Predicting Failure of Swedish and Danish Business Reorganizations

Predicting Failure of Swedish and
Danish Business Reorganizations
Master´s Thesis
Master of Science in Economics and Business Administration
Accounting, Strategy and Control
Copenhagen Business School
2014
Submitted by
Johan Bruzelius
17 th July 2014
Supervised by
Nina Sormunen
Assistant Professor and Ph.D
Department of Accounting and Auditing
Copenhagen Business School
Number of pages: 62 (Characters: 126 024; 55 standard pages equiv.)
Abstract
This thesis objective is to find variables effective in predicting failure of businesses reorganized according to the Swedish and Danish Reorganization Acts. The reorganization acts
came into force in order to save viable but financially distressed firms from liquidation. The
sample consists of 333 Swedish and 152 Danish limited companies approved by courts to
commence reorganization in 2011 and 2012. The firms are in general small, 48% of the
Swedish firms and 64 % of the Danish firms are micro firms employing less than ten
employees. This study is to a large extent based on a study made by Laitinen (2013) on
Finnish reorganizations. The purpose is to evaluate the importance of pre-filing financial and
non-financial information in predicting the outcome (failure or non-failure) of Swedish- and
Danish reorganizations. The relationship between pre-filing information and the outcome is
assessed by a binary logistic regression. By May 2014, 63 % of the Swedish firms and 71 %
of the Danish firms had been liquidated. Models tested in this study are not efficient in
predicting the outcome in Swedish reorganizations. However, the financial- and combined
financial- and non-financial model is effective and able to explain a fraction of the outcome in
Danish reorganizations.
Table of Contents
1. Introduction ......................................................................................................................................... 1
1.1 1 Background ................................................................................................................................. 1
1.2 Problem Statement........................................................................................................................ 2
1.3 Purpose .......................................................................................................................................... 3
1.4 Limitations ..................................................................................................................................... 3
2. Theoretical Framework and Research Hypotheses .............................................................................. 4
2.1 The Swedish Corporate Reorganization Act................................................................................... 4
2.1.1 Introduction ............................................................................................................................ 4
2.1.2 Requirements ......................................................................................................................... 4
2.1.3 Application .............................................................................................................................. 5
2.1.4 Reconstructor ......................................................................................................................... 5
2.1.5 Debtor..................................................................................................................................... 6
2.1.6 Creditors ................................................................................................................................. 6
2.1.7 Legal consequences ................................................................................................................ 6
2.1.8 Termination of the process ..................................................................................................... 8
2.1.9 Arrangement with creditors ................................................................................................... 8
2.2 The Danish Corporate Reorganization Act ..................................................................................... 9
2.2.1 Introduction ............................................................................................................................ 9
2.2.2 Requirements ......................................................................................................................... 9
2.2.3 Application and time limits ................................................................................................... 10
2.2.4 Reconstructor ....................................................................................................................... 12
2.2.5 Debtor................................................................................................................................... 12
2.2.6 Creditors ............................................................................................................................... 13
2.3 Main Differences between the Acts............................................................................................. 13
2.4 Prior Studies................................................................................................................................. 13
2.4.1 Casey et al. (1986)....................................................................................................................... 13
2.4.2 Sundgren (1998).................................................................................................................... 14
2.4.3 Routledge & Gadenne (2000) ............................................................................................... 15
2.4.4 LoPucki & Doherty (2002) ..................................................................................................... 16
2.4.5 Barniv et al. (2002) ................................................................................................................ 17
2.4.6 Fisher & Martel (2004) .......................................................................................................... 17
2.4.7 Laakso (2007) ........................................................................................................................ 19
2.4.8 Laitinen (2009) ...................................................................................................................... 19
2.4.9 Laitinen (2011) ...................................................................................................................... 20
2.4.10 Laitinen (2013) .......................................................................................................................... 21
2.5 Hypotheses .................................................................................................................................. 22
2.5.1 Financial variables ................................................................................................................. 23
2.2.2 Non-financial variables ......................................................................................................... 25
2.2.3 Summary of hypotheses ....................................................................................................... 27
3. Methodology ..................................................................................................................................... 30
3.1 Sample ......................................................................................................................................... 30
3.2 Data Collection ............................................................................................................................ 31
3.2.1 Firms ..................................................................................................................................... 31
3.2.2 Independent variables .......................................................................................................... 31
3.3 Reliability and Validity ................................................................................................................. 33
3.4 Statistical Methods ...................................................................................................................... 34
4. Results ............................................................................................................................................... 36
4.1 Descriptive Statistics .......................................................................................................................... 36
4.1.1 Sweden ................................................................................................................................. 36
4.1.2 Denmark ............................................................................................................................... 37
4.1.3 Comparison between Sweden and Denmark ........................................................................ 38
4.1.4 Combined set of Swedish and Danish data ........................................................................... 38
4.2 Multivariate Models .................................................................................................................... 39
4.2.1 Sweden ................................................................................................................................. 39
4.2.2 Denmark ............................................................................................................................... 43
4.2.3 Combined set of Swedish and Danish data ........................................................................... 46
4.2.4 Summary of multivariate results........................................................................................... 50
5. Analysis .............................................................................................................................................. 52
5.1 Sweden ........................................................................................................................................ 52
5.2 Denmark ...................................................................................................................................... 54
5.3 Sweden & Denmark ..................................................................................................................... 56
6. Conclusion ......................................................................................................................................... 59
6.1 Further Possibilities ..................................................................................................................... 61
7. Bibliography ....................................................................................................................................... 63
8. Appendices ........................................................................................................................................ 67
1. Introduction
1.1 Background
Bankruptcy can cause huge losses for companies, employees and the society overall. A way
for distressed firms to avoid bankruptcy is by reorganization proceedings. The purpose with
reorganization proceedings is to lower the number of bankruptcies and to create conditions for
viable firms to stay in business. (Laitinen, 2010)
Prior studies have shown that reorganization legislation in several countries is not efficient.
Reorganization failure prediction models can be used by courts in their filtering processes and
by management, investors, lending specialists, and lawyers etc. to improve their decisionmaking. During reorganization, the debtor has legal protection for rehabilitation, which
challenges traditional failure prediction models. Research on reorganization successes and
failures are thus necessary to help bankruptcy courts in their filtering processes with the
purpose to increase efficiency of the system. (Laitinen, 2013)
Most of the reorganization acts adopted around the world are based on the US reorganization
system, which was implemented already in 1898. Debt restructuring and a reorganization plan
are most often fundamental parts in reorganization legislations. Proceedings are thus similar,
however, differences between EU-countries, the US and other countries do exist (Philippe &
Deloitte, 2002). Due to the almost non-existent research in this area outside the U.S. and since
it is not possible to assume that prediction models can be generalized between countries, it’s
of highest importance to research systems in more countries (Laakso, 2007).
The purpose of the present study is thus to evaluate the usefulness of financial and nonfinancial information in predicting reorganization outcome in Sweden and Denmark. This
study will thus contribute to prior research by focusing on the reorganization procedures in
Sweden and Denmark where similar studies rarely (or never) have been done before.
Furthermore, these two countries are interesting to compare since legislation are similar and
recently adopted in Denmark (The Swedish Company Reorganization Act, 1996; The Danish
Bankruptcy Law, 2010).
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1.2 Problem Statement
Conflicts of interest and asymmetric information between debt and equity holders can ruin the
efficiency of the reorganization process, for example by forcing a viable financially distressed
firm into bankruptcy or by letting a non-viable financially distressed firm continue in
reorganization (Laitinen, 2010). Reorganization is a much better outcome for managers and
equity holders than liquidation bankruptcy, leading to incentives for the firm to claim that
they are efficient and attempt to reorganize (Sundgren, 1998). These situations are not
favorable and can cause huge economic losses. A well-functioning system, where bankruptcy
courts are able to filter out viable- from non-viable firms, is therefore of high interest for
concerned stakeholders (Laitinen, 2010).
The reorganization failure rate in Finland is approximately 50% which is much higher than
the rate in Canada but similar to the rate in the US (Laitinen, 2013; Fisher & Martel, 1995;
Jensen-Conklin, 1992). As research shows, a 50% failure rate implies that the system is not
efficient (Laitinen, 2013). The failure rate, as later shown in this study, is 63% in Sweden and
71% in Denmark, implying that the proceedings are not efficient in these two countries. Due
to the huge economic losses associated with inefficient reorganization proceedings, it’s of
high interest to find factors that influence the outcome and thus can help increase the efficiency
of reorganizations.
A reorganization failure prediction model used in prior research will be tested on Swedish and
Danish reorganizing firms to evaluate if it is effective in predicting the outcome. Both financial
and non-financial information has proven to be effective in prior studies and will thus be
tested (Laitinen, 2013). Hopefully, findings in this study can help increase efficiency of the
bankruptcy systems in Sweden and Denmark. This study’s main issue is thus:

What factors help explain and predict reorganization outcomes (failure or non-failure)
in Swedish and Danish reorganizations?
During the project, following questions will also be discussed.

Are the courts in Sweden and Denmark effective in their filtering process?

Is the reorganization process in Denmark and Sweden a solution for distressed firms
or just a postponement of bankruptcy?

