# Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Increasing the Accuracy and Interpretability of Bankruptcy-Prediction Models

## 3. Research Methodology

#### 3.1. Data Collection

#### 3.1.1. The Period Considered and the Control Period

#### 3.1.2. The Population

#### 3.1.3. Sampling and Sample Size

- bankrupt enterprises. During the period (2009–2013), these enterprises either (i) went bankrupt or (ii) started bankruptcy processes;
- non-bankrupt enterprises. The enterprises (i) did not go bankrupt or start bankruptcy processes and (ii) continued their activities and showed no indications of activity failure. This means that the enterprises were operational in 2007 and continued their activity in 2021; the enterprises were not reformed, reorganised, restructured or liquidated; they did not participate in reorganisation, separation, etc.

- (1)
- Three-hundred and twenty-one bankrupt enterprises, i.e., enterprises that (i) went bankrupt or (ii) started bankruptcy processes during the period of 2009–2013.
- (2)
- Two hundred and sixty non-bankrupt enterprises, i.e., enterprises that (i) did not go bankrupt or start bankruptcy processes during the period considered and (ii) continued their activities and showed no indications of activity failure by the year 2021.

#### 3.2. Selection of Independent Variables

#### 3.2.1. Financial Ratios

**Hypothesis**

**1 (H1).**

#### 3.2.2. Non-Financial Variables

#### 3.2.3. Construction-Sector Variables

#### 3.2.4. Macroeconomic Variables

**Hypothesis**

**2 (H2).**

#### 3.2.5. Pre-Processing Stage: Selection of Statistical Tests

_{k}

_{1}, N

_{k2}, …, N

_{ki}, … …, N

_{km}, where N

_{ki}is the independent variable k for the non-bankrupt enterprise i and m is the number of the non-bankrupt enterprises) and (ii) in the sample of the bankrupt enterprises (B

_{k}

_{1}, B

_{k}

_{2}, …, B

_{kj}, …, B

_{kl}, where B

_{kj}is the independent variable k for the bankrupt enterprise j and l is the number of the bankrupt enterprises).

- (1)
- If the assumption of normality is violated, the Mann–Whitney U test is used. The null hypothesis H0 of the Mann–Whitney U test was as follows: the distributions of the independent variables of the bankrupt and non-bankrupt enterprises are equal. The alternate hypothesis H1 was as follows: the distributions of the independent variables of the bankrupt and non-bankrupt enterprises are different. The decision was made based on the following provisions: (i) H0 is rejected, distributions of the independent variables are not equal if p < α (α = 0.05); (ii) H0 is not rejected, distributions of the independent variables are equal if p ≥ α.
- (2)
- If the assumption of normality was valid, we used the t-test. This test was applied in empirical studies (Ravisankar et al. (2011); Pustylnick (2012); Špicas et al. (2015) and others), in which, using the relative financial ratios in the financial statements, prediction of the possibility of bankruptcy was analysed.

_{k}~ N(µ

_{kN}, σ

^{2}

_{N}), B

_{k}~ N(µ

_{kB}, σ

^{2}

_{B})) and equal variances (σ

^{2}

_{N}, σ

^{2}

_{B}). Furthermore, their averages (µ

_{kN}and µ

_{kB}) and variances (σ

^{2}

_{N}and σ

^{2}

_{B}) were not known. Therefore, firstly, using Levene’s test, the equality of variances was evaluated. The equality-of-averages hypothesis was thus verified.

_{kN}= µ

_{kB}). Alternate hypothesis H1: the independent-variable averages in the samples of the non-bankrupt and bankrupt enterprises differ (µ

_{kN}≠ µ

_{kB}). The decision was made based on the following provisions: (i) H0 is rejected (i.e., the independent-variable averages are not equal if p < α (α = 0.05)); (ii) H0 is not rejected (i.e., the independent-variable averages do not differ if p ≥ α (α = 0.05)).

#### 3.3. Classificatory Devices

#### 3.3.1. The Logistic Regression Model

_{0}is the coefficient of the constant term and β

_{i}represents the particular coefficient in a linear combination of k independent variables (i = 1, …, k) in Equation (2). Independent variables X

_{i}are all potentially relevant parameters that may drive credit/bankruptcy risk (Behr and Güttler 2007). In this research, independent variables X

_{i}are (i) financial ratios, (ii) non-financial variables, (iii) macroeconomic indicators characterising the construction sector, (iv) financial indicators for the construction sector and (v) macroeconomic variables. In addition to the financial variables, this study contained a large set of other variables that could be included as control variables. Therefore, an additional set of control variables was not distinguished.

