Proceedings of World Business, Finance and Management Conference

Proceedings of World Business, Finance and Management Conference

14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand

ISBN: 978-1-922069-91-7

Effects of Cash Flow Management on SME Insolvency:

Methodological Approach

Justyna FrancDąbrowska*,

1

Małgorzata Porada-Rochoń** and Radosław

Suwała***

Purpose: The methodology for evaluating the financial management of companies against the threat of insolvency is still the topic of a wide field of scientific research.

The aim of this study was to develop models to identify factors affecting the insolvency of companies (small - and medium-sized enterprises), with a division into companies at risk of insolvency and of those not threatened with insolvency.

Design/Methodology/Approach: The study involved 273 small and 178 mediumsized enterprises from Poland in the period: 2007-2011 using financial data. It was considered reasonable to take the level of cash flow as the dependent variable and to perform panel analyses for separate groups of companies. This allowed the identification of determinants, which particularly affect the risk of insolvency in separate groups of companies.

Findings: It was found that you cannot use the same templates of evaluation for companies that exhibit symptoms of the threat of insolvency and for those which are in excellent financial condition. It should be noted, however, that some of the determinants affecting the cash flow in different groups of companies are similar

(or the same).

Originality/value : It involved the first ever preparation of dynamic panel for small and medium-sized private enterprises with a division into sub-groups – enterprises threatened with insolvency, middle and not threatened with insolvency as well as the inclusion of a dependent variable of the level of cash flow.

Keywords: cash flow, insolvency, small and medium-sized companies

This research is a part of Project financed by National Science Centre granted on the basis of decision DEC-2011/03/B/HS4/05503.

JEL Codes: G30, G32 and G33

1. Introduction

The issue of assessing the financial situation of companies against the threat of insolvency has been the same for decades. Companies are strongly influenced by shortening business cycles, resulting in more cases of crises in the economies of particular countries as well as on a global scale. The biggest challenge for entrepreneurs to remain on the market is their ability to settle current liabilities - and to maintain their capacity to cover liabilities owned by their company in the long term. Both of these phenomena - liquidity and solvency - are closely interrelated and constitute a fundamental problem in the financial management of any private company. Despite a number of studies conducted in many countries, as well as a number of methods that enable the identification of problems in the area of liquidity and solvency, there are still no conclusive answers concerning a

1

*Dr. hab. Justyna FrancDąbrowska, prof. WULS, Department of Economics and Organization of

Enterprises, Division of Finances of Enterprises and Accountancy, Warsaw University of Life Sciences –

SGGW, Poland. Email: justyna_franc_dabrowska@sggw.pl

**Dr. hab. Małgorzata Porada-Rochoń, Faculty of Management and Economics of Services, University of

Szczecin, Poland, Email: malgorzata.rochon@wzieu.pl

***mgr Radosław Arkadiusz Suwała, independent researcher, Poland. email: rs40277@gmail.com



ISBN: 978-1-922069-91-7 comprehensive assessment of the methodological approach to the problem of business insolvency.

2. Literature Review

The multitude of indicators used to predict bankruptcy in academic literature is indisputable. However, it is necessary to discuss their usefulness and their possibility of improvement. This should be done to enable the development of a methodology giving greater opportunities to identify factors pointing to the risk of business insolvency.

According to Gentry, Newbold, and Whitford (1985), a fuller picture of the financial situation of a company and the possibility of a clearer grasp of financial problems is possible if cash flow is included into the analysis. They point primarily to elements of cash flow such as dividends, investments or receivables (Gentry, Newbold, and Whitford 1985).

Similarly, in the search for the causes of the financial problems of enterprises - including bankruptcy - cash flow was specifically analyzed by Gilbert, Krishnagopal and Schwartz

(1990).

As is clear from discussions between Darby, Kaufman, Levinson, and Schaffer, there is no way out of financial problems if it is not possible to settle payments. In this case, cash flow seems to be key (Darby, Kaufman, Levinson, and Schaffer 2013). With the model solutions of Giordani, Jacobson, von Schedwin and Villani (2014) it can be observed that one of the important factors signaling the company's financial problems is excessive volatility of cash flow, which in turn carries with it an increased risk of bankruptcy. The authors also point to the increased risk of financial difficulties in situations of low cash flow

(Giordani, Jacobson, von Schedwin and Villani 2014).

In research conducted by Kane, Richardson, and Velury (2003) it was reported that companies in the growth phase, with a limited ability of using external sources of financing, particularly for obtaining favorable terms of trade credit, are reliant on internal financing. A particularly important issue is therefore access to "internal" financial resources whose level can affect the probability of financial survival. The researchers emphasize the fundamental problem of access to sufficient levels of cash flow in order to avoid the risk of financial difficulties and the risk of insolvency (Kane, Richardson, and Velury 2003).

