# The Quality of Fair Revaluation of Fixed Assets and Additional Calculations Aimed at Facilitating Prospective Investors’ Decisions

^{1}

^{2}

^{3}

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^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

**Hypothesis**

**1**

**(H1).**

**Hypothesis**

**2**

**(H2).**

**Hypothesis**

**3**

**(H3).**

**Hypothesis**

**4**

**(H4).**

**Hypothesis**

**5**

**(H5).**

## 3. Research Methodology

#### 3.1. Data

#### 3.2. Variables

#### 3.3. Model

**Static Panel Model**

**The static model exchange rate with debt to assets ratio as interaction effect is:**

#### Dynamic Panel Model

**The dynamic exchange rate with the debt-to-assets ratio as an interaction effect is:**

_{it}is 1 × K, and β is K × 1 with K a real number. μ

_{it}goes with the term’s one-way disturbance component model, μ

_{it}= λ

_{i}+ ε

_{it}. μ

_{it}is merged into λ

_{i}and ε

_{it}, where λ

_{i}has an individual specific effect to cover specific dissociation and ε

_{it}is the term error. The empirical model is considered to promote investment variables. Because equity can range from investment to firm equity or to debt in both directions and vice versa, these restrictions can be synchronized through the error term. Time-oriented firm individualities (unobserved specific effects, λ

_{i}), such as demographics and geography, can remain integrated through descriptive variables. The presence of the lagged measured variable leads to autocorrelation. There are at least two reasons for measuring a short period (t = 6), then measuring a firm in a panel dataset (n = 19): simultaneous error control makes it possible to predict some variables (associated). The firm’s specific dummy variables cannot be used while controlling the firm’s exact effects, which is due to the dynamic gathering of regression calculations. According to the theory, the experimental model promotes investment factors. It is possible to adjust these limits by using the term error, as equity investment can move from company equity to debt. It is possible to include strong time-based individuality (unobserved specific effects, such as demographics and locations, through descriptive variables). Self-correlation occurs when a measured variable is delayed. The second reason for measuring a firm (n = 19) after a short-term measurement (t = 6) of a panel dataset is that simultaneous error control capacity allows some predicting variables to be endogenous. Due to the dynamic gathering of regression computation, the company’s unique dummy variables cannot be used to control the firm’s precise impact.

_{0}(AR2). Using a two-step generalized method of moments estimate variable variance–covariance matrix (VCE) and a careful derivation of this restricted sample bias, ref. [44] defined the VCE (robust). Heteroscedasticity has little effect on reliable estimations of what has been rectified. When errors are heteroskedastic, the command estat sargan is not given following the description of the VCE, according to the Sargan test’s output (robust). After establishing the VCE, an improved version of the Arellano–Bond test for autocorrelation was created (robust).

## 4. Results and Discussion

#### 4.1. Descriptive Statistics

#### 4.2. Inferential Statistics

**Robustness test and endogeneity problem**

## 5. Conclusions

#### Contribution/Origin

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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OI | EXR | LTI | REFA | DAR | CIR | PER | ATR | ROA | |
---|---|---|---|---|---|---|---|---|---|

Mean | 9.271 | 4.655 | 4.676 | 13.450 | 0.584 | 5.749 | 17.844 | 0.801 | 4.607 |

Median | 11.294 | 4.664 | 0.000 | 13.884 | 0.657 | 1.864 | 3.901 | 0.771 | 2.820 |

Maximum | 13.577 | 4.706 | 14.477 | 16.021 | 1.248 | 56.817 | 490.791 | 2.241 | 18.084 |

Minimum | 0.000 | 4.593 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Std. Dev. | 4.706 | 0.039 | 5.923 | 2.363 | 0.322 | 10.573 | 57.821 | 0.459 | 4.636 |

Skewness | −1.328 | −0.372 | 0.528 | −4.850 | −0.589 | 3.131 | 6.070 | 0.559 | 1.128 |

Kurtosis | 3.081 | 1.796 | 1.377 | 28.193 | 2.449 | 13.472 | 44.569 | 3.369 | 3.201 |

