DETERMIANTS OF THE FINANCIAL RESULTS IN TERMS OF THE
WORLD CRISIS IN SMALL AND MEDIUM -SIZED ENTERPRISES IN
POLAND – ANALYTICAL APPROACH, PART II
Dr hab. Justyna Franc-Dąbrowska, Warsaw University of Life Science
Dr Małgorzata Porada-Rochoń, University of Szczecin
ABSTRACT
This paper analyzes the determinants affecting level of financial results, for small and
medium-sized enterprises used in our sample over the years 2006-2009. Particularly
interesting seems to establish the factors that shape these financial results (profit or loss)
enterprises under the conditions of the recent financial crisis. The study will cover Poland,
chosen at random (the sample consists of 288 commercial companies). The collected data are
a balanced panel, so it is possible to eliminate random fluctuations.Results obtained will
attempt to verify hypothesis: in terms of financial crisis on the final level of financial results
of small and medium-sized enterprises are the variables different from the classic, familiar
with the science.
To test the hypothesis, research was be carried using out extensive literature and analysis will
be applied to a model: a classical method of the smallest squares and fixed effects model.
Qualities of application of research results – in a situation in which clearly would be able to
get the determinants influencing the financial results and economic value added of small and
medium-sized enterprises in Poland – during the recent financial crisis, it would give an
opportunity for entrepreneurs to pay special attention to these factors and to influence the
final shape built up the financial result. In the longer term, it is the financial result is the goal,
or provide opportunities to achieve their development goals.
Key words: financial results, small and medium-sized enterprises
INTRODUCTION
The traditional concept of the business, which is striving to establish
profit maximization is often criticized as unrealistic, and bypass the many important aspects
of economic activity of the company. This theory claims that the unrealistic assumptions not
only about the same for the business, but also about her relationship with the environment,
lack of regard for the interests of other stakeholders, motifs of human behavior in the
company making key decisions about the procedures and rules of making those decisions, etc.
In addition:
• profit may not be the sole and a key measure of assessing the condition of the
company,
• Profit is an internal goal,
1
• Gain a synthetic indicator does not show the impact of various factors on the level,
the impact of such negative phenomena as the loss of loyal customers, the best
suppliers, reducing the value of the brand, or reacts to them too late
• Profit is susceptible to manipulation accounting.
Although the traditional approach to profit maximization as the overriding purpose of
the company, which is based on the interests of owners and is consistent with the principles of
market economy, is facing criticism, but many places in literature (in terms of theoretical and
practical) is devoted to issues of managing earnings.
EARNINGS MANAGEMENT – LITERATURE REVIEW
Potential earnings management has become a concern throughout the world.
Most of the prior studies on earnings management have focused on why firms manage
earnings. Earnings management incentives originate from diverse reasons including:

income smoothing (Moses, 1987; Yoon and Miller, 2002b),
More specifically, earnings smoothing includes two metrics: the variability of the
change in net income, and the variability of change in net income over the variability
of change in cash flow from operations. A high variability is consistent with less
earnings smoothing (Lang et al. 2003, 2006; Leuz et al. 2003; Ball & Shivakumar
2005, 2006; Barth et al. 2008; Christensen et al. 2008)1,

management compensation (Healy, 1985; Gaver et al., 1995; Holthausen et al., 1995),

ownership control (DeAngelo,1986, 1988; Perry and Williams, 1994),
Prior research has found that managerial ownership is associated with lower levels of
earnings management (Dhaliwal et al., 1982),

equity offerings (Loughran and Ritter, 1995; Loughran and Ritter, 1997; Rangan,
1998; Teoh et al.,1998; Yoon and Miller, 2002a),

maximization of earnings-based compensation (e.g., Healy 1985),

avoidance of debt-covenants violation (e.g., DeFond and Jiambalvo 1994),

to manipulate earnings to avoid losses (Burgstahler and Dichev 1997)2
1
H. Chen, Q. Tang, Y. Jiang, Z. Ling,The Role of International Financial Reporting Standards in Accounting
Quality: Evidence from the EuropeanUnion. Journal of International Financial Management & Accounting,
Volume 21 Issue 3 (Autumn 2010), pp. 1-58.
