# Financial Liquidity and Debt Recovery Efficiency Forecasting in a Small Industrial Enterprise

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Overview of the Literature on Financial Liquidity in a Small and Medium Enterprise

#### 2.2. Traditional Assessment of an Enterprise’s Financial Liquidity

- (a)
- Static approach—in relation to a specific moment, using basic parts of financial statements, such as: the balance sheet and the profit and loss account;
- (b)
- Dynamic approach—in relation to a specific reporting period, based on the cash flow statement.

#### 2.3. Financial Liquidity and Debt Recovery Efficiency Measurement in a Small Enterprise

_{t}≥ PROD

_{t}(t = 1, …, n), the enterprise possesses the cash needed to cover its liabilities in period t. A situation when CASH

_{t}< PROD

_{t}may mean a shortage of cash. It is worth noting, however, that an entrepreneur who mainly has to count on his own precaution can accumulate cash from periods of surplus over liabilities and use it during a period of current shortage. As such, consideration of the cumulative value of cash in subsequent periods of a given year and its comparison with the cumulative value of finished production may be a better solution.

_{t}= cum.CASH

_{t}− cum.PROD

_{t},

_{t}= cum.CASH

_{t-1}+ CASH

_{t}, in year t*,

_{t}= cum.PROD

_{t-1}+ PROD

_{t}, in year t*,

^{*}; t = 2, …, 12) and

_{1}= CASH

_{1}, and cum.PROD

_{1}= PROD

_{1}.

_{t}) and the value of concurrent (SBRUT

_{t}) as well as the 1-month delayed (SBRUT

_{t−1}) and two-month (SBRUT

_{t−2}) gross sales revenues. It is therefore necessary to consider the following differences (Wiśniewski 2009):

_{t}= CASH

_{t}− SBRUT

_{t},

_{t}= CASH

_{t}− SBRUT

_{t−1},

_{t}= CASH

_{t}− SBRUT

_{t−2}.

_{t}measure that are close to zero in each of the periods t (t = 1, …, n). The sum of the values of the measure ${{\displaystyle \sum}}_{\mathrm{t}=1}^{12}\mathrm{VIND}{0}_{\mathrm{t}}$ in year t * (t* = 1, …, n *)4 should be close to 0. This means that the receivables for the goods and services sold were converted into cash. ${{\displaystyle \sum}}_{\mathrm{t}=1}^{12}\mathrm{VIND}{0}_{\mathrm{t}}$ cannot be expected to be positive. If, on the other hand, ${{\displaystyle \sum}}_{\mathrm{t}=1}^{12}\mathrm{VIND}{0}_{\mathrm{t}}$ is significantly lower than zero, it means inefficient debt recovery in the enterprise, which can even threaten its existence.

_{t}) is the arithmetic (moving) average of the detailed measures of debt recovery efficiency:

_{t}= (VIND0

_{t}+ VIND1

_{t}+ VIND2

_{t})/3.

_{t}, having the nature of a moving average, is characterized by a much lower dispersion—compared with the detailed measures of debt recovery efficiency.

#### 2.4. The Econometric Model Describing the Interdependence between Financial Liquidity and Debt Recovery Efficiency in an Enterprise

_{t}and EVIND

_{t}form direct feedback, i.e.,

_{t}and EVIND

_{t}is identifiable ambiguously.

_{t}. In addition, the following predetermined variables occur in the equation:

_{t-1}, EVIND

_{t-2}, …, EVIND

_{t-12}− debt recovery efficiency measures delayed by 1, 2, …, 12 months;

_{t}and delayed endogenous variables LIQ

_{t−1}, LIQ

_{t−2}, …, LIQ

_{t−12}appear naturally. Additionally, the following is taken into account: autoregression up to the twelfth order inclusive, dummy variables describing monthly fluctuations, and the time variable t. In both equations, a variable SBRUT

_{t}representing activity in the sales network—gross sales revenues (in PLN thousand) along with its delays from 1 to 12 months (SBRUT

_{t−1}, SBRUT

_{t−2}, …, SBRUT

_{t−12})—occurs as well. The variable SBRUT

_{t}(along with delays) provides information on the intensity of the sales network service, which is always connected with concurrent debt recovery.

_{t}and EVIND

_{t}from the structural-form equations, using an iterative method,6 also called the “snail” method. The calculations were carried out using the GRETL package.

