Parametric Conditions of High Financial Risk in the SME Sector
Abstract
:1. Introduction
2. The Financial Situation of SMEs
3. Financial Risk in SMEs
4. Methodology and Research Sample
5. The Assessment of the Intensity of Financial Risk in the SME Sector
5.1. The Input Data of the Logit Model
- Does the size of the company affect the intensity of individual sources of financial risk?
- Does internationalization of SMEs affect the intensity of the perception of sources of financial risk?
- Does the operating time in the market affect the intensity of financial risk?
- Does experience in risk management affect the intensity of financial risk?
- Do the levels of sources of financial risk interdepend financially?
- Yn—dependent variable,
- x1, … x4—independent variables,
- B1, … B4—structural parameters of the model.
5.1.1. The Results of the Estimation of the Y1 Model
- The larger the company, the lower the probability of the occurrence of high risk.
- If the company operates in the international market, there is higher probability of the occurrence of high risk.
- The longer the company operates in the market, the higher the probability of the occurrence of high risk.
- If the company deals with risk management, the probability of the occurrence of high risk is lower.
5.1.2. The Results of the Estimation of the Y2 Model
- The larger the company, the lower the probability of the occurrence of high risk.
- If the company operates in the international market, the probability of the occurrence of high risk is higher.
- The longer the company operates in the market, the higher the probability of the occurrence of high risk.
- If the company deals with risk management, the probability of the occurrence of high risk is lower.
5.1.3. The Results of the Estimation of the Y3 Model
- The larger the company, the lower the probability of the occurrence of high risk.
- If the company operates in the international market, the probability of the occurrence of high risk is higher.
- The longer the company operates in the market, the higher the probability of the occurrence of high risk.
- If the company deals with risk management, the probability of the occurrence of high risk is lower.
5.1.4. The Results of the Estimation of the Y4 Model
- The larger the company, the lower the probability of the occurrence of high risk
- If the company operates in the international market, the probability of the occurrence of high risk is higher.
- The longer the company operates in the market, the higher the probability of the occurrence of high risk.
- If the company deals with risk management, the probability of the occurrence of high risk is lower.
6. Discussion on the Results
7. Conclusions
8. Limitations and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Sources of Financial Risk | Intensity of Risk Sources | Insufficient Profit of the Company | Foreign Capital (High Share of Foreign Capital) | Unpaid Claims | Inability to Pay Obligations (Insolvency) |
---|---|---|---|---|---|
Size of the company | |||||
micro- (up to 9 employees) | Low | 23.4 | 52 | 42.1 | 39 |
High | 37.8 | 11.1 | 18.3 | 20.1 | |
small (10–49 employees) | Low | 14.1 | 19.4 | 18.6 | 20.9 |
High | 14.1 | 5.1 | 9.7 | 10 | |
medium (50–249 employees) | Low | 2.0 | 9.7 | 6.1 | 5.8 |
High | 7.9 | 2.6 | 4.7 | 4.2 | |
Internationalization of the company | |||||
Internationalization | Low | 38.5 | 80.3 | 64.7 | 63.8 |
High | 60.2 | 18.1 | 33 | 33.4 | |
No internationalization | Low | 1 | 0.9 | 1.9 | 1.9 |