Studying working capital is very important since it can affect profitability, risks and liquidity of companies. Liquidity and profitability are primary goals of the company following value maximization because they play an essential role in ensuring financial sustainability of an enterprise especially in times of financial distress and economic downturn. Furthermore both goals have to be reached simultaneously (Dong and Su 2010).

Financial Forecasting

Cash flow forecasting is becoming more common and from different perspectives: internal, external as well as the relationship between stakeholders and their information needs.

Moreover, Abed at al. (2014) has expressed that having taken into consideration recent research of literature, the disclosure of cash flow information should be wider than the economic perspective and should involve social, political institutional context (Abed at al.

2014).

The usefulness of cash flow forecasts is crucial for the decision making process and depends on information from the environment. Research conducted by De Fond and Hung

(2003) implicates that analysts have incentives to make cash flow forecasts in addition to



ISBN: 978-1-922069-91-7 earnings forecasts when market participants have a relatively greater demand for cash flow information in valuing securities. They discovered that the following determine cash flow forecasts: 1) large accruals 2) more heterogeneous accounting choices relative to their industry peers 3) high earnings volatility 4) high capital intensity 5) poor financial health (DeFond and Hung 2003). According to Jung‟s (2015) findings, a cash flow forecast combined with an earnings forecasts provide market participants with high quality information and in consequence the cost of equity capital is reduced (Jung 2015).

Methods Used to Investigate Relationship between Cash Flow and Other Variable

At least three different groups of methods used to investigate cash flow management strategies can be found in the existing body of cash flow management literature: 1) correlation and regression models 2) case studies 3) hypothetical optimization model

(Kroes, Manikas 2014).

According to the first group, Deloof (2004) used a sample of 1009 large Belgian nonfinancial firms during the period between 1992 and 1996 and worked on the relationship between working capital management and corporate profitability using Pearson correlation and multiple regression. He found that the Cash Conversion Cycle (CCC) is not significantly associated with Gross Operating Income but shorter Days of Inventory

Outstanding DIO, Days of Sales Outstanding DSO and Days of Payables Outstanding

DPO are each associated with it (Deloof 2004). Similar conclusions were made by

Karaduman et al. (2004).

The analysis performed by Ebben and Johanson (2011) consists of financial data for 833 small U.S. retail manufacturing companies using the regression model. The authors‟ findings show that there is positive relationship between longer CCC and invested capital and negative relationship with Asset Turnover, ROI and Net Balance Position (Ebben and

Johanson 2011). A much larger sample of 5884 companies during the period between

1986-2001 was used by Farris and Hutchinson (2003). Their primary finding was that median CCC duration diminished from 1986-2001 (Farris and Hutchinson 2003).

Garcia-Teruel and Marino-Solano (2007) take into account 8872 small and medium companies from 1996-2002. The methodology used in this study to investigate the relationship between CCC and profitability is the Pearson correlation and the multiple regression analysis. They present similar results as Deloof (2004), shorter CCC, DIO,

DPO, DSO are associated with better ROA. DPO loses significance when lagged values are included (Garcia-Teruel and Marino-Solano 2007).

A strong negative relationship between profitability and the cash conversion cycle, the average collection period and payable deferral period were observed in the research performed by Lazaridis and Tryfonidis (2006). The authors used a sample of 131 listed companies in the Athens stock exchange (ASE) for the period 2001- 2004 (Lazaridis and

Tryfonidis 2006). Aguenaou et al. (2015), used a sample of 43 non-financial firms listed at the Casablanca Stock Exchange from 2006 to 2012. The research findings show that there is a statistically significant positive relationship between ROA and the average collection period. Moreover, they found a statistically negative relationship between the gross operating income and both the average collection period and the payables deferral period.

They state that the management of these working capital components can have a positive impact on the profitability of Moroccan firms (Aguenaou et al. 2015).



ISBN: 978-1-922069-91-7

In contrast to most of the presented studies, the results of research conducted by Arbidane and Ignatjeva (2013) failed to confirm a negative correlation between PDP, ICP, RCP,

CCC and corporate profitability. A significant negative correlation was observed in tested companies only between ROA and RCP and less significant between GOP and RCP.

Moreover, the CCC and ROA also had a negative but insignificant coefficient. The authors used data of annual reports of 182 companies of the Latvian manufacturing sector for the years 2004-2010 (Arbidane and Ignatjeva 2013).

Exactly the same findings according to the relationship between CCC and profitability measure were observed in research performed by Enqvist, Graham and Nikkine (2011).

They used a panel of Finnish listed companies for the years between 1990 and 2008. The sample consisted of 1136 companies (Enqvist, Graham and Nikkine 2011).

According to the size of the company Moss and Stine (1993) used 1717 publicly traded

U.S. retail firms covering the period from 1971 to 1990. They found that CCC was shorter for larger companies (Moss and Stine 1993).

The second group of methods is observed in the works of Churchil and Mullins (2001).