## 3. Results and Discussion

_{t}and EVIND

_{t}. They result from parameter estimation carried out via OLS using the GRETL package.

_{t}, EVIND

_{t}), an external variable (SBRUT

_{t}) appears, both current and with delays. Construction of financial liquidity and debt recovery efficiency forecasts requires prior determination of the variable SBRUT

_{t}. The requisite of a systemic approach to the mechanisms in the enterprise forces consideration of a cycle containing the value of gross sales revenues. The following mechanism possible to occur in a small manufacturing enterprise should therefore be considered:

_{t}, which is presented in Table 3. The statistically significant impact of the concurrent variable PROD

_{t}on the gross sales revenues can be noticed. It therefore becomes necessary to describe finished production with an adequate empirical equation, which is presented in Table 4. In the equation describing PROD

_{t}, the concurrent variable EMP

_{t}turned out to be statistically insignificant, which reduces the system to a recursive mechanism. In Table 4, the volume of employment, delayed by 8 months, is statistically significant. It thus becomes possible to employ a recursive (chain) procedure to estimate PROD

_{Tp}and SBRUT

_{Tp}forecasts for the next 12 months.7 These forecasts are presented in Table 5. Having the PROD

_{Tp}and SBRUT

_{Tp}forecasts, it is possible to use an iterative procedure to estimate the LIQ

_{Tp}and EVIND

_{Tp}forecasts.

_{Tp}forecasts can be estimated assuming realistic hypothetical values of the EVIND

_{Tp}forecasts. In the first iteration, perfect efficiency of debt recovery was presumed, assuming that the forecasts values of EVIND

_{Tp}= 0 (T = 1, 2, …, 12). The forecasts obtained under this assumption are presented in column It.1, Table 6.

_{Tp}forecasts, which are presented in the It.1 column, Table 7. These EVIND

_{Tp}forecasts allow construction of LIQ

_{Tp}forecasts in the second iteration (It.2 column, Table 6) and their comparison with the forecasts from the first iteration. In the case of a difference, the LIQ

_{Tp}forecasts from the second iteration are used to build EVIND

_{Tp}forecasts in the second iteration. The results are presented in the It.2 column, Table 7. A succeeding comparison ends the procedure, if the forecast values are repeated or a third iteration for the LIQ

_{Tp}forecasts is performed. The calculations are continued until convergence is achieved, i.e., the forecast values are repeated in a subsequent iteration.

_{Tp}forecasts for January was obtained after five iterations and for February after six iterations. Successively, after seven iterations, the forecasts for March, April, May, June, July, and August converged. Eight iterations were needed to obtain convergence of the EVIND

_{Tp}forecasts for September, October, and November. Finally, after nine iterations, the EVIND

_{Tp}forecast for December 2004 converged.

_{Tp}forecasts converged slightly differently. The convergent forecast for January appeared after six iterations, whereas for February after seven iterations. After eight iterations, convergent forecasts were obtained for the period from March to August. In turn, after nine iterations, convergent LIQ

_{Tp}forecasts were obtained for the last four months of the year. The iterative procedure applied led to an automatic synchronization of the EVIND

_{Tp}and LIQ

_{Tp}forecast values, as part of the feedback (9).

## 4. Conclusions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Ethical Approval

## Notes

1 | “Recovery (v.)—regain or secure money by legal means or the making of profits”, Paperback Oxford English Dictionary, Oxford University Press, Oxford, 2012, p. 604. “ Debt recovery—the process of making people or companies pay the money that they owe to other people or companies, when they have not paid back the debt at the time that was arranged”, Cambridge Business English Dictionary, Cambridge University Press, Cambridge, 2011, p. 213. In business practice, debt recovery is often confused with debt collection, implemented, inter alia, by debt collection companies/agencies. Meanwhile, “collect (v.)—ask for money”, Paperback Oxford English Dictionary, Oxford University Press, Oxford, 2012, p. 134. “Debt collection—the job of collecting payments from people who have failed to pay the money they owe for goods, services, etc. that they have already received”, Cambridge Business English Dictionary, Cambridge University Press, Cambridge, 2011, p. 212. |

2 | The importance of interpreting and forecasting a company’s financial position has received considerable attention from Bodie and Merton (2000, chp. 3). |

