- Email: [email protected]
Corporate bankruptcy prediction in Japan
Journal
of Banking
and Finance
CORPORATE Kichinosuke
8 (1948) 229-247.
BANKRUPTCY TAKAHASHI Keio University.
Institute
PREDICTION and Yukiharu
Yokohama
Kazunori Nagasaki
North-Holland
IN JAPAN
KUROKAWA
and Tokyo, Japan
WATASE
of Applied Science, Nagasaki,
Japan
The purpose of this study is to highlight the financial characteristics of failed firms in Japan, and to construct corporate bankruptcy prediction models with greater prediction accuracy. Our principal component analysis indicated that failed firms in Japan could be classified into two groups: a group having negative financial structures and a group having a declining flow of funds. Additionally, they can be classified into two other different categories of groups: one whose financial position during three years before shows a ‘V’ shape and another group that shows a ‘A’ shape. Our discriminant analysis indicated that improved prediction accuracy could be obtained by using, as predictor variables, both ratios and absolute amounts based on cash base financial statement data three years before failure. This data was adjusted to properly reflect the exceptrons, reservations, and qualifications appearing in the audit reports and those based on accrual base financial statement data.
1. Introduction Study of the development of corporate bankruptcy prediction models started in America in the mid-1960’s. Virtually all studies so far published use multivariate discriminant functions, except for that of Beaver (1966) who used a univariate prediction model and that of Wilcox (1973) who tried to predict corporations’ meantime to failure through the study of their states and the probability of their moving. Included in this related group are Altman (1968), Deakin (1972), Cooley’s (1975) examination of theoretical models, Altman et al. (1977), Altman (1980), and Ohlson (1980). In Japan, work started in the early 1970’s using the achievements of the studies in America as a base. The Japanese studies so far published include Nomura Research Institute (1973), Toda (1974), Itoh (1977), Ohta (197Q Tanaka and Wakagi (1978), Murakami (1979), Igarashi (1979), and Ozeki and Ohno (1980). They all use multivariate prediction models using discriminant functions, except for Tanaka and Wakagi (1978), who used a multiple regression model and Ozeki and Ohno (1980), who used a model combining principal component analysis and cluster analysis. They invariably deal with listed corporations as their sample firms, except for 03784266/84/$3.00
0
1984, Elsevier
Science Publishers
B.V. (North-Holland)
230
K. Takahashi
et al., Bankruptcy
prediction
in Japan
Igarashi (1979) and Ozeki and Ohno (1980) who deal only with non-listed corporations of relatively smaller sizes. Among those studies, Toda (1974) deals with the smallest number of failed sample firms 15 firms - while Murakami (1979) deals with the largest number of failed sample firms - 55 firms. Ohta (1978) uses the smallest number of indicator variables - four while Igarashi (1979) uses the largest number of indicator variables 14. They all use as indicator variables financial data ratios for the first year before failure alone, except for Murakami (1979) and Igarashi (1979), who use both financial data ratios and absolute amounts. Though Itoh (1977) and Igarashi (1979) use two or more cutoff points to have five or ten areas into which to classify sample firms, the other four models use only one cutoff point each. Here we can see great influence of the early American studies such as Altman (1968) and Deakin (1972). Since bankruptcy is very rare in the listed corporations in Japan, none of the studies mentioned above, with the exception of Igarashi (1979), uses any verification test samples (secondary samples) apart from samples for analysis purposes (initial samples). The purpose of this study is to clarify financial characteristics of failed firms in Japan and to construct a bankruptcy prediction model or models with greater accuracy than those previously developed.