Are there any significant differences between the legislation in Denmark and Sweden?
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
Is the process more efficient in one country, and if so, what might explain the
differences?
1.3 Purpose
The purpose of the present study is to evaluate the usefulness of financial and non-financial
information in predicting reorganization outcome in Sweden and Denmark. A secondary
purpose is to evaluate whether the reorganization process is a solution for a distressed firm or
just a postponement of a bankruptcy.
1.4 Limitations
This study will focus on Swedish and Danish reorganizations since a similar study has not
been made before. Firms filing for reorganization in 2011 and 2012 will be examined. Since
the Danish act came into force 2011 data for other years are not obtainable. It’s therefore not
possible to evaluate potential differences during boom and recession.
The effect of different reorganization actions (management change, cost cutting etc.) is not
possible to evaluate as the requested data are not available. Prior studies have shown the
effect of different reorganization actions on the outcome (Laitinen, 2011, 2013). Furthermore,
findings in this study are based on statistical analysis and conclusions are thus limited in some
circumstances. Interviews with key stakeholders could have been a complementary element in
order to be able to draw further conclusions. The variable referring to industry
(manufacturing or service) is self-assessed and is thus subjective. Furthermore, no validation
data are available so all results are based on estimations. One should therefore be careful
when generalizing results outside the sample.
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2. Theoretical Framework
Research Hypotheses
and
In this chapter the reorganization acts in Sweden and Denmark will be presented and relevant
earlier studies will be briefly discussed. Finally, research hypotheses based on prior research
will be developed and presented.
2.1 The Swedish Corporate Reorganization Act
2.1.1 Introduction
The Company Reorganization Act was introduced in 1996 due to rapid increase in the number
of bankruptcies. The government’s idea was to enable reorganization in order to avoid loss in
asset values (Johansson, 2004).The purpose with the Swedish reorganization act is thus to
save viable firms from bankruptcy. Firms accessed as viable are firms with a functioning
business concept, an intention to survive and potential to find a solution to its payment
difficulties. Prior to the new act, it was more favorable for a distressed firm to liquidate
compared to finding an arrangement with creditors due to the State guaranteed salary during
bankruptcies. Obviously, this was costly and not advantageous for the government.
(Insolvensutredningen, 1992). The process aims to reorganize the debt as well as, if
necessary, the company’s business operations. (Mellqvist, 2011)
2.1.2 Requirements
A prerequisite to commence reorganization is that the filing firm has payment difficulties.
Payment difficulties exist when a firm fails to pay its overdue debt or when that inability is
expected to occur in the near future. (Swedish Company Reorganization Act, 1996) This
inability, illiquidity, is not the same as insolvency which is the terminology for a firm not able
to pay its overdue debt and when that inability is expected to be long-lasting. Illiquidity
occurs when a firm currently doesn’t have access to cash funds covering the short-term debt
but the condition is transitory. Another prerequisite is that the debtor is conducting businesses
domiciled in Sweden. Certain businesses in the financial and public sectors, e.g. banking
companies, insurance companies and debtors controlled by the State are not allowed to file for
reorganization. Furthermore, reorganization will only begin if the court finds it reasonable to
assume that the purpose with the reorganization is possible to achieve. The court has to
examine the firms future prospects and forecast if the reorganization is economical
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sustainable. This prerequisite was introduced to avoid misuse of the act, for example to
prevent companies filing only to reach a respite on its payments. (Persson & Karlsson-Tuula,
2012)
2.1.3 Application
It’s possible for both the debtor and creditors to apply for reorganization. When the debtor
applies it has to hand in information about its economic condition, preferably the last annual
report and a balance sheet made for liquidation purposes. Furthermore, the firm has to inform
why the payment difficulties occurred as well as how the firm intends to operate profitable in
the future. The debtor also has to supply a list with all creditors and a draft showing how an
agreement with them can be reached. Lastly, a suitable reconstructor has to be suggested.
(The Swedish Government, 1995)
The debtor has to admit to the application when a creditor files for reorganization (Swedish
Company Reorganization Act, 1996). In these cases the creditor also has to hand in
information about its claim as well as all its knowledge about the debtor’s payment
difficulties. Furthermore, a suitable reconstructor has to be suggested (Mellqvist, 2011)
(Persson & Karlsson-Tuula, 2012).
Application is to be handed in to the authorized court. If the court approves the application, it
has to nominate a reconstructor as well as decide when a meeting with the creditors is to be
held (Mellqvist, 2011). However, the court has to examine the debtors’ payment difficulties,
the firm’s future prospects and if it’s reasonably that the firm will achieve the purpose with
the reorganization (Persson & Karlsson-Tuula, 2012). The court starts and ends the procedure
and also has some influence during the process (Mellqvist, 2011).
2.1.4 Reconstructor
The court nominates one reconstructor or a couple of reconstructors when debtor´s operations
are complex. The reconstructor shall, in collaboration with the debtor, examine the debtor’s
economic condition and form a reorganization plan consisting of a solution to the payment
difficulties and a suggestion how the firm’s profitability can be improved. The reconstructor
has to inform all known creditors within a week after the decision to commence
reorganization has been made. (Mellqvist, 2011)
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The reconstructor’s legal responsibility is not dealt with in the act. However, according to the
doctrine, the reconstructor is accountable if he or she knew or should have been aware about
economic harmful consequences caused by him, or if he or she breached a provision in favor
of a third party. (Mellqvist, 2011)
2.1.5 Debtor
The reorganization is dependent upon the debtors own will and the firm can basically terminate
the process at any time. The debtor keeps resourcefulness over the firm during the process.
However, the debtor is forced to follow the reconstructors’ advice and guidance and to provide
him/her with necessary documents for the process. Furthermore, the debtor needs permission
before he/she carries out certain tasks such as payment of liabilities, entering into new
agreements and transfer and pledges of property. It’s possible for the court to limit the
debtor’s resourcefulness if it is reasonably to believe that creditor rights are threatened by the
debtor. (The Swedish Government, 1995)
2.1.6 Creditors
Creditors have some power during the process to protect their rights and receive payments for
their claims. Furthermore, the debtor has to provide creditors with certain information about
the firm and its future prospects. A creditor can also report irregularities in the debtor’s
application and hand in information to the court that puts the purpose of the reorganization
into question. Creditors can in addition demand the court to nominate a committee consisting
of three persons, most often one person representing the largest creditor, one representative
from the state and one person among the firm´s vendors. The purpose of the committee is to
act as a discussion partner to the reconstructor. Furthermore, creditors can file for
reorganization and can also influence who is selected as reconstructor. (The Swedish
Government, 1995; Mellqvist, 2011)
2.1.7 Legal consequences
The debtor is protected against proceedings from creditors during the reorganization process
in order to give the debtor time to find a solution to its economic situation. Furthermore, the
debtor is protected against enforcement procedures and the firm´s contracts are protected
against revocation. (Mellqvist, 2011)
The debtor is protected against distraint and other types of enforcements during the
reorganization process. However, enforcement procedures can be used if a creditor has a
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pledge or right of retention. One exception to the prohibition to use enforcement procedures is
mentioned in the act. According to the paragraph the court has the right to take appropriate
actions if the debtor takes actions or does not take actions which are threatening the creditor’s
rights. (Swedish Company Reorganization Act, 1996)
It is possible to initiate a court case against the debtor and an ongoing court case between the
debtor and creditors are allowed to proceed. However, the reorganization process is dependent
upon the debtor’s free will; a cease of the reorganization is therefore most likely when a
debtor is not collaborating. (Mellqvist, 2011)
Contracts necessary for the debtor’s future viability such as electricity and water supply are
protected against cancellation if the debtor is late with payments or agreed performances. The
paragraph protects agreements settled before the decision to commence reorganization was
made by the court. However, creditors have the right to cancel contracts if the debtor acts in
an incorrect way according to agreed performance. The debtor has to provide collateral in
those cases where the agreement continues even though creditors demand for cancellation.
(The Swedish Government, 1995)
These rules don’t apply to employment contracts. Employees are free to terminate their
contracts if they do not receive their salary. However, the state guarantees salary for firms
during reorganization proceedings. (Swedish Company Reorganization Act, 1996; Persson &
Karlsson-Tuula, 2012)
A seller has the right to take back goods if it is sold to a firm currently in a reorganization
process. However, the seller loses its right of repossession if the goods were forwarded before
the application for reorganization was sent to the court, or if the buyer immediately pays or
puts collateral for the claim. (The Swedish Sales of Goods Act, 1990)
The debtor’s possibilities to terminate contracts are not dealt with in the act. This is not
consistent with rules in many other countries were the debtor has the possibility to terminate
ongoing contracts in advance. However, there is consensus in the Swedish doctrine that the
debtor has some rights to cancellation. (Tuula, 2001)
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2.1.8 Termination of the process
The reorganization ends when the purpose with the reorganization is fulfilled (Tuula, 2001).
The process is also terminated if creditors or the reconstructor demands it and it is not likely
that the purpose with the process can be fulfilled. It’s possible for the debtor to terminate the
contract if no decision about composition proceedings with creditors has been made.
Furthermore, the process is not permitted to last more than three months. It’s possible to
extend this deadline with three months a time up to one year if there is a clear reason and the
debtor is in favor of it (Swedish Company Reorganization Act, 1996). It is only possible for a
process to last more than one year if negotiations concerning composition proceedings with
creditors are ongoing (Swedish Company Reorganization Act, 1996).
2.1.9 Arrangement with creditors
An arrangement with creditors to write down the debt is basically a requirement for a
reorganization to be successful and is always a part of the reorganization process. An
application has to be sent to the court with a proposal stating how much each creditor will
receive. The application has to be supplemented with a record of the debtor’s assets and
liabilities as well as a statement where the reconstructor explains the debtor’s financial
condition. Furthermore, the reconstructor has to show that at least 40 % of the creditors are in
favor of the arrangement. (The Swedish Government, 1995; Swedish Company
Reorganization Act, 1996)
The court has to gather all creditors, the debtor and the reconstructor for composition
proceedings as soon as they receive the application (The Swedish Government, 1995). During
the meeting a voting about the arrangement has to take place. The arrangement shall be
adopted with a certain majority depending on the composition dividend, which is the
percentage to which the claims are to be paid. If the composition dividend is more than 50 %,
60 % of the creditors and of the total claims must be in favor for the arrangement to be settled.
If the composition dividend is lower, 80 % has to give their approval. The composition
dividend is not allowed to be less than 25 % of the total claims without approval from the
creditors. An arrangement is only mandatory for unsecured creditors. (Tuula, 2001) The
arrangement can be forfeited if the debtor has disfavored or favored one or more of the
creditors or if the debtor has neglected its commitment according to the agreement (Mellqvist,
2011).
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2.2 The Danish Corporate Reorganization Act
2.2.1 Introduction
The number of bankruptcies escalated during the last financial crisis and reached its highest
level ever measured in Denmark 2010 (Danmarks Statistik). The prior act was criticized for
protecting non-viable firms instead of closing them down and as a consequence, the Danish
government decided to change the bankruptcy law with the intention to give insolvent firms
better possibilities to survive. The reorganization act came into force 1st of April 2011. The
purpose with the new legislation was to save insolvent but viable firms and to close down
non-viable firms. The debtor, creditors and the public would be in favor of the reform since
financial distressed but viable firms are given time to solve their financial problems which
might increase the overall investment desire in the society and also save employment.
(Kjaergaard & Dyhr, 2010)
2.2.2 Requirements
A firm has to be insolvent to obtain approval to commence reorganization. Insolvency is the
lack of liquidity to pay debt as they fall due. Hence, balance sheet insolvency which occurs
when a firm has negative net assets is not of interest. A high degree of likelihood of a
distressed firm’s solvency is required to protect the distressed firm from reorganization if the
application is made by a creditor. (Danish Bankruptcy Law, 2010)
The reorganization has to result in a composition arrangement with the creditors and/or a
transfer of business (Danish Bankruptcy Law, 2010). A composition can be a total or a partial
reduction of debt or a moratorium. There is no requirement in regards to minimum
composition dividend that has to be paid. However, if the debtors business is liquidated due to
the compulsory arrangement, a percentage for distribution to creditors is compulsory. This is
consistent with the bankruptcy rules to avoid debtors from gaining on liquidation during a
reorganization process compared to during bankruptcy.
Some claims can never be part of compulsory composition agreement. For example: claims
encountered after the court received the application, claims secured by a pledge and claims
with priority in case of bankruptcy. (Lindencrona et al., 2010)
A transfer of business according to the definition in (§ 2:10) should be the debtors ongoing
activities or part of it, which maintains its identity and forms an economic entity. This allows
9
the debtor to select and maintain the healthy part of its business. It is clear from the definition
that it is not possible to classify all sales of debtors’ assets as transfer of business. The transfer
of assets is not decided by the debtor or the reconstructor, instead it’s decided by the creditors
through the adaption of a reorganization proposal. (Danish Bankruptcy Law, 2010)
2.2.3 Application and time limits
The debtor, creditors and employees can apply for reorganization. Creditors need to have an
individual and essential interest in the reorganization and provide collateral up to 30 000
DKK for the costs associated with the reorganization if they take act. This rule was introduced
to avoid creditors from applying when it’s not necessary. Furthermore, the debtor needs to
give its permission immediately or after a meeting with the court, otherwise, bankruptcy
proceedings will commence. A debtor that is not personally liable, a limited company, can be
forced into reorganization. In such cases, the reconstuctor takes control over the company to
ensure an effective reorganization process. A successful reorganization is not achievable
without the debtors’ collaboration, which makes this requirement logical. An opened process
always ends with a reorganization of the debtor or bankruptcy. Consequently, there is no way
back once the process has begun and it is not possible to return an application. The only
possible way to end the process is by proving that the debtor is solvent. (Danish Bankruptcy
Law, 2010)
The appointed reconstructor has to inform all creditors and other stakeholders affected by the
reorganization within a week after the decision to commence reorganization was made. The
firms last annual report, the reasons behind the payment difficulties and proposed method to
reorganize the firm, i.e. transfer of business or composition with creditors have to be
communicated. (Danish Bankruptcy Law, 2010)
After four weeks a meeting with the creditors is held with the purpose to vote about the
reorganization plan which must contain information whether a composition or transfer of
business is chosen as a solution to the firm’s problems. Furthermore, the debtor or
reconstructors in cases where they have taken control over the business must approve the
plan. A majority of the creditors representing at least 25% of the claims must vote against the
plan, otherwise the plan is approved. Both the reconstructor and the person taking care of the
company’s financial records have to communicate whether they consider that the process can
end satisfactorily for the creditors. (Danish Bankruptcy Law, 2010)
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Within three months after the process begun the reconstructor has to inform creditors how the
process is advancing and when they can expect the reorganization proposal to be finished.
A meeting about the reorganization proposal, i.e. the final meeting about the closure of the
reorganization must be held within 6 months after the adoption of the plan. If the period of 6
months has expired, the process ends and the debtor is transferred into bankruptcy. The
deadline may be extended two times two months if it is likely that a reorganization proposal
will be produced. Maximum duration of the process is thus 12 months. The strict time limits
were introduced to reduce creditor losses and to secure the survival of viable firms. (Danish
Bankruptcy Law, 2010)
The reorganization proposal has to contain sufficient information for creditors to be able to
make a decision about the suggested solution. For example, if a transfer of business is chosen,
information about the price and the acquirer has to be supplied. Furthermore, an estimated
dividend if the reorganization ends in bankruptcy has to be provided. This information will
allow creditors to decide whether to vote for reorganization or bankruptcy. The debtor has to
accept the proposal; otherwise bankruptcy is the only option. As for the reorganization plan,
the proposal is adopted as long as a majority is not voting against it. If a composition is
chosen, a vote for claims covered by the composition has to be made. A proposal has to be
approved by the bankruptcy court before the proposal is final. (Danish Bankruptcy Law,
2010)
The process can end due to a number of reasons. For example, timeliness is an essential
requirement; the process ends if the reorganization plan, proposal and additional material are
not available on time. The process can also end if the debtor acts in a non-loyal manner.
Another reason for termination is if the debtor proves to be solvent. The debtor can also
request for termination, but since there is no way back, the only option left is bankruptcy. The
reconstructor can also end the process if it is not likely that the purpose with the
reorganization can be attained. (Danish Bankruptcy Law, 2010)
The main rule is that the process ends when the bankruptcy court has confirmed the proposal,
but it depends on whether it is a transfer of business or composition. When it is a transfer of
business the treatment can continue if there are negotiations about further transfer of
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businesses or compositions. If the proposal only contains composition, the process terminates
when the proposal is confirmed. (Danish Bankruptcy Law, 2010)
2.2.4 Reconstructor
The court nominates one or more reconstructors and a person with accounting knowledge to
take care of the debtors’ financial records. No formal requirements about necessary education
are mentioned in the act but it is clear that it is supposed to be a lawyer and a certified
accountant. The reconstructor has to be business oriented and capable enough to advise the
firm about their future possibilities to operate. The reconstructor takes control of the firm if it
is necessary and if the debtor gives its approval. The appointed reconstructor is legally
responsible for inappropriate behavior caused by him intentionally or negligently. It is a
requirement that the damage caused to the debtor or creditors was a foreseeable consequence
of the reconstructors’ actions. Furthermore, the reconstructor has the responsibility to ensure
that an aimless reorganization is closed down. (Danish Bankruptcy Law, 2010)
The reconstructors’ first task is to evaluate possibilities for the distressed firm to survive. The
