Prediction of Financial Distress among Swedish Listed Companies
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Bachelor’s Thesis in Accounting and Financial Management
Stockholm School of Economics
Spring 2012
Prediction of Financial Distress among Swedish Listed Companies
- A study of Ohlson’s O-score and an alteration of the model using Swedish data
Niklas Alyhr* Mattias Holmberg
Instructor: Håkan Thorsell
Date: 30 May 2012
Location: Stockholm School of Economics, Room 538
*21817@student.hhs.se 21806@student.hhs.se
Stockholm School of Economics
Spring 2012
Prediction of Financial Distress among Swedish Listed Companies
- A study of Ohlson’s O-score and an alteration of the model using Swedish data
Niklas Alyhr* Mattias Holmberg
Instructor: Håkan Thorsell
Date: 30 May 2012
Location: Stockholm School of Economics, Room 538
*21817@student.hhs.se 21806@student.hhs.se
2
ABSTRACT
The prediction of bankruptcy and financial distress related to companies has interested many
researchers and two of the more notable published contributions are attributable to Altman
(1968) and Ohlson (1980). As the studies within this field of research are many, few can be
been related to Swedish listed companies. This thesis mimics the research done by Ohlson in
the pursuit of trying to find a model suitable for the Swedish stock market, with a starting
point in the studies of the same. By comparing the results of three different models derived
through logistic regression analysis, Ohlson’s model still seems to provide some usable input.
However, the results indicate that a revised model, incorporating information related to the
audit report and the opinion expressed by the auditor, improves the prediction of financial
distress among Swedish listed companies. We found this model, Model B, to be the most
suitable one amongst the three.
Key words: Bankruptcy, Financial distress, Prediction model, Audit report, O-score, Swedish
listed companies
ABSTRACT
The prediction of bankruptcy and financial distress related to companies has interested many
researchers and two of the more notable published contributions are attributable to Altman
(1968) and Ohlson (1980). As the studies within this field of research are many, few can be
been related to Swedish listed companies. This thesis mimics the research done by Ohlson in
the pursuit of trying to find a model suitable for the Swedish stock market, with a starting
point in the studies of the same. By comparing the results of three different models derived
through logistic regression analysis, Ohlson’s model still seems to provide some usable input.
However, the results indicate that a revised model, incorporating information related to the
audit report and the opinion expressed by the auditor, improves the prediction of financial
distress among Swedish listed companies. We found this model, Model B, to be the most
suitable one amongst the three.
Key words: Bankruptcy, Financial distress, Prediction model, Audit report, O-score, Swedish
listed companies
3
TABLE OF CONTENTS
Abstract ...................................................................................................................................... 2
1 Introduction ............................................................................................................................. 5
1.1 Purpose ................................................................................................................................. 7
1.2 Question Formulations ......................................................................................................... 7
2 Literature Review .................................................................................................................... 9
2.1 Early Seminal Research: Beaver and Altman................................................................... 9
2.2 Logit Models: Ohlson..................................................................................................... 10
2.3 Swedish Bankruptcy Prediction Models: Skogsvik ........................................................ 11
2.4 Research Considering Audit Reports and Bankruptcy ................................................... 12
2.4.1 Hopwood, McKeown & Mutchler ........................................................................... 13
2.4.2 Gaeremynck & Willekens ........................................................................................ 13
2.5 Relevant Recent Bachelor and Master Theses ............................................................... 14
3 Method .................................................................................................................................. 16
3.1 The Sample ..................................................................................................................... 16
3.1.1 The Focus Group ..................................................................................................... 20
3.2 Statistical Methods ......................................................................................................... 22
3.2.1 The Logistic Regression .......................................................................................... 23
3.2.2 The Wald Chi-Square Test ...................................................................................... 24
3.2.3 The Likelihood Ratio Test ....................................................................................... 25
3.2.4 The Pseudo R-squared – McFadden’s R-squared ................................................... 25
3.2.5 The Z-Statistics ....................................................................................................... 26
4 Analysis ................................................................................................................................. 27
4.1 Ratios and Basic Results ................................................................................................ 27
4.2 Evaluation of Predictive Performance ............................................................................ 34
5 Conclusions ........................................................................................................................... 40
TABLE OF CONTENTS
Abstract ...................................................................................................................................... 2
1 Introduction ............................................................................................................................. 5
1.1 Purpose ................................................................................................................................. 7
1.2 Question Formulations ......................................................................................................... 7
2 Literature Review .................................................................................................................... 9