2. Approach and methods of analysis 2.1. Data and indices used Among the listed Japanese firms which went bankrupt between 1961 and 1977, we have selected 40 corporations whose financial statements for at least three years before failure were available. The ‘first year before failure’ as used herein with respect to any failed firm means its fiscal year preceding the day of its bankruptcy. As Ohlson (1980) pointed out, there is an important relation between the date when a firm goes bankrupt and the date when it last publishes its financial statements. One of the features of this study is that light was cast on such a relation. Most prediction models developed in America, including Beaver (1966), use financial statement data from five years before failure. Most prediction models developed in Japan use data from three or four years before failure. This is obviously because of the fact that listed firms which have gone bankrupt are limited in number in this country and that many of those bankrupt firms had less than five years of business history between the date of their initial listing and the date of failure. There are numerous cases in this country where the auditors’ reports of failed firms contain exceptions, reservations and/or qualifications due primarily to window dressing (mostly overstatement of profits). In an attempt
K. Takahashi et al., Bankruptcy prediction in Japan
231
to properly reflect those exceptions in our models, we made such adjustments to the financial statement data of the sample firms as we considered appropriate under the circumstances. To our knowledge, there are few prior studies in this country in which such adjustments were made to the financial statement data.l 2.2. Principal component analysis We employed what is called ‘pair sampling method’. We selected an equal number of non-failed firms (non-failed mates) and failed tirms,2 and compared the failed and non-failed groups to find the characteristics, if any, peculiar to the failed group. For this purpose, we employed ‘principal component analysis’ to classify the failed sample firms into several pattern groups. Initially we selected 80 indices based on the following criteria:
(i)
whether or not the particular index is expected to show any meaningful change before failure, and (ii) whether or not the particular index performed well in any one of the previously developed prediction models. We calculated the value of each of the indices selected, using the financial statement data of the sample firms for each of the three years before failure. Then we indexed and determined the value of the ‘level’, ‘trend’, and ‘behavior’ (‘V’ or ‘/\’ shape) of every index based upon its values during the three-year period before failure. 3 A principal component analysis was then conducted in the following two different methods: ‘The average amount of operating losses of the failed sample group (40 failed firms) for the first year before failure calculated based on non-adjusted financial statement data was about Y 240 million per firm, which was approximately equal to $1.0 million, if translated at a rate of $l.OO=Y240.00. When the average amount of operating losses of those 21 failed sample firms, out of the 40 failed sample firms, which had exceptions, reservations and/or qualifications in their audit reports, was recalculated based on their financial statement data for the first year before failure, as adjusted to properly reflect those exceptions, reservations and/or qualifications, a downward adjustment of some Y 120 million ($500 thousand) per firm appeared to be necessary to correct overstatement of their operating profits and losses. ‘For the purpose of our study, pair sampling was conducted to select one solvent firm for each failed sample firm on the basis (i) that they both belonged to the one and same industry, (ii) that they had substantially the same fiscal year systems, and (iii) that they were as close to each other as possible in total assets size. 31f Xi, is the value of any given index for j year before failure, and wherein i= 1 to 80 and j= 1, 2 or 3, thee the following functions will stand: level of the index: x1, = 5 X,,/3, j=l
trend of the index: x2, = X, 1 -X, behavior
of the index: xa, =Xi,
3, +Xi,-2X,,,
K. Takahashi et al., Bankruptcy prediction in Japan
232
(i) (ii)
the method where the values of the indices for each of the three years before failure are used as they are, and the method where the values showing their ‘levels’, ‘trends’ and ‘behaviors’ are used instead.
In both methods, we used six indices (or 18 variables) selected original list of indices in accordance with the following procedures: (i)
(ii)
out of the
to determine the correlation coefficient of each index in the original list of indices, to pick up indices having higher correlation coefficients among them to form a group, and to select from among indices in each of the groups one representative index; and to select, from among the indices selected in accordance with the procedures set fourth in (i) above, six indices which show the most significant differences between the failed and non-failed groups.4
2.3. Discriminant
analysis
The initial sample we selected for our discriminant analysis consisted of 72 firms; 36 failed firms selected from the 40 failed firms mentioned above, which went bankrupt before 1977, and 36 non-failed firms selected by pair sampling. The secondary sample which we selected for verification test purposes consisted of 48 firms, namely, the other four failed firms which went bankrupt in 1977, four non-failed firms which were selected by pair sampling, and 40 other firms then existing in 1977. We selected for our discriminant analysis 75 accrual base financial data indices (61 ratios and 14 absolute amounts) and 54 cash base financial data indices (45 ratios and nine absolute amounts).
2.3.1. Construction
of prediction
models
One can develop several different types of prediction models depending upon what financial statement data and indices one will use - (a) nonadjusted data or data adjusted to reflect the exceptions, reservations and/or qualifications appearing in the audit reports; (b) accrual or cash base financial data indices; (c) index values for three years before failure or only for the first year before failure; or (d) ratios alone, or a combination of ratios and absolute amounts. Theoretically, combination of (a), (b), (c), and (d) above could produce 16 different model types. The prediction models developed by Altman (1968) and Altman et al. (1977) are different in that the former uses financial statement data ratios for the first year before failure alone, while the latter uses both financial 4The “T’-test’ and Mann-Whitney’s
‘rank sum test’.