risk of taking on new costs to increase the possibility for the distressed firm to survive has to
be balanced against the risk of causing more losses to creditors. The reconstructor is
responsible to ensure that the process is ended as quickly as possible by a successful transfer
of business, a compulsory composition with the creditors or by bankruptcy if continued
survival is unrealistic. (Danish Bankruptcy Law, 2010)
The person appointed to handle the debtors’ financial records has to be neutral and authorized
to organize compositions. His or her function is to ensure the financial records trustworthiness
in order to increase the confidence among creditors and to gain necessary support from them.
Furthermore, the person should evaluate whether it’s possible to fulfill the reorganization plan
and make a statement together with the reconstructor about the reorganizations process.
(Danish Bankruptcy Law, 2010)
2.2.5 Debtor
The debtor retains right of disposal of the firms’ assets but is not allowed to make any major
transactions without the reconstructors approval. If the debtor does not leave its approval to
commence reorganization and it is a limited company, control over the firm is taken over by
the reconstructor to ensure an effective process. This could also happen if the reconstructor or
any creditor suspects that the debtors’ management will not cooperate or will work against the
12
reorganization process. In such cases the only alternative for the distressed firm is bankruptcy
proceedings. (Danish Bankruptcy Law, 2010)
2.2.6 Creditors
As mentioned above, creditors have the power to apply for reorganization and suggest
reconstructors to the bankruptcy court. Creditors need to have an individual and essential
interest in the reorganization and provide collateral up to 30 000 DKK for the costs associated
with the reorganization. Creditor can also demand that management of the distressed firm is
taken over by a reconstructor in cases where they suspect that the current management acts in
disfavor of the process. (Danish Bankruptcy Law, 2010)
2.3 Main Differences between the Acts
The reorganization process has to end with a transfer of business or composition proceedings
according to the Danish reorganization act. A similar requirement is not stated in the Swedish
act. However, an arrangement with creditors is basically a requirement for a successful process
and is always a part of the process. In Denmark employees are able to apply for
reorganization which is not the case in Sweden, most likely due to the State guaranteed salary
in Sweden. In Denmark, it’s possible to force a limited company into reorganization; in such
cases the reconstructor takes control of the company. In Denmark a person with accounting
knowledge is appointed to take care of the debtors’ financial records. In the Swedish act, no
such person is mentioned except that the reconstructor himself is supposed to have accounting
knowledge. In Denmark, creditors that apply have to provide collateral up to 30 000 DKK and
in Sweden, it’s possible for creditors to demand the court to nominate a committee acting as a
discussion partner to the reconstructor.
2.4 Prior Studies
The following earlier studies are relevant to gain knowledge about findings made in other
countries and in developing the research hypotheses.
2.4.1 asey et al. (1986)
In this study the authors examined a predictive model of bankruptcy reorganizations
anticipated by White (1981, 1984). Variables suggested by White were tested to evaluate
whether they were successful in distinguishing between firms that reorganize or liquidate.
13
White demonstrates that firms that successfully reorganize have more free assets available as
collateral for new borrowing, are larger which increase borrowing capacity, have more
attractive earnings prospects and have stronger equity commitment by management.
The authors defined a “success” as a firm that survived at least three years after the
confirmation date. “Liquidation” was defined as a firm that either didn’t get their application
approved or voluntarily decided to liquidate within a pre-defined time limit. The final sample
consisted of 113 firms that went bankrupt 1970 to 1981 in the US. Compared to prior
bankruptcy studies, a sample of this size is large. The most important results from this study
were its support for two of the factors White suggested as relevant for predicting failure. The
free asset percentage was a significant discriminating variable as well as earnings prospects.
The two other factors, size and equity commitment by management, were not significant
according to the study. One reason for the insignificance of equity commitment by
management could be due to the measure used, stock option percentage, which might not
have been an appropriate variable according to the authors. (Casey et al., 1986)
2.4.2 Sundgren (1998)
Reorganization proceedings have been heavily criticized for saving firms that ought to be shut
down and that the process is long and costly. Some researchers argue that market based
insolvency is more efficient and that distressed firms should be auctioned in the open market
instead of protected in a reorganization. However, during financial distress it’s fair to believe
that assets can be sold below their fundamental values and that there as a consequence is a net
gain to creditors in reorganization.
A study based on Japanese firms found that creditors receive a better payoff in a
reorganization compared to immediate liquidation even though only
34 % of the
reorganizations were successful. Results from the US are similar, that creditors on average
receive a better payoff in reorganization compared to immediate liquidation. This study was
based on the administrators and the company’s estimates of what creditors would receive in
liquidation. Especially the firm could have incentives to produce biased estimates of what
creditors would receive in liquidation. Thorburn (1997) compared payments to creditors in
Swedish liquidation bankruptcies with payments to creditors in US reorganizations and found
no difference in payments within these two different proceedings.
14
The study made by Sundgren is based on a sample of Finnish small and mid-sized firms filing
for reorganization 1998-1994 and on a sample of firms that filed for liquidation bankruptcy.
The paper has two main contributions: whether creditors yield a net gain in reorganizations
and whether there is a difference in costs related to reorganizations and liquidation
bankruptcies. The evidence shows that the average payoff in reorganization is significantly
higher than liquidation bankruptcy. The net gain is 10,7% when payments in reorganization
are compared with what creditors received in liquidation bankruptcy, in cases when the firm
was sold as a going concern. One possible reason why creditors receive better payoff in
reorganizations could be the difference in administrative costs. The result shows that
reorganizations are less costly, however, this difference is much smaller than the total gain.
Other factors, such as the underpricing of assets in liquidation bankruptcy also explain the
difference. Similar results have been found in other countries. However, Thorburn (1997)
found no such difference in administrative costs when comparing Swedish bankruptcies with
U.S. reorganizations.
In general the evidence suggests that the reorganization law improves the efficiency of the
insolvency law. Even though a significant amount of firms fail during the reorganization
process, the average payoff for creditors is higher for firms being reorganized. (Sundgren,
1998)
2.4.3 ge & Gadenne (2000)
Routledge and Gadenne investigated whether reorganized firms can be distinguished from
liquidated firms under voluntary administration (VA) proceedings in Australia. The VA came
into force in June 1993 with the purpose to provide a flexible and inexpensive procedure in
which a firm can find an arrangement with its creditors. The objective is to save a firm from
liquidation or if that is not possible, to improve the return to creditors.
Variables and hypotheses examined by the authors are based on the coalition theory as well as
on prior research. Following variables were examined to evaluate whether they influence the
success or failure of reorganizations: Leverage, short-term liquidity, earnings prospects, equity
commitment, debt structure and company size. The sample consisted of 20 reorganized and 20
liquidated Australian firms.
Results show that it was possible to distinguish reorganized firms from liquidated firms.
Contrary to the hypothesis, firms were more likely to reorganize successfully as leverage
15
increased. An explanation for this unexpected effect could be the interaction of the two
measures of leverage included in the model. It was shown that firms with higher level of short
term liquidity were more likely to reorganize, which was anticipated according to the
hypothesis. Profitability had no significant impact on the outcome. The reason for this could
be that the reorganization decision may not be based on indicators of firms’ long term
prospects. Industry had an effect on the outcome with a higher proportion of retail firms
failing compared to other industries. The equity commitment and size variables were not
effective in predicting failure or success. (Routledge & Gadenne, 2000)
2.4.4 oPucki & Doherty (2002)
The authors investigated the high rate of failure in Delaware, USA. The number of companies
filing for reorganization in Delaware increased heavily during the 90’s, from 7% of the cases
1990 to 87% of the cases in 1996. At the same time the failure rate was significantly higher in
Delaware compared to other courts in the U.S. What could be the reason for this? Could the
firms filing in Delaware be more difficult to reorganize because they had more serious
business problems or more complex capital structure or was Delaware´s high failure rate
efficient implying that other courts should accept more applications?
The study included large public firms filing for reorganization 1991-1996, in total the sample
consisted of 26 Delaware reorganizations, 16 New York reorganizations and 56
reorganizations from other courts.
Results show that firms filing in Delaware failed more often. Results also showed that prefilling
characteristics of the firms filing in Delaware cannot explain the high failure rates. Firms that
emerged from Delaware showed higher leverage and lower earnings and since there were
no significant differences in characteristics before filing, the cause of difference is not the
input but the effect that Delaware had on the cases.
The data presented in the study are not enough to completely state what causes the high
failure rates in Delaware but they provide support for some speculations according to the
authors. For example, if parties are in agreement on a plan, the Delaware court will confirm it.
This is, however, true for other courts as well. Furthermore, several factors suggest that the
process in Delaware is less comprehensive and detailed compared to other courts. For example,
the process is quicker in Delaware and slightly less expensive even though
professionals are paid at higher rates in Delaware. Another difference is the fee distributions
16
to professionals. In Delaware, 60% of the fees go to financial advisors, in other courts the
proportion is 40%. These data suggest that investment bankers rather than lawyers control
Delaware cases and they may spend less time and finish more quickly, which is consistent
with the greater simplicity of Delaware plans.
The cause of the higher failure rates seems be the Delaware´s less effective procedures. The
question is why firms choose to take their cases to the court that is least likely to successfully
reorganize a firm. A possible reason could be firms´ desire to appear to reorganize without in
fact doing so. Involved parties hope to benefit from the signaling effect the court’s decision
has - that the firm has dealt with its problem. But in fact, actually none of the involved parties
want the firm to face up its problems. (LoPucki & Doherty, 2002)
2.4.5 Barniv et al. (2002)
The purpose of this study was to classify and predict the final bankruptcy outcome under the
U.S Chapter 11 bankruptcy code. Firms allowed protection under the act face three possible
resolutions: A firm can be acquired by other firms, emerge as an independent firm or be
liquidated. Since firms filing share similar financial characteristics of financial distress it is
expected that it will be hard to predict the outcome. The authors therefore add non-accounting
variables to increase the likelihood of finding significant relationships. The model examined
by the authors consisted of five accounting and five non-accounting variables used by
bankruptcy courts.
The sample consisted of 237 publicly traded firms that filed for bankruptcy in the U.S 1980 to
1995. The model performed quite well, correctly classifying approximately 61 % of all firms
into the three possible resolutions. Only few of the accounting variables were effective in
predicting the resolution, which was in accordance with the authors’ expectations. However,
the non-accounting variables such as fraud, resignation of executives, and investors’ past
losses were shown to be effective. This highlights the importance of enclosing nonaccounting information in financial statements. (Barniv et al., 2002)
2.4.6 isher & Martel (2004)
The authors examined the efficiency of the bankruptcy system in Canada. A well-working
system let viable firms restructure their business and send non-viable firms into liquidation.
Two types of filtering errors are defined that can occur during the process. Type 1 error, when
a non-viable firm is allowed to reorganize and Type 2 when a viable firm is sent into
17
liquidation. The process can be divided into three stages in systems like the U.S. Bankruptcy
Code and the Canadian Bankruptcy Act. In the first step, managers have to decide between
reorganization and liquidation. In the second step, if reorganization is selected, a proposal has
to be sent to the court and creditors vote on the proposal. In the third step, if creditors accept
the proposal, the court confirms the plan and the firm must meet the terms of the contract in
order to avoid bankruptcy. Filtering failures can happen during first two steps of the process
and it is obvious that failures happen in Canada according to prior studies. A study made by
Fisher (1995) and one made by Martel (1998) report failure rates of 19% and 28%
respectively for firms allowed into reorganization.
The Fisher & Martel study aims to find measures of filtering failure that can be applied to any
court-supervised reorganization system. A sample of 303 reorganization proposals during
1977-1988 is used to quantify the filtering failures in the Canadian system. The ex post
measure indicates that the probability of filtering failure is 18-41% and that Type 1 errors are
approximately four times more likely to occur than Type 2 errors. According to the ex-ante
measure the probability of filtering failure is 22-53% and that Type 1 error is four times more
likely to occur than type a 2 error. This suggests that the Canadian is inclined to let too many
firms into reorganization, which is consistent with findings with the U.S. Chapter 11
procedure. Prior studies in Canada and the US has shown signs of tighter screening in the
Canadian system leading to less Type 1 errors and more Type 2 errors compared to the U.S.
system.
The results show that Type 1 errors are four times more likely to occur compared to Type 2
errors under the Canadian Bankruptcy act. However, commercial reorganization cases
represent only 5% of all filings under the Canadian Bankruptcy act which suggests that the
Canadian system has good screening at the first stage of the process. Furthermore the results
show that creditors are more likely to accept non-viable firm than rejecting viable firms. This
is not surprising since studies have shown that the pay-off for creditors in the event of
liquidation is low. The possibility of higher pay-off associated with a successful reorganization
therefore leads to more Type 1 errors and fewer Type 2 errors.
The main cause of the filtering failures in the system may be due to asymmetric information
about a firm’s true value and thus viability which could be caused by the structure of the law,
18
the inability of creditors to make correct decisions or factors not directly visible but which are
affecting the outcome. (Fisher & Martel, 2004)
2.4.7 aakso (2007)
The focus of this article is on a study made by Laitinen (2007). In addition to variables used
by Laitinen, the author uses a new variable, which describes the progress of the process to
create and confirm the reorganization plan. The sample consisting of 85 reorganization
proposals confirmed by the court in 2000 is close to the same as in Laitinen (2007). A success
in the study is defined as if the plan has been consummated or continues at the end of 2006.
The results show that the new variable supersedes almost all of the other variables used by
Laitinen (2007).
The process to produce a reorganization plan can be divided into three main phases. Three
variables measuring duration in the progress are used in this study: total duration of the
process, delay in submitting the plan proposal and duration of the feasibility tests.
The most essential finding in Laitinen (2007) was that non-financial variables improve the
ability to predict failures and the most essential finding in this study is the issue of the
progression in the process to produce a reorganization plan. The dummy variable used to
describe the normal progression proved to be very powerful in explaining failure of a
reorganization plan. All of the factors describing the characteristics of the debtor, apart from
being a limited company, were less powerful compared to the characteristics of the process. A
reason for this finding could be that a non-perfect progress increases the risk to create
weaknesses in the plan. Furthermore, it is likely to suppose that the risk for a disordered
progress is higher when challenges with the reorganization increases. Therefore, it’s not
meaningful to evaluate whether a firm has the potential to recover without taking into account
the process according to the author. (Laakso, 2007)
2.4.8 aitinen (2009)
The purpose with this study was to assess viability of firms that filed for reorganization and
bankruptcy in Finland. The reorganization act was taken into force to recover distressed firms
that are viable. The act is not efficient if a viable firm is liquidated or non-viable firm is given
approval for reorganization. Inefficiencies in the filtering process are cost some for the society,
creditors, employees and other stakeholders. It is clear that the filtering process is not working
perfectly in Finland. On average 60% of the reorganization applications are approved
19
by the court and approximately 75% of them will lead to a confirmed plan. Around 40-50% of
the firms that begun with the plan went bankrupt during the program.
During 2004, the event period, 109 firms applied for reorganization and 497 firms for
bankruptcy. A majority of the reorganization applications were handed in by the debtor
(97,2%) and a majority of the bankruptcy applications were handed in by the creditors
(66,4%).
Results proved that non-financial variables provide incremental information for viability
assessment over financial ratios. Non-financial information includes updated information as
well as information about payment behavior of the firm, which might be the reason. Examples
of non-financial variables used are industry, age, number of board members, number of
payment defaults during last 12 months. This result shows how important it is to assess nonfinancial information and not only financial information when assessing the viability of a
firm. Furthermore, results showed that approximately 75% of the reorganization filers are
classified as non-viable ex ante, which reflects a high rate of filtering failure. Approximately
35% of the non-bankrupted firms were viable which is consistent with prior studies were
approximately 65% of the firms will fail during the program. (Laitinen, 2009)
2.4.9 aitinen (2011)
In this paper, Laitinen examined the effect of different reorganization actions on long-term
financial performance of small entrepreneurial firms in Finland. More specifically, impact of
organizational change and management accounting change on financial performance was
examined. Examples of organizational change used in the study are: cutting down of idle
activities, changes in management and changes in firm structure. Examples of management
accounting change used in the study are: cost cutting, pricing, capital budgeting etc.
Reorganizing firms are mainly distressed micro firms with limited resources for
reorganization. These firms may be forced to carry out short-term actions such as asset
liquidation that may decrease their long-term survival possibilities. It is therefore important to
analyze the effect of different reorganization actions on long-term viability for firms to better
select which actions a firm should undertake.
Actions were classified into financial (debt) and business (restructuring) actions. Management
accounting change and organizational change are carried out to increase efficiency of the
20
business and are thus examples of restructuring actions. Financial actions are done in
cooperation with the creditors and are thus simple and less risky than restructuring actions.
Prior research has shown that financial actions mainly have a short-term impact. However,
financial actions are necessary for a firm to be able to cope with its financial distress. On the
other hand, business actions are strategic actions that may have a long-term impact.
A sample of 93 small entrepreneurial firms was examined, mainly surviving ones due to lack
of response from failed firms. As a consequence, the study mainly reflects the effect of
actions in surviving firms. The main findings were as follows: debt restructuring had a
positive impact on performance, which is logical since stronger debt restructuring gives more
financial resources to undertake other actions. Asset liquidation was not shown to have a
positive impact since small firms most often are very dependent upon its competitive
advantage with limited resources. Organizational change had a positive total impact but its