3 | The use of cumulative amounts results from the assumption of proper precaution on the part of the small enterprise owner. He/she collects funds during periods of financial surpluses for the time of reduced cash inflows. An owner who does not have the ability to accumulate funds usually is unable to maintain the company’s position under conditions of strong competition in the market. The symbol t* denotes the number of the year, whereas t is the number of the month in year t*. |

4 | The symbol t* denotes the year number, while n* denotes the number of the years considered. |

5 | Goldberger (1964), in his work Econometric theory, John Willey and Sons, New York, writes that: “(…) despite their inconsistency, classical least-squares estimators a minimum variance property” (p. 359). In a later section the author states that: “This analysis suggests that for small samples the second moments of the classical least-squares estimators (about the true parameter values) may be less than those of the 2SLS estimators—their variances may be sufficiently small to compensate for their bias” (p. 360). See also: Wiśniewski (2011). |

6 | This method has been described in the works: Wiśniewski (2016, pp. 39–45), Wiśniewski (2018, chp. 3) and Wiśniewski (2021). |

7 | The forecasted period (T = 1, 2, …, 12) is denoted by the symbol T. The forecast is denoted by the symbol p. As such, forecasts of PROD _{Tp}, SBRUT_{Tp}, LIQ_{Tp,} and EVIND_{Tp} are constructed. The markings PROD_{T}, SBRUT_{T}, LIQ_{T,} and EVIND_{T} (i.e., without the p-index) are reserved for the realization of these variables. Realizations, i.e., the future actual values of the variables forecasted, enable assessment of the forecasts constructed. |

8 |

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**Figure 1.**Convergent monthly forecasts of the company’s financial liquidity (LIQ

_{Tp}) for the year 2004. Source: own calculations using the GRETL package.

**Figure 2.**Convergent monthly forecast of the company’s debt recovery efficiency (EVIND

_{Tp}) for the year 2004. Source: own calculations using the GRETL package.

Variable | Coefficient | Std. Error | t-Statistic | Prob. p | |
---|---|---|---|---|---|

const | 35.7892 | 17.3869 | 2.0584 | 0.0433 | ** |

SBRUT_1 | 0.264044 | 0.0980122 | 2.6940 | 0.0088 | *** |

SBRUT_2 | 0.335448 | 0.0777751 | 4.3131 | <0.0001 | *** |

EVIND | 1.11045 | 0.138434 | 8.0216 | <0.0001 | *** |

EVIND_11 | −0.559677 | 0.16928 | −3.3062 | 0.0015 | *** |

dm1 | −121.502 | 12.7075 | −9.5614 | <0.0001 | *** |

dm9 | −71.2396 | 15.4927 | −4.5983 | <0.0001 | *** |

dm10 | −79.0165 | 17.8276 | −4.4323 | <0.0001 | *** |

LIQ_1 | 0.784919 | 0.0565033 | 13.8916 | <0.0001 | *** |

LIQ_4 | −0.178276 | 0.054603 | −3.2649 | 0.0017 | *** |

LIQ_8 | −0.142219 | 0.0560837 | −2.5358 | 0.0135 | ** |

LIQ_12 | −0.19808 | 0.0589236 | −3.3616 | 0.0013 | *** |

Mean dependent var. | 115.6671 | S.D. dependent var. | 61.88753 | ||

Sum squared resid. | 46,539.15 | S.E. of regression | 25.78459 | ||

R-squared | 0.849988 | Adjusted R-squared | 0.826414 | ||

F(11,70) | 36.05710 | Prob(F-statistic) | 1.71 × 10^{−24} | ||

Log likelihood | −376.3475 | Akaike info criterion | 776.6950 | ||

Schwarz criterion | 805.5756 | Hannan-Quinn criterion | 788.2901 | ||

Autocorrel. coeff. (rho1) | −0.093267 | Durbin h-statistic | −0.982984 |

Variable | Coefficient | Std. Error | t-Statistic | Prob. p | |
---|---|---|---|---|---|