2
Burgstahler and Dichev (1997) present two theories that can explain loss avoidance (and earnings decrease
avoidance). First, their transactions costs theory predicts that stakeholders, such as customers, suppliers, shortterm creditors, and employees, use heuristic cut-offs at zero earnings or zero earnings changes to value their
implicit claim on the firm and to determine the terms of transactions with the firm. As a consequence, the firm
manipulates reported earnings beyond such cut-offs to improve its terms of transactions (see also Bowen,
2
They find that firms appear to manipulate earnings to avoid losses and earnings
decreases as there is a higher than expected frequency of firms reporting slightly
positive earnings changes and a lower than expected frequency of firms reporting
small decreases in earnings.

political costs (Liberty and Zimmerman,1986; Jones, 1991; Cahan, 1992; Maydew,
1997; Han and Wang, 1998).
A number of academic studies have argued that earnings management may be beneficial
because it potentially enhances the information value of earnings. Managers may exercise
discretion over earnings to communicate private information to stockholders and the public
(Subramanyam, 1996; Watts and Zimmerman, 1986; Healy and Palepu, 1993; Guay, Kothari,
and Watts, 1996; Demski, 1998; Arya, Glover, and Sunder, 2003). If this is the case, then,
earnings management may not be harmful to the stockholders and the public. In fact, the
empirical evidence in Subramanyam (1996) supports the contention that managers exercise
their discretion to improve the ability of earnings to reflect fundamental value. Other studies,
nevertheless, argue in favor of the opportunistic use of earnings management (Healy and
Palepu, 1993, for instance). Misalignment of managers’ and shareholders’ incentives could
induce managers to use the flexibility provided by the Generally Accepted Accounting
Principals (GAAP) to manage income opportunistically, thereby creating distortions in the
reported earnings. Thus, earnings management can be viewed as either opportunistic or
beneficial3.
Some studies take a more general approach by investigating whether under all
circumstances managers have an incentive to exceed certain thresholds, such as prior year
earnings (Burgstahler and Dichev 1997; Degeorge, Patel, and Zeckhauser 1999). These
studies find that managers are inclined to manage earnings upward when this would lead to
earnings that are just above the thresholds, while previously being below these thresholds.
DuCharme, and Shores 1995). Second, prospect theory indicates that when individuals’ wealth is at risk, they
tend to evaluate potential wealth changes in reference to their current wealth, and attach a higher disutility to an
actual decrease in wealth than to a foregone increase in wealth (Kahneman and Tversky 1979). In the research
setting of Burgstahler and Dichev (1997), it is the value of shareholders’ and creditors’ explicit claim on the
outcomes of the firm that is at risk. Shareholders and creditors who use accounting earnings to estimate the
change in the value of their claim on the firm (economic performance), may experience higher disutility from an
accounting loss of one dollar than they experience utility from an accounting profit of the same amount. As a
consequence, shareholders and creditors may be averse to (small) accounting losses, if they perceive these losses
to be persistent.
L.Coppens E. Peek , An analysis of earnings management by European private firms Journal of International
Accounting, Auditing and Taxation Volume 14, Issue 1, 2005, pp. 1-17.
3
Jiraporn, Pornsit & Miller, Gary A. & Yoon, Soon Suk & Kim, Young S., 2008. Is earnings management
opportunistic or beneficial? An agency theory perspective, International Review of Financial Analysis, Elsevier,
vol. 17(3), pp. 622-634.
3
Burgstahler and Eames (1998) suggest that managers tend to report earnings that meet or
beat analyst estimates. It is found that there is a psychological distinction between positive
and negative numbers (or zero). Individuals usually form a negative perception when they see
negative reported earnings; hence it is important to report positive profits to avoid unfavorable
perceptions. A drop in reported earnings that are benchmarked against past year's
performance will give a negative signal to the investors, thus it is equally important for the
firm to sustain its recent performance. By meeting analysts’ consensus forecast, firms are
considered to be meeting the industry norm, as well as having comparable performance
against other firms in the same industry.4
Earnings management may take the form of either income-increasing
or income-
decreasing
accounting choices.
Under certain conditions, managers may prefer to engage in income decreasing earnings
management. Healy (1985), Gaver et al. (1995) and Holthausen et al. (1995) report evidence
of income-decreasing accounting choices when managers’ accounting-based bonuses are at
their maximum. Several explanations for these income-decreasing accounting choices have
been suggested. One possibility is that managers prefer to shift abnormal positive earnings
forward in order to make future thresholds easier to attain. Another possibility is that
managers are reluctant
to report large gains because of fears that their performance target will be ratcheted up in the
future. If downward manipulation imposes significant costs on external parties, boards should
be as concerned with income-decreasing manipulations as they are with income-increasing
earnings management5.