2.1 Early Seminal Research: Beaver and Altman................................................................... 9
2.2 Logit Models: Ohlson..................................................................................................... 10
2.3 Swedish Bankruptcy Prediction Models: Skogsvik ........................................................ 11
2.4 Research Considering Audit Reports and Bankruptcy ................................................... 12
2.4.1 Hopwood, McKeown & Mutchler ........................................................................... 13
2.4.2 Gaeremynck & Willekens ........................................................................................ 13
2.5 Relevant Recent Bachelor and Master Theses ............................................................... 14
3 Method .................................................................................................................................. 16
3.1 The Sample ..................................................................................................................... 16
3.1.1 The Focus Group ..................................................................................................... 20
3.2 Statistical Methods ......................................................................................................... 22
3.2.1 The Logistic Regression .......................................................................................... 23
3.2.2 The Wald Chi-Square Test ...................................................................................... 24
3.2.3 The Likelihood Ratio Test ....................................................................................... 25
3.2.4 The Pseudo R-squared – McFadden’s R-squared ................................................... 25
3.2.5 The Z-Statistics ....................................................................................................... 26
4 Analysis ................................................................................................................................. 27
4.1 Ratios and Basic Results ................................................................................................ 27
4.2 Evaluation of Predictive Performance ............................................................................ 34
5 Conclusions ........................................................................................................................... 40
4
6 References ............................................................................................................................. 41
Published Articles ................................................................................................................ 41
Non-published articles .......................................................................................................... 42
Theses ................................................................................................................................... 42
Bachelor Theses ............................................................................................................... 42
Master Theses ................................................................................................................... 42
Literature .............................................................................................................................. 42
Internet ................................................................................................................................. 42
Sites .................................................................................................................................. 42
Reports ............................................................................................................................. 43
News Articles ................................................................................................................... 43
Appendix A: Summary of the Financially Distressed Companies ........................................... 44
6 References ............................................................................................................................. 41
Published Articles ................................................................................................................ 41
Non-published articles .......................................................................................................... 42
Theses ................................................................................................................................... 42
Bachelor Theses ............................................................................................................... 42
Master Theses ................................................................................................................... 42
Literature .............................................................................................................................. 42
Internet ................................................................................................................................. 42
Sites .................................................................................................................................. 42
Reports ............................................................................................................................. 43
News Articles ................................................................................................................... 43
Appendix A: Summary of the Financially Distressed Companies ........................................... 44
5
1 INTRODUCTION
Without exaggerating, money is by many means a fundamental cornerstone of our society.
Many have at some point come across the expression “money makes the world go around”,
once sung in the musical, Cabaret. This expression can easily be applied to and observed
within the reality of corporations. Without funds, the world of the company will simply stop
spinning, since without funds one will have trouble paying suppliers’ bills and in most cases
this results in the suppliers discontinuing supplying their goods and services to the company.
A recent case involves the subcontractors of the Swedish car manufacturer Saab Automobile,
who amongst others, was negatively affected by the financial distress presented by this big
industrial actor.1 One of the more alarming indications of a forthcoming bankruptcy of Saab
Automobile, was its inability to pay its subcontractors which consequently lead to material
shortages.2 Without the input provided by suppliers a business will not sustain for long and
operations will be forced to cease, as in the case of Saab Automobile. Such an undesirable
situation could, as observed, ultimately lead to the company being liquidated or forced into
bankruptcy.