effect was mainly indirect. Management control system change had a strong positive impact
meaning that control systems are important for small firms to be successful. Management
accounting change had a negative impact. One reason for this could be that management
focuses and uses resources for these kinds of actions instead of focusing on strategic actions.
Furthermore, results showed that performance improves if a firm follows its reorganization
plan. (Laitinen, 2011)
2.4.10 Laitinen (2013)
Laitinen made a study on the Finnish market where he examined reorganization failures and
successes. The finish reorganization act came into force 1993 to save viable but distressed
firms from bankruptcy. A reorganization program in Finland last five to ten years in general.
Laitinens study contributes to prior made studies by focusing on small firms in contrast to a
majority of earlier studies.
The sample used consisted of 80 firms for which reorganization plans were confirmed by the
court 2000. These firms are all small entrepreneurial firms from different industry categories,
with on average 2-5 employees and generally less than 20 years old. The purpose with
Laitinens study was to assess the importance of financial and non-financial variables in
predicting survival or failure in the reorganization process. To do this, Laitinen used the Cox
proportional hazards regression analysis and the binary logistic regression analysis.
21
Almost half (47,5%) of the sample firms went bankrupt during the process. Financial variables
such as profitability, leverage, liquidity were not efficient in predicting failure. Probably
because a pre-requisite for reorganization is that the firm is financially distressed. Firms
applying should thus share same financial characteristics. Non-financial variables such as the
use of active reorganization actions were effective in predicting failures. Also characteristics
of the manager and the firm can help to explain failures in small business reorganizations.
Furthermore, the result supports the hypothesis that efficiency-oriented actions such as cost
cutting are critical success factors. Efficiency-oriented actions are made to improve the firms’
profitability which in turn improves the firms’ financial position. Financial restructuring alone
without efficiency-oriented actions is an inefficient way to reorganize a small firm. Even if
financial actions like remission of debt do not play a significant role, it is usually necessary in
the beginning of the process.
Female managed and couple managed firms are shown to have higher survival rates compared
to male-managed firms. The reason to this is not clear but it could be because female personality
makes entrepreneurship stronger than a male personality does. According to prior studies,
entrepreneurship has shown to be a success factor. Furthermore, location of the court has an
impact on the success rate. Courts in southern Finland receive more applications, which
provide them with better opportunities to extract potentially successful firms. (Laitinen, 2013)
2.5 Hypotheses
A majority of earlier studies on reorganization failure have focused on financial variables due
to the easiness of collecting this type of data. However, financial distress is a pre-requisite for
firms filing for reorganization so firms filing should thus share the same pre-filing financial
characteristics. As a consequence, financial variables may not be effective in predicting
reorganization failures. Prior research has shown that non-financial variables could provide
additional information and thus be helpful in prediction failure (Barniv et al., 2002; Laitinen,
2009, 2013). Non-financial variables may include characteristics of the firm, manager,
22
entrepreneur etc. This kind of information is not always easily available and has as a
consequence not been measured as frequently in prior studies (Barniv et al., 2002).
Characteristics of the reorganization plan, which Laitinen (2013) divide into financial and
business restructuring, was shown to be significant in predicting reorganization failure in his
study. Unfortunately, this information is not possible to collect and hence not possible to
measure in scope of the present study. For example, it took Laitinen almost 6 months to
collect his data with a sample much smaller than the one in the present study. The present
study will as much as possible be based on Laitinen, (2013) and thus use both financial and
non-financial variables in predicting the outcome. Variables and hypotheses used by Laitinen
(2013) as well as other relevant variables will be tested in the present study to evaluate the
relationship with failure in Swedish and Danish reorganizations.
2.5.1 Financial variables
White (1984, 1989) used coalition behavior theory to examine decision making under the US
reorganization procedure. She assumed that a coalition consisting of equity holders and secured
and unsecured creditors control the behavior of a distressed firm. This assumption operates
when the firm is distressed and in need of new finance to avoid liquidation. White found
evidence that the liquidation risk depends on the following factors in such circumstances:
equity commitments, leverage position, payoff in reorganization compared to liquidation,
future profitability and secured debt in the capital structure (White, 1989). The coalition
behavior theory is frequently used in predicting reorganization outcomes (Laitinen, 2013).
The following hypotheses are drawn from prior studies on the relationship between financial
variables and the likelihood of failure in reorganizations.
Liquidity
A study made by Routledge & Gadenne (2000) showed that firms with higher short-term
liquidity are more likely to reorganize successfully. This is consistent with findings made by
Fisher & Martel (1995). In contrast, Laitinen (2013) and Sundgren (1998) did not find support
for the importance of liquidity in predicting failure, probably due to the fact that firms
applying for reorganization share same pre-filing financial characteristics.
23
Reorganizing firms are mainly distressed micro firms with limited resources for
reorganization. Higher liquidity should provide firms with better possibilities to tackle its
payment difficulties. Furthermore, higher liquidity could help prevent firms from being forced
to carry out short-term actions such as asset liquidation that may decrease their long-term
survival potential. (Laitinen, 2011) Following hypothesis is drawn:
Hypothesis 1: Pre-filing liquidity has a negative relationship with the likelihood of failure in
reorganization.
Leverage
Routledge & Gadenne (2000) found evidence that firms that reorganize successfully are more
highly leveraged. One possible explanation according to the authors is the financial
restructuring that most likely will take place during the reorganization process. This effect is
the opposite from what is expected according to the coalition behavior theory which suggest
that it will be more difficult for highly levered firms to gain support for reorganization and
funding from various creditor coalitions.
Additional funding is normally necessary for a firm attempting to survive its financial distress.
However, it might be hard for a firm to obtain additional funding if the firm has a high level
of debt and no unsecured assets available to offer as security (Routledge & Gadenne, 2000).
Furthermore, Casey et al. (1986) found that firms with higher level of free assets are more
likely to reorganize. Laitinen (2013) and LoPucki & Doherty (2002) found no evidence that
the amount of leverage influence the reorganization outcome. Following hypothesis is
formulated in the present study:
Hypothesis 2: Pre-filing leverage has a positive relationship with the likelihood of failure in
reorganization.
Pre-filing profitability
White (1984, 1989) and Casey et al. (1986) found that companies with more attractive
earnings prospects are more likely to reorganize successfully which is consistent with the
coalition behavior theory. For example, a profitable firm with a well-functioning business
concept may become insolvent due to temporary cash-flow problems caused by rapid
expansion. Such a firm would in fact be an ultimate candidate for reorganization (Routledge
& Gadenne, 2000).
24
Routledge & Gadenne (2000) found evidence that profitable firms more often reorganize
successfully. Laitinen (2013) did not find any significant effect. The following hypothesis is
formulated:
Hypothesis 3: Pre-filing profitability has a negative relationship with the likelihood of failure
in reorganization.
Size
Laitinen (2013) did not find any significant difference in distribution of size between failed
and non-failed firms. However, several studies have found evidence that large firms are more
likely to successfully reorganize (LoPucki, 1983ab; Eisenberg & Tagashira, 1994; Campbell,
1996; Sundgren, 1998). Cost savings in reorganization could be related to firm size. It might
be harder for large firms to attract bidders which may limit the possibility to sell the firm`s
assets at a reasonable price in liquidation bankruptcy. Hence, reorganization would be a better
solution for larger firms in financial distress (Sundgren, 1998). Following hypothesis is
formulated:
Hypothesis 4: Pre-filing size has a negative relationship with the likelihood of failure in
reorganization.
2.2.2 Non-financial variables
Prior studies have shown that non-financial characteristics could provide additional
information to financial variables and thus be helpful in predicting failure (Routledge &
Gadenne, 2000; Barniv et al., 2002; LoPucki & Doherty, 2002; Sundgren, 1998; Laitinen
(2009, 2013). As far as possible the same variables as used in Laitinen (2013) will be used in
the present study to measure the impact of non-financial variables. However, the sample in
this study does not only consist of small entrepreneurial firms and it is therefore not possible
to use gender of the entrepreneur (owner-manager) as Laitinen (2013). Instead the gender of
the CEO and chairman of the board will be used in the present study. Laitinen (2013)
measured legal form of the firm; however, the present study is focused on limited companies
alone due to limited access to data of non-limited firms. In addition to Laitinen (2013),
auditor remarks which were tested by Laitinen (2009) will be used. Laitinen is the only one,
or one among few, who have measured the impact of the age of the firm, gender of the
manager and auditor remarks, making these variables even more interesting to examine.
25
The following hypotheses are drawn from prior studies on the relationship between nonfinancial variables and the likelihood of failure in reorganizations.
Industry
Industries differ in several ways when it comes to complexity of business processes, risk,
management style etc. Furthermore, industry reflects the business environment of a firm and
may therefore have an impact on the difficulty to reorganize. Industry has been examined
frequently before with mixed results. Prior studies made by Campbell (1996), Hotchkiss
(1995), LoPucki (1983ab), LoPucki & Kalinn (2004) and Routledge & Gadenne (2000) found
evidence that industry effected the outcome. Another study by LoPucki & Doherty (2002)
found no significant industry effect.
Laitinen (2009, 2013) found some evidence that industry affected the outcome of
reorganizations in Finland. Following hypothesis is formulated:
Hypothesis 5: Industry of the firm has a relationship with the likelihood of failure in
reorganization.
Age of the firm
Younger firms fail in general more often; age of the firm may therefore be an effective
variable in predicting failure. Furthermore, the impact of different variables may be affected
by the life cycle stages of a firm (Brinckmann et al., 2010; Lussier, 1996). The age factor has
been examined in failure prediction studies with mixed results. For example Laitinen (2005)
found that younger firms are more likely to default, in contrast, Shumway, 2001 found no
significant effect. Following hypothesis is formulated:
Hypothesis 6: Age of the firm has a negative relationship with the likelihood of failure in
reorganization.
Gender of the CEO and chairman of the board.
Prior studies have shown that the gender of the manager may affect the chances of a successful
reorganization. Du Rietz & Henrekson (2000) showed that female-owned firms in general do
not perform as well as male-owned firms on measures such as profit, revenue and failure.
However, empirical results on the relative performance of genders are not clear cut. Several
potential systematic differences between female- and male-owned firms such as
26
industry, age of business, access to capital, education, experience, risk aversion and less
concern with financial rewards may cause differences between the genders (Watson, 2003).
A small firm’s performance is largely affected by its owner’s entrepreneurial abilities which
might differ due to features related to the gender of the owner-manager. Sexton & BowmanUpton (1990) showed that female entrepreneurs are less willing to go into situations with
uncertain outcomes and have less energy level needed to maintain a growth oriented business.
Hence, the gender of the manager may be effective in predicting failure. Following hypothesis
is formulated:
Hypothesis 7: Gender of the CEO and Chairman of the Board has a relationship with the
likelihood of failure in reorganization.
Auditor remark
Kennedy & Shaw (1991) found evidence that going concern audit opinion is effective in
predicting failure. However, the evidence was week with no greater accuracy than a naive
mechanical model. Auditors are supposed to focus on impeding financial distress and not on
predicting a bankruptcy filing. If auditors focus on financial distress rather than the possibility
of a bankruptcy filing, the audit opinion should be effective in predicting outcome (Kennedy
& Shaw, 1991). Hopwood et al. (1989) found evidence that going concern opinion is more
effective than a set of financial variables in predicting bankruptcy filing. Overall, Hopwood et
al. proved that a qualified opinion has the ability to serve as an early warning signal for
failure. The present study measure the impact of audit remarks in addition to the other
variables to determine whether this variable provides additional information in predicting
reorganization failure. The following hypothesis is formulated:
Hypothesis 8: Auditor remark has a relationship with the likelihood of failure in
reorganization.
2.2.3 Summary of hypotheses
The financial variables used in the present study are based on prior studies, the coalition
behavior theory and bankruptcy theories and are almost the same as used by Laitinen (2013).
Firms filing in Sweden and Denmark are in general small financially distressed firms with
poor capital structure, liquidity and profitability. A prerequisite to let a firm into
reorganization in Sweden and Denmark is that the firm has payment difficulties and is not
27
able to pay its debt as it falls due. Hence, firms allowed into reorganization should share the
same financial characteristics, making it hard to predict failure by using financial variables
(Barniv et al., 2002; Laitinen, 2013). Furthermore, the reorganization process often begin with
a financial restructuring to strengthen the capital structure which most often is necessary for
the firm to be able to continue its business. This will to a large extent eliminate the effect of
pre-filing liquidity and leverage. As a consequence, the relationship of failure to pre-filing
leverage and pre-filing liquidity is not expected to be strong. (Laitinen, 2013)
The main risk during the reorganization process is related to actions necessary to improve the
profitability of the firm. These actions, referred to as business restructuring actions, are more
challenging then financial restructuring actions but are most often necessary in a successful
reorganization. A firm with higher pre-filing profitability should thus have better prospects to
successfully complete the reorganization program compared to a firm with lower pre-filing
profitability. Pre-filing liquidity and leverage can be mechanically improved and are thus not
as important as pre-filing profitability for a reorganization to be successful. As a consequence,
the relationship between pre-filing profitability and failure is expected to be strong.
Furthermore, a majority of the Swedish and Danish reorganization filers are small firms,
which most probably will weaken the size to failure effect. The fact that three out of four
financial variables are expected to have a weak impact on survival/failure makes hypothesis
number 9 trustworthy. (Laitinen, 2013)
Hypothesis 9: The overall performance of a prediction model based on pre-filing financial
variables is low.
The present study also evaluates the impact of four non-financial variables, industry, age of
the firm, gender of the CEO/Chairman of the Board and auditor remarks. Results in prior
studies have been mixed for these variables making it even more interesting to measure the
impact of these variables on Swedish and Danish reorganization firms. As mentioned before,
it’s not possible to collect data for reorganization actions as in Laitinen (2013). However,
since prior studies have shown the effect of selected non-financial variables it’s expected that
the overall performance of a prediction model based on non-financial variables will be good.
It is likely that the effect won’t be as strong as Laitinen (2013) since reorganization actions
were the most significant variables in his study.
28
Hypothesis 10: The overall performance of a prediction model based on non-financial
variables is high
Lastly, it is expected that financial variables bring incremental information over non-financial
variables in reorganization failure prediction. To measure this, financial variables and nonfinancial variables will be measured in the multivariate model at the same time. (Laitinen,
2013)
Hypothesis 11: Financial variables bring incremental information over non-financial
variables in reorganization failure prediction.
Table
1
Hypothesis
Nr
Financial variables
1
2
3
4
Pre-filing liquidity has a negative relationship with the likelihood of failure in reorganization.
Pre-filing leverage has a positive relationship with the likelihood of failure in reorganization.
Pre-filing profitability has a negative relationship with the likelihood of failure in reorganization.
Pre-filing size has a negative relationship with the likelihood of failure in reorganization.
Non-financial variables
5
6
7
8
Industry of the firm has a relationship with the likelihood of failure in reorganization.
Age of the firm has a negative relationship with the likelihood of failure in reorganization.
Gender of the CEO and Chairman of the Board has a relationship with the likelihood of failure in reorganization.
Auditor remarks have a relationship with the likelihood of failure in reorganization.
Models
9
10
11
The overall performance of a prediction model based on pre-filing financial variables is low.
The overall performance of a prediction model based on non-financial variables is high
Financial variables bring incremental information over non-financial variables in reorganization failure prediction.
29
3. Methodology
3.1 Sample
The sample consists of Swedish and Danish limited companies approved by the court to
commence reorganization in 2011 and 2012. The Danish reorganization act was taken into
force April 2011, making another time period impossible to use. A longer time period would
otherwise be appealing due to possible differences during economic booms and recessions.
The reorganization process last approximately one year in Sweden and Denmark. It was thus
possible to evaluate success of these firms. Success is measured by firm survival given that
the purpose with the reorganization act is to save viable firms from liquidation. Firms still
alive in May 2014 are assessed as successful. A process can last more than one year under
certain circumstances which made it necessary to evaluate the resolution slightly more than
one year after the last firms was approved by the court. Only limited companies are included
due to restricted access to data for other legal forms.
The original sample consisted of 365 Swedish and 188 Danish firms. Out of these 32 Swedish
and 36 Danish firms were excluded from the sample. The reason for the shortfall was missing
pre-filing financial statements and missing information whether the firm was liquidated or
still in business.
The final sample consists of 333 Swedish and 152 Danish firms. The sample is thus larger
than the study made by Laitinen (2013) and other reorganization studies in general (Casey,
McGee, & Stickney, 1986). Of the Swedish final sample 124 (37%) were still in business in
May 2014. Among the Danish firms 44 (29%) were still in business. These survival rates are
much lower than the study made by Laitinen (2013) on Finish firms, where 52,2% survived.
The difference is not surprising since a reorganization program in general last five to ten years
in Finland. This additional time should give the firms better possibilities to handle its financial
distress. Sundgren (1998) used a shorter observation period compared to Laitinen (2013)
and found that about 30% of the Finnish firms survived. LoPucki (1983a) measured the
failure in the US and found that only 27% of the firms reorganized successfully. A reason for
the high failure rates might be that firms’ tries to avoid negative publicity associated with
reorganizations and thus postpone their application until their business is out of rescue
(Philippe & Deloitte, 2002).
30
Firms filing and approved into reorganization in Sweden and Denmark are in general small
firms. A majority of prior research have focused on large public firms making this study even
more interesting. The study made by Laitinen (2013) is focused on small entrepreneurial
firms. This study thus contributes to prior research by examining reorganization failure in
Sweden and Denmark and by examining a sample mainly consisting of small firms. The prefiling average number of employees for the Swedish firms is 37,7 and the median 10. For the
Danish firms, the average number of employees is 11 and the median 3. Furthermore, 48% of
the Swedish firms and 64,4 % of The Danish firms are micro firms employing less than ten
employees.
The sample fulfills the following conditions:

The firm must have been allowed to commence reorganization by a court in Sweden
or Denmark during 2011-2012.

Pre-filing financial data must be obtainable.

It must be possible to evaluate whether the firm was liquidated or still in business in
May 2014.
3.2 Data Collection
3.2.1 Firms
Information about companies that filed and had their application approved is not easily
available in Sweden and Denmark. District courts handle the documents, making it impossible
to collect the information in person. For Swedish firms, Affärsdata was used where it was
possible to find all companies let into reorganization during the time period.
By using Statstidende it was possible to find Danish firms let into reorganization during the
time period. The district court has to post a notification into Statstidende when a decision to
commence reorganization is made in order to gather the creditors (Danish Bankruptcy Law,
2010). Figures for the independent variables were collected by using the Central Business
Register, NN Markedsdata, Bisnode and the companies’ financial statements.
3.2.2 endent variables
The financial variables for the Swedish firms were extracted from Affärsdata as well as from
the firms pre-filing financial statement. For the Danish firms figures for the independent
31
variables were collected from the Central Business Register, NN Markedsdata, Bisnode and
the companies’ financial statements.
Most of the variables are common financial ratios:

The size of the firm is measured by the natural logarithm of total assets. This
transformation is done because the size allocation is skewed. Consistent with Laitinen
(2013).

The profitability is measured by the return on investment ratio (EBIT to total assets).
Also consistent with Laitinen (2013).

The leverage is measured by the equity ratio (book equity to total assets). It’s not
possible to use market values of equity due to the use of private firms. Consistent with
Laitinen (2013)

The liquidity is measured by the current ratio (current assets to current liabilities). This
is consistent with Routledge & Gadenne (2000).
In addition, Laitinen (2013) controls for technology (type of production) by including the
ratio of net sales to total assets in the analysis. This is done due to the fact that the sample
firms represent many different production technologies which affect the relation between the
income statement and balance sheet. This is also controlled for in the present study.
The non-financial variables were extracted using the same sources as the financial variables.
Most of them are defined as in Laitinen (2013):

The industry is measured by a dummy variable. Firms are divided into two sub
groups, the manufacturing and service industry.