Observations | 114 | 114 | 114 | 114 | 113 | 113 | 113 | 114 | 114 |

OI | EXR | LTI | REFA | DAR | CIR | PER | ATR | ROA | |
---|---|---|---|---|---|---|---|---|---|

OI | 1.000 | ||||||||

EXR | −0.223 *** | 1.000 | |||||||

LTI | 0.038 *** | 0.022 *** | 1.000 | ||||||

RVFA | 0.067 *** | −0.087 *** | 0.178 *** | 1.000 | |||||

DAR | 0.804 *** | −0.290 *** | −0.145 *** | 0.082 *** | 1.000 | ||||

CIR | 0.068 *** | −0.297 *** | 0.058 *** | 0.121 *** | 0.156 *** | 1.000 | |||

PER | 0.021 *** | −0.287 *** | 0.127 *** | 0.109 *** | 0.019 *** | 0.826 *** | 1.000 | ||

ATR | −0.166 *** | 0.234 *** | −0.008 *** | −0.214 *** | −0.227 *** | −0.081 *** | −0.079 *** | 1.000 | |

ROA | 0.133 *** | 0.018 *** | −0.126 *** | −0.061 *** | 0.102 *** | −0.423 *** | −0.261 *** | −0.206 *** | 1.000 |

(1) | (2) | (3) | (4) | (5) | (6) | |
---|---|---|---|---|---|---|

Variables | OLS | RE | FE | PCSE AR1 | PCSE AR1 HetOnly | 2StepSys GMM |

OI = L | 0.048 * | |||||

(0.025) | ||||||

EXR | 0.159 | 0.159 | 0.668 | 0.289 | 0.289 | 3.233 *** |

(7.265) | (7.265) | (5.739) | (6.043) | (6.137) | (1.157) | |

LTI | 0.127 *** | 0.127 *** | −0.149 | 0.125 *** | 0.125 *** | 0.128 ** |

(0.045) | (0.045) | (0.111) | (0.031) | (0.048) | (0.064) | |

REFA | −0.036 | −0.036 | −0.317 ** | −0.076 | −0.076 | −0.572 *** |

(0.113) | (0.113) | (0.130) | (0.193) | (0.182) | (0.017) | |

DAR | 12.34 *** | 12.34 *** | 15.25 *** | 13.18 *** | 13.18 *** | 0.578 |

(0.888) | (0.888) | (0.836) | (1.290) | (0.918) | (0.435) | |

CIR | −0.071 | −0.073 | −0.135 *** | −0.101 ** | −0.101 ** | −0.068 *** |

(0.049) | (0.049) | (0.042) | (0.040) | (0.047) | (0.014) | |

PER | 0.011 | 0.011 | 0.014* | 0.0119 * | 0.012 * | 0.005 *** |

(0.008) | (0.008) | (0.008) | (0.007) | (0.007) | (0.002) | |

ATR | 0.306 | 0.306 | 0.818 | 0.520 | 0.520 | 0.933 ** |

(0.610) | (0.610) | (0.909) | (0.699) | (0.701) | (0.472) | |

ROA | 0.036 | 0.036 | 0.007 | 0.030 | 0.031 | 0.212 *** |

(0.066) | (0.066) | (0.069) | (0.044) | (0.069) | (0.030) | |

Constant | 1.100 | 1.100 | 2.149 | 0.465 | 0.465 | 1.692 |

(33.95) | (33.95) | (26.84) | (28.08) | (28.73) | (5.355) | |

Observations | 113 | 113 | 113 | 113 | 113 | 95 |

R-squared | 0.684 | 0.684 | 0.828 | 0.633 | 0.633 | |

Number of firms | 19 | 19 | 19 | 19 | 19 | 19 |

Diagnostic Checks | ||||||

Breusch and Pagan LM test for random effects | (8) *** | |||||

Hausman test | (18) *** | |||||

Multicollinearity test (VIF) | 1.87 | |||||

Heteroskedasticity test | 333.59 *** | |||||

Wooldridge test | 11.583/(0.0032) | |||||

Sargan test chi2(9)/(p-Value) | (7.178) (0.618) | |||||

Arellano Bond Test AR (1) (Z) p-Value | (−1.8675) (0.0618) | |||||

AR (2) (z) p-Value | (1.557) (0.1195) |

(1) | (2) | (3) | (4) | (5) | (6) | |
---|---|---|---|---|---|---|

Variables | OLS | RE | FE | PCSE AR1 | PCSE Hetonly | Twostep Sys GMM |

OI = L | 0.0810 *** | |||||

(0.0158) | ||||||