K. Takahashi et al., Bankruptcy prediction in Japan
233
statement data ratios and absolute amounts for more than one year before failure. In the case of Japanese firms, as mentioned before, it would be necessary and appropriate for discriminant analysis purposes to adjust financial statement data to reflect the exceptions, reservations and/or qualifications appearing in audit reports, and cash base financial data is more reliable because it is less susceptible to window dressing by management. Therefore, in addition to the 16 models, we also developed another discriminant function, as the 17th model, which uses both ratios and absolute amounts derived from adjusted accrual base and cash base financial statement data for three years before failure. Those 17 models are shown in fig. 1. For each of the 17 models constructed, we used eight indices (or 24 variables) or 24 indices which we selected on the same base as the one we used for our principal component analysis purpose.5 2.3.2. Verljkation of prediction models There is a big difference between the Type I and Type II errors in costs of errors.6 Type I errors cause investors and creditors to suffer actual economic loss resulting from their inability to collect loans, or the charging off of their equity investments, resulting from the depreciation of their market value. However, no actual damage is recognized from type II errors, other than opportunity costs resulting from the loss of investment opportunities due to too conservative investment decisions. Generally the probability of failure is far smaller than the probability of non-failure. Cooley (1975) has demonstrated by using a simulation model that the cutoff point varies depending on whether or not misclassification costs and/or the probability of misclassification are to be taken into consideration. To select the best cutoff point or points, Altman et al. (1977) and Altman (1980) determined misclassification costs based on the results of rather extensive inquiries conducted by them with respect to commercial bank lendings alone. It is, however, practically very difficult to determine misclassification costs of this kind. 5During the course of our study we encountered cases where we could not reduce the number of variables to 2.5 [the maximum number permitted by the computer program we used (BMD 04M)] when we attempted to select variables merely by those standards. (The variables selected merely by those standards are hereinafter referred to as ‘potentially useful variables’.) Therefore we prepared several combinations of potentially useful variables, each having variables not exceeding the said limit. A discriminant function was then constructed for each of the combinations. The discriminant functions were then tested and compared for discriminant efficiency as determined by Mahalanobis’ square distance between the respective barycenters of the two groups to choose a discriminant function having the highest discriminant efficiency. This procedure was followed for each of the 17 prediction model types we constructed. 6‘Type I error’ means misclassification of a failed firm as non-failed, and ‘Type II error’ means misclassification of a non-failed firm as failed.
K. Takahashi
234
Materials studied
Data
et al., Bankruptcy
prediction
Period before failure covered
used
in Japan
Indices
Model code name
Ratios
(1)
Ratios/absolute amounts
(2)
3 years I Non-adjusted
data
(31 1st year <~~~;bsolute (4)
Accrual base financial statements
Ratios
(5)
3 years < t
Adjusted
Ratios/absolute amounts
data ( lst year <~~~~~/absolute amounts Ratios
(81 (91
3 years < Non-adjusted
data
Rattos/absolute amounts
(101 (11)
( 1st year Cash base financial statements
amounts
(12)
Ratios
(13)
3 years Adjusted
data
Ratios/absolute amounts
(14)
Ratios
(151
< Ratios/absolute amounts Accrual and cash bases financial statements
Adjusted
data-
3 years before failure
Fig. 1. 17 prediction
models
Ratios/absolute amounts
used for the study.
(I71
K. Takahashi
et al., Bankruptcy
prediction in Japan
235
When the Japanese actually try to predict bankruptcy of a firm, we do not believe they will make such predictions using a single cutoff point. Instead, they would prefer to have more than one cutoff point, such as the most conservative point, the most optimistic point, or the point where bankruptcy may occur with a certain statistical probability percentage. We therefore adopted six cutoff points which have their own justification for existence in our actual prediction models. This will enable the users of our models to have plural prediction results depending on which one of the six cutoff points they will use, leaving the final judgment up to them. Those six cutoff points are: the point at which the overall probability of (A) cutoff point C, misclassification becomes minimal, assuming that both populations have normal distribution, (B) cutoff point C, - the point at which the probability of the Type I error (misclassification of failed firms as non-failed firms) becomes lx, assuming that both populations have normal distribution, (C) cutoff point C, - the point at which the probability of the Type I error becomes 5%, assuming that both populations have normal distribution, (D) cutoff point C, - the point at which the number of firms misclassified becomes minimal, (E) cutoff point C, - the point at which the number of failed firms misclassified as non-failed becomes zero, and (F) cutoff point Cf - the point at which the number of non-failed firms misclassified as failed (Type II error) becomes zero. We applied each of the 17 discriminant functions to the verification (secondary) sample to verify their prediction accuracy, using the six different cutoff points. To determine the predictive ability ranking of the 17 discriminant functions by cutoff points, the following two criteria were employed: (i)
that the smaller its ratio of the Type I error was, the higher it ranked, and (ii) that the smaller its ratio of the Type II error was, the higher it ranked.
