.53 | −0.94 | −0.16 | −0.23 | ||||

MVE | 0.102 | 0.108 | 0.095 | −0.055 | −0.064 | |||

(1.44) | (1.51) | (1.31) | (0.70) | (0.83) | ||||

VOL_MVE | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |||

(0.33) | (0.26) | (0.22) | (0.29) | (0.32) | ||||

LEVERAGE | −0.173 | −0.167 | −0.143 | −0.027 | −0.011 | |||

(1.60) | (1.53) | (1.30) | (0.19) | (0.08) | ||||

INFLATION | 0.039 | 0.024 | 0.036 | |||||

(2.66) *** | (1.88) * | (2.34) ** | ||||||

SHTBRDEF | 0.095 | 0.094 | 0.093 | |||||

(2.54) ** | (2.79) *** | (2.16) ** | ||||||

_cons | 0.004 | −3.525 | −0.472 | −0.818 | −4.006 | −4.475 | 0.257 | −0.055 |

(0.01) | (4.37) *** | (1.33) | (1.84) * | (4.61) *** | (4.90) *** | (0.26) | (0.05) | |

lnsig2u_cons | 0.057 | 1.127 | 0.291 | 0.203 | 1.184 | 1.223 | 0.333 | 0.264 |

(0.09) | (4.48) *** | (0.51) | (0.35) | (4.68) *** | (4.84) *** | (0.59) | (0.46) | |

N | 1572 | 1572 | 1572 | 1572 | 1572 | 1572 | 1572 | 1572 |

**Note:**Standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1. Source: Authors’ analyses.

Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
---|---|---|---|---|---|---|---|---|

WCTA | −0.91 | −0.883 | −0.878 | −0.866 | −0.856 | |||

(2.71) *** | (2.60) *** | (2.58) *** | (2.54) ** | (2.51) ** | ||||

RETA | −2.617 | −3.048 | −3.119 | −2.822 | −2.834 | |||

(4.31) *** | (4.71) *** | (4.78) *** | (4.28) *** | (4.31) *** | ||||

EBITTA | −181.231 | −185.327 | −186.046 | −184.921 | −185.308 | |||

(23.00) *** | (22.79) *** | (22.80) *** | (22.58) *** | (22.61) *** | ||||

BVETL | −0.652 | −0.665 | −0.676 | −0.973 | −0.965 | |||

(2.44) ** | (2.45) ** | (2.49) ** | (3.07) *** | (3.04) *** | ||||

PRICE | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |||

(6.19) *** | (6.29) *** | (6.15) *** | −0.71 | −1.1 | ||||

MVE | −0.295 | −0.306 | −0.309 | −0.051 | −0.051 | |||

(6.01) *** | (6.17) *** | (6.23) *** | (1.03) | (1.04) | ||||

VOL_MVE | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |||

(0.76) | (0.82) | (0.82) | (0.07) | (0.11) | ||||

LEVERAGE | −0.181 | −0.181 | −0.171 | 0.189 | 0.175 | |||

(2.26) ** | (2.25) ** | (2.12) ** | (1.88) * | (1.75) * | ||||

INFLATION | 0.054 | −0.02 | 0.053 | |||||

(5.43) *** | (2.60) *** | (5.26) *** | ||||||

SHTBRDEF | 0.095 | −0.048 | 0.094 | |||||

(5.39) *** | (3.62) *** | (5.26) *** | ||||||

_cons | 1.129 | 2.507 | 0.782 | 0.49 | 2.795 | 3.019 | 1.496 | 1.239 |

(6.38) *** | (4.58) *** | (4.14) *** | (2.30) ** | (4.98) *** | (5.30) *** | (2.42) ** | (1.97) ** | |

lnsig2u_cons | 0.622 | 1.298 | 0.651 | 0.647 | 1.302 | 1.308 | 0.675 | 0.673 |

(3.78) *** | (12.38) *** | (3.91) *** | (3.88) *** | (12.39) *** | (12.46) *** | (4.05) *** | (4.02) *** | |

N | 5077 | 5077 | 5077 | 5077 | 5077 | 5077 | 5077 | 5077 |

**Note:**Standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1. Source: Authors’ analyses.

Measure | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
---|---|---|---|---|---|---|---|---|

ROC | 0.9061 | 0.6041 | 0.9075 | 0.9072 | 0.5888 | 0.5962 | 0.9073 | 0.9071 |

-2 Log Likelihood | 736 | 1201 | 730 | 731 | 1199 | 1197 | 730 | 731 |

Cox and Snell’s R^{2} | 0.264 | 0.01 | 0.267 | 0.266 | 0.011 | 0.013 | 0.266 | 0.266 |