EXR | −28.07 * | −28.07 * | −40.99 *** | −32.13 | −32.13 * | 26.01 *** |

(16.96) | (16.96) | (14.19) | (19.94) | (18.40) | (3.121) | |

LTI | 0.123 *** | 0.123 *** | −0.142 | 0.122 *** | 0.122 *** | 0.170 *** |

(0.0446) | (0.0446) | (0.105) | (0.0251) | (0.0460) | (0.0612) | |

REFA | −0.0349 | −0.0349 | −0.392 *** | −0.0762 | −0.0762 | −0.577 *** |

(0.112) | (0.112) | (0.126) | (0.207) | (0.189) | (0.0163) | |

DAR | −189.9 * | −189.9 * | −275.0 *** | −215.3 * | −215.3 ** | 157.0 *** |

(110.0) | (110.0) | (91.24) | (114.5) | (109.4) | (18.97) | |

CIR | −0.0745 | −0.0745 | −0.143 *** | −0.105 *** | −0.105 ** | −0.0477 *** |

(0.0488) | (0.0488) | (0.0406) | (0.0408) | (0.0453) | (0.0119) | |

PER | 0.00990 | 0.00990 | 0.0126 * | 0.0113 | 0.0113 * | 0.00477 *** |

(0.00825) | (0.00825) | (0.00694) | (0.00703) | (0.00645) | (0.00158) | |

ATR | 0.364 | 0.364 | 0.904 | 0.566 | 0.566 | 1.680 *** |

(0.604) | (0.604) | (0.865) | (0.650) | (0.704) | (0.273) | |

ROA | 0.0388 | 0.0388 | −0.0292 | 0.0267 | 0.0267 | 0.223 *** |

(0.0649) | (0.0649) | (0.0667) | (0.0473) | (0.0700) | (0.0270) | |

EXDAR | 43.31 * | 43.31 * | 62.12 *** | 48.90 ** | 48.90 ** | −33.36 *** |

(23.57) | (23.57) | (19.52) | (24.48) | (23.37) | (4.076) | |

Constant | 133.0 * | 133.0 * | 198.1 *** | 152.1 | 152.1 * | −106.5 *** |

(79.23) | (79.23) | (66.66) | (93.20) | (86.19) | (14.56) | |

Observations | 113 | 113 | 113 | 113 | 113 | 95 |

R-squared | 0.6593 | 0.6937 | 0.846 | 0.652 | 0.652 | |

Diagnostic Checks | ||||||

Breusch and Pagan LM test for random effects | (9) *** | |||||

Hausman test | (18) *** | |||||

Multicollinearity test (VIF) | 1.86 | |||||

Heteroskedasticity test | 255.26 *** | |||||

Wooldridge test | 10.423/(0.0047) | |||||

Sargan test chi2(9)/(p-Value) | (11.77) (0.462) | |||||

Arellano Bond Test AR (1) (Z) p-Value | (−1.658) (0.095) | |||||

AR (2) (z) p-Value | (1.278) (0.201) |

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## Share and Cite

**MDPI and ACS Style**

Hussain, S.; Hoque, M.E.; Susanto, P.; Watto, W.A.; Haque, S.; Mishra, P.
The Quality of Fair Revaluation of Fixed Assets and Additional Calculations Aimed at Facilitating Prospective Investors’ Decisions. *Sustainability* **2022**, *14*, 10334.
https://doi.org/10.3390/su141610334

**AMA Style**

Hussain S, Hoque ME, Susanto P, Watto WA, Haque S, Mishra P.
The Quality of Fair Revaluation of Fixed Assets and Additional Calculations Aimed at Facilitating Prospective Investors’ Decisions. *Sustainability*. 2022; 14(16):10334.
https://doi.org/10.3390/su141610334

**Chicago/Turabian Style**

Hussain, Sarfraz, Mohammad Enamul Hoque, Perengki Susanto, Waqas Ahmad Watto, Samina Haque, and Pradeep Mishra.
2022. "The Quality of Fair Revaluation of Fixed Assets and Additional Calculations Aimed at Facilitating Prospective Investors’ Decisions" *Sustainability* 14, no. 16: 10334.
https://doi.org/10.3390/su141610334