Const | 1.4823 | 12.6127 | 0.1175 | 0.9068 | |

SBRUT | 0.104745 | 0.0498141 | 2.1027 | 0.0395 | ** |

SBRUT_1 | −0.165433 | 0.0577255 | −2.8659 | 0.0056 | *** |

SBRUT_2 | −0.343855 | 0.0654157 | −5.2565 | <0.0001 | *** |

SBRUT_9 | 0.144591 | 0.0503366 | 2.8725 | 0.0055 | *** |

LIQ | 0.231299 | 0.0342227 | 6.7586 | <0.0001 | *** |

LIQ_2 | −0.227186 | 0.0391187 | −5.8076 | <0.0001 | *** |

LIQ_4 | 0.0773338 | 0.0342058 | 2.2608 | 0.0272 | ** |

LIQ_8 | 0.0680115 | 0.0283642 | 2.3978 | 0.0195 | ** |

LIQ_12 | 0.0823227 | 0.0301587 | 2.7297 | 0.0082 | *** |

dm1 | 42.8764 | 8.72728 | 4.9129 | <0.0001 | *** |

dm2 | 28.7364 | 9.08009 | 3.1648 | 0.0024 | *** |

dm3 | 23.8075 | 7.36668 | 3.2318 | 0.0020 | *** |

dm6 | −27.0287 | 8.3487 | −3.2375 | 0.0019 | *** |

dm7 | −30.5163 | 7.37474 | −4.1379 | 0.0001 | *** |

dm8 | −18.4091 | 7.1938 | −2.5590 | 0.0129 | ** |

dm11 | −22.8948 | 7.9052 | −2.8962 | 0.0052 | *** |

EVIND_1 | −0.262529 | 0.0822912 | −3.1902 | 0.0022 | *** |

EVIND_11 | 0.24579 | 0.0916589 | 2.6816 | 0.0093 | *** |

Mean dependent var. | −4.453659 | S.D. dependent var. | 28.56555 | ||

Sum squared resid. | 10,462.94 | S.E. of regression | 12.88714 | ||

R-squared | 0.841699 | Adjusted R-squared | 0.796470 | ||

F(18,63) | 18.60978 | Prob(F-statistic) | 1.10 × 10^{−18} | ||

Log likelihood | −315.1569 | Akaike info criterion | 668.3137 | ||

Schwarz criterion | 714.0414 | Hannan–Quinn criterion | 686.6727 | ||

Autocorrel. coeff. (rho1) | 0.096296 | Durbin h-statistic | 1.307605 |

Variable | Coefficient | Std. Error | t-Statistic | Prob. p | |
---|---|---|---|---|---|

Const | 49.3006 | 17.0179 | 2.8970 | 0.0051 | *** |

PROD | 0.314418 | 0.0822437 | 3.8230 | 0.0003 | *** |

PROD_2 | 0.192879 | 0.0664748 | 2.9015 | 0.0050 | *** |

PROD_4 | 0.266735 | 0.0627707 | 4.2494 | <0.0001 | *** |

PROD_6 | 0.260442 | 0.082139 | 3.1707 | 0.0023 | *** |

PROD_10 | −0.208976 | 0.0646183 | −3.2340 | 0.0019 | *** |

PROD_12 | 0.386798 | 0.0777756 | 4.9733 | <0.0001 | *** |

dm2 | −56.4333 | 10.0217 | −5.6311 | <0.0001 | *** |

dm3 | −85.5183 | 13.8924 | −6.1558 | <0.0001 | *** |

dm4 | −119.789 | 13.0028 | −9.2126 | <0.0001 | *** |

dm5 | −85.7246 | 10.6554 | −8.0452 | <0.0001 | *** |

dm6 | −48.7422 | 9.82865 | −4.9592 | <0.0001 | *** |

dm8 | 73.7642 | 10.896 | 6.7699 | <0.0001 | *** |

dm9 | 52.2021 | 9.87142 | 5.2882 | <0.0001 | *** |

SBRUT_11 | −0.272401 | 0.0812169 | −3.3540 | 0.0013 | *** |

Mean dependent var. | 119.9622 | S.D. dependent var. | 57.15374 | ||

Sum squared resid. | 24,216.31 | S.E. of regression | 19.01151 | ||

R-squared | 0.908476 | Adjusted R-squared | 0.889352 | ||

F(14,67) | 47.50362 | Prob(F-statistic) | 3.30 × 10^{−29} | ||

Log likelihood | −349.5635 | Akaike info criterion | 729.1270 | ||

Schwarz criterion | 765.2278 | Hannan–Quinn criterion | 743.6210 | ||

Autocorrel. coeff. (rho1) | −0.003106 | Durbin–Watson stat. | 1.955127 |

Variable | Coefficient | Std. Error | t-Statistic | Prob. p | |
---|---|---|---|---|---|