Without any doubt, it looks different approach to earnings management in healthy than
distressed companies. Due to the timeliness of the issue of global crisis further part of the
considerations will be concentrated among the issues the earnings management of distressed
companies.
Recent studies show, that generally during recession or crisis, companies generatelower
financial result compare to economic expansion. Earnings is sensitive to the business cycle.
In particular, the Earnings Response Coefficient (ERC) has been shown to be higher during
economic expansions than during contractions ( Johnson, 1999). This result can be largely
4
Ch. Charoenwong, J. Pornsit, Earnings Management to Exceed Thresholds: Evidence from Singapore and
Thailand, 2008 Electronic copy available at: http://ssrn.com/abstract=1104523.
5
K.V. Peasnell, P.F. Pope, S. Young. Board Monitoring and Earnings Management: Do Outside Directors
Influence Abnormal Accruals? Journal of Business Finance & Accounting.Vol 32/2005, pp. 1311-1346.
4
explained by the defining characteristic of macroeconomic expansion – broad-based
economic growth. Furthermore, expansions are typically longer and thus more persistent than
contractions. The higher growth and persistence associated with macroeconomic expansions
alter the contextual expectations related to reported corporate earnings, resulting in a higher
ERC6
There are a number of reasons to believe that conditional earnings conservatism is higher
during periods of economic recession:

securities litigation typically occurs following economic declines, when events such
as sharp declines in stock prices are more likely. To address the heightened litigation
risk during economic contractions, firms generally report more conservatively.

the threat of increased regulatory scrutiny, resultant from more public uncertainty
about the existence and impact of hidden bad news, arguably motivates the reporting
of conservative accounting numbers. During such periods of uncertainty and
heightened interest in information about bad news, regulators tend to respond with
increased scrutiny.

firms normally prefer internal sources of funding to external sources, and of the
external sources, debt over equity financing. This is the result of adverse selection and
increased information costs associated with obtaining external financing, especially as
it relates to equity financing
Prior studies suggest that the management of firms in distress may have various
incentives or feel pressure to manage financial information in different ways. The literature
provides mixed results with regard to the earnings behaviour of distressed firms. Some studies
find that the management of financially distressed firms adjusts earnings upwards (DeFond
and Jiambalvo, 1994; Rosner, 2003; Beneish et al. 2004) while other studies show that the
earnings of such firms are shifted downwards (DeAngelo et al., 1994)7.
Management of distressed firms has motivation (acting on behalf of shareholders or
institutional investors) to report higher earnings in order to avoid debt covenant violations and
probable bankruptcy. In such cases managers are primarily concerned with the short-term
survival of their firm (which does not necessarily imply self-interested manipulation). For
instance, DeFond and Jiambalvo (1994) show positive unexpected accruals in the year prior to
6
D. S. Jenkins, G. D. Kane and U. Velury.Earnings Conservatism and Value Relevance Across the Business
Cycle Journal of Business Finance & Accounting, 36(9) & (10), 1041–1058, November/December 2009, pp.
1041-1057.
7
A. Charitou, N. Lambertides and L.Trigeorgis Earnings Behaviour of Financially Distressed Firms: The Role of
Institutional Ownership ABACUS, Vol. 43, No. 3, 2007, pp. 271-296.
5
default, consistent with managers manipulating earnings to prevent default. Dichev and
Skinner (2002) also provide evidence that managers take actions to avoid debt covenant
violations. An alternative explanation is that management of financially distressed firms may
manipulate earnings upwards out of self-interest for various reasons, such as to avoid
management turnover during the distressed period, or temporarily inflate the market price to
increase their compensation or gain from cashing stock-based compensation holdings8.
Prior literature shows that earnings management of financially-distressed firms is mixed
(Bradbury, 2007)– distressed firms may engage in income-increasing accruals manipulation to
reduce the impact of negative signals from financial distress (Burgstahler & Dichev, 1997;
DeFond & Jiambalvo, 1994; Sweeney, 1994), or engage in incomedecreasing accruals
manipulation for renegotiating debt contracts with lenders (DeAngelo, DeAngelo, & Skinner,
1994; Saleh & Ahmed, 2005). Jaggi and Lee (2002) further argue that the choice of incomeincreasing (or -decreasing) is influenced by the severity of financial distress9.