#### 3.3.2. Artificial Neural Network Model

#### 3.3.3. Multivariate Adaptive Regression Splines Model

## 4. Research Results and Findings

#### 4.1. Using Statistical Tests: Estimation of Independent Variables

#### 4.2. Logistic Regression EBP Models

#### 4.3. Two-Stage Hybrid-Model Development

#### 4.4. MLP and RBF Neural Network Models

#### 4.5. The MARS Model

## 5. Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

**Table A1.**Financial variables used to develop the EBP models for Lithuanian MiSEs in the construction sector.

Financial Variable (Financial Ratio) | Calculation Formula |
---|---|

1a. Profitability ratios (return from sales) | |

Gross profit/sales | GP/S |

EBIT/sales | EBIT/S |

EBT/sales | EBT/S |

Net profit/sales | NP/S |

1b. Profitability ratios (return on investment) | |

Gross profit/total assets | GP/TA |

EBIT/total assets | EBIT/TA |

EBIT/current liabilities | EBIT/CL |

EBT/total assets | EBT/TA |

EBT/equity | EBT/Eq |

EBT/(equity–current liabilities) | EBT/(Eq-CL) |

Net profit/total assets | ROA |

Net profit/equity | ROE |

2. Liquidity ratios | |

Current assets/current liabilities | CA/CL |

(Current assets–inventories)/current liabilities | (CA-INV)/CL |

Inventories/current liabilities | INV/CL |

Accounts receivable/total liabilities | AR/TL |

Accounts receivable/(total liabilities–cash) | AR/(TL-Cash) |

Cash/current liabilities | Cash/CL |

(Cash–inventories)/current liabilities | (Cash-INV)/CL |

Cash/total liabilities | Cash/TL |

Cash/equity | Cash/Eq |

Working capital/total assets | WC/TA |

Working capital/equity | WC/Eq |

(Current liabilities–cash)/total assets | (CL-Cash)/TA |

3. Solvency ratios | |

Total liabilities/total assets | TL/TA |

Equity/total assets | Eq/TA |

Equity/(equity + long term liabilities) | Eq/(Eq+LTL) |

Equity/total liabilities | Eq/TL |

Fixed assets/equity | FA/Eq |

Current assets/total liabilities | CA/TL |

Current assets/(total liabilities–cash) | CA/(TL-Cash) |

(Equity− Intangible Assets)/(Total Assets–Intangible Assets–Fixed assets–Cash) | (Eq-IA)/(TA-IA-FA-Cash) |

4. Activity ratios | |

4a. Activity ratios (assets turnover) | |

Sales/inventories | S/INV |

Sales/accounts receivable | S/AR |

Sales/fixed assets | S/FA |

Sales/current assets | S/CA |

Sales/total assets | S/TA |

Sales/cash | S/Cash |

Sales/working capital | S/WC |

4b. Activity ratios (equity and liabilities turnover) | |

Sales/equity | S/Eq |

Sales/capital | S/C |

Sales/current liabilities | S/CL |

Sales/total liabilities | S/TL |

4c. Activity ratios (level of expenses) | |

Cost of sales/sales | CS/S |

Working capital/operating expenses | WC/OE |

5a. Structure ratios (total assets structure ratios) | |

Current assets/total assets | CA/TA |

Accounts receivable/inventories | AR/INV |

Inventories/total assets | INV/TA |

Cash/total assets | Cash/TA |

5b. Structure ratios (equity and liabilities structure ratios) | |

Retained earnings/total assets | RE/TA |

Current liabilities/(total liabilities−cash) | CL/(TL-Cash) |

6 Other ratios (size of enterprise) | |

Logarithm of total assets | LogTA |

Logarithm of total sales | LogS |

**Table A2.**Non-financial variables used to develop the EBP models for Lithuanian MiSEs in the construction sector.

Abbreviation | Variables | Description | Data Source |
---|---|---|---|

AUDIT | Audit of financial statements | Whether the annual financial statements were audited: (i) the audit has been carried out or (ii) the audit has not been carried out (Yes—1; No—0) | SECR ^{1} |

SHARE | Sole shareholder | (i) The company has a single shareholder or (ii) more than one shareholder has acquired shares in the company (Yes—1; No—0) | SECR ^{1} |

RECORDS | Number of records | The number of records published in the Register of Legal Entities | SECR ^{1} |

SUBMISSION_FS | Late submission of financial statements | Financial statements were submitted late (days) | SECR ^{1} |

AGE | The age of the enterprise | The difference between the financial year and the enterprise’s establishment year | SECR ^{1} |

^{1}—State Enterprise Centre of Registers (SECR), Register of Legal Entities. Available online: https://www.registrucentras.lt/jar/ (accessed July–August 2021).