Nagelkerke’s R^{2} | 0.489 | 0.019 | 0.495 | 0.494 | 0.021 | 0.024 | 0.494 | 0.494 |

Measure | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
---|---|---|---|---|---|---|---|---|

ROC | 0.9379 | 0.682 | 0.9395 | 0.9394 | 0.682 | 0.6813 | 0.9384 | 0.9385 |

-2 Log likelihood | 3154.15 | 5473.52 | 3125.7 | 3125.04 | 5465.1 | 5462.87 | 3121.37 | 3120.03 |

Cox and Snell’s R^{2} | 0.418 | 0.08 | 0.422 | 0.421 | 0.082 | 0.082 | 0.421 | 0.422 |

Nagelkerke’s R^{2} | 0.608 | 0.117 | 0.613 | 0.613 | 0.119 | 0.12 | 0.613 | 0.612 |

Sector | No. of Observations | Safe | Grey | Bankruptcy |
---|---|---|---|---|

Energy | 319 | 41.4% | 6.6% | 52.0% |

Materials | 885 | 47.2% | 5.2% | 47.6% |

Industrials | 2596 | 47.1% | 5.5% | 47.5% |

Consumer Discretionary | 484 | 46.9% | 6.8% | 46.3% |

Consumer Staples | 911 | 40.4% | 7.0% | 52.6% |

Health & education | 176 | 62.5% | 3.4% | 34.1% |

Financials | 882 | 49.2% | 4.3% | 46.5% |

Technology | 115 | 51.3% | 5.2% | 43.5% |

Telecommunication | 104 | 53.8% | 2.9% | 43.3% |

Utilities | 139 | 41.2% | 7.6% | 51.1% |

Total | 6611 |

Sector | No. of Observations | Safe | Grey | Bankruptcy |
---|---|---|---|---|

Energy | 73 | 38.4% | 12.3% | 49.3% |

Materials | 197 | 42.6% | 5.1% | 52.3% |

Industrials | 646 | 42.0% | 6.0% | 52.0% |

Consumer Discretionary | 115 | 37.4% | 8.7% | 53.9% |

Consumer Staples | 229 | 40.2% | 8.3% | 51.5% |

Health & education | 50 | 20.0% | 8.0% | 72.0% |

Financials | 163 | 49.1% | 3.7% | 47.2% |

Technology | 31 | 35.5% | 6.5% | 58.1% |

Telecommunication | 27 | 33.3% | 7.4% | 59.3% |

Utilities | 34 | 61.8% | 17.6% | 20.6% |

Total | 1565 |

Sector | No. of Observations | Safe | Grey | Bankruptcy |
---|---|---|---|---|

Energy | 246 | 42.3% | 4.9% | 52.8% |

Materials | 688 | 48.5% | 5.2% | 46.2% |

Industrials | 1950 | 48.8% | 5.3% | 45.9% |

Consumer Discretionary | 369 | 49.9% | 6.2% | 43.9% |

Consumer Staples | 682 | 40.5% | 6.6% | 52.9% |

Health & education | 126 | 79.4% | 1.6% | 19.0% |

Financials | 719 | 49.2% | 4.5% | 46.3% |

Technology | 84 | 57.1% | 4.8% | 38.1% |

Telecommunication | 77 | 61.0% | 1.3% | 37.7% |

Utilities | 105 | 36.2% | 5.7% | 58.1% |

Total | 5056 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Vo, D.H.; Pham, B.N.V.; Ho, C.M.; McAleer, M.
Corporate Financial Distress of Industry Level Listings in Vietnam. *J. Risk Financial Manag.* **2019**, *12*, 155.
https://doi.org/10.3390/jrfm12040155

**AMA Style**

Vo DH, Pham BNV, Ho CM, McAleer M.
Corporate Financial Distress of Industry Level Listings in Vietnam. *Journal of Risk and Financial Management*. 2019; 12(4):155.
https://doi.org/10.3390/jrfm12040155

**Chicago/Turabian Style**

Vo, Duc Hong, Binh Ninh Vo Pham, Chi Minh Ho, and Michael McAleer.
2019. "Corporate Financial Distress of Industry Level Listings in Vietnam" *Journal of Risk and Financial Management* 12, no. 4: 155.
https://doi.org/10.3390/jrfm12040155