Const | −3.90858 | 18.1577 | −0.2153 | 0.8302 | |

EMP_8 | 2.73307 | 0.920238 | 2.9700 | 0.0040 | *** |

dm6 | −25.1642 | 9.04259 | −2.7828 | 0.0068 | *** |

dm9 | 69.4314 | 9.99448 | 6.9470 | <0.0001 | *** |

dm10 | 61.4042 | 8.88592 | 6.9103 | <0.0001 | *** |

dm11 | 23.6176 | 8.78676 | 2.6879 | 0.0089 | *** |

PROD_3 | 0.206534 | 0.0649295 | 3.1809 | 0.0021 | *** |

PROD_11 | 0.235135 | 0.0640667 | 3.6702 | 0.0005 | *** |

Mean dependent var. | 104.7747 | S.D. dependent var. | 38.66778 | ||

Sum squared resid. | 35,906.28 | S.E. of regression | 21.88037 | ||

R-squared | 0.707141 | Adjusted R-squared | 0.679808 | ||

F(7,75) | 25.87089 | Prob(F-statistic) | 1.25 × 10^{−17} | ||

Log likelihood | −369.6697 | Akaike info criterion | 755.3394 | ||

Schwarz criterion | 774.6902 | Hannan–Quinn criterion | 763.1135 | ||

Autocorrel. coeff. (rho1) | 0.042344 | Durbin-Watson stat. | 1.910283 |

**Table 5.**Monthly forecasts of production (PROD

_{Tp}) gross sales revenues (SBRUT

_{Tp}) for the year 2004.

Forecasting Period (T) | PROD_{Tp} | Standard Error | SBRUT_{Tp} | Standard Error |
---|---|---|---|---|

2004:01 | 88.684 | 21.880 | 168.648 | 19.011 |

2004:02 | 85.928 | 21.880 | 95.923 | 19.011 |

2004:03 | 90.416 | 21.880 | 93.099 | 19.011 |

2004:04 | 77.993 | 22.342 | 47.225 | 19.011 |

2004:05 | 77.377 | 22.342 | 43.865 | 19.011 |

2004:06 | 55.791 | 22.342 | 54.994 | 19.011 |

2004:07 | 86.220 | 22.362 | 102.083 | 19.011 |

2004:08 | 92.371 | 22.362 | 164.770 | 19.011 |

2004:09 | 156.544 | 22.362 | 203.091 | 19.011 |

2004:10 | 149.041 | 22.363 | 146.991 | 19.011 |

2004:11 | 111.608 | 22.363 | 156.410 | 19.011 |

2004:12 | 95.7382 | 22.947 | 126.777 | 19.704 |

**Table 6.**Monthly forecasts of the company’s financial liquidity (LIQ

_{Tp}) for the year 2004 in subsequent iterations.

Forecasting Period (T) | It.1 | It.2 | It.3 | It.4 | It.5 | It.6 | It.7 | It.8 | It.9 |
---|---|---|---|---|---|---|---|---|---|

2004:01 | 89.921 | 94.333 | 95.464 | 95.746 | 95.823 | 95.849 | 95.849 | 95.849 | 95.849 |

2004:02 | 127.35 | 132.973 | 135.027 | 135.688 | 135.909 | 135.974 | 135.999 | 135.999 | 135.999 |

2004:03 | 137.833 | 162.894 | 169.537 | 171.359 | 171.902 | 172.045 | 172.084 | 172.109 | 172.109 |

2004:04 | 123.109 | 145.515 | 153.723 | 156.413 | 157.259 | 157.528 | 157.580 | 157.619 | 157.619 |

2004:05 | 121.819 | 146.299 | 152.093 | 153.742 | 154.240 | 154.415 | 154.477 | 154.503 | 154.503 |

2004:06 | 102.503 | 127.248 | 132.730 | 133.530 | 133.506 | 133.430 | 133.402 | 133.398 | 133.398 |

2004:07 | 67.6134 | 81.926 | 84.351 | 84.499 | 84.268 | 84.100 | 83.997 | 83.954 | 83.954 |

2004:08 | 76.199 | 86.111 | 84.967 | 83.679 | 83.073 | 82.839 | 82.732 | 82.685 | 82.685 |

2004:09 | 43.403 | 40.402 | 36.784 | 34.708 | 33.800 | 33.465 | 33.365 | 33.333 | 33.308 |

2004:10 | 57.774 | 13.359 | −1.699 | −6.352 | −7.761 | −8.183 | −8.286 | −8.314 | −8.328 |

2004:11 | 115.165 | 43.355 | 18.479 | 10.492 | 8.152 | 7.531 | 7.355 | 7.337 | 7.325 |

2004:12 | 150.758 | 98.993 | 81.718 | 76.176 | 74.487 | 74.008 | 73.871 | 73.859 | 73.849 |

**Table 7.**Monthly forecasts of the company’s debt recovery efficiency (EVIND

_{Tp}) for the year 2004, in nine subsequent iterations.