**Table A3.**Macroeconomic indicators characterising the construction sector used to develop the EBP models for Lithuanian MiSEs in the construction sector.

Abbreviation | Full Name, Description |
---|---|

ICW | Index of construction work carried out within the country (2015 = 100) |

ICW_CHG | Annual change in index of construction work carried out within the country (2015 = 100) (%); calculation = [X(t)/X(t − 1)] − 1 |

CW | Construction work carried out within the country at current prices (thousands of EUR) |

CW_CHG | Annual change in construction work carried out within the country at current prices (%); calculation = [X(t)/X(t − 1)] − 1 |

TCA | Turnover from construction activities in non-financial enterprises (thousands of EUR) |

TCA_CHG | Annual change in turnover from construction activities in non-financial enterprises (%); calculation = [X(t)/X(t − 1)] − 1 |

SCAinC | The share of the construction activity in the country in the total construction-activity revenue (at current prices) (%) |

SCAinC_CHG | Annual change in the share of the construction activity in the country in the total construction-activity revenue (%); calculation = [X(t)/X(t − 1)] − 1 |

IWS | Index of wages and salaries in construction enterprises (2015 = 100) |

IWS_CHG | Annual change in index of wages and salaries in construction enterprises (2015 = 100) (%); calculation = [X(t)/X(t − 1)] − 1 |

INPE | Index of number of persons employed in construction enterprises (2015 = 100) |

INPE_CHG | Annual change in index of the number of persons employed in construction enterprises (2015 = 100); calculation = [X(t)/X(t − 1)] − 1 |

**Table A4.**Financial indicators for the construction sector used to develop the EBP models for Lithuanian MiSEs in the construction sector.

Abbreviation | Variable, Calculation Formula |
---|---|

GP/S_CS | Gross profit margin (%). Calculation: gross profit/sales |

NP/S_CS | Net profit margin (%). Calculation: net profit/sales |

ROA_CS | Return on assets (ROA) (%) |

ROE_CS | Return on equity (ROE) (%) |

CA/CL_CS | Current ratio. Calculation: current assets/current liabilities |

TL/TA_CS | Total liabilities-to-total assets ratio. Calculation: total liabilities/total assets |

S/AR_CS | Receivables turnover ratio (times). Calculation: sales/accounts receivable |

S/TA_CS | Total asset turnover (times). Calculation: sales/total assets |

CCI_CS | Change in customer insolvency and late payments over the last three months (increasing) (%) |

**Table A5.**Macroeconomic variables of the country (Lithuania) used to develop the EBP models for Lithuanian MiSEs in the construction sector.

Abbreviation | Full Name, Description |
---|---|

GDP | GDP (at 2010 constant prices) |

GDP_CHG | GDP yearly change (at 2010 constant prices) (%); calculation = [X(t)/X(t − 1)] − 1 |

GDP_index | GDP index (at 2010 constant prices, 2010 = 100) |

GDP_index_CHG | GDP index annual change (at 2010 constant prices, 2010 = 100) (%); calculation = [X(t)/X(t − 1)] − 1 |

GDP(MP) | GDP at market prices (EUR per capita) |

GDP(MP)_CHG | Annual change in GDP at market prices (Euro per capita) (%); calculation = [X(t)/X(t − 1)] − 1 |

HICP | The harmonised index of consumer prices at constant tax rates (2015 = 100) |

INF | Annual inflation |

INF_A | Average annual inflation |

HPI | House-price index (2015 = 100) |

HPI_CHG | Annual change of house price index (%); calculation = [X(t)/X(t − 1)] − 1 |

UR | Unemployment rate |

CIPI | Construction-input-price index (CIPI) (%) |

**Table A6.**Variables used to develop the EBP models for Lithuanian MiSEs in the construction sector: LR and MARS models.