They used case examples examining financial data for several firms. They examine how the CCC can be used as a metric to determine the growth potential of firms (Churchil and

Mullins 2001). Farris and Hutchinson (2002) used financial data for several firms demonstrating the importance of measuring CCC. The research done by Randall and

Farris (2009) provides examples of how a reduced CCC is associated with improved profitability (Randall and Farris 2009). Stewart (1995) confirms that CCC is fully used as a benchmarking metric for supply chain firms (Stewart 1995).

The third group of methods is represented by Hofmann and Kotzab (2010). They used linear optimization and got a result which showed that reducing CCC for a single firm in a supply chain did not implicate added value to all of the members of the supply chain

(Hofmann and Kotzab 2010).

According to Mauchi et al. (2011) there are three ways of improving cash management: 1) postponing capital expenditure 2) concentration banking 3) cash operating cycle.

3. The Methodology and Model

The aim of this study was to develop models to identify factors affecting the insolvency of companies (small and medium-sized enterprises). These were divided into companies at risk of insolvency and not threatened with insolvency. The following specific objectives were formulated:



developing models for small businesses, taking into account their division into three groups: o quartile 1, companies at risk of insolvency (Model 1b) o for middle values, business not at risk of insolvency in an average financial condition (Model 1a), o quartile 3 - business not at risk of insolvency in the best financial condition

(Model 1c).



developing models for medium businesses, taking into account their division into three groups:



ISBN: 978-1-922069-91-7 o quartile 1, businesses at risk of insolvency (Model 2b) o for middle values, businesses not at risk of insolvency in an average financial condition (Model 2a), o 3 quartile

– businesses not at risk of insolvency – in best financial condition

(Model 2c).

Due to the key significance of liquidity problems in determining insolvency, the level of cash flow was used as the dependent variable in all models (the basis for such a variable selection was the results of previous studies, which show a strong influence of cash flow on the risk of insolvency)3. In order to select a sample of companies in a better and a worse financial situation, a division into quartiles was conducted for both the group of small enterprises and medium-sized ones4. It was thus possible to obtain models in which groups of businesses with low, medium and high cash flow, were recorded.

The following research hypothesis was formulated in the course of the research:

H1 - There are significantly different determinants influencing the insolvency of small and medium enterprises, and therefore different variables determine the insolvency of small enterprises, and others determine the insolvency of medium ones.

Moreover, two auxiliary hypotheses were formulated within the scope of the selected groups of businesses:

H2 - There are significant differences in the list of factors affecting the insolvency of small businesses, depending on their financial condition.

H3 - There are significant differences in the list of factors affecting the insolvency of medium-sized enterprises, depending on their financial condition.

Financial and economic micro-data can be found in the collected research materials.

These data are in the form of panel data, in particular a micro-panel of a balanced structure. The panel data is a collection of observations containing information on the phenomenon studied for a specific unit in subsequent periods. For each unit in the test data, its value can be assigned in specific periods, for example: months, years, etc. The size of the panel is determined by two dimensions: the number of units surveyed in the data set (N) and the number of periods in which each of these units has been studied (T).

Thus, the variables used to build the financial econometric models have a double notation, e.g. for the implementation of the dependent variable in the model it is:

( 𝑖 = 1,;= 1, 𝑇 ) (Gruszczyński 2012).

The database contains annual summary information from the years 2007 to 2011 for 273 small businesses and 178 medium-sized enterprises, amounting to a total of 2255 observations.

The enterprises are included into the group of small and medium-sized enterprises (SMEs) pursuant to Commission Regulation (EC) No 70/2001 (Acts. Office. EU I. 10 of

13.01.2001), as amended by Regulation 364/2004 (Acts .Office. EU I. 63 of 28.02.2004), which classifies the company as:



medium, when employing fewer than 250 employees and has an annual turnover not exceeding € 50 million or an annual balance sheet total not exceeding € 43 million;



ISBN: 978-1-922069-91-7



small, when employing fewer than 50 employees and has an annual turnover not exceeding € 10 million or annual balance sheet total does not exceed € 10 million.

The use of panel data enables the removal of the load estimator resulting from mistakenly omitted variables

– in other words the problem of missing an important factor in the model.

Considering the function of Cobb-Douglas production – in the form of: where: is the logarithm of production value,

is the vector of logarithmic expenditures on production factors,

is a random component,

is a model constant.

Mundlak (1961) expressed that businesses obtain various levels of profit despite using the same expenditures of factors of production. He regarded that this difference is the result of the influence of the management of the business and its resources – in other words the skills of the manager. In the case of cross-sectional data the identification of this effect is not possible because there are as many effects as there are units in the sample and it itself becomes part of the random component. The effect of the manager‟s skills is correlated with ,which is expenditures on production factors. The high skills of the manager usually lead to high profits, or , and these will lead to a greater investment in .