Forecasting Period (T) | It.1 | It.2 | It.3 | It.4 | It.5 | It.6 | It.7 | It.8 | It.9 |
---|---|---|---|---|---|---|---|---|---|

2004:01 | 3.973 | 4.991 | 5.245 | 5.3147 | 5.338 | 5.338 | 5.338 | 5.338 | 5.338 |

2004:02 | 1.949 | 3.000 | 3.396 | 3.5396 | 3.580 | 3.603 | 3.603 | 3.603 | 3.603 |

2004:03 | 18.591 | 23.121 | 24.294 | 24.627 | 24.710 | 24.727 | 24.750 | 24.750 | 24.750 |

2004:04 | 2.463 | 5.159 | 6.294 | 6.672 | 6.813 | 6.832 | 6.849 | 6.849 | 6.849 |

2004:05 | 6.916 | 6.513 | 6.142 | 6.005 | 5.977 | 5.996 | 5.992 | 5.992 | 5.992 |

2004:06 | 5.883 | 7.054 | 6.715 | 6.377 | 6.195 | 6.130 | 6.108 | 6.108 | 6.108 |

2004:07 | −0.578 | −1.203 | −1.343 | −1.447 | −1.521 | −1.588 | −1.620 | −1.620 | −1.620 |

2004:08 | 2.406 | 0.980 | 0.147 | −0.099 | −0.149 | −0.163 | −0.169 | −0.169 | −0.169 |

2004:09 | −5.213 | −6.588 | −7.246 | −7.546 | −7.650 | −7.655 | −7.646 | −7.669 | −7.669 |

2004:10 | −33.183 | −43.043 | −45.552 | −46.155 | −46.302 | −46.326 | −46.329 | −46.323 | −46.323 |

2004:11 | −27.765 | −38.289 | −41.930 | −43.008 | −43.278 | −43.375 | −43.375 | −43.377 | −43.377 |

2004:12 | 10.606 | 14.014 | 14.934 | 15.113 | 15.129 | 15.120 | 15.119 | 15.119 | 15.096 |

**Table 8.**Convergent monthly forecasts of the company’s financial liquidity (LIQ

_{Tp}) and debt recovery efficiency (EVIND

_{Tp}) for the year 2004.

Forecasting Period (T) | Forecasts LIQ _{Tp} | Forecasts EVIND _{Tp} |
---|---|---|

2004:01 | 95.849 | 5.338 |

2004:02 | 135.999 | 3.603 |

2004:03 | 172.109 | 24.750 |

2004:04 | 157.619 | 6.849 |

2004:05 | 154.503 | 5.992 |

2004:06 | 133.398 | 6.108 |

2004:07 | 83.954 | −1.620 |

2004:08 | 82.685 | −0.169 |

2004:09 | 33.308 | −7.669 |

2004:10 | −8.328 | −46.323 |

2004:11 | 7.325 | −43.377 |

2004:12 | 73.849 | 15.096 |

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**MDPI and ACS Style**

Wiśniewski, J.W.
Financial Liquidity and Debt Recovery Efficiency Forecasting in a Small Industrial Enterprise. *Risks* **2022**, *10*, 66.
https://doi.org/10.3390/risks10030066

**AMA Style**

Wiśniewski JW.
Financial Liquidity and Debt Recovery Efficiency Forecasting in a Small Industrial Enterprise. *Risks*. 2022; 10(3):66.
https://doi.org/10.3390/risks10030066

**Chicago/Turabian Style**

Wiśniewski, Jerzy Witold.
2022. "Financial Liquidity and Debt Recovery Efficiency Forecasting in a Small Industrial Enterprise" *Risks* 10, no. 3: 66.
https://doi.org/10.3390/risks10030066