Variables | Variable Is Used in | ||
---|---|---|---|

LR Model | MARS Model | ||

I. Financial Variables | |||

1a. Profitability ratios (return from sales) | |||

EBIT/Sales | EBIT/S | x | |

1b. Profitability ratios (return on investment) | |||

Gross Profit/Total Assets | GP/TA | x | |

EBIT/Total Assets | EBIT/TA | x | |

Net Profit/Total Assets | ROA | x | |

2. Liquidity ratios | |||

Accounts Receivable/Total Liabilities | AR/TL | x | |

(Cash−Inventories)/Current Liabilities | (CASH-INV)/CL | x | |

(Current Liabilities−Cash)/Total Assets | (CL-Cash)/TA | x | |

3. Solvency ratios | |||

Total Liabilities/Total Assets | TL/TA | x | x |

4. Activity ratios | |||

4a. Activity ratios (assets turnover) | |||

Sales/Fixed Assets | S/FA | x | |

Sales/Total Assets | S/TA | x | x |

4b. Activity ratios (equity and liabilities turnover) | |||

Sales/Current Liabilities | S/CL | x | |

Sales/Total Liabilities | S/TL | x | |

4c. Activity ratios (level of expenses) | |||

Cost of Sales/Sales | CS/S | x | |

5a. Structure ratios (total-assets-structure ratios) | |||

Current Assets/Total Assets | CA/TA | x | x |

Inventory/Total Assets | INV/TA | x | |

Cash/Total Assets | Cash/TA | x | |

II. Macroeconomic Variables | |||

GDP index | GDP_index | x | |

The harmonised index of consumer prices at constant tax rates | HICP | x | x |

Average annual inflation | INF_A | x | |

House-price index | HPI | x | |

III. Construction-Sector Variables | |||

Macroeconomic indicators characterising the construction sector | |||

Annual change in index of construction work carried out within the country | ICW_CHG | x | |

Annual change in the share of the construction activity in the country in the total construction activity revenue | SCAinC_CHG | x | |

IV. Financial Indicators for the Construction Sector | |||

Gross profit margin (%) Gross profit/sales | GP/S_CS | x | |

Total-asset-turnover ratio (times) Sales/total assets | S/TA_CS | x | |

Change in customer insolvency and late payments over the last three months | CCI_CS | x | |

V. Non-Financial Variables | |||

The age of the enterprise | AGE | x | x |

Sole shareholder | SHARE | x | |

Number of records | RECORDS | x |

## Notes

1 | Until 2019, the State Data Agency of the Republic of Lithuania collected data on bankruptcy (more precisely, the number of bankruptcy proceedings initiated in the corresponding year)—source: The State Data Agency of the Republic of Lithuania. Bankruptcy processes instituted and completed by economic activity. Available online: http://university2.taylors.edu.my/tbr/uploaded/2015_vol5_issue2_p3.pdfhttps://osp.stat.gov.lt/statistiniu-rodikliu-analize?hash=46cbb9e7-57e9-485d-9ae3-56ae2458ccd4#/ (accessed on 22 July 2022). |

2 | The titles of the financial statements and financial items are used in accordance with International Financial Reporting Standards (IFRSs). |

3 | According to Statistics Lithuania, an operating enterprise (or) working enterprise is an enterprise operating with a specific number of employees and (or) annual revenue. |

4 | Construction work carried out refers to the value (VAT excluded) of all kinds of work performed when building a new structure or reconstructing, repairing (restoring) or demolishing an existing structure for a customer (sale) or for own needs—source: State Data Agency (Statistics Lithuania). (2023). Construction work carried out (Metadata). https://osp.stat.gov.lt/documents/10180/5118910/Statybos+%C4%AFmoni%C5%B3+atlikt%C5%B3+darb%C5%B3+rodikliai+%5BEN%5D+645.html (accessed on 24 April 2023). |

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**Figure 1.**ROC curve and AUC for (1) two-stage hybrid models based on (

**a**) MLP and (

**b**) RBF, for (2) neural network models based on (

**a**) MLP and (

**b**) RBF and for (

**c**) all models.

Independent Variables | Model: | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

M1.1 | M1.2 | M2.1 | M2.2 | M3.1 | M3.2 | M4 | |||||||||

Coef. | Sign. | Coef. | Sign. | Coef. | Sign. | Coef. | Sign. | Coef. | Sign. | Coef. | Sign. | Coef. | Sign. | ||

Constant | −2.212 | 0.000 | −2.550 | 0.000 | 1.271 | 0.574 | 2.427 | 0.205 | −3.068 | 0.023 | −0.719 | 0.748 | −2.372 | 0.567 | |