Age of the firm is calculated in years from the firm was founded until the
reorganization decision.

Gender of the CEO and Chairman of the Board is measured by a dummy variable, 0 if
both are males, 1 if both of them are females or one of them is a female and one of
them is a male.

An auditor remark is measured by a dummy variable and is equal to 1 if a remark was
made and 0 if no remark was made. A remark is done when the auditor believes that
items included in the financial report are either regulatory/policy/legal violations or
32
present an unacceptable high risk of financial loss or adverse public/political exposure
(University of NothernIowa).
Court location is also controlled by a dummy variable to access potential differences between
the district courts in Sweden and Denmark as well as in between the two countries. The
district courts in Sweden and Denmark handling a majority of the reorganization filings will
be controlled against the other courts in those countries. In Sweden the courts in Attunda,
Malmö, Göteborg and Stockholm have handled at least 20 filings each during the time-period.
In Denmark the only court that stands out is Copenhagen with 40 filings. For the combined
set consisting of both the Swedish and Danish firms, court location will instead refer to the
two different countries. Prior studies made by for example Laitinen (2013), LoPucki and
Doherty (2002) and by Fisher and Martel (2004) found that court location may have an
impact on failure. Courts handling a larger amount of filings should have better opportunities
to pick successful candidates and thus select firms that are more viable (Laitinen, 2013).
Furthermore a dummy representing the two types of limited companies existing in Denmark
and Sweden will be tested. In Denmark, rules for ApS-firms are simpler compared to A/Sfirms. ApS are most often small to middle-sized firms with a minimum capital requirement of
DKK 50 000 (DKK 80 000 until 2014) and A/S middle-sized to large firms with a minimum
capital requirement of DKK 500 000. Only an A/S can be listed on the Copenhagen Stock
Exchange. (Ministry of Foreign Affairs of Denmark)
Similar legislation exists in Sweden. Private limited liability companies are required to have a
minimum share capital of SEK 50 000 and public limited companies SEK 500 000. Only
public firms are allowed to sell its shares to other people and register its shares on a stock
exchange. (ViaVästerbotten)
3.3 Reliability and Validity
Reliability measures the risk that random error occurs, i.e. how reliable the study is. It must be
possible to redo a study with the same results for a study to be trustworthy. High reliability
decreases the likelihood of incorrect results caused by random errors. It is necessary to double
check data and calculations to make sure that the study is as reliable as possible (Dahmström,
2005; Kaplan & Saccuzzo, 2005). To ensure that the study has a high degree of reliability, a
33
selection of the financial data and calculations were double checked with firms pre-filing
financial statement.
Validity measures if any systematic errors exist. If systematic errors exist it does not matter if
one redoes a study with same result since the method used is incorrect. It’s necessary with a
high degree of validity (low risk of systematic errors) for a study to be trustworthy
(Dahmström, 2005). The risk of systematic errors decreases since similar studies have been
done in other countries and since the present study follows the method used in Laitinen
(2013) to a large extent.
3.4 Statistical Methods
To ensure validity, the preset study will use the same statistical method as Laitinen (2013).
Laitinen used two statistical methods in order analyze reorganization failure. A logistic
regression model (LRM), focusing on survival or failure, was shown to be more accurate than
a Cox proportional hazards model (PHM) which focuses on survival time. Due to this fact and
since it was not possible to determine survival time for the Danish firms; a logistic regression
model is used in the present study. Furthermore, LRA provides a similar benchmark to PMH
in reorganization research (Fischer, 2007). LRA can be used to predict a binary dependent
variable and to find variance in the dependent variable caused by the independent variables.
The logistic regression analysis creates a score, L, for every firm. It’s assumed that the
independent variables are related to L. The risk score is used to determine the probability of
membership in failed firms as follows:
Where (j = 0, …, n) are regression coefficients and n is the number of independent
variables (i = 1, …, n). (Gujrati & Porter, 2010)
The dependent variable (Z) in LRA is a binary variable referring to failure or survival. A
successful reorganization is equal to 0 (Z = 0) and unsuccessful reorganizations to 1 (Z = 1).
The models are estimated by the maximum likelihood method in SPSS. Following tests will
be conducted to assess the hypotheses: The significance of the coefficients will be tested by
the Wald test statistic. The strength of relationship is assessed by the standard test for LRA,
34
for example the Negelkerke R square, Cox and Snell R square and -2 log likelihood. The
goodness-of-fit is tested by the Hosmer and Lemeshow chi-square test. (Laitinen, 2013)
The present study continue to follow the method used in Laitinen (2013) by estimating the
LRA-model with different independent variable sets to test the hypotheses stated in chapter 2.
One set consist of only the financial variables, one set of the non-financial variables and one
of a combination of the financial and non-financial variables for the Swedish and Danish
samples. Furthermore, these sets will also be tested in a model loaded with a combination of
Danish and Swedish data.
35
4. Results
4.1 Descriptive Statistics
Descriptive statistics for the samples shows mean- and median values for all independent
variables divided into non-failed and failed firms. By conduction a Mann-Whitney U-tests,
univariate differences between non-failed and failed firms are tested. The Mann Whitney Utest is chosen since it has greater efficiency than the t-test on non-normal distributions and is
nearly as efficient as the t-test on normal distributions. (Laitinen, 2013).
4.1.1 Sweden
None of the financial variables differ significantly between failed and-non failed firms which
is in accordance with the assumption that firms filing for reorganization share the same
characteristics of financial distress. Mean profitability (-28%), leverage (-13%) and current
ratio (0, 37) for Swedish failed and non-failed firms are very low indicating that firms are in
financial distress when filing.
For the non-financial variables no significant differences is shown between failed and nonfailed firms.
In summary, the univariate analysis for the Swedish sample shows no significant difference in
between failed and non-failed firms. Later, the hypotheses will be tested in a multivariate
setting to expose the real relationships.
Table 2
36
4.1.2 nmark
The size-variable is shown to be almost the same for failed and non-failed firms. The mean
profitability is negative and almost identical for both non-failed and failed firms. The big
difference between the mean- and median value indicates a skewed distribution. Non-failed
firms are shown to have much worse pre-filing leverage, -102% compared to – 25% for failed
firms. Again the distribution is skewed but the difference between the groups is still
significant if extreme outliers are removed. The mean current ratio is worse for non-failed
firms than for failed firms and the difference is proven to be significant. As expected, the
mean profitability (-32%), leverage (-48%) and liquidity (0,8) for firms filing are very low
indicating that firms are in financial distress.
As concerned to the non-financial variables, the only significant difference between failed and
non-failed firms is found in the variable referring to the type of limited company which shows
that A/S-firms on average fail more often. In a univariate setting, the other variables are not
shown to significantly differ between failed and non-failed firms.
In summary, the univariate analysis for the Danish sample shows that more highly leveraged
firms that have lower current ratio and are defined as ApS-firms are more likely to survive.
Later, the hypotheses will be tested in a multivariate setting to expose the true relationships.
Table 3
Descriptive statistics Danish firms
Variable
Financial variables
Size
Profitability
Leverage
Current ratio
Net sales/Total assets
Non-financial variables
Age
Industry
Gender
Court location
Auditor remark
Type of limited company
Non-Failed firms
Mean Median Stadard deviation
9,00
8,90
-35,63% -6,94%
-102,44% -26,27%
0,63
0,45
0,5
0,21
10,73
0,61
0,27
0,27
0,05
0,32
9,00
1,00
0,00
0,00
0,00
0,00
Failed firms
Mann Whitney U test
Mean Median Stadard deviation Test statistics Z
p-value
2,10
70,12%
220,57%
0,96
1,06
9,32
-30,17%
-25,39%
0,88
0,34
9,15
-3,41%
4,04%
0,74
0,23
1,49
106,22%
147,90%
1,07
0,63
-0,88
-1,53
-3,85
-2,95
-0,63
0,38
0,13
0,00
0,00
0,53
7,95
0,49
0,45
0,45
0,21
0,47
13,39
0,50
0,24
0,26
0,05
0,52
8,50
0,50
0,00
0,00
0,00
1,00
0,50
0,50
0,43
0,44
0,21
0,50
-0,71
-1,27
-0,41
-0,17
-0,02
-2,24
0,48
0,20
0,68
0,87
0,98
0,03
37
4.1.3 omparison between Sweden and Denmark
As we can see in the descriptive statistics Danish firms have on average a worse leverage ratio
than Swedish firms but a higher current ratio. Mean profitability and size are approximately
the same for both sub samples. Firms in both countries have on average been operating
approximately 13 years and a higher proportion of Danish firms belongs to the service
industry and are managed by a female or co-managed by a female and male. Since a
prerequisite to commence reorganization in both countries is that the filing firm is not able to
pay debt as it falls due it’s not a surprising that firms in both countries share similar financial
characteristics even though they differ slightly in degree of distress.
4.1.4 ombined set of Swedish and Danish data
Some changes are made for the combined set consisting of Swedish and Danish firms. The
limited company variable is erased due to differences between the different types of limited
companies in the two countries and the court dummy is replaced and instead referring to the
two countries, Sweden and Denmark.
The profitability variable falls just outside the 10% significant level with a p-value of 10,7%.
Although it signals that non-failed firms in general have lower pre-filing profitability than
failed firms. The leverage variable is significant with a p-value less than 1% and shows that
non-failed firms in general have worse pre-filing leverage than non-failed firms. The current
ratio is also significant with a p-value less than 1% showing that non-failed firms have lower
current ratio compared to failed firms. The significant difference between failed and nonfailed firms in the leverage- and liquidity- ratio violates the assumption that firms filing for
reorganization share same financial characteristics of financial distress. As mentioned before
the reason for this could be the financial restructuring in the beginning of the reorganization
process which alters these positions. As expected, the mean profitability (-29%), leverage (24%) and liquidity (0,5) for firms filing are very low indicating that firms let into
reorganization are in financial distress.
The only significant difference among the non-financial variables is found in the country
dummy. It shows that Swedish firms are more likely to survive compared to Danish firms.
In summary, a univariate analysis for the combined sample shows that firms that more highly
leveraged firms that have lower current ratio and are from Sweden are more likely to survive.
Later, the hypotheses will be tested in a multivariate setting to expose the real relationships.
38
Table 4
Descriptive statistics combined sample
Variable
Financial variables
Size
Profitability
Leverage
Current ratio
Net sales/Total assets
Non-financial variables
Age
Industry
Gender
Country
Auditor remark
Non-Failed firms
Failed firms
Mann Whitney U test
Mean Median Stadard deviation Mean Median Stadard deviation Test statistics Z
p-value
9,08
-33,87%
-39,88%
0,42
1,72
9,00
-6,97%
2,05%
0,3
1,31
1,63
88,97%
147,38%
0,59
1,8
9,28
-26,28%
-15,00%
0,55
1,73
9,15
-5,90%
5,83%
0,41
1,26
1,57
76,20%
115,68%
0,77
1,98
-1,16
-1,61
-2,91
-3,11
-0,31
0,25
0,11
0,00
0,00
0,76
13,29
0,48
0,13
0,26
0,02
10,00
0,00
0,00
0,00
0,00
11,18
0,50
0,34
0,44
0,15
13,59
0,41
0,13
0,34
0,03
9,00
0,00
0,00
0,00
0,00
13,39
0,49
0,34
0,48
0,16
-0,79
-1,4
-0,05
-1,79
-0,1
0,43
0,16
0,96
0,08
0,92
4.2 Multivariate Models
A logistic regression model is conducted to test the hypotheses and to evaluate the true
relationship between the independent variables and the likelihood of failure. First, Swedish
data will be presented, after that Danish data and finally the combined Swedish and Danish
data.
4.2.1 Sweden
Financial variables
Table 5 presents the results for the model consisting of financial variables. The Hosmer and
Lemeshow test shows that the linear logit fits with the data. However, this test is sensitive to
sample size. A sample should consist of at least 400 observations while the used sample only
consists of 333 observations (SPSS Statistics). The Negelkerke R Square shows that only
2,2% of the variability in the dependent variable (failure or non-failure) can be explained by
the five financial independent variables, thus showing that pre-filing financial explains almost
nothing of failure risk in reorganization.
The only independent variable that is significant is the net sales to total assets variable which
is included to control for differences in the relation between the income statement and the
balance sheet (type of production). The variable is significant at the p-level of 10% and
39
signals that firms with a higher ratio are more likely to fail. Rests of the financial variables are
not significant and does therefore not support hypothesis 1 to 4 on the relationship between
financial variables and the likelihood of failure. The Chi-square value of 5,4 with a p-value of
37% tell us that the model as a whole is not significantly better than a model with no
predictors. The overall model is weak and insignificant which is consistent with hypothesis 9
i.e. that the overall performance of a prediction model based on pre-filing financial variables
is poor.
Table 5
Omnibus Tests of Model Coefficients
Chi-square
Step 1
Step
Block
Model
df
5,378
5,378
5,378
Sig.
5
5
5
,372
,372
,372
Model Summary
-2 Log
likelihood
Step
1
Cox & Snell Nagelkerke
R Square
R Square
,016
434,320a
,022
Hosmer and Lemeshow Test
Step
Chi-square
1
df
6,883
Sig.
8
,549
Variables in the Equation
B
Step 1a
S.E.
Wald
df
Sig.
Exp(B)
Logaritmofto
talassets
Profitability
,092
,083
1,213
1
,271
1,096
,002
,002
,638
1
,425
1,002
Leverage
,000
,002
,002
1
,968
1,000
CurrentRatio
,164
,294
,310
1
,578
1,178
NetSalesAs
sets
Constant
,119
,067
3,114
1
,078
1,126
-,601
,842
,509
1
,475
,548
Non-financial variables
A new logistic regression model is estimated consisting pf the non-financial variables for the
Swedish sample. Results are shown in table 6. The Hosmer and Lemeshow test is significant
which implies that our model is good at predicting failure. However, as mentioned before, the
test is sensitive to sample size. The Negelkerke R Square shows that only 0,9% of the
40
variability in the dependent variable (failure or non-failure) can be explained by the nonfinancial variables, thus showing that non-financial variables explain almost nothing of failure
risk in reorganization. The Chi-square value of 2,1 with a p-value of 91% tells us that the
model as a whole is not significantly better than a model with no predictors. Furthermore, all
of the non-financial variables are insignificant implying that we have to reject research
hypotheses 5-8 on the relationship between non-financial variables and the likelihood of
failure. Research hypothesis 10 is also rejected since the model is insignificant.
Table 6
Omnibus Tests of Model Coefficients
Chi-square
Step 1
Step
Block
Model
df
2,085
2,085
2,085
Sig.
6
6
6
,912
,912
,912
Model Summary
-2 Log
likelihood
Step
Cox & Snell Nagelkerke
R Square
R Square
437,613a
1
,006
,009
Hosmer and Lemeshow Test
Step
Chi-square
1
df
17,299
Sig.
8
,027
Variables in the Equation
B
a
Step 1
S.E.
Wald
df
Sig.
Exp(B)
Age
-,004
,008
,175
1
,676
,996
IndustryDum
my
Gender
-,262
,235
1,239
1
,266
,770
-,140
,427
,108
1
,742
,869
CourtDumm
y
AuditorRem
arkDummy
Limitedcom
panydummy
Constant
,020
,239
,007
1
,932
1,021
-,180
,924
,038
1
,846
,836
-,490
,721
,461
1
,497
,613
,693
,220
9,932
1
,002
2,000
Non-financial and financial variables
Another model is estimated with the combined set of financial and non-financial variables.
Results are shown in 7. The Hosmer and Lemeshow test shows that the linear logit fits with
the data. However, again, this test is sensitive to sample size. The Nagelkerke R Square of
41
3,2% shows that the strength of dependence in the model is very low. However, it’s slightly
higher than it was for the non-financial model, 0,9%, suggesting that it is better than the nonfinancial model. For the combined model, the only significant variable is the Net sales to total
assets ratio which is significant on the p-level 10%. In summary, including financial variables
slightly improves the non-financial model, but it’s still insignificant and worthless. Thus,
weak or no evidence is found in support of hypothesis 11: that financial variables bring
incremental information over non-financial variables in predicting failure.
Table 7
Omnibus Tests of Model Coefficients
Chi-square
Step 1
Step
Block
Model
df
7,988
7,988
7,988
Sig.
11
11
11
,714
,714
,714
Model Summary
-2 Log
likelihood
Step
Cox & Snell Nagelkerke
R Square
R Square
431,710a
1
,024
,032
Hosmer and Lemeshow Test
Step
Chi-square
1
df
5,871
Sig.
8
,662
Variables in the Equation
B
a
Step 1
S.E.
Wald
df
Sig.
Exp(B)
Logaritmofto
talassets
Profitability
Leverage
CurrentRatio
,108
,086
1,558
1
,212
1,114
,002
,000
,141
,002
,002
,291
,617
,000
,234
1
1
1
,432
,995
,629
1,002
1,000
1,151
NetSalesAs
sets
Age
IndustryDum
my
Gender
CourtDumm
y
AuditorRem
arkDummy
Limitedcom
panydummy
Constant
,129
,069
3,496
1
,062
1,138
-,007
-,285
,009
,238
,619
1,437
1
1
,432
,231
,993
,752
-,198
-,017
,439
,243
,204
,005
1
1
,652
,944
,820
,983
-,333
,949
,123
1
,726
,717
-,415
,737
,317
1
,573
,660
-,521
,862
,366
1
,545
,594
42
4.2.2 nmark
Financial variables
A model consisting of the financial variables are tested on the Danish sample. Results are
shown in Table 8. The Hosmer and Lemeshow test shows that we have a good model,
however, the test is sensitive to sample size. The Negelkerke R Square shows that the model
explains 10,5 % of the variability in the dependent variable (failure or non-failure). Implying
that pre-filing financial explains only a fraction of failure risk in Danish reorganizations. The
Chi-square value of 11,6 with a p-value of less than 5% tell us that the model as a whole fits
significantly better than a model with no predictors. The overall model is weak but
significant.
The model includes two variables with high statistical significance. Profitability is significant
on a p-level of 5%. The negative coefficient shows that firms with higher pre-filing
profitability are less likely to fail which supports our Hypothesis 3. Furthermore, leverage is
significant on the p-level of 5%, implying that firms with higher equity ratio are more likely
to fail. Thus it shows that more highly leveraged firms are more likely to survive which was
not expected according to our hypothesis. No statistical support is found for hypothesis
number 1 and 4.
Table 8
Omnibus Tests of Model Coefficients
Chi-square
Step 1
df
Sig.
Step
Block
11,598
11,598
5
5
,041
,041
Model
11,598
5
,041
Model Summary
Step
1
-2 Log
likelihood
Cox &
Snell R
Square
171,312
Nagelkerke
R Square
,073
,105
Hosmer and Lemeshow Test
Step
1
Chi-square
5,093
df
Sig.
8
,748
43
Variables in the Equation
B
a
Step 1
Logaritmofto
talassets
Profitability
Leverage
CurrentRatio
NetSalesAs
sets
Constant
S.E.
Wald
df
Sig.
Exp(B)
,070
,131
,284
1
,594
1,072
-,008
,006
,323
,004
,002
,267
3,923
5,715
1,459
1
1
1
,048
,017
,227
,992
1,006
1,381
,343
,372
,851
1
,356
1,410
-,070
1,359
,003
1
,959
,932
Non-financial variables
A logistic regression model is estimated with non-financial Danish data. Results are shown in
table 9. The Hosmer and Lemeshow test is insignificant which suggest that the model fits our
data well. However, as mentioned before, the test is sensitive to sample size. The Negelkerke
R Square shows that 6,3% of the variability in the dependent variable (failure or non-failure)
can be explained by the non-financial variables, thus showing that non-financial variables
only explains a fraction of failure risk in reorganization. The Chi-square value of 6,8 with a pvalue of 34% tell us that the model as a whole is not significantly better than a model with no
predictors. The only predictor that is significant is the limited company variable. It’s
significant at a p-level 10% and shows that A/S-firms are more likely to default. Rests of the
non-financial variables are insignificant implying that we have to reject research hypotheses
5-8 on the relationship between non-financial variables and the likelihood of failure. Research
hypothesis 10 is also rejected since the model as a whole is insignificant.
Table 9
Omnibus Tests of Model Coefficients
Chi-square
Step 1
df
Sig.
Step
Block
6,825
6,825
6
6
,337
,337
Model
6,825
6
,337
Model Summary
Step
1
-2 Log
likelihood
176,085
Cox &
Snell R
Square
,044
Nagelkerke
R Square
,063
44
Hosmer and Lemeshow Test
Step
Chi-square
1
df
7,698
Sig.
8
,464
Variables in the Equation
B
Step 1
a
Age
IndustryDu
mmy
Gender
CourtDum
my
AuditorRe
markDum
my
Limitedco
mpanydum
my
Constant
S.E.
Wald
df
Sig.
Exp(B)
,014
-,433
,020
,388
,471
1,246
1
1
,493
,264
1,014
,648
-,476
-,005
,446
,424
1,138
,000
1
1
,286
,990
,621
,995
,024
,887
,001
1
,979
1,024
,694
,417
2,768
1
,096
2,002
,800
,407
3,875
1
,049
2,226
Non-financial and Financial variables