When applying the criteria, priority was given to criterion (i) over criterion (ii). Their predictive abilities were compared with each other by applying criterion (i) first. If, as a result of such comparison, any two or more discriminant functions ranked pari passu, we then applied criterion (ii) to determine the ranking among them. The reason why we gave priority to criterion (i) was that we thought that the cost of misclassifying failed firms as non-failed would be far greater than that of misclassifying non-failed firms as failed.
236
K. Takahashi
et al., Bankruptcy
prediction
in Japan
3. Results 3.1. Results of principal component analysis The following six indices component analysis:
(or
18 variables)
were
used
in our
net worth to total assets, working capital to total assets, borrowed expenses to sales, value added to total assets at the beginning of the year, cash provided from operations7 to total assets at the beginning year, and working capital provided from operations’ to total assets beginning of the year. As shown in table 1, the components Z- 1 and 2-2 cover as much as 54%.of all information, when method when we used method (ii).
principal
of the at
the
together alone could (i) was used and 48%
Table 1 Elgenvalue
and ratio accumulated
Component
contribution.
Z-l
z-2
z-3
z-4
z-5
1.32
2.47
190
1.54
1.32
0.41
0.54
0.65
0.74
0.81
4.97
3.75
1.92
1.38
1.08
0.28
0.48
0.59
0.67
0.73
Method (i) Eigenvalue Ratio of accumulated
contribution
Method (ii) Eigenvalue Ratio of accumulated
contribution
We then determined the meaning, if any, of the components based on the respective factor loadings of their relevant original variables. The results of such determination are shown in table 2. The Z-1 component under method (i) seemed to indicate the ‘financial structure (or static financial condition)’ of the sample firms, and the 2-2 component ‘flow of funds (or dynamic “Cash provided from operations’ as used herein for any given year means the balance between cash revenues and cash expenses for that year accruing from transactions of the recurring nature in the ordinary course of business. “Working capital provided from operations’ as used herein for any given year means net increase (decrease) in working capital for that year accruing from transactions of the recurring nature in the ordinary course of business.
K. Takahashi et al., Bankruptcy prediction in Japan
237
Table 2 Component
Variables
_
loadings.
Year before failure
Components Z-1
z-2
z-3
3 2 1 3 2 1 3 2 1 3 2 1
0.711 0.836 0.846 0.446 0.604 0.706 -0.624 - 0.706 -0.695 0.583 0.698 0.430
0.308 0.199 0.155 - 0.409 - 0.458 - 0.026 0.117 0.243 -0.001 - 0.076 -0.050 0.781
0.278 0.158 0.200 0.694 0.517 0.415 0.492 0.475 0.396 -0.126 -0.108 -0.021
0.441 0.432 0.461
-0.119 - 0.208 0.588
- 0.340 - 0.025 0.145
0.725 0.712 0.572
- 0.428 -0.392 0.713
-0.187 -0.101 -0.054
Level Trend Behavior
0.499 0.161 0.026
- 0.596 -0.644 0.250
- 0.069 0.132 0.688
Level Trend Behavior
0.168 0.601 0.566
-0.541 - 0.226 0.211
0.087 -0.329 0.395
0.585 0.154 -0.392
0.202 - 0.665 0.105
Method (i) Net worth
Working
Borrowed
to total assets
capital
to total assets
expenses
to sales
Value added to total assets
Cash provided to total assets
from operations
Working capital provided operations to total assets
from
Method (ii) Net worth
to total assets
Working capital total assets
Borrowed
to
Value added
Cash provided to total assets
-0.311 -0.393 -0.462
to sales
Level Trend Behavior
to total assets
Level Trend Behavior
0.647 0.831 0.682
-0.332 0.245 0.564
-0.131 0.114 - 0.064
Level Trend Behavior
0.365 0.429 0.248
-0.604 0.387 0.142
0.174 0.046 -0.755
Level Trend Behavior
0.535 0.868 0.725
-0.785 0.266 0.550
-0.011 0.059 -0.148
expenses
from operations
Working capital provided operations to total assets
from
238
K. Takahashi et al., Bankruptcy prediction in Japan
financial condition)‘. And Z- 1 and 2-2 components under method (ii) seemed to indicate the ‘levels of the indices’ and ‘changes of the indices in time series’. Finally we tried to find if the failed sample firms could be classified into several pattern groups, using a map showing the distribution of the sample scores. Under method (i), it seemed that the failed sample firms9 could be classified into the following three groups: (A) Type A, (B) Type B, (C) Type C, -
Firms having bad financial structure. 27 failed sample firms were classified into this type. Firms having declining flow of funds. Nine failed sample firms were classified into this type. Firms having neither bad financial structure nor declining flow of funds. Three failed sample firms were classified into this type.