As a consequence the random component and expenditures are correlated and that in the case of cross-sectional data leads to an inconsistency of the estimator. This is known as the Mundlak effect. The use of panel data enables the acknowledgment of the influence of the skills of the manager. In such a case the model is expressed as:

Where: 𝑖 𝑇 , and have an influence on and is associated with . The introduction of the manager to the model eliminates the load of the estimator.

(Gruszczyński 2012).

Due to the nature of business processes, in particular the impact of the decisions of managers and internal development strategies and sales, dynamic models were used for data models. In the case of panel data the "dynamic model" (due to the nature of the problems associated with the estimation) is identified with the "auto-regressive model".

The simplest example of a one-way panel auto-regressive model - AR (1) - is expressed as:

(1)

Where: is the delayed dependent variable in the model which assumes the form of the dependent variable.

This means that in the classical method of estimation – the fixed and random estimators cannot be applied, because the variable is not exogenous. As a consequence - in the case of the random estimator – it leads to a load and an estimator inconsistency. In the case of the model with the fixed effect – there is a correlation of the random component and dependent variable , which appeared after a transformation inside the group. In



ISBN: 978-1-922069-91-7 order to solve the problem of estimator inconsistency resulting from the endogeneity of dependent variables, a basic estimator of the generalized method of moments was used – proposed by Arellando and Bond (1991). This approach constituted an alternative to the instrumental variables method, which was of low-effectiveness. Let us once again consider the simple model AR(1) with the formula (1) and its form after a differentiation:

(2)

The authors of this approach observed that if: is a suitable instrument, than all earlier values may be used as instruments such as that – , additionally .

Therefore the number of instruments available in the following periods increases. In the case of the first two periods, no instruments are available, therefore one can limit the periods to whilst noting the vector of the random component in equation (2). This vector is expressed as:

( )

The matrix of the instruments is expressed as:

(3)

[ ]

, (4) where: each – of the block column consists of the placed k in the – th line of the vector of instruments appropriate for period , and consists of zeroes in the remaining lines.

It is worth noting that every next delay of the variable will be a successively weaker instrument because the relationship between the next delays and the instrumentalized variable is successively weaker. Due to the autoregressive process, the correlation of the next with the instrumental variable will still exist. Using the denotation that is a vector of increases and is a vector of delayed initial increases starting from period t=3, which is the first for which instruments are available, we have

{ } (5)

The estimator of parameter is determined in such a way which minimizes the equation (5), in which the average from the test replaces the expected value. Due to the fact that the researcher may acknowledge that the applied instruments are not of equal significance, a weight matrix is used in the minimized function . To estimate the models a two-step procedure of estimating elements of the matrix was used. It is based on the estimation of the model using a random symmetrical weight matrix, and next on determining the rest of the model estimated on increases, treated as ̂ and determining the estimation of the variance – covariance matrix of moments expressed as:

̂{ } ∑ ̂ ̂ (6)



ISBN: 978-1-922069-91-7

The corrected weight matrix is determined as inversion (6) and finally placed to the estimator expressed as:

̂ ∑ ∑ [(∑ ) (∑ )] (7)

The models are not strictly auto-regressive expressed as AR(1), but have additional dependent variables. Such a model is expressed as:

After differentiation the model is expressed as:

(8)

(9)

The estimation of model (9) requires the broadening of the matrix of instruments expressed as (4) by instruments needed to identify vector . In the case of variables , which are strictly exogenous, only variables may be used as instruments. This involves adding to the matrix for every period . For variables determined in advance, in other words associared with earlier values of the random component, the appropriate instruments will be delayed by at least one horizontal period or increases of dependent variables (for ). However for endogenous variables , appropriate instruments for increases are delayed y by at least two horizontal periods or increases (for ). The next steps in the formulation of the estimator remain unchanged in comparison to the model describing the simple process of AR(1)

(Gruszczyński 2012).

A lack of autocorrelation and the actual exogeneity of individual instruments is a necessary condition for the fulfillment of (5), or maintaining estimator consistency. A Sargan test was used to investigate the exogeneity of the instruments. If the zero hypothesis H

0

proves correct, this test (stating that all instruments are exogenous) has a distribution of .

However, the autocorrelation test of the random component tests the autocorrelation of the random component in the AR(2) process. Assuming that the zero hypothesis is true H

0

– stating that there is no second order autocorrelation

– it is of normal distribution.

The R

2

coefficient of determination is a squared coefficient of the correlation between empirical and theoretical (calculated on the basis of the model) values of the dependent variable. The values of the determination coefficient (with the appropriate assumptions) range between 0 and 1 and indicate the quality of the KMNK estimation model. However, the application of the standard determination coefficient R

2

or its corrected version, based on models which do not take into account individual effects right through to those models which do, is incorrect (Verbeek 2004).