Financial disterss is often contected with debt. It is interesting how debt influence on
earnings management. When debt is relatively high, managers have strong incentives to make
accounting
choices and reporting decisions that reduce the likelihood of possible debt
covenant violations (Watts and Zimmerman, 1986).1 Opportunistic managers are more likely
to use their financial reporting discretion because:
1. financial leverage frequently serves as a proxy for closeness to accounting-based
covenant violations (Billett et al., 2007; Dichev and Skinner, 2002; Press and Weintrop,
1990; and Smith, 1993),
2.
the cost of violating debt covenants is large (Beneish and Press, 1993).
Therefore, when debt is high, accounting numbers may not represent faithfully the underlying
future economic performance because of the aggressive use of accruals to manage earnings in
an effort to avoid covenant violations (Sweeney, 1994; and DeFond and Jiambalvo, 1994)10,
11
.
8
oA. Charitou, N. Lambertides and L.Trigeorgis Earnings Behaviour of Financially Distressed Firms: The Role
of Institutional Ownership ABACUS, Vol. 43, No. 3, 2007, pp. 271-296.
9
P. Cheng, W. Aerts, A. Jorissen, Earnings Management, Asset Restructuring, and the Threat of Exchange
Delisting in an Earnings-based Regulatory Regime Corporate Governance: An International Review, 2010,
18(5), pp. 438–456.
10
The two conflicting perspectives of debt suggest that the relationship between debt financing and earnings
quality is ultimately determined by the interactions of the positive and negative influence of debt.
11
Aloke (Al) Ghosh and Doocheol Moon Corporate Debt Financing and Earnings Quality Journal of Business
Finance & Accounting, 37(5) & (6), pp. 538–559, June/July 2010.
6
Prior accounting literature (e.g., Leuz, Nanda, &Wysocki, 2003) shows that earnings
management is a pervasive corporate phenomenon under particular market regulations. In
order to meet a pre-determined earnings target, earnings management is often the first choice:
1. a firm’s earnings manageability may be constrained on some occasions. For example,
severe loss firms may not be able to manage accruals (such as credit sales) at full
discretion if operations are partially discontinued or substantially reduced.
2. loss firms threatened by delisting risk are subject to stronger scrutiny from auditors
and stock exchanges. For example, financial statements in the loss reversal year
should receive an unqualified audit opinion.
3. Earnings management does not improve economic viability, and firms turning into
profits through earnings management could easily move back to losses again in the
near future. In this vein, distressed firms may opt to use alternatives to enhance
operating performance: such as employee layoffs and asset restructuring activities12,
Individual firms may have different incentives for earnings management. However, the
tools that firms use in managing earnings can be systematically associated with the
decomposition of total accruals.
PURPOSE AND METHODOLOGY OF RESEARCH
The article is a continuation of the analysis described in the study: Determinants of
financial result in terms of a global crisis in small and medium-sized enterprises in Poland an analytical approach, part I
This paper analyzes the determinants affecting level of financial results, for small and
medium-sized enterprises used in our sample over the years 2006 to 2009. Particularly
interesting seems to establish the factors that shape these financial results (profit or loss)
enterprises under the conditions of the recent financial crisis. The study will cover Poland,
chosen at random (the sample consists of 288 commercial companies). The collected data are
a balanced panel, so it is possible to eliminate random fluctuations. These analysis are
possible for a constant group of companies.Results obtained will attempt to verify hypothesis:
in terms of financial crisis on the final level of financial results of small and medium-sized
enterprises are the variables different from the classic, familiar with the science.
12
P. Cheng, W. Aerts, A. Jorissen, Earnings Management, Asset Restructuring, and the Threat of Exchange
Delisting in an Earnings-based Regulatory Regime Corporate Governance: An International Review, 2010,
18(5), pp. 438–456.
7
To test the hypothesis, research was be carried using out extensive literature and
analysis will be applied to a model - panel models: a classical method of the smallest squares
and fixed effects model.
The classic estimation by the smallest square method of the panel model is realized
with the use of the following formula:
y it  xit   vit
where: y it – endogenous variable,
x it - exogenous variable (generally a vector of exogenous variables),
 - N -dimensional vector of structural parameters of the model,
v it - total random error consisting of a totally random part  it and an individual effect
u i related to a specific “it” panel unit ( vit   it  u i ) [Kufel, 2007].
