Swedish Firms Financial Distress: An Ohlson’s O-Score Study

This bachelor's thesis in Accounting and Financial Management from the Stockholm School of Economics explores the prediction of financial distress among Swedish listed companies using Ohlson's O-score. It compares the results of three different models derived through logistic regression analysis, finding that Ohlson's model provides usable input. The study indicates that a revised model, incorporating information related to the audit report and the auditor's opinion, improves the prediction of financial distress. The research analyzes the applicability of Ohlson’s O-score to Swedish data and modern Swedish firms, examining differences between the original model and an altered model based on the same statistical methods and variables. The thesis concludes by assessing whether incorporating audit report quality enhances the predictability of corporate failure.

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
*[e-mail] [e-mail]

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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

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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

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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

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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)

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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 methods 6 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

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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

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o What are the implications of adding information regarding the quality of the
annual reports to the model?

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2 LITERATURE REVIEW
2.1 EARLY SEMINAL RESEARCH: BEAVER AND ALTMAN
During the 1960s the foundation of the research related to ratio analysis and how the use of it
could help determine the probability of firm-related bankruptcy was laid. In 1966 Beaver
published a study where he analyzed a set of thirty financial ratios in order to distinguish their
uses when evaluating the probability of companies going bankrupt. He found that there were a
lot of financial ratios that could be used as predictors of this matter.10 Soon after Beaver’s
study, Altman published a paper based on a study analyzing 66 different companies of which
33 were defined as healthy companies that had not gone bankrupt. The other 33 were instead
defined as bankrupt companies. Based on this sample and the method of multiple discriminant
analysis (MDA) he developed the Z-score model. Unlike Beaver he analyzed a set of financial
ratios at the same time and after having run numerous different ratio profiles through the
computer, the final appearance of his model was the following:11
12
The final summarized product, the so called Z-score, was then compared to an accompanying
discriminant value which determined whether the firm would go bankrupt or not.13 All of
these variables were based upon accounting data, except for the fourth variable which
considers the market value of equity. The traditional Z-score has later on been followed by the
ZETA model14, as well as a number of other similar models.15
10 Beaver (1966), “Financial Ratios as Predictors of Failure”, pp. 106ff
11 Altman (1968), “Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy”, pp. 593ff.
12 Ibid., p. 594
13 Ibid., p. 592
14 Altman, E., Haldeman, R. and Narayanan, P. “ZETA™ ANALYSIS, A New Model to Identify Bankruptcy
Risk of Corporations”, Journal of Banking and Finance, Vol. 1, No. 1, 1977, pp. 29-54
15 Altman, E. “Predicting Financial Distress of Companies: Revisiting the Z-Score and ZETA® Models”, Stern
School of Business, New York University, July 2000, pp. 25ff., http://pages.stern.nyu.edu/~ealtman/Zscores.pdf,
(Downloaded 2012-05-05)

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2.2 LOGIT MODELS: OHLSON
Apart from Beaver and Altman, an extensive array of studies have been conducted within this
research area and there are now a wide variety of models that have been developed and which
attempts to predict the bankruptcy of firms. The most important one regarding this study is the
one conducted by Ohlson, who presents a binary model. It is similar to Altman’s original Z-
score in the sense that it is meant to predict corporate failure as evidenced by the event of
bankruptcy. Another similarity is the almost exclusionary use of key ratios and accounting-
based information. However, this binary model uses a different statistical approach enabling a
more unhampered sample and sampling method. The statistical approach enabling this is the
logistic approach (or the logit approach) and Ohlson has, unlike Altman, not used a small
sample were failing companies and non-failing companies have been paired together. Instead
he has used a sample consisting of 105 failing companies and 2,058 non-failing companies,
all of which had been or was listed companies.16
Instead of five variables, like Altman’s Z-score model, 17 Ohlson’s so called O-score is
constituted out of nine variables, including both financial ratios and specific dummies
attempting to enhance predictability of his model. 18 Each variable is described below by the
words of Ohlson himself:
1. SIZE = log(total assets/GNP price-level index). The index assumes a base value of
100 for 1968. Total assets are as reported in dollars. The index year is as of the year
prior to the year of the balance sheet date. The procedure assures a real-time
implementation of the model. The log transform has an important implication.
Suppose two firms, A and B, have a balance sheet date in the same year, then the
sign of P A - P B is independent of the price-level index. (This will not follow unless
the log transform is applied.) The latter is, of course, a desirable property.
2. TLTA = Total liabilities divided by total assets.
3. WCTA = Working capital divided by total assets.
4. CLCA = Current liabilities divided by current assets.
5. OENEG = One if total liabilities exceeds total assets, zero otherwise.
6. NITA = Net income divided by total assets.
7. FUTL = Funds provided by operations divided by total liabilities.
16 Ohlson (1980), “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, pp. 109f
17 Altman (1968), “Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy”, p. 594
18 Ohlson (1980), “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, pp. 118f