The general determination coefficient R

2

indicates the total quality of adjustment of the model to analyzed data and is a squared correlation coefficient between theoretical values of the dependent variable ( ̂

( ). It is expressed as:

) and empirical values of the dependent variable in the panel

{ ̂ } .

The Wald test of the significance of parameters is used to verify significance or a lack of it in all estimated parameters of the model. The zero hypothesis of this test H

0

states a simultaneous lack of significance of all estimated parameters alongside the dependent



ISBN: 978-1-922069-91-7 variables. An alternative hypothesis H

1

states that at least one parameter is statistically significant. If we assume hypothesis zero H

0 distribution with levels of freedom, where

as correct, the statistics of the test has a

is the number of tested parameters.

An analysis of the panel data was conducted with the methodological assumptions taken into account. A model solution was proposed for the investigated groups of small and medium sized enterprises. Table 1 presents the results of the model solutions for small enterprises (1a – a model for middle values, 1b – a model for 1Q and 1 c – a model for

3Q).

Table 1: Determinants Shaping Cash Flow in Small Enterprises

Model 1a.

Coefficients Estimate Std. Error t value Pr(>|t|) Significance

Balanced Panel: n=137, T=5, N=685

Number of Observations Used: 411

Residuals

Min. 1st Qu. Median Mean 3rd Qu. Max.

-124.5000 -6.0330 0.1368 -0.2829 6.6090 91.8500 lag(CashflowthEUR, 1) 0.8218328 0.1699520 4.8357 1.327e-06 lag(CurrentAssets to_TotalAssets, 1) lag(EBITDAMargin, 0)

0.1721331 0.0941655 1.8280 0.0675519

***

. lag(EBITDAMargin, 1) lag(PLaftertaxthEUR, 0) lag(PLaftertaxthEUR, 1)

1.1464127 0.4781517

-2.6366007 0.9173960

0.8906634 0.0700262

-0.8099081 0.1675674

2.3976 0.0165032

-2.8740 0.0040530

12.7190 < 2.2e-16

-4.8333 1.343e-06

*

**

***

***

**

***

* lag(ROS, 1) lag(FinancialPLthEUR, 1) lag(AsstesTurnoverRatio, 1)

2.9880706 0.9403073

-0.2821855 0.0801328

-0.0087451 0.0039040

Model parameters

3.1778 0.0014842

-3.5215 0.0004292

-2.2400 0.0250881

Sargan Test: chisq(5) = 7.305573 (p.value=0.19889)

Autocorrelation test (1): normal = -2.429445 (p.value=0.015122)


Wald test for coefficients: chisq(9) = 3282.663 (p.value=< 2.22e-16)

Wald test for time dummies: chisq(3) = 10.74066 (p.value=0.013214)

R

2

= 0.9155349

Signif. codes:

„***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

Model 1b.



ISBN: 978-1-922069-91-7

Coefficients Estimate Std. Error t value



Residuals


-69.0200 -3.3340 0.1071 -0.1230 3.7980 55.4400

0.0558028 0.0218687

0.0614103 0.0099981

2.5517

Pr(>|t|)

0.01072

6.1422 8.137e-10

Significance

*

*** lag(CashflowthEUR, 1) lag(CapitalthEUR, 0) lag(TangiblefixedassetsthEUR,

0) lag(CurrentAssets_to_TotalAss ets, 1) lag(EBITDAMargin, 1) lag(PLaftertaxthEUR, 0)

0.0135365 0.0065632

0.3506844 0.0765658

-0.3387146 0.1567701

0.9910260 0.0154865

Model parameters

2.0625

4.5802

-2.1606

63.9928

0.03916

4.646e-06

0.03073

< 2.2e-16

*

***

*

***






R

2

= 0.9726035

Signif. codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

Model 1c.

Coefficients Estimate Std. Error



Residuals


-319.800 -45.260 1.482 2.407 35.730 585.000 t value lag(CashflowthEUR, 1) lag(EBITDAMargin, 0) lag(ROS, 0) lag(CurrentliabilitiesthEUR, 1)

0.154507 0.073354

7.485007 1.868593

1.445226 0.645564

0.113140 0.041377

Pr(>|t|)

2.1063 0.035176

4.0057 6.184e-05

2.2387 0.025175

2.7344 0.006250

Significance

*

***

*

** lag(LialiabitiesTurnoverRatioDa ys, 0)

0.257572 0.090253 2.8539

Model parameters



Autocorrelation test (2): normal = 0.4348388 (p.value=0.66368)

Wald test for coefficients: chisq(5) = 37.78574 (p.value=4.1663e-07)


R

2

= 0.3413218

Signif. codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

0.004319 **

Source: own research.