In the smallest square estimation it is assumed that the index i  1,..., N signifies
consecutive objects, whereas the index t  1,..., T stands for units of time. The estimation is
permissible when there is no individual effect and the panel is treated as the cross-sectional
data collection [Kufel, 2007].
Next the fixed-effects panel analysis in the following form was used:
yit  xit   ui   it
where u i - individual effect,
 it - pure random error.
In the fixed-effects panel model fixed individual effects are eliminated by averaging
the model against time ( t index).
Explanatory variable is the variable financial result (profit or loss) among the variables
under consideration will be 30 variables (metrics and financial ratios), which will undergo an
initial substantive and statistical verification
Qualities of application of research results - in a situation in which clearly would be
able to get the determinants influencing the financial results and economic value added of
small and medium-sized enterprises in Poland – during the recent financial crisis, it would
give an opportunity for entrepreneurs to pay special attention to these factors and to influence
8
the final shape built up the financial result. In the longer term, it is the financial result is the
goal, or provide opportunities to achieve their development goals.
DESCRIPTION OF THE EXAMINED COMMUNITY
The first stage of the project involved panel data analyses using the least squares estimation
method. Table 1 contains the results of the calculations, and graph 1 shows a normal Q-Q plot
for the model. It has been found that during the great financial crisis, the majority of the
determinants had a negative effect on profit at the small enterprises included in the research
project, reducing the amount of profit. The strongest variables reducing the level of profit
were as follows: (a) interest paid on loans and credit facilities and (b) loss on business
operations. The determinants that improved the level of net profit included operating profit
and financial revenue. The model as developed must be considered only as an initial model to
be used for further analyses, as it is not fully correctly conditioned. Although the basic
parameters of the model are satisfactory, the normal Q-Q plot distribution is not appropriate.
Although it is not a critical condition for the correctness of the model, model 1 cannot be
considered as a fully satisfactory model.
Table 1. Estimation results of the panel analysis – classical method of the smallest square
Variable
const
y70
y110
y113
y114
y119
y197
y200
y201
y202
y205
y210
y216
y6
y39
y78
Model 1: Panel estimation – method of the smallest square
Dependent variable: net profit
Resistant standard errors (robust HAC)
Standard
Coefficient
t-Student
p-value
error
-965,771
9825,51
-0,0983
0,92181
0,0507186
0,0151122
3,3561
0,00097
-0,0189225
0,00941649
-2,0095
0,04603
0,0635082
0,025819
2,4597
0,01488
-0,0516411
0,0230364
-2,2417
0,02624
0,414568
0,198462
2,0889
0,03817
-0,324515
0,171207
-1,8955
0,05969
0,317484
0,171132
1,8552
0,06526
1,89586
0,400526
4,7334
<0,00001
2,0275
0,377963
5,3643
<0,00001
-1,33171
0,415901
-3,2020
0,00162
-1,73515
0,302629
-5,7336
<0,00001
-1,05856
0,379643
-2,7883
0,00589
-0,591495
0,194723
-3,0376
0,00275
0,0234505
0,0106518
2,2015
0,02901
-0,0104427
0,0032354
-3,2276
0,00149
Significance*)
***
**
**
**
**
*
*
***
***
***
***
***
***
**
***
Total Sum of Squares = 1,7736e+012
9
R2 = 0,99971
Adjusted R2 = 0,99964
F (41, 174) = 14471,9 (p < 0,00001)
*)
* significant at the 10 percent level, ** significant at the 5 percent level, *** significant at the 1 percent level.
Source: own work.
Where:
y2
y6
y15
y38
y39
y70
y78
y84
y98
y104
y110
y113
y114
y118
y119
y190
y197
y200
y201
y202
y210
y211
y216
fixed assets,
other intangible assets,
fixed assets under construction,
current assets,
inventory,
cash and cash assets,
share capital,
previous years’ profit (loss),
long-term liabilities from other parties,
short-term liabilities from related parties,
loans and credit,
loans and credit from contractors,
loans and credit facilities maturing up to 12 months,
liabilities arising from taxes, customs, national insurance and other benefits,
payroll liabilities,
other costs by type,
the other operating costs,
other operating costs,
operating profit (loss),
financial revenue,
financial expenses,
interest paid,
profit (loss) on business operations.