From 2007 to 2009 the numbers of corporate bankruptcies in Sweden increased by almost 32
percents from 5 791 to 7 638. Since then the numbers have somewhat decreased reaching a
level of 6 958 bankruptcies treated by the district courts in 2011.3 Apart from the suppliers
and subcontractors, a bankruptcy might have serious implications also for other stakeholders.
For one, the employees and managers might lose their jobs and thus their income if their
employer has to close operations and can no longer provide work nor pay out salaries.
Customers whose operations are dependent on the financially distressed company and the
goods or services that it purchases will naturally also be affected. Further down the line of
economic instability, the company might also have to cancel its pending payments towards
creditors, thus not fulfilling its debt obligations.
With this in mind, it could very well be considered an advantage if one with great ease and
certainty was able to determine the likelihood of a firm, in a not too distant future, reaching a
1 TT, ”Saab går i konkurs”, Dagens Nyheter, 2011-12-19, http://www.dn.se/ekonomi/saab-begars-i-konkurs,
(Downloaded 2012-04-17)
2 Rabe, M., ”Saab har problem – betalar inte underleverantörer”, Teknikens värld, 2011-03-29,
http://www.teknikensvarld.se/2011/03/29/12375/saab-har-problem--betalar-inte-underleverantorer/,
(Downloaded 2012-05-10)
3 Tillväxtanalys, Konkurser och offentliga ackord 2011: Statistikrapport 2012:01, 2012-02-13,
http://www.tillvaxtanalys.se/tua/export/sv/filer/statistik/konkurser/Statistik_2012_01.pdf, (Downloaded 2012-
04-17)
1 INTRODUCTION
Without exaggerating, money is by many means a fundamental cornerstone of our society.
Many have at some point come across the expression “money makes the world go around”,
once sung in the musical, Cabaret. This expression can easily be applied to and observed
within the reality of corporations. Without funds, the world of the company will simply stop
spinning, since without funds one will have trouble paying suppliers’ bills and in most cases
this results in the suppliers discontinuing supplying their goods and services to the company.
A recent case involves the subcontractors of the Swedish car manufacturer Saab Automobile,
who amongst others, was negatively affected by the financial distress presented by this big
industrial actor.1 One of the more alarming indications of a forthcoming bankruptcy of Saab
Automobile, was its inability to pay its subcontractors which consequently lead to material
shortages.2 Without the input provided by suppliers a business will not sustain for long and
operations will be forced to cease, as in the case of Saab Automobile. Such an undesirable
situation could, as observed, ultimately lead to the company being liquidated or forced into
bankruptcy.
From 2007 to 2009 the numbers of corporate bankruptcies in Sweden increased by almost 32
percents from 5 791 to 7 638. Since then the numbers have somewhat decreased reaching a
level of 6 958 bankruptcies treated by the district courts in 2011.3 Apart from the suppliers
and subcontractors, a bankruptcy might have serious implications also for other stakeholders.
For one, the employees and managers might lose their jobs and thus their income if their
employer has to close operations and can no longer provide work nor pay out salaries.
Customers whose operations are dependent on the financially distressed company and the
goods or services that it purchases will naturally also be affected. Further down the line of
economic instability, the company might also have to cancel its pending payments towards
creditors, thus not fulfilling its debt obligations.
With this in mind, it could very well be considered an advantage if one with great ease and
certainty was able to determine the likelihood of a firm, in a not too distant future, reaching a
1 TT, ”Saab går i konkurs”, Dagens Nyheter, 2011-12-19, http://www.dn.se/ekonomi/saab-begars-i-konkurs,
(Downloaded 2012-04-17)
2 Rabe, M., ”Saab har problem – betalar inte underleverantörer”, Teknikens värld, 2011-03-29,
http://www.teknikensvarld.se/2011/03/29/12375/saab-har-problem--betalar-inte-underleverantorer/,
(Downloaded 2012-05-10)
3 Tillväxtanalys, Konkurser och offentliga ackord 2011: Statistikrapport 2012:01, 2012-02-13,
http://www.tillvaxtanalys.se/tua/export/sv/filer/statistik/konkurser/Statistik_2012_01.pdf, (Downloaded 2012-
04-17)
6
state of financial distress. Because, as described above, in such a state the future of the
company is by many means at risk and there is often a significant possibility that this will lead
to a situation where the company has to discontinue its operations. This could have serious
implications for all these stakeholders of the company. However, the stakeholder often
assuming the riskiest position is the shareholder investing equity capital in exchange for
shares.