ISBN: 978-1-922069-91-7

By taking into consideration the model solutions for small businesses and by treating business in Q1 as most at risk of insolvency, and Q3 as the least at risk, it was stated that in all the models an important determinant of the level of cash flows in the current period is the value of the cash flows from the previous period. This is a correct result which is confirmed in the observations and statistical analyses of financial reports from enterprises and is also confirmed in scientific literature. It is frequently the case that the situation of small enterprises in the next year is determined by the level of financial means which were saved in the previous period. Even a financial deterioration in the enterprise does not have to lead to insolvency – on condition that the enterprise disposes of financial means saved from previous years.

One should include the EBITDA-Margin (from the base year - without a delay) to the variables specific to the groups of companies not threatened with insolvency. In the group of small businesses that showed symptoms of the threat of insolvency (Model 1b), the determinant adversely affecting cash flow was only the EBITDA-Margin from previous years. It follows that the EBITDA-Margin, and therefore the effects worked out by entrepreneurs at the operating level in previous years, if insufficient, may consequently contribute to the deterioration of the financial situation of enterprises. Also this determinant is confirmed in practice, as well as in numerous scientific studies. It should be noted that in the group of companies threatened with insolvency, those determinants that contribute to improved cash flow were distinguished, and among them are profit after tax, the level of capital, the level of tangible fixed assets (all of these variables included are in current values - without delays). What is very important is that in particular, factors of the current period positively affect the level of cash flow (in the group of small business). Essentially it may be generalized that in a group of small enterprises threatened with insolvency a decisive meaning in the deterioration of their financial situation towards insolvency can be found in the EBITDA-Margin variable of the previous period. This means that in a business, if a low (or decreasing) level of the EBITDA-Margin is observed the financial situation in the next year will continue to deteriorate.

Observations concerning small businesses facing insolvency are indirectly confirmed by the results obtained for “middle” enterprises (Model 1a) and Q3 (Model 1c). In these models, one of the interesting variables that determine the cash flow is the Return on

Sales (ROS). The situation in the middle enterprises is different to those in the best financial situation. In “middle” enterprises the ROS from the previous period has a significant impact (one should remember that this is a group of companies not threatened with insolvency, but with lower cash flows than group Q3), and thus it can be concluded that in this case - as in enterprises threatened with insolvency - operating activities - closely linked to the sale - carry serious implications for the level of cash flow in the following year. The situation is somewhat different in the middle enterprises than those in the best financial condition. In “middle” enterprises ROS from the previous period is of great significance. It is worth noting that this is the group of enterprises not threatened with insolvency, but still with a lower cash flow than group (3Q). One can acknowledge that in this case

– as with enterprises not threatened with insolvency – the operating activity closely associated with sales – has major consequences for the level of cash flow in the next year. A deteriorating ROS may also lower cash flow in the group of companies in good financial condition. This – combined with other unfavourable factors - may be the beginning of problems with insolvency. The Return on Sales also has a statistically significant meaning for group 3Q in the current aspect. Therefore, it is not a factor which has a significant influence on insolvency. This is justified if we also take into account the question of separating incoming financial means from sales transactions in cashless circulation (with deferred payment deadlines).



ISBN: 978-1-922069-91-7

Among the variables that determine the cash flow of “middle” enterprises is the asset turnover ratio from the previous period. This is a variable that has a negative impact on the level of cash flow. Such a result should be regarded as justified as any expenses related to the investment process, which is reflected in the asset turnover ratio, doubtlessly affect the level of cash flow in the following year. It may also be assumed that amongst the "middle enterprises" the securing of funds for capital expenditures is insufficient, and certainly adversely affected by the investment process (of course, in the long term, the vast majority of entities should feel the positive effects of the investment - in terms of cash flow).

In the group of small companies with the highest level of cash flow an interesting variable affecting the cash flow is: current liabilities of the previous year and the time of settling payment obligations in the days of the current year. In this case, it can be concluded that companies at least threatened with insolvency, in a proper and safe way for their financial situation, took out trade credit and regulated current liabilities. One can even assume that the policy of managing working capital in these companies is correct, and its effects can be seen by a high level of cash flow that ensures the financial security of this group of companies.

On summarizing the analysis of the model solutions for small businesses, one should acknowledge that research hypothesis 2 was confirmed and a significant variable which differentiates the investigated group of enterprises is the EBITDA-Margin from the previous period. This variable appears in both businesses threatened with insolvency (Model 1b) and middle businesses (Model 1a), however in the second case it is balanced by the

EBITDA-Margin level of the current year. This relation is not present in the businesses in the weakest financial condition.

Table 2 presents the results of model solutions for medium-sized enterprises (2a

– model for middle values, 2b

– model for 1Q and 2c – model for 3Q)

Table 2. Determinants shaping cash flow in medium enterprises

Model 2a.