10
Figure 1. Normal plot of residuals for the model 1
Source: own.
In order to achieve fully reliable results, panel data estimation was carried out, i.e. fixed
effects estimation, and the results are shown in Table 2. In addition, figure 2 shows a normal
Q-Q plot for model 2. The model covers a large number of the variables defined as net profit
determinants in model 1. Note should be taken, as is the case of model 1, of the following
variables: financial expenses, interest paid and loss on business operations. These variables
significantly reduced the level of net profit. Variables that improved net profit include, in
particular, the following: operating profit and financial revenue. The results achieved did not
allow for specifying “new” variables, but they did allow for confirming the common truths
that the source of value growth for shareholders should be operation revenue and that
additional benefits can be achieved by investing available cash in alternative portfolios (and
not only in core business operations and fixed assets). The parameters of the model should be
considered as satisfactory, and the normal Q-Q plot as close to normal, thus also acceptable.
In order to verify the observations and whether or not the fact that the variables relate to a
given period affects the final shape of the model, panel estimations (panel model with fixed
effects) were carried out, taking into account a time variable. The results achieved are shown
in Table 3 (the normal Q-Q plot is shown in figure 3).
11
Table 2. Estimation results of the panel analysis – panel model with fixed effects
const
y2
y15
y38
y70
y84
y98
y104
y110
y113
y114
y118
y119
y190
y197
y200
y201
y202
y205
y210
y211
Model 2: Panel estimation – method with fixed effects
Dependent variable: net profit
Resistant standard errors (robust HAC)
Standard
Coefficient
t-Student
p-value
error
-420944
129725
-3,2449
0,00158
0,00670375
0,00203489
3,2944
0,00135
0,0161985
0,00742004
2,1831
0,03130
0,0109055
0,0043135
2,5282
0,01298
0,0721854
0,0113101
6,3824
<0,00001
-0,0660716
0,0206269
-3,2032
0,00181
0,14662
0,0826953
1,7730
0,07918
-0,0229831
0,0111882
-2,0542
0,04249
-0,0598053
0,0105139
-5,6882
<0,00001
0,0945454
0,0304501
3,1049
0,00246
-0,11628
0,024809
-4,6870
<0,00001
-0,219808
0,0437321
-5,0262
<0,00001
-0,480582
0,135075
-3,5579
0,00057
-0,198211
0,0929296
-2,1329
0,03531
-0,290398
0,147678
-1,9664
0,05194
0,249767
0,150342
1,6613
0,09969
1,90984
0,31609
6,0421
<0,00001
2,11072
0,323798
6,5186
<0,00001
-1,42467
0,385928
-3,6916
0,00036
-1,80391
0,238699
-7,5572
<0,00001
-0,338364
0,185217
-1,8269
0,07062
***
***
**
**
***
***
*
**
***
***
***
***
***
**
*
*
***
***
***
***
*
y216
-1,04832
***
Variable
0,316198
-3,3154
0,00126
Significance*)
R2 = 0,99995
Adjusted R2 = 0,99960
F (112, 103) = 19901,4 (p < 0,00001)
*)
* significant at the 10 percent level, ** significant at the 5 percent level, *** significant at the 1 percent level.
Source: own work.
12
Figure 2. Normal plot of residuals for the model 2
Source: own.
The results achieved allowed for confirming the earlier observations, and the final modelling
effects were relatively slightly affected by the fact that the data related to the year 2008. It can
therefore be expected that the middle year of the great financial crisis (2007-2009) was
specific enough to affect the model, taking into account the financial results achieved by the
group of enterprises included in the project. The parameters of the model should be
considered as satisfactory, and the model as correctly conditioned, with a high degree of
explanation of the process studied as part of the project.