Financial variables (Financial ratios) | |||||||||||||||

1a. Profitability ratios (return from sales) | |||||||||||||||

EBIT/Sales | EBIT/S | −0.554 | 0.003 | ||||||||||||

1b. Profitability ratios (return on investment) | |||||||||||||||

Gross Profit/Total Assets | GP/TA | 0.504 | 0.000 | 1.652 | 0.000 | ||||||||||

EBIT/Total Assets | EBIT/TA | 1.155 | 0.000 | ||||||||||||

Net Profit/Total Assets | ROA | 1.040 | 0.000 | ||||||||||||

2. Liquidity ratios | |||||||||||||||

Accounts Receivable/Total Liabilities | AR/TL | 0.139 | 0.000 | 1.140 | 0.000 | 0.268 | 0.000 | ||||||||

(Cash − Inventories)/Current Liabilities | (CASH-INV)/CL | −0.330 | 0.109 | ||||||||||||

3. Solvency ratios | |||||||||||||||

Total Liabilities/Total Assets | TL/TA | 3.334 | 0.000 | 4.136 | 0.000 | 3.440 | 0.000 | 3.532 | 0.000 | 3.875 | 0.000 | 2.806 | 0.000 | 2.720 | 0.000 |

4. Activity ratios | |||||||||||||||

4a. Activity ratios (assets turnover) | |||||||||||||||

Sales/Total Assets | S/TA | 1.390 | 0.000 | 0.306 | 0.000 | ||||||||||

4b. Activity ratios (equity and liabilities turnover) | |||||||||||||||

Sales/Current Liabilities | S/CL | −0.453 | 0.000 | ||||||||||||

Sales/Total Liabilities | S/TL | −0.064 | 0.003 | −0.311 | 0.000 | −0.107 | 0.000 | ||||||||

4c. Activity ratios (Level of expenses) | |||||||||||||||

Cost of Sales/Sales | CS/S | 0.911 | 0.000 | 1.841 | 0.000 | −0.778 | 0.002 | ||||||||

5a. Structure ratios (total assets structure ratios) | |||||||||||||||

Current Assets/Total Assets | CA/TA | −1.188 | 0.000 | −3.048 | 0.000 | −2.013 | 0.000 | −2.030 | 0.000 | −2.178 | 0.000 | −2.192 | 0.000 | −2.395 | 0.000 |

Inventory/Total Assets | INV/TA | 2.934 | 0.000 | 4.128 | 0.000 | 1.265 | 0.061 | 3.086 | 0.000 | 2.691 | 0.000 | ||||

Macroeconomic variables | |||||||||||||||

GDP index | GDP_index | −0.096 | 0.000 | ||||||||||||

The harmonised index of consumer prices at constant tax rates | HICP | −0.023 | 0.327 | −0.036 | 0.081 | ||||||||||

Average annual inflation | INF_A | 0.158 | 0.000 | 0.251 | 0.000 | ||||||||||

Construction-sector variables | |||||||||||||||

Macroeconomic indicators characterising the construction sector | |||||||||||||||

Annual change of index of construction work carried out within the country | ICW_CHG | 0.012 | 0.218 | ||||||||||||

Annual change in the share of the construction activity in the country in the total-construction-activity revenue | SCAinC_CHG | −0.041 | 0.148 | ||||||||||||

Financial indicators for the construction sector | |||||||||||||||

Gross profit margin | GP/S_CS | 0.111 | 0.089 | 0.438 | 0.000 | 0.311 | 0.000 | ||||||||

Total-asset-turnover ratio (times) | S/TA_CS | −3.575 | 0.237 | ||||||||||||

Change in customer insolvency and late payments over the last three months | CCI_CS | 0.038 | 0.000 | ||||||||||||

Non-financial variables | |||||||||||||||

The age of the enterprise | AGE | −0.137 | 0.000 | ||||||||||||

The sole shareholder | SHARE | −0.785 | 0.000 | ||||||||||||

Chi-square p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||||||||

Cox-and-Snell R square | 0.543 | 0.583 | 0.590 | 0.562 | 0.570 | 0.568 | 0.585 | ||||||||

Nagelkerke R square | 0.738 | 0.793 | 0.803 | 0.763 | 0.774 | 0.771 | 0.792 | ||||||||

Model test sample (dataset): | |||||||||||||||

The total percentage of the model’s correctly classified cases | 68.1 | 75.8 | 90.4 | 91.7 | 69.4 | 84.2 | 77.7 | ||||||||

The percentage of the model’s correctly classified bankrupt-enterprises cases | 82.8 | 85.7 | 85.2 | 80.3 | 85.7 | 90.0 | 89.0 | ||||||||

The percentage of the model’s correctly classified non-bankrupt-enterprises cases | 59.0 | 69.7 | 93.8 | 98.8 | 59.3 | 80.4 | 70.3 | ||||||||