From the perspective of an equity investor, it might be sufficient if the company simply
performs poorly and gets itself into a financially troublesome situation, in order for the
investment to become worth close to nothing as the share price could be very vulnerable to
such implications. With the possibility of losing a lot or even everything, it would thus
probably serve them best if a tool could be used to predict when and if a company could get
itself into a state related to financial distress. By taking such information into account one
could perhaps easier evade such events.
Such tools do in fact exist and the research considering them is in fact rather extensive. When
describing the research conducted regarding this area there are however a couple of seminal
studies that needs to be mentioned. In 1966 Beaver sought to analyze single financial key
ratios to find whether these could predict bankruptcy on their own, using univariate analysis.4
Later on, Altman conducted a study where he instead of using only single key ratios,
developed a model consisting of five different ratios.5 Afterwards, Ohlson wrote a paper
where he developed another model, promoting other statistical methods6 and seeking to better
predict corporate bankruptcy.7
This leads us to the this thesis, which presents some empirical results of a study evaluating
and focusing on Ohlson’s O-score and its performance and uses when depicting the
probability of financial distress. The study centres around Swedish listed firms and the
probability that such will, within one year, reach a state of financial distress. We present
results describing how well Ohlson’s original model, based upon American data originating
from the 70’s,8 functions when studying Swedish firms of today. We also develop an updated
4 Beaver, W. 1966. “Financial Ratios as Predictors of Failure”, Journal of Accounting Research, Vol. 4, No. 3, p.
100
5 Altman, E. “Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy”, Journal of
Finance, Vol. 23, No. 4, 1968, pp. 594ff.
6 Ohlson, J. “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, Journal of Accounting Research,
Vol. 18, No. 1, 1980, pp. 111ff.
7 Ibid., p. 109
8 Ohlson (1980), “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, p. 109
state of financial distress. Because, as described above, in such a state the future of the
company is by many means at risk and there is often a significant possibility that this will lead
to a situation where the company has to discontinue its operations. This could have serious
implications for all these stakeholders of the company. However, the stakeholder often
assuming the riskiest position is the shareholder investing equity capital in exchange for
shares.
From the perspective of an equity investor, it might be sufficient if the company simply
performs poorly and gets itself into a financially troublesome situation, in order for the
investment to become worth close to nothing as the share price could be very vulnerable to
such implications. With the possibility of losing a lot or even everything, it would thus
probably serve them best if a tool could be used to predict when and if a company could get
itself into a state related to financial distress. By taking such information into account one
could perhaps easier evade such events.
Such tools do in fact exist and the research considering them is in fact rather extensive. When
describing the research conducted regarding this area there are however a couple of seminal
studies that needs to be mentioned. In 1966 Beaver sought to analyze single financial key
ratios to find whether these could predict bankruptcy on their own, using univariate analysis.4
Later on, Altman conducted a study where he instead of using only single key ratios,
developed a model consisting of five different ratios.5 Afterwards, Ohlson wrote a paper
where he developed another model, promoting other statistical methods6 and seeking to better
predict corporate bankruptcy.7
This leads us to the this thesis, which presents some empirical results of a study evaluating
and focusing on Ohlson’s O-score and its performance and uses when depicting the
probability of financial distress. The study centres around Swedish listed firms and the
probability that such will, within one year, reach a state of financial distress. We present
results describing how well Ohlson’s original model, based upon American data originating
from the 70’s,8 functions when studying Swedish firms of today. We also develop an updated
4 Beaver, W. 1966. “Financial Ratios as Predictors of Failure”, Journal of Accounting Research, Vol. 4, No. 3, p.
100
5 Altman, E. “Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy”, Journal of
Finance, Vol. 23, No. 4, 1968, pp. 594ff.