Table 3. Estimation results of the panel analysis – panel model with fixed effects taking into
account a time variable
Variable
const
y2
y15
y38
y70
y84
y98
Model 3: Panel estimation – method with fixed effects
Dependent variable: net profit
Resistant standard errors (robust HAC)
Standard
Coefficient
t-Student
p-value
error
-435321
126763
-3,4341
0,00086
0,00691576
0,00210773
3,2811
0,00142
0,0166976
0,00742624
2,2485
0,02672
0,0118643
0,00425756
2,7867
0,00636
0,0720946
0,0113883
6,3306
<0,00001
-0,0680601
0,0201926
-3,3706
0,00106
0,161408
0,0847652
1,9042
0,05973
Significance*)
***
***
**
***
***
***
*
13
y104
y110
y113
y114
y118
y119
y190
y197
y201
y202
y205
y210
y211
-0,0227171
-0,0588719
0,0945063
-0,118598
-0,222575
-0,481218
-0,182451
-0,290894
1,89563
2,09735
-1,40873
-1,79685
-0,315909
0,0100362
0,0101474
0,030746
0,0249968
0,044945
0,128263
0,0915559
0,152259
0,317326
0,325261
0,387423
0,246522
0,182196
-2,2635
-5,8017
3,0738
-4,7445
-4,9522
-3,7518
-1,9928
-1,9105
5,9738
6,4482
-3,6362
-7,2888
-1,7339
0,02574
<0,00001
0,00272
<0,00001
<0,00001
0,00029
0,04898
0,05890
<0,00001
<0,00001
0,00044
<0,00001
0,08599
**
***
***
***
***
***
**
*
***
***
***
***
*
y216
dt_2
dt_3
-1,0319
-16351,7
-9559,19
0,317237
7618,7
9109,37
-3,2528
-2,1463
-1,0494
0,00155
0,03425
0,29651
***
**
R2 = 0,99995
Adjusted R2 = 0,99990
F (114, 101) = 19654,8 (p < 0,00001)
*)
* significant at the 10 percent level, ** significant at the 5 percent level, *** significant at the 1 percent level.
Source: own work.
Figure 3. Normal plot of residuals for the model 3
Source: own.
14
Small enterprises are a specific group of business entities. Therefore, an attempt was made to
define the determinants affecting net profit using panel data estimation (random effects
estimation). The results achieved are shown in Table 4. The analyses carried out confirm the
significance of the determinants defined in models 2 and 3. However, model 4 is not correctly
conditioned. As it can be seen from graph 4, the normal Q-Q plot is significantly different
from the level that could be considered as close to normal. In order to verify whether or not
the inclusion of a time variable improves the parameters of the model, panel data estimations
(random effects) were carried out, taking into account a time variable. The results achieved
are shown in Table 5.
Table 4. Estimation results of the panel analysis – panel model with random effects
const
y2
y70
y110
y113
y114
y118
y190
y197
y200
y201
y202
y205
y210
Model 4: Panel estimation – method with random effects
Dependent variable: net profit
Resistant standard errors (robust HAC)
Standard
Coefficient
t-Student
p-value
error
-8755,99
12394,9
-0,7064
0,48087
0,00567957
0,00260709
2,1785
0,03071
0,0576738
0,00937502
6,1519
<0,00001
-0,0264198
0,00801269
-3,2972
0,00118
0,0651298
0,0174373
3,7351
0,00025
-0,0561579
0,0150685
-3,7268
0,00026
-0,0917751
0,0322998
-2,8414
0,00503
-0,0738379
0,0415275
-1,7780
0,07714
-0,346064
0,0972337
-3,5591
0,00048
0,336097
0,0975126
3,4467
0,00071
2,01648
0,220369
9,1505
<0,00001
2,17999
0,214126
10,1809
<0,00001
-1,5202
0,212228
-7,1630
<0,00001
-1,86736
0,209812
-8,9002
<0,00001
**
***
***
***
***
***
*
***
***
***
***
***
***
y216
y6
y39
y78
y215
-1,17519
-0,55727
0,0173628
-0,0099851
-0,249447
***
***
*
***
*
Variable
0,214781
0,122888
0,00910862
0,00330106
0,139886
-5,4716
-4,5348
1,9062
-3,0248
-1,7832
<0,00001
0,00001
0,05827
0,00286
0,07629
Significance*)
Akaike = 5640,72
Schwarz = 5782,48
Hannan-Quinn = 5697,99
*)
* significant at the 10 percent level, ** significant at the 5 percent level, *** significant at the 1 percent level.
Source: own work.
15
Figure 4. Normal plot of residuals for the model 4
Source: own.
It is evident that the inclusion of a time variable did not significantly affect the determinants
and their effect on the variable in question, i.e. net profit. Moreover, the d2_2 and dt_3 time
variables were not statistically significant in the model. Additionally, this did not improve the
parameters of model 5 (cf: the normal Q-Q plot – figure 5). It can therefore be stated that
models 2 and 3 (fixed effects and fixed effects with a time variable) best describe the level of
net profit achieved by small enterprises.