AUC | 0.751 | 0.821 | 0.952 | 0.985 | 0.746 | 0.907 | 0.838 | ||||||||

Model training sample (dataset): | |||||||||||||||

The total percentage of the model’s correctly classified cases | 71.4 | 79.2 | 94.2 | 94.2 | 75.3 | 86.2 | 80.2 | ||||||||

The percentage of the model’s correctly classified bankrupt-enterprises cases | 84.7 | 89.8 | 91.7 | 85.6 | 90.6 | 89.5 | 90.6 | ||||||||

The percentage of the model’s correctly classified non-bankrupt-enterprises cases | 63.1 | 72.6 | 95.7 | 99.5 | 65.8 | 84.2 | 73.4 | ||||||||

AUC | 0.803 | 0.868 | 0.988 | 0.996 | 0.817 | 0.935 | 0.876 |

Model | M1.2+ M1 MLP | M1.2+ M1 RBF | M2.2+ M2 MLP | M2.2+ M2 RBF | ||||

Stage | St. I | St. II | St. I | St. II | St. I | St. II | St. I | St. II |

M1.2 | M1 MLP | M1.2 | M1 RBF | M2.2 | M2 MLP | M2.2 | M2 RBF | |

Model-test sample (dataset): | ||||||||

The total percentage of the model’s correctly classified cases (Accuracy rate) | 75.8 | 76.4 | 75.8 | 78.1 | 91.7 | 80.6 | 91.7 | 79.4 |

The percentage of the model’s correctly classified bankrupt-enterprises cases (Sensitivity) | 85.7 | 61.6 | 85.7 | 76.4 | 80.3 | 71.9 | 80.3 | 92.1 |

The percentage of the model’s correctly classified non-bankrupt-enterprises cases (Specificity) | 69.7 | 85.6 | 69.7 | 79.2 | 98.8 | 85.9 | 98.8 | 71.6 |

AUC | 0.821 | 0.736 | 0.821 | 0.860 | 0.985 | 0.789 | 0.985 | 0.882 |

Model | M3.2+ M3 MLP | M3.2+ M3 RBF | M4+ M4 MLP | M4+ M4 RBF | ||||

Stage | St. I | St. II | St. I | St. II | St. I | St. II | St. I | St. II |

M3.2 | M3 MLP | M3.2 | M3 RBF | M4 | M4 MLP | M4 | M4 RBF | |

Model-test sample (dataset): | ||||||||

The total percentage of the model’s correctly classified cases (Accuracy rate) | 84.2 | 78.4 | 84.2 | 74.1 | 77.7 | 80.6 | 77.7 | 79.3 |

The percentage of the model’s correctly classified bankrupt-enterprises cases (Sensitivity) | 90.0 | 67.6 | 90.0 | 77.6 | 89.0 | 75.2 | 89.0 | 82.4 |

The percentage of the model’s correctly classified non-bankrupt-enterprises cases (Specificity) | 80.4 | 85.3 | 80.4 | 71.9 | 70.3 | 84.1 | 70.3 | 77.4 |

AUC | 0.907 | 0.765 | 0.907 | 0.831 | 0.838 | 0.797 | 0.838 | 0.873 |

**Table 3.**EBP models for MiSEs in the construction sector: MLP and RBF neural network models and the MARS Model.

Model | M(ALL.MLP) | M(ALL.RBF) | M(MARS) |
---|---|---|---|

Model-test sample (dataset): | |||

The total percentage of the cases correctly classified by the models | 81.6 | 70.1 | 93.9 |

The percentage of the bankrupt-enterprise cases correctly classified by the models | 74.8 | 76.4 | 93.8 |

The percentage of the non-bankrupt-enterprise cases correctly classified by the model | 84.1 | 66.9 | 93.9 |

AUC | 0.799 | 0.787 | 0.987 |

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**MDPI and ACS Style**

Kanapickienė, R.; Kanapickas, T.; Nečiūnas, A.
Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector. *Risks* **2023**, *11*, 97.
https://doi.org/10.3390/risks11050097

**AMA Style**

Kanapickienė R, Kanapickas T, Nečiūnas A.
Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector. *Risks*. 2023; 11(5):97.
https://doi.org/10.3390/risks11050097

**Chicago/Turabian Style**

Kanapickienė, Rasa, Tomas Kanapickas, and Audrius Nečiūnas.
2023. "Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector" *Risks* 11, no. 5: 97.
https://doi.org/10.3390/risks11050097