6 Ohlson, J. “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, Journal of Accounting Research,
Vol. 18, No. 1, 1980, pp. 111ff.
7 Ibid., p. 109
8 Ohlson (1980), “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, p. 109
7
model and analyze the differences between it and the original model. This updated model is
fabricated using the same statistical methods and variables as used by Ohlson when
constructing his O-score. However, our sampling method is somewhat different from
Ohlson’s.9 Finally, we incorporate variables describing the quality of the financial reports as
described by the auditor in the audit report. The effects are consequently sought out and
analyzed.
1.1 PURPOSE
The purpose of this study is to examine the possible uses of Ohlson’s O-score in relation to
Swedish data and to observe how it applies to modern Swedish firms of today. That includes
analyzing the differences between the original model, published in a paper in 1980 and an
altered model based upon the same statistical methods and variables, but on Swedish listed
firms being active during recent years. Basically, we aim to examine how Ohlson’s studies
can be employed today in order to determine the probability of a Swedish listed firm
becoming bankrupt or reaching another condition related to financial distress. Furthermore,
since the models are almost entirely relying on accounting-based information derived from
the annual reports, we would like to examine whether there is a gain to be attained in also
considering the quality of these reports, with regard to the model. That is, can we increase the
predictability of corporate failure by inducing an audit element and incorporating data
regarding the audit reports into the model?
Above all this, the underlying purpose is, with Ohlson’s O-score as a starting point, to find a
model that can easily be understood and applied by investors investing in Swedish stocks on
as many levels as possible. This also includes that the data should be easily acquirable and
used as input in the model.
1.2 QUESTION FORMULATIONS
The questions that are connected to the purpose of the study are formulated as follow:
How can Ohlson’s O-score be applied when examining the probability of Swedish
listed firms reaching a state of financial distress?
o How would a similar model, based on the same statistical methods and
variables look like and would it differ from the original model?
9 Ibid., p. 114
model and analyze the differences between it and the original model. This updated model is
fabricated using the same statistical methods and variables as used by Ohlson when
constructing his O-score. However, our sampling method is somewhat different from
Ohlson’s.9 Finally, we incorporate variables describing the quality of the financial reports as
described by the auditor in the audit report. The effects are consequently sought out and
analyzed.
1.1 PURPOSE
The purpose of this study is to examine the possible uses of Ohlson’s O-score in relation to
Swedish data and to observe how it applies to modern Swedish firms of today. That includes
analyzing the differences between the original model, published in a paper in 1980 and an
altered model based upon the same statistical methods and variables, but on Swedish listed
firms being active during recent years. Basically, we aim to examine how Ohlson’s studies
can be employed today in order to determine the probability of a Swedish listed firm
becoming bankrupt or reaching another condition related to financial distress. Furthermore,
since the models are almost entirely relying on accounting-based information derived from
the annual reports, we would like to examine whether there is a gain to be attained in also
considering the quality of these reports, with regard to the model. That is, can we increase the
predictability of corporate failure by inducing an audit element and incorporating data
regarding the audit reports into the model?
Above all this, the underlying purpose is, with Ohlson’s O-score as a starting point, to find a
model that can easily be understood and applied by investors investing in Swedish stocks on
as many levels as possible. This also includes that the data should be easily acquirable and
used as input in the model.
1.2 QUESTION FORMULATIONS
The questions that are connected to the purpose of the study are formulated as follow:
How can Ohlson’s O-score be applied when examining the probability of Swedish
listed firms reaching a state of financial distress?
o How would a similar model, based on the same statistical methods and
variables look like and would it differ from the original model?
9 Ibid., p. 114
8
o What are the implications of adding information regarding the quality of the
annual reports to the model?
o What are the implications of adding information regarding the quality of the
annual reports to the model?
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