Under method (ii), it seemed that the failed sample firms could classified also into the following three groups: (A) Type A, -
(B) Type B, -
(C) Type Cz -
be
Firms in bad financial position with the time series change of their financial statements data indices behavior approximately showing ‘V’ shape.l’ 28 failed sample firms were classified into this type. Firms in bad financial position with the time series change of their financial statements data indices behavior approximately showing ‘/\, shape.” Seven failed sample firms were classified into this type. Firms whose financial position was not bad. Four failed sample firms were classified into this type.
3.2. Results of discriminant analysis All 17 prediction models constructed by us were made subject to a prediction accuracy test. The results of the test are set forth in table 3. The test showed that models (6) and (17) had relatively higher prediction accuracy regardless of the cutoff points used. Each of the 17 models was then subjected to a comparative study to see 9For this classification purpose, one failed sample firm whose financial statement data indices showed extraordinary values was excluded from the failed sample group. ioIf the behavior of the time series change of any index shows ‘V’ shape, it means that the value of the index for the first year and that for the third year before failure were both greater than that for the second year before failure. “If the behavior of the time series change of any index shows ‘A’ shape, it means that the value of the index for the first year and that for the third year before failure were both less than for the second year before failure.
K. Takahashi et al., Bankruptcy prediction in Japan
239
Table 3 Ranking
in terms of prediction
ability.”
Cutoff points Initial sample Model code name
sample
ABCDEFABCDEF
(l) 3 4 ii; (4) (5) (6) (7) (8) (9)
Secondary
2
3 1
2
;’
1 31 31241412
314314 2
4
4
4 1 4
2
1
1 4
3 4 2 4
2
2
2 3
(10) (11) I::; (14) (15) I:;;
2332321231
“Only the first through shown in this table.
1 fourth
places in the ranking
are
whether or not its prediction accuracy would show any significant change, if different components, different financial statement data, different number of years before failure, and/or financial indices of different types were used. The comparative study revealed the following facts:
0) that prediction
models using adjusted financial statement data had greater prediction accuracy than those using non-adjusted financial statement data, (ii) that prediction models using accrual base indices had greater prediction accuracy than those using cash base ones, (iii) that prediction models using financial statement data for three years before failure had greater prediction accuracy than those using financial statement data for only the first year before failure, and that there was no significant difference in prediction accuracy between (iv) prediction models using only ratios and those using both ratios and absolute amounts. Also, when we applied the 17 prediction models to the verification (secondary) samples, they showed virtually the same discriminant efficiencies.
K. Takahashi et al., Bankruptcy prediction in Japan
240
Althou~ it seemed at first glance that models (4) and (17) had almost equal prediction ability, we consider model (17) to be the best prediction model because of its relatively small variance in ranking, caused by the use of different cutoff points (table 3). Tables 4 and 5 show the prediction ability of the model (17) and its discriminant function, respectively. As shown in these tables, the discriminant efficiency was si~i~~ant at the level of 0.5%. Model (17) showed, when it was applied to the verification (secondary) sample, no Type I errors at all, regardless of which one of the six cutoff points was used, while its Type II error varied within a range of 20.5% to 43.2% depending on which one of those cutoff points was used. Table 4 Results of prediction ability test of best prediction model.”
Cutoff points
G
Secondary sample
Initial sample
Numbers Ratios
Numbers Ratios
Type I error Type II error
0 9
Type I error Type II error
0
0
0
0
19
43.2
19
52.8
Type I error Type II error
0 13
0 29.5
1 9
2.x 25.0
13.9
Type I error Type IT error
0 20.5 0 31.8
1 3
2.8 8.3
5.6
Type I error Type II error
0 9 0 14
16
44.4
22.2
Type I error Type II error
0 9
0 20.5
6 -
16.7 -
8.4
0 20.5
3 2
“Numbers renresent the number of misclassified represent percentage of mtsclassification ratios.
