# The Effect of Inventory Leanness on Firms’ Credit Ratings: The Case of Pakistan

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^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review and Construction of Hypothesis

**Hypothesis**

**1.**

## 3. Materials and Methods

#### 3.1. Data Collection

#### 3.2. Econometric Model

#### 3.2.1. RATING—Credit Rating

#### 3.2.2. INV ELI

#### 3.2.3. SIZE

#### 3.2.4. LEVERAGE

#### 3.2.5. CAPINT

#### 3.2.6. LOSS

#### 3.2.7. SUBORD

#### 3.3. Estimation Technique

_{1}, 1 if µ

_{1}< y* ≤ µ

_{2}, 2 if µ

_{2}˂ y* ≤ µ

_{3}, …, N if µ

_{N}< y*, where µ denotes the restricted parameters for categorical variables, and y* is the dependent category variable. Thus, the model represented in Equation (1) can be used to estimate the coefficient values.

#### 3.4. Research Design

#### 3.5. Target Population

#### 3.6. Sampling

#### 3.7. Data Sources

## 4. Results

^{2}to assess the goodness of fit. As per our results, the value is 0.356 (Table 7), indicating it is a good fit.

_{1}) is 0.942, with the respective p-value (0.000) confirming that ELI has a significant relation with RATING. Accordingly, a one unit increase in ELI will tend to bring a 0.942 change in RATING, corresponding to an upgrade of approximately one rating level. The estimated coefficient of SIZE is 0.898, which denotes the change in RATING related to a one unit increase in SIZE. The estimated coefficient value of LEVERAGE is −3.770, which represents the change in RATING as a reflection of a one-unit increase in LEVERAGE. Regarding CAPINT, we also detect a significant but negative association with RATING. In this case, RATING decreases by 4.428 when CAPINT increases one unit. Both dummy variables, SUBORD and LOSS, show nonsignificant associations with RATING, though revealing opposite signs.

## 5. Discussion

## 6. Conclusions and Limitations

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Variable | Definition | Relevant Literature |
---|---|---|

Dependent variable | ||

RATING | Ordinal variable based on eight rating classes of PACRA long-term issuer credit rating: 8 for AAA, 7 for AA, 6 for A, 5 for BBB, 4 for BB, 3 for B, 2 for CCC, and 1 for CC | (Ashbaugh-Skaife et al. 2006; Attig et al. 2013; Kisgen 2009) |

Independent variable- Leanness | ||

INV ELI | Inventory empirical leanness indicator; residuals of a regression of the natural logarithm of firm sales on the natural logarithm of the total inventory for each year separately. The studentized residuals are multiplied by −1 | (Eroglu and Hofer 2011a, 2011b, 2014; Isaksson and Seifert 2014) |

Control variables | ||

SIZE | Natural logarithm of total assets | (Alissa et al. 2013; Ashbaugh-Skaife et al. 2006; Kim et al. 2013; Kisgen 2009) |

LEVERAGE | Total long-term debt divided by total firm assets | (Ashbaugh-Skaife et al. 2006; Attig et al. 2013; Kim et al. 2013; Kisgen 2009) |

CAPINT | Gross property, plant, and equipment divided by total firm assets | (Ashbaugh-Skaife et al. 2006; Attig et al. 2013; Kim et al. 2013) |

Dummy variables | ||

LOSS | The binary variable takes the value of 1 if the firm has a negative net income in the current year as well as in the previous year and 0 if otherwise | (Ashbaugh-Skaife et al. 2006; Attig et al. 2013) |

SUBORD | The binary variable takes the value 1 if the firm has subordinated debt and 0 otherwise | (Ashbaugh-Skaife et al. 2006; Scott 1977; Ziebart and Reiter 1992) |