Coefficients Estimate Std. Error t value



Residuals


-477.400 -24.610 4.411 1.089 29.750 380.300

0.453114 0.139254 3.2539

Pr(>|t|)

0.001138

Significance

** lag(CashflowthEUR, 1) lag(OthercurrentassetsthEUR,

0) lag(CurrentAssets_to_TotalAss ets, 0) lag(EBITDAMargin, 0) lag(PLaftertaxthEUR, 0) lag(PLaftertaxthEUR, 1)

0.054634

1.575411

6.866727

0.022596

0.799006

2.146412

-0.403578 0.131928

2.4179

1.9717

3.1992

-3.0591

0.015612

0.048642

0.001378

0.891522 0.030505 29.2254 < 2.2e-16

0.002220

*

*

**

***

**

Model parameters


Autocorrelation test (1): normal = 0.1119513 (p.value=0.91086)





ISBN: 978-1-922069-91-7


R

2

= 0.9254626

Signif. codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

Model 2b.




Residuals


-1008.000 -81.940 23.780 1.118 96.640 754.900 lag(CashflowthEUR, 1) 0.743232 0.214328 t value Pr(>|t|) lag(EBITDAMargin, 0) lag(EBITDAMargin, 1)

28.970711 6.658083

-21.290785 6.425310

-0.149513 0.026213

3.4677 0.0005249

4.3512 1.354e-05

-3.3136 0.0009211 lag(MaterialcoststhEUR, 1) lag(ReceivableToAssets, 0) -3.375568 1.923728

Model parameters

-5.7038 1.172e-08

-1.7547 0.0793103






R

2

= 0.9155349

Signif. codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

Significance

***

***

***

***

.

Model 2c.




Residuals


-368.900 -51.370 1.186 -2.737 44.370 307.500 lag(CashflowthEUR, 1) 0.429273 0.121287 t value Pr(>|t|)

3.5393 0.0004012 lag(TangiblefixedassetsthEUR,

0) lag(EBITDAMargin, 0)

0.065301 0.020584 lag(EBITDAMargin, 1) lag(PLaftertaxthEUR, 0) lag(PLaftertaxthEUR, 1) lag(ROA, 1) lag(CurrentliabilitiesthEUR, 0) lag(FinancialPLthEUR, 0) lag(AssetTurnoverRatio, 0)

21.918462 1.547282

-8.324040 3.926292

0.689743 0.031194

-0.452646 0.102248

6.957988

0.026958

1.545814

0.012463

0.300837 0.039598

0.576831 0.268324

Model parameters




3.1725 0.0015114

14.1658 < 2.2e-16

-2.1201 0.0339996

22.1111 < 2.2e-16

-4.4269 9.558e-06

4.5012 6.758e-06

2.1631 0.0305321

7.5973 3.023e-14

2.1498 0.0315750

Significance

***

**

***

*

***

***

***

*

***

*



ISBN: 978-1-922069-91-7



R

2

=0.94802

Signif. codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1

Source: own research.

The situation is somewhat different in the group of medium enterprises – taking into consideration the division into lowest level of cash flow (most threatened with insolvency) and „middle” as well as those of the highest level of cash flow. In medium sized businesses – regardless of their financial condition – the cash flow variable of the previous period has an influence on the level of cash flow in the ongoing (current) period. This is a natural feature of every enterprise. However, other variables (at various levels of intensity) influence the level of cash flow.

In the group of enterprises at most risk of insolvency (Model 2b), the EBITDA-Margin of the current year has a positive influence, whereas it has a very negative influence in the previous year. It is thus worth emphasizing that entrepreneurs from this group of enterprises should pay special attention to the effectiveness of the conducted operating activity, because its level determines the threat of insolvency. Moreover, the material cost variable from the previous year has a negative influence on cash flow. Therefore, special attention should be paid to this area in order to prevent problems with solvency. Scientific literature in this area, as well as methods of business practice recommend the DuPont analysis to estimate the influence of changes in material costs on ROA or ROE.

Entrepreneurs running medium-sized business in a poor financial condition should pay special attention to this area.

An interesting phenomenon appeared in medium-sized enterprises – the so called

''middle'' ones (Model 2a). In this group it occurred that a variable which has a negative influence on cash flow is profit after tax from the previous year. This phenomenon may have two basic reasons. One of them may be numerous cases of losses in the investigated group of enterprises. The second reason may be payments from the profit in the shape of bonuses, rewards, royalties or dividends. Though the first aforementioned reason may not have a direct influence on the level of cash, if we take into consideration the way of formulating financial balance sheets – it has an indirect influence on cash flow.

In turn, payments of bonuses, rewards, royalties or dividends frequently occur in such enterprises. Thus, it can be inferred that the payments made from profits have a negative impact on the level of cash flow1.

It is worth emphasizing that mediumsized enterprises from the ''middle group” managed other current asset and current assets to total assets in a way that had a favourable influence on the level of cash flow. This is analogical to the group of small enterprises – belonging to the so called middle(Model 1a). This may indicate a certain similarity in groups of enterprises of various sizes which qualify to the same group, namely those of the criterion ''threatened with insolvency” (in other words certain features from Model 1a are similar to those in Model 2a).