A last model for the Danish sample is estimated with the combined set of financial and nonfinancial variables. Results are shown in table 10. The Hosmer and Lemeshow test shows that
the linear logit fits with the data. However, again, this test is sensitive to sample size. The
Nagelkerke R Square of 16,9% is higher than it was for the non-financial model, 6,3%, which
shows that the combined model outperform the non-financial model. Furthermore, the model
as a whole is significantly better than a model with no predictors at a p-level of 10%.
Four independent variables are statistically significant at a p-level of 10%: profitability,
leverage, gender and type of limited company. Compared to the prior estimated financial and
non-financial models, gender is added among the significant variables. The coefficient shows
that firms with a female, or a female and a male, as CEO and Chairman of the Board are less
likely to fail. Thus giving support to the hypothesis that gender has a relationship with the
likelihood of failure. The model is significant and the inclusion of financial variables improves
the performance of the non-financial model, thus supporting our hypothesis that financial
information brings incremental information over non-financial information in predicting the
outcome.
45
Table 10
Omnibus Tests of Model Coefficients
Chi-square
Step 1
df
Sig.
Step
19,117
11
,059
Block
Model
19,117
19,117
11
11
,059
,059
Model Summary
Step
1
-2 Log
likelihood
Cox &
Snell R
Square
163,794
Nagelkerke
R Square
,118
,169
Hosmer and Lemeshow Test
Step
Chi-square
1
df
7,597
Sig.
8
,474
Variables in the Equation
B
Step 1
Logaritmofto
talassets
Profitability
S.E.
Wald
df
Sig.
Exp(B)
-,083
,159
,273
1
,602
,920
-,009
,004
5,144
1
,023
,991
Leverage
,007
,003
7,612
1
,006
1,007
CurrentRatio
,363
,277
1,717
1
,190
1,438
NetSalesAs
sets
Age
,433
,412
1,103
1
,294
1,542
,016
,022
,553
1
,457
1,016
Industry
-,240
,409
,345
1
,557
,786
Gender
-,874
,488
3,214
1
,073
,417
Court
,517
,492
1,100
1
,294
1,676
AuditorRem
ark
,349
1,054
,109
1
,741
1,417
LimitedCom
pany
Constant
,804
,484
2,756
1
,097
2,234
,981
1,512
,421
1
,517
2,667
4.2.3 Combined set of Swedish and Danish data
Finally, models consisting of combined Swedish and Danish data are estimated. This is done
to evaluate whether predictive capacity increases when the data is combined. One can question
the relevance of this combination due to slightly different legislation and other differences
between the countries. However, most of the reorganization acts are based on the
46
US Chapter 11 Act and in general the legislation in Denmark and Sweden share similar
characteristics. (Philippe & Deloitte, 2002)
Financial variables
Results for the financial-model are shown in table 11. The Hosmer and Lemeshow test shows
that the model does not adequately fit the data, indicating that the model predicts output that
is significantly different from observed values. The Negelkerke R Square shows that the
model explains 2,8 % of the variability in the dependent variable (failure or non-failure).
Implying that pre-filing financial explains almost nothing of failure risk in this combined
Swedish and Danish set. The Chi-square value of 9,9 with a p-value of less than 10% tell us
that the model as a whole fits significantly better than a model with no predictors. The overall
model is weak.
The model includes one predictor with high statistical significance. The liquidity-ratio is
shown to be is significant at a p-level of 5%. The positive coefficient shows that firms with
higher pre-filing liquidity are more likely to fail which is opposite from expected according to
hypothesis 1. No statistical support is found for hypothesis number 2 - 4.
Table 11
Omnibus Tests of Model Coefficients
Chi-square
Step 1
Step
Block
Model
df
Sig.
9,963
9,963
9,963
5
5
5
,076
,076
,076
Model Summary
Step
1
-2 Log
likelihood
Cox &
Snell R
Square
615,866
Nagelkerke
R Square
,020
,028
Hosmer and Lemeshow Test
Step
1
Chi-square
14,203
df
Sig.
8
,077
47
Variables in the Equation
B
a
Step 1
Logaritmoft
otalassets
Profitability
Leverage
CurrentRati
o
NetSalesA
ssets
Constant
S.E.
Wald
df
Sig.
Exp(B)
,065
,068
,926
1
,336
1,068
-,001
,002
,002
,001
,519
1,931
1
1
,471
,165
,999
1,002
,419
,215
3,799
1
,051
1,520
,043
,054
,644
1
,422
1,044
-,234
,691
,114
1
,735
,792
Non-financial variables
Results for the non-financial set are shown in table 12. The Hosmer and Lemeshow test is
insignificant which suggest that the model fits our data well. The Negelkerke R Square shows
that 1,8% of the variability in the dependent variable (failure or non-failure) can be explained
by the non-financial variables, thus showing that non-financial variables explains almost
nothing of failure risk in reorganization. The Chi-square value of 6,4 with a p-value of 27%
tell us that the model as a whole is not significantly better than a model with no predictors.
The model consists of two significant predictors. The industry dummy is significant at the
10% level, indicating that firms within the service industry are less likely to fail. The country
dummy is significant with a positive coefficient at the p-level of 5%, showing that Danish
firms are more likely to fail compared to Swedish firms. Rests of the non-financial variables
are insignificant implying that we have to reject the other research hypotheses on the
relationship between non-financial variables and the likelihood of failure. Research
hypothesis 10 is also rejected since the model as a whole is weak and insignificant.
Table 12
Omnibus Tests of Model Coefficients
Chi-square
Step 1
Step
Block
Model
6,420
6,420
6,420
df
Sig.
5
5
5
,267
,267
,267
48
Model Summary
Step
1
-2 Log
likelihood
Cox &
Snell R
Square
619,409
Nagelkerke
R Square
,013
,018
Hosmer and Lemeshow Test
Step
1
Chi-square
df
Sig.
11,885
8
,156
Variables in the Equation
B
Step 1
Age
IndustryDum
my
Gender
Country
AuditorRem
arkDummy
Constant
S.E.
Wald
df
Sig.
Exp(B)
,002
,008
,047
1
,829
1,002
-,330
,196
2,825
1
,093
,719
-,185
,463
-,058
,296
,223
,626
,391
4,303
,009
1
1
1
,532
,038
,926
,831
1,588
,944
,645
,180
12,799
1
,000
1,906
Non-financial and financial variables
Lastly, a last model is estimated with a combined set of financial and non-financial variables.
Results are shown in table 13. The Hosmer and Lemeshow test shows that the linear logit fits
with the data. The Nagelkerke R Square of 5% is higher than it was for the non-financial
model, 1,8%, which shows that the combined model explains more of the variability in
outcome than the non-financial model. Furthermore, the model as a whole is significantly
better than a model with no predictors at a p-value of 5,8%.
Three independent variables are statistically significant at a p-level of 10%: leverage, net sales
to total assets and the country-dummy. Compared to the prior estimated financial and nonfinancial models for the combined data, leverage and net sales to total assets are added among
the significant predictors. Leverage is significant on the p-level of 10%, implying that firms
with higher equity ratio are more likely to fail. Thus it shows that more highly leveraged firms
are more likely to survive. The net sales to total assets variable is significant at a p-level of
10%. The positive coefficient implies that firms with a higher ratio are more likely to fail. The
industry dummy is no longer a significant predictor but shows a p-value of 11,2% when
financial and non-financial data are combined.
49
The model as a whole is significant and the inclusion of financial variables slightly improves
the performance of the non-financial model, thus weakly supporting our hypothesis that
financial information brings incremental information over non-financial information in
predicting reorganization failure.
Table 13
Omnibus Tests of Model Coefficients
Chi-square
Step 1
Step
Block
Model
df
Sig.
17,846
17,846
17,846
10
10
10
,058
,058
,058
Model Summary
Step
1
-2 Log
likelihood
Cox &
Snell R
Square
607,983
Nagelkerke
R Square
,036
,050
Hosmer and Lemeshow Test
Step
1
Chi-square
df
7,663
Sig.
8
,467
Variables in the Equation
B
Step 1
S.E.
Wald
df
Sig.
Exp(B)
Logaritmofto
talassets
Profitability
,078
,070
1,230
1
,267
1,081
-,002
,002
,963
1
,326
,998
Leverage
CurrentRatio
,002
,260
,001
,200
3,312
1,703
1
1
,069
,192
1,002
1,297
NetSalesAs
sets
Age
,120
,065
3,457
1
,063
1,128
-,001
,008
,027
1
,869
,999
-,317
,199
2,527
1
,112
,728
-,297
,690
-,090
,304
,281
,656
,954
6,032
,019
1
1
1
,329
,014
,890
,743
1,995
,913
-,417
,715
,340
1
,560
,659
IndustryDum
my
Gender
Country
AuditorRem
arkDummy
Constant
4.2.4 Summary of multivariate results
A model consisting only of financial variables is shown to be effective in predicting
reorganization failure in Denmark. The model is significant for the Danish sample and explains
10,5% of the outcome; failure or non-failure. The model is not significant for the
50
Swedish sample. The control variable for production technology is the only significant
independent variable for the Swedish sample, no similar effect is found for the Danish
sample. However, two predictors are statistically significant for the model consisting of
Danish firms. Profitability is shown to have a negative relationship with the likelihood of
failure which was expected according to the hypothesis. Leverage is shown to have a positive
relationship with failure which also was expected, implying that more highly leveraged firms
and more profitable firms are less likely to fail.
The non-financial models are shown to be no better than a model without any predictors for
both the Swedish and Danish samples. They are thus worthless in predicting the outcome. The
only significant predictor was the one referring to type of limited company in Denmark which
shows that A/S-firms are more likely to fail compared to ApS-firms.
The combined model consisting of both financial and non-financial data for the Swedish
sample is weak, insignificant and thus worthless. In contrast, the same model for the Danish
sample is shown to be significant and shown to outperform a model consisting of only nonfinancial variables, thus supporting our hypothesis nr 11. A new significant predictor is also
detected which shows that firms with a female, or a female and a male, as CEO and Chairman
of the Board are less likely to fail in Danish reorganizations.
A model consisting of financial variables for the combined sample of Swedish and Danish
firms is shown to be weak and thus worthless. The only significant predictor is the current
ratio, implying that firms with higher pre-filing liquidity are more likely to fail. The nonfinancial model is also weak and useless. Firms within the service industry and firms from
Sweden are shown to be less likely to fail. A model estimated by both financial and nonfinancial variables is shown to be significant and explains slightly more of the outcome
compared to a non-financial model. However, only 5% of the outcome can be explained by
the predictors.
In summary, it has been shown that a model consisting of tested predictors is more effective
in predicting the outcome in Denmark and that financial data or a combination of financial
and non-financial data is preferable compared to non-financial data.
51
5. Analysis
In this chapter findings from the estimated logistic regression models are analyzed. Whilst the
present study follows Laitinen (2013) to a large extent, it’s important to have in mind that we
should expect our non-financial and combined models to be weaker than Laitinens model
since reorganization actions, which was not possible to include in the present study, were the
most effective predictors in his model. It’s hard to analyze and explain all findings since our
study only examines statistical data. A complement could have been interviews with both
failed and non-failed firms and with bankruptcy courts to gain further knowledge. However, it
was not possible in the scope of this study. No validation data are available; as a consequence
one should be careful when generalizing results outside the sample (Wong et al., 2007).
5.1 Sweden
The financial model was shown to be weak, insignificant and thus not useful in predicting the
outcome in Sweden. This finding confirms our hypothesis 9; that the overall performance of a
prediction model based on pre-filing financial variables is low.
The reasonable explanation is that firms filing are in financial distress and thus share same
financial characteristics. Furthermore, the reorganization process often begins with a financial
restructuring which to a large extent eliminates the effect of pre-filing liquidity and leverage
(Barniv et al., 2002; Laitinen, 2013).
One financial independent variable was shown to be significant; the control variable for type
of technology (net sales to total assets). It is shown that firms with a higher ratio are more
likely to fail. This implies that firms within a sector associated with high net sales and
relatively small asset base are more likely to fail compared to firms within a sector associated
with higher asset bases. Higher asset bases could make it easier for firms to find additional
funding which could be an explanation to this finding. For example Casey et al. (1986) and
White (1984) found evidence that firms with higher level of free assets are more likely to
reorganize successfully.
52
The non-financial model was shown to be even weaker than the financial model and no
predictor was shown to be significant. Thus we have to reject hypotheses 5-8 and 10
concerning the relationship between non-financial variables and the reorganization outcome.
Laitinen (2013) found support for his non-financial model; however the significant
predicators in his model apart from gender were not possible to include in the present study.
It’s thus reasonable to believe that results would have been more similar if exactly equivalent
non-financial variables had been available.
Other prior studies have shown that non-financial characteristics could be helpful in
predicting failure, Routledge and Gadenne (2000), Barniv et al. (2002), LoPucki and Doherty
(2002), Sundgren (1998) (Laitinen (2007, 2009, 2013). However, tested predictors in the
present study (industry, age, gender and auditor remark) are not effective in predicting the
outcome in Swedish reorganizations. These variables were chosen since they had proven to be
significant in prior studies; however, other non-financial variables not included in this study
may be effective. For example different reorganization actions as shown by Laitinen
(2011,2013) and the process to produce a reorganization plan as shown by Laakso (2007).
The insignificance of the auditor remark-dummy might be due to the fact that small
companies don not necessarily have to use an auditor. This might explain the few remarks
given and thus the insignificance of this variable (ViaVästerbotten) (Ministry of Foreign
Affairs of Denmark).
Adding financial variables to the non-financial model only slightly improved the nonfinancial model, but they are still insignificant and not useful. Thus, weak or no evidence is
found in support of hypothesis 11 i.e. that financial variables bring incremental information
over non-financial variables in predicting failure.
In summary, empirical evidence strongly shows that a financial model, a non-financial model
and a combination of both models consisting of chosen variables, are not effective in predicting
the outcome in Swedish reorganizations. Thus, tested pre-filing financial information and
non-financial characteristics are not shown to be critical factors for courts and insolvency
experts in their filtering process in Sweden.
53
5.2 Denmark
The financial model was shown to be significant and able to explain a fraction (10,5%) of
failure in Danish reorganizations. These results make us reject (or question) hypothesis 9.
This finding is interesting since the same financial model was shown to be inefficient in
Sweden and similar financial models in other countries, for example Laitinen (2013) in
Finland. Legislation in countries are similar but differences do exist which could help explain
unlike findings. However, the same prerequisites do exist in Sweden and Denmark; that a firm
has to be unable to pay debt as it falls due and similar in Finland i.e. that a firm has financial
difficulties and characterized by indebtedness. Firms should thus share similar financial
characteristics when filing, which should lead to the insignificance of pre-filing financial
variables.
Two predictors were shown to be significant; firms with higher pre-filing profitability and
more highly leveraged firms were shown to be more likely to reorganize successfully. Thus
giving support to hypothesis 2 and 3. Routledge and Gadenne (2000) found evidence that
profitable firms more often reorganize successfully which is consistent with the coalition
behavior theory and logical to expect since profitable firms should be able to find support and
funding more easily. Furthermore, a profitable firm with a well-functioning business concept
may become insolvent due to temporary cash-flow problems caused by rapid expansion. Such
a firm would in fact be an ultimate candidate for reorganization and more likely to succeed
compared to a non-profitable firm (Routledge and Gadenne, 2000).
According to the coalition theory it should be harder for a leveraged firm to find additional
funding and support for reorganization, which most often is necessary for a firm to survive.
One possible reason behind the finding in the present study, that more highly leveraged firms
are more likely to survive, could be the financial restructuring in the beginning of the process
which alters a firms capital structure (Routledge and Gadenne, 2000).
The non-financial model is insignificant and weaker than the financial model. Hypothesis 10
on the relationship between non-financial variables and the outcome is thus rejected. This is
consistent to the present study’s findings in Sweden. The analysis in that section (5:1) is thus
applicable here. The only difference is the significance of the dummy referring to type of
limited company. Results show that A/S-firms are more likely to default compared to ApSfirms. A/S-firms are in general larger and could be publicly traded, which might explain this
54
finding. It is likely that ApS-firms more often are owner-managed and that management in
such firms in general has a closer relation to the firm. These factors could increase
entrepreneurial motivation which has shown to be a success factor (Keats & Bracker, 1988;
Laitinen, 2013). Furthermore, even if losses are limited to the shareholder capital for both
types of limited companies, it is likely that owner-managers in general are worse affected
since they probably will find it harder to find new livelihood. It is also reasonable to believe
that managers for an A/S find it easier to leave a firm into bankruptcy and continue the
business in a new corporation or to liquidate it and transfer the healthy parts to another
business. Similar findings were discussed by Laitinen (2013) although in this case the
discussion concerned limited and non-incorporated firms.
Adding financial variables to the non-financial variables improved the non-financial model
and it was shown to be significantly better than a model without any predictors. Thus,
evidence was found in support of hypothesis 11 i.e. that financial variables bring incremental
information over non-financial variables in predicting failure.
Compared to prior estimated financial and non-financial models for Denmark, gender was
shown to be a significant predictor in the combined model. The coefficient shows that firms
with a female, or a female and a male, as CEO and chairman of the board are less likely to
fail, thus giving support to the hypothesis that gender has a relationship with the likelihood of
failure. Laitinen (2013) found evidence that female-managed and couple-managed firms are
less likely to fail compared to male-managed firms. Prior research in this area is mixed.
Studies have found evidence that female-managed firms fail more often and other studies that
male-managed firm fails more often (Watson, 2003). A reason for the gender effect could be
related to industry differences. Rosa et al., (1996) found evidence that female-managed firm
fails more often because women tend to start up business in sectors associated with low
returns. When controlled for industry effects, Watson (2003) could not find support that
female-managed firms fail more often. Another factor explaining the gender effect could be
related to differences in risk aversion as females on average tend to be more risk averse than
males (Anna et al., 1999). The few observations of female-managed firms filing in the present
study might be explained by the fact that females in general are more risk averse, thus falling
into financial difficulties less often. Furthermore, females might on average file for
reorganization earlier due to higher risk aversion which could improve firm’s chances to
55
restructure its business before it is too late. This is however not measured in the present study
and thus only a speculation. Another reason could be that females in general are better on
entrepreneurship than males. Entrepreneurship has proven to be a success factor for small
firms (Keats and Bracker, 1988).
In summary, empirical evidence shows that the financial model and the combined financialand non-financial model are effective in predicting the outcome in Danish reorganizations,
even though it only explains a fraction of failure or non-failure. Thus, pre-filing financial
information (leverage and profitability) and non-financial characteristics (gender and type of
limited company) are shown to be critical factors for a court in their filtering process in
Denmark. However, reasons for the gender- and type of limited company- effect are not
obvious, and might thus not necessarily play a central role in predicting the outcome. Courts
and other stakeholders should thus be careful in discriminating between firms in terms of the
risk they represent based on gender and type of limited company.
5.3 Sweden & Denmark
The financial model consisting of both Swedish and Danish data was shown to be significant