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8. INTWO = One if net income was negative for the last two years, zero otherwise.
9. CHIN = (NIt – NIt-1)/(|NIt| + |NIt-1|), where NI is net income for the most recent
period. The denominator acts as a level indicator. The variable is thus intended to
measure change in net income.19
In his study he presented results for three different models, which sought to predict the
probability of bankruptcy one year in advance, two years in advance given that the firm did
not go bankrupt during the first year and either one or two years in advance. 20 However, it is
only the first model which is of interest in this study as it is devoted to analyzing the
probabilities of financial distress one year in advance. Ohlson’s first model was presented
with the following appearance, describing the O-score:
21
Regarding the signs preceding these coefficients they could very well be logically explained
with regard to the essence of what these variables are trying to capture. This is however not
explained by Ohlson in his study. SIZE, WCTA, NITA, FUTL and CHIN are although ratios
that decreases the probability of failure, consequently presented as negative coefficients. As
the value they assume increase they are considered as being healthier, which is reflected in a
decreased probability of bankruptcy. OENEG is a variable trying to compensate for
companies whom present negative equity, or alternatively has total liabilities which are
exceeding total assets. This variable is by Ohlson described as neither truly negative nor truly
positive and its interpretation is thus not as clear-cut as for the others. TLTA, CLCA and
INTWO do understandably are positive coefficients, since a higher share of liabilities over
assets and a negative net income do not typically have a positive effect on a company’s well-
being. This means that they have an increasing effect on the probability of bankruptcy. 22
2.3 SWEDISH BANKRUPTCY PREDICTION MODELS: SKOGSVIK
Skogsvik has developed two models based upon a statistical method related to the logit
analysis – the probit analysis. His sample, aside from being Swedish, consists of industrial
companies with a data period ranging from 1966 to 1980. The observations were however
obliged to have had assets amounting to at least 200 million SEK or, as a minimum, 200
19 Ohlson (1980), “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, pp. 118f
20 Ibid., p. 120
21 Ibid., p. 121
22 Ibid., p. 119

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employees any year between 1966 and 1971. Comparable as to our study he defined the
failing firms, or the firms that had reached a state of financial distress, as either having gone
bankrupt, reached a composition agreement, voluntarily shut down the primary production
activity or received a receipt of a substantial subsidy provided by the state. His sample did
eventually contain 51 failing companies and 328 non-failing companies. 23 One of the models
was based upon current cost accounting (CCA), whilst the second one on the more commonly
used historical cost accounting (HCA) approach. This second one was intended to be used as
a reference model. The two models were developed independently and had slightly differing
sets of variables. 24 What he found was however that their performances were very alike, at
least as to their Type-I and Type-II error rates. 25 HCA ratios thus seemed to still prove to be
good predictors of business failure, in the sense that they have been employed earlier in the
studies of Beaver, Altman and Ohlson amongst others.26
2.4 RESEARCH CONSIDERING AUDIT REPORTS AND BANKRUPTCY
Auditing can be defined as “a systematic process of objectively obtaining and evaluating
evidence regarding assertions about economic actions and events to ascertain the degree of
correspondence between those assertions and established criteria and communicating the
results to interested users”.27 The results communicated to interested users are the audit
reports as well as the opinions derived from the financial statements of the company. The
most common opinion is an unmodified opinion, proclaiming that the statements are
presented fairly according to the auditor. If this is not the case, but rather that a modified
opinion is presented, there is probably something wrong with regards to the financial
statements and the accounting procedures of the company. It could be that the auditor has
found a material misstatement, but the company has refused to change it. It could also be that
the auditor has not been able to perform a satisfying evaluation. There can be several other
underlying reasons resulting in a modified opinion 28 and it is the relationship between these
reasons and bankruptcy which the following studies seek to undertake.
23 Skogsvik, K. “Current Cost Accounting Ratios as Predictors of Business Failure: The Swedish Case”, Journal
of Business Finance & Accounting, Vol. 17, No. 1, 1990, pp. 141f
24 Ibid., pp. 137f
25 Ibid., p. 152
26 Ibid., p. 138
27 Eilifsen, A., Messier, W., Glover, S. and Prawitt, D. Auditing & Assurance Services, 2nd revised ed., Berkshire:
McGraw-Hill Education, (2010), p. 10
28 Ibid., pp. 18-20