Rating Notation | RATING Score | Grade |
---|---|---|

AAA | 8 | Investment |

AA+ | 7 | Investment |

AA | 7 | Investment |

AA− | 7 | Investment |

A+ | 6 | Investment |

A | 6 | Investment |

A− | 6 | Investment |

BBB+ | 5 | Investment |

BBB | 5 | Investment |

BBB− | 5 | Investment |

BB+ | 4 | Speculative |

BB | 4 | Speculative |

BB− | 4 | Speculative |

B+ | 3 | Speculative |

B | 3 | Speculative |

B− | 3 | Speculative |

CCC+ | 2 | Speculative |

CCC | 2 | Speculative |

CCC− | 2 | Speculative |

CC | 1 | Speculative |

N | Minimum | Maximum | Mean | Std. Deviation | |
---|---|---|---|---|---|

SIZE | 151 | 21.138 | 26.505 | 24.067 | 1.299 |

LEVERAGE | 151 | 0.000 | 0.730 | 0.213 | 0.181 |

CAPINT | 151 | 0.124 | 1.300 | 0.715 | 0.290 |

SUBORD | 151 | 0 | 1 | 0.05 | 0.211 |

LOSS | 151 | 0 | 1 | 0.07 | 0.250 |

RATING | 151 | 4 | 8 | 6.46 | 7.19 |

Eli | 151 | −2.29 | 2.38 | 0.001 | 1.004 |

Valid N (listwise) | 151 |

SIZE | LEVERAGE | CAPINT | SUBORD | LOSS | RATING | Eli | |
---|---|---|---|---|---|---|---|

SIZE | 1 | ||||||

LEVERAGE | −0.020 | 1 | |||||

CAPINT | −0.374 | 0.500 | 1 | ||||

SUBORD | −0.083 | 0.297 | 0.102 | 1 | |||

LOSS | −0.129 | 0.407 | 0.246 | 0.068 | 1 | ||

RATING | 0.488 | −0.377 | −0.522 | −0.143 | −0.321 | 1 | |

Eli | −0.011 | 0.252 | 0.392 | −0.026 | −0.216 | 0.047 | 1 |

Model | 2-Log Likelihood | Chi-Square | Df | Sig. |
---|---|---|---|---|

Intercept Only | 312.521 | |||

Final | 201.208 | 111.313 | 6 | 0.000 |

Chi-Square | Df | Sig. | |
---|---|---|---|

Pearson | 492.005 | 594 | 0.999 |

Deviance | 201.201 | 594 | 1.000 |

Cox and Snell | 0.522 |

Nagelkerke | 0.597 |

Mc Fadden | 0.356 |

Estimate | Std. Error | Wald | Df | Sig. | 95% Confidence Level | |||
---|---|---|---|---|---|---|---|---|

Lower Bound | Upper Bound | |||||||

Threshold | [RATING = 4] | 11.507 | 4.417 | 6.788 | 1 | 0.009 | 2.851 | 20.163 |

[RATING = 5] | 12.777 | 4.440 | 8.283 | 1 | 0.004 | 4.076 | 21.479 | |

[RATING = 6] | 17.505 | 4.691 | 13.927 | 1 | 0.000 | 8.311 | 26.698 | |

[RATING = 7] | 22.773 | 4.798 | 22.532 | 1 | 0.000 | 13.370 | 32.177 | |

Location | Eli | 0.942 | 0.235 | 16.064 | 1 | 0.000 | 0.481 | 1.403 |

SIZE | 0.898 | 0.190 | 22.417 | 1 | 0.000 | 0.526 | 1.270 | |

LEVERAGE | −3.770 | 1.441 | 6.842 | 1 | 0.009 | −6.595 | −0.945 | |

CAPINT | −4.428 | 1.031 | 18.455 | 1 | 0.000 | −6.448 | −2.408 | |

SUBORD | 0.134 | 0.901 | 0.022 | 1 | 0.882 | −1.631 | 1.900 | |

LOSS | −1.051 | 0.832 | 1.597 | 1 | 0.206 | −2.682 | 0.579 |

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**MDPI and ACS Style**

Carvalho, P.V.; Shah, S.S.H.; Zaheer, A.; Mata, M.N.; Morão Lourenço, A.
The Effect of Inventory Leanness on Firms’ Credit Ratings: The Case of Pakistan. *Risks* **2022**, *10*, 226.
https://doi.org/10.3390/risks10120226

**AMA Style**

Carvalho PV, Shah SSH, Zaheer A, Mata MN, Morão Lourenço A.
The Effect of Inventory Leanness on Firms’ Credit Ratings: The Case of Pakistan. *Risks*. 2022; 10(12):226.
https://doi.org/10.3390/risks10120226

**Chicago/Turabian Style**

Carvalho, Paulo Viegas, Sayyed Sadaqat Hussain Shah, Abrish Zaheer, Mário Nuno Mata, and António Morão Lourenço.
2022. "The Effect of Inventory Leanness on Firms’ Credit Ratings: The Case of Pakistan" *Risks* 10, no. 12: 226.
https://doi.org/10.3390/risks10120226