but very weak and thus not useful in predicting the outcome in Sweden. This finding confirms
our hypothesis 9.
The financial model includes one significant predictor, the liquidity-ratio. The positive
coefficient implies that firms with higher pre-filing liquidity are more likely to fail which is
contrary to what we expected. A reasonable explanation is the financial restructuring which
alters a firm’s financial position and thus changes a firm’s liquidity position in the beginning
of a reorganization process (Laitinen, 2013).
The non-financial model is insignificant and weak. Hypothesis 10 on the relationship between
non-financial variables and the outcome is thus rejected. This is consistent to the present
study’s findings in Sweden and Denmark. The analysis in section (5.1) is thus applicable here
as well. The only thing that is different in this model is the significance of the country- and
industry-dummy. Results show that Danish reorganizations are more likely to fail compared
to Swedish reorganizations. The legislations in both these countries are similar. However, one
possible explanation behind the higher failure rate in Denmark could be the concept of
56
transfer of business, which is one of two alternatives in Danish reorganizations, the other one
being composition proceedings. The higher likelihood of failure in Denmark could thus be
caused by the fact that an amount of firms decide to liquidate the distressed firm and transfer
or sell the healthy parts to another firm. Another possible explanation could be that Danish
firms in general are in worse condition when filing. However, descriptive statistics do not
give strong support for this conclusion which indicates that Danish firms are performing
worse during the process. Furthermore, courts in Sweden have been working with
reorganization proceedings for more than 20 years whereas the act is new in Denmark. Thus,
courts in Sweden have worked with more filings and as a result have more routine and better
knowledge compared to courts in Denmark. This fact should provide them with better
opportunities to extract more viable and less risky candidates. A similar discussion was made
by Laitinen (2013) on court location in Finland were it was shown that courts handling more
filings were better in the filtering process. Comparable findings have also been made in the
US by LoPucki and Doherty (2002) and in Canada by Fisher (2007).
Results show that firms within the service industry are less likely to fail compared to firms
within the manufacturing industry. Industries differ in several ways when it comes to
complexity of business processes, risk, management style etc. Furthermore, industry reflects
the business environment of a firm which might have an impact on the difficulty to reorganize
Laitinen (2013). It could be that firms within the manufacturing industry are more complex to
restructure due to different reasons. For example, it is reasonable to believe that firms within
the service industry have more flexible operations and thus are better able to alter their
processes to new circumstances than manufacturing firms. However, it’s not possible to truly
determine the cause of this finding with the scope of this study.
The combined model as a whole is significant and the inclusion of financial variables slightly
improves the performance of the non-financial model, thus weakly supporting our hypothesis
that financial information brings incremental information over non-financial information in
predicting reorganization failure.
Three independent variables are shown to be statistically significant, leverage, net sales to
total assets and the country-dummy variable. Compared to the prior estimated models with
Swedish and Danish data, leverage and net sales to total assets are added among the significant
predictors. It is shown that more highly leveraged firms are more likely to survive.
57
Furthermore, it was shown that firms with higher net sales to total assets ratio are more likely
to fail. Similar findings have been discussed earlier in this chapter. When adding financialvariables to the non-financial model, the industry dummy is no longer a significant predictor
but shows a p-value of 11,2%.
In summary, empirical evidence shows that the financial model and the financial- and nonfinancial model for the combined Swedish and Danish dataset are statistically significant.
However they are weak and almost explain nothing of failure. The following predictors are
shown to be significant in the combined set; liquidity, industry, country, net sales/total assets,
leverage.
58
6. Conclusion
The present study contributes to earlier reorganization studies by examining failure prediction
models for Swedish and Danish reorganizing firms which never (or rarely) have been done in
a similar setting before. Furthermore, the sample largely consists of small firms which rarely
have been studied before. The study also contributes by examining a couple of variables
which have hardly ever been examined before in the context of reorganization.
The Swedish and Danish reorganization acts came into force in order to save viable but
financially distressed firms from bankruptcy. The present study shows that failure rates are
high in both countries, implying that the systems are inefficient in saving viable firms or/and
courts not effective in their filtering process. The main cause of the filtering failures in the
system may be due to asymmetric information about a firm’s true value and thus viability
which could be caused by the structure of the law, the inability of creditors to make correct
decisions or factors not directly visible but which are affecting the outcome.
It’s clear that the process works more as a postponement of bankruptcy than a solution for
distressed firms. Creditors might vote in favor for reorganization proceedings since payoff on
average is better in reorganization compared to immediate liquidation. The possibility of
higher pay-off associated with a successful reorganization therefore leads to more Type 1
errors and fewer Type 2 errors. It’s obvious that courts allow too many firms into
reorganization. Decisions do not appear to be based on an assessment of a firm’s prospect to
operate profitable in the future. This is supported by the fact that profitability is shown to be a
significant variable in distinguishing successful from unsuccessful firms in Denmark. We can
only speculate what causes this massive market failure. One reason might be the fact that
payoff to creditors and other stakeholders in general are better in reorganization compared to
bankruptcy. It is thus possible that courts prefer to give firms chance to reorganize than to
reject them even though the firms are not suitable for reorganization proceedings. Other
reasons might be that firms want to avoid the negative publicity associated with a proceeding
and thus delay their application until the point that their business is out of rescue or that
reorganizing micro firms have limited resources for reorganization.
The higher failure rate in Denmark compared to Sweden might be explained by the fact that
Danish courts are poor in managing the process, have less routine of handling it or by the
59
possibility in Denmark to use transfer of business were a distressed firm is liquidated and
healthy parts transferred to a new business. Apart from this, legislation and the process is
similar in Sweden and Denmark. Measured failure rates are high compared to rates measured
in Finland by Laitinen (2013). This might be caused by the fact that Finnish firms are allowed
protection on average 5-10 years, which gives them additional time to find a solution to its
distress. However, reasons behind different failure rates between countries are not truly
possible to make in scope of this study. Legislations around the world are similar, but
differences do exist, courts in respective country can thus not completely trust factors proven
to be significant predictors of reorganization outcome in other countries. This study’s purpose
was as a consequence to determine factors that can help explain and predict failure of firms
reorganizing in Sweden and Demark.
The analysis was based on 333 Swedish and 152 Danish firms filing for reorganization 20112012. By May 2014, 63 % of the Swedish firms and 71 % of the Danish firms were bankrupt.
Models tested in this study were not efficient in predicting Swedish reorganization outcomes.
Thus, tested pre-filing financial information and non-financial characteristics are not shown to
be critical factors for a court in their filtering process in Sweden. It’s, however, possible that
other factors not measured in the preset study would be effective. Reorganization actions as
shown in prior studies, for example by Laitinen (2011,2013) and the process to create a
reorganization plan Laakso (2007) might be critical success factors and are thus interesting to
examine for insolvency experts and other stakeholders in further studies.
The financial- and combined financial- and non-financial model was effective and able to
explain a fraction of Danish reorganization outcomes. Thus, pre-filing financial information
alone and combined with non-financial characteristics are shown to be critical factors for a
court in their filtering process in Denmark. However, reasons for the gender- and type of
limited company- effect are not obvious, and might thus not necessarily play a central role in
predicting the outcome. Although, tested models may be of help for insolvency expert and
other stakeholders when estimating probability of survival.
Tested variables were thus shown to be more important predictors in Denmark than in Sweden.
The reason for this is out of the present study but could be due to slightly different business
culture/environment, differences in courts filtering process or in the overall reorganization
procedure.
60
It’s clear that the reorganization process is not well-working in Sweden and Denmark.
Stakeholders would gain on a more efficient procedure were non-viable firms are liquidated
instead of protected in reorganization. A solution might be to examine and benchmark other
jurisdictions that are more stringent in their filtering process, or jurisdictions that give firms
better possibilities to survive, in order to increase efficiency. Findings in this study can assist
courts in making the filtering process more efficient by providing some evidence about
potential success factors. However, more studies are necessary in order to truly understand
what causes the high failure rates and to truly determine success factors. This study, however,
shows that the systems are not optimal and that something has to be done in order to achieve
what a reorganization process is all about; to create conditions for viable firms to stay in
business.
6.1 Further Possibilities
The study analyzed both non-financial and financial variables proven to be significant
predictors in prior studies. However, most of them were not effective, and the models were in
general weak. The challenge for future researcher is to discover the source of the high failure
rates and to find significant models that explain a larger fraction of the outcome.
It would thus be interesting to examine other predictors that might be significant. For example
reorganization actions as measured by Laitinen (2011, 2013), the process to create a
reorganization plan Laakso (2007) and other non-financial variables. Interviews with
stakeholders and bankruptcy courts would be interesting to understand how they work in
order to make it possible to draw further conclusions. This would make it possible to evaluate
how courts in Sweden and Denmark handle a case and thus find differences that might
explain unlike findings. A throughout comparison between acts in different countries would
also be interesting to truly measure and find reasons behind failure rates between different
legislations. A comparison like this would make it possible for countries to benchmark “the
ideal” process and legislation and thus increase the overall efficiency. Additional elements to
statistical findings, like interviews, could also help explain findings, such as why gender and
type of limited company were shown to be effective predictors in Danish reorganizations.
61
A further examination how courts handles a process and how they discriminate between
viable and non-viable firms would also be interesting to determine the source of this massive
market failure of letting non-viable firms into reorganization.
It would also be interesting to compare average payoff for different resolutions. For example
average pay off when transfer of business is used in Denmark. Even though a significant
amount of firms fail during the reorganization process, the average payoff for creditors might
be higher in reorganization as shown in other countries.
62
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8. Appendices
Sweden Non-Failed Firms
Stage Magic Production i Stockholm AB
Alternativ Media Stockholm AB
Tumba Bokhandel AB
Something Corporate AB
Dackebygdens Kött i Virserum AB
Bo Bäst i Sverige AB
Noås Industri AB
Danne Näslund Åkeri AB
LENNART BJÖRKLUND BYGG AB
Sundsvalls UV-Konsult AB
QCC Management AB
Mediacta AB
Tappen i Varekil AB
Ground Zero AB
SEAB Stockholms Elbyrå AB
Doxa Dental AB
Nya Sidensjöhus Förvaltnings AB
Frog Marine Service AB
South of France Communication AB
Håkansson & Bresch Åkeri AB
Professionell Säkerhet i Mälardalen AB
HP Däckservice AB
Trudes Frisörer AB
Erik Karlsson Såg & Hyvleri AB
Europe Investor Direct AB
Vimmerby bageri o konditori AB
Förstoringsateljén AB
Nilssons Mark & Anläggning i Jokkmokk AB
Liberty Equestrian & Education Centre LEEC AB
N G Forest AB
Swedblo Trans AB
Hydropulsor AB (publ)
Kanvicom AB
Kiddies i Stockholm AB
NL Mat & Inspiration AB
Gmki Elkonsult AB
Kött & viltspecialisten i Sverige AB
Vrigstad Buss & Taxi AB
AllTele Företag Sverige AB
Brandt Fastigheter i Hunnebostrand AB
J B L Mekan AB
67
Strand Packaging AB
Kakelgruvan i Borlänge AB
Englunds Måleri AB
Nässjö Bud Service AB
Käkeläs Finn-Möbler AB
Trälens Byggnads AB
AW Frakt AB
Nordic Protection AB
AQ Fastighet i Torslanda AB
Betong o Smide Projekt i Trelleborg AB
IMS Bildbyrå AB
Din Båt i Tjuvkil AB
Husindustrier i Värmland AB
Rowi Wines AB
SDA Control Systems AB
Gimgården LGC AB
Metallvärden i Lesjöfors AB
Systematisk Kapitalförvaltning i Sverige AB
(publ)
M.V. Metallvärden AB
Expo för hållbart byggande i Gråbo AB
Mattsons Mekaniska AB
ONE CC AB
I3TEX AB
Scandinavian event and furniture group AB
S. Eskilsons Åkeri AB
PRESTANDO I TRELLEBORG AB
Fläkt-Teknik AB
Matskaparna Leverans & Catering i Stockholm
AB
Sparkök ND Syd AB
Möbelboning i Tibro AB
Exidus i Stockholm AB
Tryckopia AB
Två Stenar Fastigheter AB
SDA KV Teknik AB
PEHJUS AB
E Edvardsson Bygg AB
JVA Lastbilstransporter AB
Södertörns Marktjänst AB
Sieglo AB
AQ Retor Engineering AB
BTB Data AB
Hotell Tänninge AB
Anebyhus AB
68
Arsizio AB
Eds Bruk Holding AB
Visual A.C.T Scandinavia AB
Backen i Bankeryd AB
Ferromet Ferrous Raw Materials Trading AB
Hangaren Industri AB
Spatial Technology AB
Inmotion Intelligence AB
Leksands IF Ishockey AB (publ)
3BAS AB
Greatline Boats of Sweden AB
Master Die Casting AB
Nordström och Karlsson El AB
Ferrotex Holding AB
Malmö Redhawks Ishockey AB
Digitala Vaktbolaget Nordic AB
Food Factory Europé AB
Fönstergruppen Sverige AB
Leksands IF Fastighets AB
Skåne Hansa Bygg och Service AB
Omnitor AB
AdOperator AB
Promocar i Stockholm AB
Candles Scandinavia AB
Abelco Nordic AB
JIMROL Media AB
Aqua Terrena International AB
Leksand Sommarland AB
Malö Yachts AB
Solarus Sunpower Sweden AB
Safe at Sea AB
Corebus AB
Obducat AB
AQ 3-Elite AB
AQ Retor Production AB
Metallvärden i Sverige AB (publ)
Funäsdalen Mountain Resort AB
XPR Medical Services AB
Prestando Holding AB
Doxa AB
69
Sweden
Failed Firms
Radius Control Systems AB
MOTIVAR AB
Extentit Produktion AB
STOREBRO SERVICE-SYSTEM AB
The Studio Group Sweden AB
MFK Förvaltning AB
Alvedoor International AB
Fastighets Energi Service och Tillsyn i Skåne AB
Foxtrot Broadband AB
Jerker Möllerstrandhs Frakt AB
Lilla Helfwetet AB
Priority Aero Maintenance (Sweden) AB
AB Henrics Gräv & Schakt
DPNOVA AB
Elektriska Flexservice i Norden AB
Boacasa AB
Vasa Prefab AB
Dredging i Göteborg AB
Tepro Print Products AB
LG Gard Bildelar & Bilservice AB
Rederi Allandia AB
Ronneby Fjärrtransporter AB
Clear Communications (EurAust) AB
Svets o Produktion i Täby AB
Blidö Entreprenad Maskinservice AB
CMI Composites AB
Grävtjänst i Järfälla AB
Liser Solutions AB
Europabagaren Sverige AB
FREGADO AB
Lars Claessons Måleri AB
Stormhavet 1 AB
Västeråsbagar'n AB
Alvedoor AB
AZUARIT AB
Bactus AB
Falköpings Fashion AB
Food Hanholmsvägen AB
Holje Mekaniska AB
Miljardmåleriet AB
NANOxIS AB
Rör- och Oljeservice i Hagfors AB
70
SIBAB Borås Ventilations och Skorstensrenovering AB
Tryckeri AB Knappen
Westbahr AB
VVS & Industrirör i Trelleborg AB
Arkivator AB
Bröderna Arvidsson Orsa AB
CoolFast AB
Exir Telecom AB
GSM International AB
Mark & Service M.S. AB
PABART AB
The Olive House AB
Alfa Stenhus Production AB
ASP Logistic AB
Atomgruppen AB
Borgstaden Fastigheter AB
Färg & Interiör i Simrishamn AB LKonsult International AB
Peter Mattsson Betong i Oxie AB
Trend & Design i Gävle AB
AB Jobblyftet
Altero AB
Altero Energy Solutions AB
AT Outdoor AB
Caselin AB
Elimag Radarmekan AB
ESM Eksjö Svetsmontage AB
Järfälla Byggeliter AB
Järna Anläggnings AB
Limex AB
BPJ Transport AB
Brabo Möbelvaruhus AB
Bygg & Konsult i Grythyttan AB
gw design AB
Next Generation Broadcasting NGB AB
Proximion Fiber Systems AB
Regenersis Nordic AB
Sensor Control AB
Sjölands Bilservice AB
Terrestrial Broadcasting Investments AB
WoodHouse of Siljan AB
Anytec AB
Bergslagens Destilleri AB
Boardstore Sverige AB
Borrbolaget i Järna AB
71
Budfrakt Billesholm AB
Delifour AB
Eco Supplies Europe AB
Ecovena AB
Gargnäs Snickeri AB
Kenneth & Ingers Åkeri AB
Mellansvenska Rör Per Kardell AB
Shuttleservice Local i Tidaholm AB
Solar Design AB
Trinity Försäljnings AB
TräningsAgenten i Stockholm AB
AB Sigfr. Anderssons Elektriska
CR Gruppen AB
Dansken's Lampkultur AB
EMA Lundberg AB
Lilla Mirakel AB
LunchExpress i Sverige AB (publ)
MatPartner Svenska AB
PLB i Östersund AB
PODAB Print i Småland AB
Radius Sweden AB
Restaurang 68 i Mölndal AB
S.P. Injection AB
Sunne Virkestransport AB
Svanstein Gård AB
Technofibre i Lysekil AB
AI Group AB
Air Sweden Aviation AB
Brandworld Sverige AB (publ)
Food Center i Stockholm AB
Gösta Palm Entreprenad AB
HJ:s Rep & Montageservice AB
Ikator Entreprenad Stockholm AB
JBP Enterprises AB
Ljungsarps Verkstads AB
Skeppsbroskeppet AB
Bageri Beila AB
Forward Förenade Bygg AB
Gisip AB
Health Choice AB
SANKON Elektronik AB
Skogrand Skyltdesign AB
Skyltbolaget Roos Neon i Stockholm AB
Streamtel AB
Suncore AB
72
Svenska Miljödäck AB
Vitech Virkeshantering AB
AB Möckelns Sågverk
AB Wilhelm Kindwall
Anders Källström Entreprenad AB
AUGUSTA ENTREPRENAD AB
Awinto Bygg AB
B Hansen Media AB
Bakefield Paper Products AB
Blidö Entreprenad AB
Carl Svanberg Bokbinderi AB
Forza Förvaltning AB
Higeti AB
Iggesund Treasury AB
iNovacia AB
Kakelgruvan i Falun AB
Noaks Teknikpool AB
Proforma Conveyor System AB
R.Rum AB
SAAB Automobile AB
Saab Automobile Powertrain AB
Saab Automobile Tools AB
Sankon Sverige AB
SeaNet Maritime Communications AB (publ)
Svenskt Fågelkött AB
TABO Incinerator AB
Ardy Electronics AB
Bedroom & Bathroom Fashion i Stockholm AB
BioResonator AB
Boj Transportvagnar AB
Bryland Transport AB
Dax Door Produktion AB
Duogruppen i Helsingborg AB
Hammar Invest AB (publ)
Jerker Antoni Musik AB
KADAX Produktion AB
Karlit AB
RLE International Sweden AB
Skebo Kött AB
Stefan Olsson i Nynäshamn AB
STÖS Måleri AB
Superbo AB
Swegrand Production AB
TOR Air AB
Åcool AB
73
Bedroom & Bathroom i Malmö AB
BeOutdoors Nordic AB
Cellcomb AB
Cellcomb Scandinavia AB
DO tryckerierna AB
Eco Rental Sverige AB
Eco Supplies Solar AB
Frog Construction & Diving AB
Frog Marine Group AB
G.I.A.-Gården AB
Hotell i Bergslagen AB
L. Heino Livs AB
Latitude Solar AB
LB Hus AB
Melk Stockholm AB
Mikaels Livlina AB
Nordisk Bevakningstjänst AB
Norra Stockholms Fastighetsförvaltning AB
ONE Sthlm AB
Ori On AB
PA Resins AB
Q-vision AB
Vida Paper AB
Ailas Hembageri AB
Bjuvs Snickerifabrik AB
Duo fasader Nord AB
Duofasader AB
E.S.S. PETROLSERV AB
Fredriksson Grävmaskiner i Torsby AB
JAM Linköping AB
Mark o VA i Malmö AB
Star Public Relations AB
Denmark Non-Failed Firms
A&V ApS
Aalborg DH A/S
Akirema ApS (tidl: M/S Amerika Restaurant med Bar ApS)
Aqua Miljø Hyrup ApS
BB Invest af 20.09.2007 ApS
Come Back ApS
Conson Elektronik A/S
Cornega ApS
Dapeca ApS
74
DAT af 2006 ApS
Ejendomsselskabet Aalborg ApS
Ejendomsselskabet Odense-Slagelse-Aabenraa A/S
Esbjerg Bedding ApS
Foodshop No. 55 ApS
Gartneriet Kronborg ApS
GIB Sleehw A/S (tidl. Big Wheels A/S)
HP Gruppen af 1953 ApS
Hvidebæk Slagteri A/S
IP. Cons ApS
JB Holding A/S
JKJ Ejendomsinvest A/S
K & K Vejle ApS
K og K Blåvand ApS
K og K Holding ApS
K&H Administration ApS
Lars Pedersen Foto ApS
LB international, Næstved ApS
Lindbo ApS
Lindelund Holding ApS
Lunderskov Entreprenørforretning ApS
Petunia A/S
Photonic Energy A/S
PVH Finér A/S
Ramblaselskabet ApS
Rossi International A/S
Sagafjord ApS
Selskabet af 1. februar 2011 ApS
SG Ejendomsselskab A/S
Supersonic CPH ApS
T.G. PRODUKTION ApS
Trautmann Holding Erhverv ApS
Uldjysk Isolering A/S
Updata Danmark A/S
Østbirk Turist A/S
Denmark Failed Firms
Agio Finans A/S
Agio Invest ApS
Agis Fire & Security A/S
A-Line Estate ApS
Allocator A/S
Allright Agency A/S
75
ApS af 01.082006
AR Luftteknik ApS
Bendt Wikke Marketing A/S
Berent ApS
Billet Centralen A/S
Bomaxx Bolighus A/S
Brøndum Energy A/S
Bøssemagerne Nystrøm & Krabbe A/S
Cafe Ketchup i Tivoli ApS
Camann ApS
Carepoint Middelfart Biler ApS
Carl Wine A/S
Center Ellested A/S
Cyncron A/S
Danfrig Køleteknik A/S
Dansk Vognfjeder Fabrik ApS
Dreyer Gruppen ApS
Driftsselskabet af 14.10.2010 ApS (tidl. Agnes Cupcakes
ApS)
Eastwood Denmark ApS
Edderup Autoophug ApS
Ejendommen Præstekilde ApS
Ejstrupholm Dambrug A/S
El-firma Kjeld Jacobsen A/S
Entreprenør Bjarne Winther ApS
Entreprenørfirmaet Brian Ventrup ApS
Erik Bresling ApS
Fiskeriselskabet Kingfisher A/S
Fluxome Sciences A/S
Fluxome Sciences A/S
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