High | 0.3 | 0.9 | 0.3 | 0.8 | |
Operating time in the market | |||||
Up to 4 years in the market | Low | 17.1 | 38.8 | 29.9 | 30.6 |
High | 27.6 | 6.6 | 14.9 | 15.9 | |
Up to 5 years in the market | Low | 22.3 | 42.3 | 36.9 | 35.1 |
High | 32.9 | 12.3 | 18.3 | 18.4 | |
Risk management | |||||
It does manage risk | Low | 18.4 | 28 | 25.7 | 22.8 |
High | 17.7 | 6.6 | 11.1 | 12.1 | |
It does not manage risk | Low | 21.2 | 53.1 | 41 | 42.9 |
High | 42.8 | 12.3 | 22.2 | 22.3 |
Predictor | Variable Type | Variable Categories | Category Code |
---|---|---|---|
Size of the entity (X1) | Continuous variable | Micro (up to 10 employees) | 1 |
Small (10–49 employees) | 2 | ||
Medium (50–249 employees) | 3 | ||
Operating time in the market (X2) | Continuous variable | Less than 1 year | 1 |
1–4 years | 2 | ||
5–9 years | 3 | ||
More than 10 years | 4 | ||
Activities of the company in the international market (X3) | Continuous variable | The company does not operate in the international market | 0 |
The company has been operating in the international for less than 1 year | 1 | ||
The company has been operating in the international market for 1–4 years | 2 | ||
The company has been operating in the international market for 5–9 years | 3 | ||
The company has been operating in the international market for more than 10 years | 4 | ||
Risk management in the company (X4) | Continuous variable | Not identified | 0 |
Yes, less than 1 year | 1 | ||
Yes, 1–4 years | 2 | ||
Yes, 5–9 years | 3 | ||
Yes, more than 10 years | 4 |
Chi-square test results | ||
Chi-square | df | Sig. |
15.365 | 3 | 0.002 |
Log likelihood value and peudo-R2 | ||
−2 log likelihood | Cox-Snell R2 | Nagelkerke R2 |
390.629 | 0.049 | 0.43 |
Hosmer-Lemeshow test | ||
Chi-square | df | Sig. |
6.685 | 8 | 0.571 |
Observed | Predicted | Percentage of Correct Classification | |
---|---|---|---|
Low Risk Intensity | High Risk Intensity | ||
Low risk intensity | 26 | 93 | 21.8 |
High risk intensity | 19 | 165 | 89.7 |
General classification accuracy | 63 |
Variables | B | Standard Error | Wald | df | Significance | Exp(B) |
---|---|---|---|---|---|---|
X1 | 0.234 | 0.170 | 1.901 | 1 | 0.168 | 1.264 |
X2 | 0.026 | 0.148 | 0.030 | 1 | 0.862 | 1.026 |
X3 | 0.218 | 0.073 | 8.970 | 1 | 0.003 | 1.243 |
X4 | −0.226 | 0.084 | 7.295 | 1 | 0.007 | 0.798 |
Constant | −0.150 | 0.537 | 0.078 | 1 | 0.780 | 0.861 |
Chi-square test results | ||
Chi-square | df | Sig. |
2.700 | 4 | 0.609 |
Log likelihood value and peudo-R2 | ||
−2 log likelihood | Cox-Snell R2 | Nagelkerke R2 |
332.852 | 0.008 | 0.012 |
Chi-square test results | ||
Chi-square | df | Sig. |
6.230 | 4 | 0.183 |
Log likelihood value and peudo-R2 | ||
−2 log likelihood | Cox-Snell R2 | Nagelkerke R2 |
452.870 | 0.017 | 0.024 |
Chi-square test results | ||
Chi-square | df | Sig. |
1.060 | 4 | 0.901 |
Log likelihood value and peudo-R2 | ||
−2 log likelihood | Cox-Snell R2 | Nagelkerke R2 |
459.599 | 0.003 | 0.004 |
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Ślusarczyk, B.; Grondys, K. Parametric Conditions of High Financial Risk in the SME Sector. Risks 2019, 7, 84. https://doi.org/10.3390/risks7030084
Ślusarczyk B, Grondys K. Parametric Conditions of High Financial Risk in the SME Sector. Risks. 2019; 7(3):84. https://doi.org/10.3390/risks7030084
Chicago/Turabian StyleŚlusarczyk, Beata, and Katarzyna Grondys. 2019. "Parametric Conditions of High Financial Risk in the SME Sector" Risks 7, no. 3: 84. https://doi.org/10.3390/risks7030084