An interesting result was obtained in Model 2c - worked out for medium-sized enterprises at least risk of insolvency. The factors which strongly determine the level of cash flow (in a negative way) are EBITDA-Margin from the previous year and profit after tax from the previous year. It is worth noting that in medium-sized enterprises of good financial health, the effectiveness of operating activities and decisions concerning the division of net



ISBN: 978-1-922069-91-7 financial results have a significant influence and may lead to the deterioration of the level of cash flow. In such cases, it can be unequivocally stated the reason for the negative influence of net profits after tax on cash flow in the current year is decisions concerning payments made from profit (bonuses, rewards, royalties and dividends), for the Return on

Assets from the previous year has a positive influence on cash flow. Therefore in this group of enterprises net losses generally were not recorded. The effects of payments made from net profit are usually felt in the next or following years.

After the analysis of panel models, hypothesis 3 was confirmed. There are significant differences in the list of factors determining the level of cash flow, depending on the financial condition of medium enterprises.

The conducted research also makes it possible to partly confirm the main research hypothesis. It was stated that the list of variable determining the level of cash flow in small and medium-sized businesses are differentiated but not completely different. Some of the variables are characteristic in all of the groups of enterprises regardless of their size. In particular this includes cash flow from the previous period, EBITDA-Margin (in some models the current value in others values from the previous year). There are however variables which are only characteristic for small enterprises – such as Return on Sales

(Model 1a and Model 1c, so groups of enterprises not threatened with insolvency). Other variables are particular only to medium enterprises and these include material costs

(Model 2b – enterprises threatened with insolvency) and return on assets (Model 2c).

4. Limitations

The conducted research concerned private small and medium sized businesses. The results obtained may not be uncritically transferred to large enterprises or entities out of the private sector. Small and medium-sized businesses undoubtedly constitute a characteristic group of entities of specific features (such as relatively low scale of activity, low number of employed and relative low relative value of assets and capital). Because there are numerous studies concerning the insolvency of large companies, especially those listed on the stock exchange, this research should be acknowledged as a valuable addition to previously observed dependencies described in scientific literature.

5. Findings and Conclusions

The research which was conducted is innovative. It involved the first ever preparation of dynamic panel for small and medium-sized private enterprises with a division into subgroups – enterprises threatened with insolvency (Model 1b and 2b), middle (Model 1a and

2a) and not threatened with insolvency (Model 1c and 2c) as well as the inclusion of a dependent variable of the level of cash flow. This division was classified into enterprises threatened with insolvency (Model 1b and 2b), enterprises in good financial condition

(Model 1a and 2a) and very good financial condition (Model 1c and 2c).

The inclusion of a dependent variable on the level of cash flow and the conducting of a panel analysis on the selected groups of private enterprises was assumed as justifiable. It enabled the identification of a determinant which particularly influences the threat of insolvency in the various groups of enterprises. It is important that the same templates of assessment are not used in enterprises which display symptoms of being at risk of insolvency and towards those enterprises which are in excellent financial condition. This



ISBN: 978-1-922069-91-7 does not mean that they should be submitted to assessment in other completely other areas of their activity.

In the assessment of the authors of this work, it is necessary to pay particular attention to those determinants of insolvency which are characteristic for a specific group of enterprises. A particular variable in the group of small enterprises at risk of insolvency is the EBITDA-Margin from the previous year (Model 1b). Material costs are a characteristic feature in the group of medium-sized enterprises at risk of insolvency (Model 2b).

The conducted research enabled the identification of a determinant which makes it possible to define the positive influence on the financial condition of enterprises, measured by the level of cash flow. ROS is such a variable for small enterprises and ROA is such a variable for medium-sized enterprises.

It was also ascertained that in all of the analyzed cases – the value of cash flow from the previous year has a positive influence (and may protect against insolvency). It should be stated that this is a most ''natural variable” which emerged after the application of the dynamic approach to the considered phenomenon, and that savings of financial means from previous periods will always constitute a buffer especially protecting small and medium-sized businesses against insolvency in the short-term.

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Proceedings of World Business, Finance and Management Conference

Effects of Cash Flow Management on SME Insolvency:

Methodological Approach

Justyna FrancDąbrowska*,

Małgorzata Porada-Rochoń** and Radosław

Suwała***

1. Introduction

2. Literature Review

3. The Methodology and Model

4. Limitations

5. Findings and Conclusions

References

Related documents

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Proceedings of World Business, Finance and Management Conference

Effects of Cash Flow Management on SME Insolvency:

Methodological Approach

Justyna FrancDąbrowska*,

Małgorzata Porada-Rochoń** and Radosław

Suwała***

1. Introduction

2. Literature Review

3. The Methodology and Model

4. Limitations

5. Findings and Conclusions

References

Related documents

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