Table 5. Estimation results of the panel analysis – panel model with random effects taking into
account a time variable
Variable
const
y2
y70
y110
y113
y114
y118
Model 5: Panel estimation – method with random effects
Dependent variable: net profit
Resistant standard errors (robust HAC)
Standard
Coefficient
t-Student
p-value
error
-9495,79
14707,5
-0,6456
0,51937
0,00559175
0,00264015
2,1180
0,03561
0,057784
0,00944667
6,1169
<0,00001
-0,0266434
0,00811966
-3,2813
0,00125
0,0655139
0,0176457
3,7127
0,00028
-0,0568687
0,0153271
-3,7103
0,00028
-0,0934546
0,0327979
-2,8494
0,00492
Significance*)
**
***
***
***
***
***
16
y190
-0,0746482
0,0418408
-1,7841
0,07617
*
y197
y200
y201
y202
y205
y210
y216
y6
y39
y78
y215
dt_2
dt_3
-0,349864
0,339858
2,027
2,19051
-1,5278
-1,88554
-1,18591
-0,560497
0,0170801
-0,00983882
-0,245254
-871,616
4103,6
0,0984677
0,0987328
0,223609
0,21733
0,214937
0,217595
0,218158
0,124147
0,00920235
0,00334981
0,141307
15150,2
15987,5
-3,5531
3,4422
9,0649
10,0792
-7,1082
-8,6654
-5,4360
-4,5148
1,8561
-2,9371
-1,7356
-0,0575
0,2567
0,00049
0,00072
<0,00001
<0,00001
<0,00001
<0,00001
<0,00001
0,00001
0,06516
0,00377
0,08442
0,95419
0,79774
***
***
***
***
***
***
***
***
*
***
*
Akaike = 5644,98
Schwarza = 5793,5
Hannan-Quinn = 5704,98
*)
* significant at the 10 percent level, ** significant at the 5 percent level, *** significant at the 1 percent level.
Source: own work.
Figure 5. Normal plot of residuals for the model 5
Source: own.
FINDINGS OF RESEARCH
17
The results of the research allowed for defining net profit determinants at small enterprises
during the great financial crisis. Determination of the factors that affect the level of net profit
achieved by enterprises, particularly during a financial crisis, is of great practical significance.
Knowing such factors, managers can include, in their decision-making processes, those
variable which most significantly affect net profit levels achieved by enterprises.
SUMMARY AND CONCLUSIONS
1. It can be concluded based on the research that no such non-standard variables were
observed during the great financial crisis which might significantly affect net profit
levels achieved by small enterprises. Therefore, despite the difficult macroeconomic
circumstances, managers continued to be interested in those areas which require
special attention also under non-extreme conditions.
2. Those variables which had a particularly strong adverse effect on the level of net profit
included interest paid on loans and credit facilities. Therefore, it is not advisable for
small enterprises to take out debt during a financial crisis, as it may, in extreme cases,
lead to the enterprise losing its financial liquidity and going bankrupt. The negative
effect of incurring considerable financial expenses reflected in some of the enterprises
included in the project recording losses on business operations. Because, in the
opinion of the authors, profit/loss on business operations is that type of results which
should be subject to in-dept analysis (as the level of net profit/loss on business
operations depends, above all, on the managers/owners’ decisions), this type of results
should be given special attention during crisis years.
3. The determinants that positively affected the final results of the enterprises’ operations
were operating profit and financial revenue. This, therefore, confirms the common rule
accepted in economics that special attention should be given to core business
operations and that any other revenue should be considered as supplementary,
particularly during crisis years. The positive effect of financial revenue shows that the
managers were enterprising in their decisions and worked to generate additional
benefits from alternative sources, in particular from bank term deposits.
4. It can be concluded based on the research that in the case of the enterprises included in
the research project, the most appropriate method was panel data estimation (fixed
18
effects panel models) and that the models developed best describe the process in
question and are best conditioned.
5. Based on the model analyses conducted as part of the project, it can concluded that the
“outstanding” year during the great financial crisis was the year 2008, and the fact that
the variables related to the year 2008 was of significance in the final shape of the
model. Therefore, in the group of enterprises included in the research project, special
attention should be given to the variability of data between the years 2007-2008 and
2008-2009.
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