4.
8.3 5.6
6.9 26.4
firms, and ratios
Discussion
4.1.
Ch~ra~~eri~~i~~
p~c~liur to failed
~arnpl~bus
Our principal component analysis revealed two major characteristics peculiar to the failed sample firms. First, they could be classified into two major types - (i) those suffering persistent aggravation of the financial structure (Type-A, tirms”), and (ii) those suffering aggravation of flow of funds within a relatively short period of time (‘Type-B, firms’). These phenomena have often been pointed out in literature concerning corporate bankruptcy. Second, from the viewpoint of the time series change, they also
241
K. Takahashi et al., Bankruptcy prediction in Japan Table 5 Discriminant
function
of best prediction
model.
Year before failure Variables
l(xt 1)
x r, =Net worth to fixed assets xZj = Current liabilities ratio X3, = Voluntary reserves plus unappropriated surplus to total assets x 4, = Borrowed expenses to sales xsj = Earned surplus x 6, = Increase in residual value to cash sales” xTj =Cash provided from operations to total assetsb xsj = Cash sales’- cash purchase@ Mahalanobis’ square distance F-value (sigmticance level) Mean value Standard deviation
2(-k)
0.01039 -0.05687
0.07658 0.05147
-0.040231
0.22167
3(x,,) -0.01444 -0.03447
0.13213
1.00945 0.008 14
-0.72363 -0.01366
-0.34111 0.00685
0.14522
-0.03596
0.00545
-0.13034 0.0003 1
0.07848 0.00010
3.13721
6.22992 (0.005)
Non-failed Failed Non-failed Failed
0.02901 - 0.00007
0.06469 -0.02431 0.03353 0.03767
“Increase in residual value to cash sales ratio= [operating and other income (cash sales + interest and dividends received + cash proceeds realized from sale of fixed assets+funds generated by net decrease in short-term loans) -operating and other expenses (cash purchases +personnel expenses outlaid+cost and expenses outlaid+funds outlaid due to net mcrease in short-term loans) -cost of capital (dividends paid f interest paid + bond premiums paid + bond issue cost paid + stock issue cost paid) -original cost of long-term investment liquidated] -cash sales. bCash provided from operations to total assets = [(cash sales + interest and dividends received) -(cash purchases + personnel expenses outlaid + expenses outlaid+cost of capital)] L(cash on hand and at bank+long-term investment). “Cash sales = sales -net increase in account receivables-trade + net increase in advance received. %ash purchases = total purchases of raw materials and merchandise-net increase in account payables-trade + net increase in advance payments.
could be classified into two major types showed some improvement in their financial second year before failure, instead of straight and (ii) those which temporarily showed some position sometime during the first year before improvements seem to indicate that the firms rescue operation before failure.
(i) those which temporarily position sometime during the aggravation (‘Type-B, firms’); improvement in their financial failure (Type-A, firms’). These underwent some unsuccessful
242
K. Takahashi
et al., Bankruptcy
prediction
in Japan
The foregoing seems to indicate that in order to make accurate corporate bankruptcy predictions, we should use both cash base financial statement data indices, which can better show short-term flow of funds, and accrual base financial indices. To reflect the changes in the financial position before failure, in our prediction models, we should use financial statement data for two or more years before failure. Model (17), which we consider the best among the 17 prediction models, satisfies these requirements. Among the failed sample firms, however, were certain firms which showed no significant difference from the non-failed sample firms, as far as the results of the principal component analysis of their financial statement data were concerned. (We named these failed sample firms ‘Type-C, firms’ failed firms which had neither bad financial structure nor bad flow of funds - and ‘Type-C, firms’ - failed firms whose financial position was not so bad.) We then carefully reviewed those failed firms to see if they had been subject to any special conditions or circumstances to which none of the other failed sample firms had been subject. 4.2. Failed sample firms which showed sample firms
no signijkant
difference from non-failed
(1) Included in those failed sample firms were Nichiman Kogyo, Tokyo Tokei Seizo, Taio Seishi, Nippon Card Clothing and Giken Kogyo. Among the failed sample firms named above, Nichiman Kogyo and Taio Seishi were Type C, firms as well as Type C, firms. In the case of Tokyo Tokei Seizo, a staggering window dressing of financial statement was revealed after its bankruptcy, and its outside auditors were punished for having overlooked this window dressing. This case illustrates that no one can be aware of the true financial status of any corporation from false or twisted financial statements. In the case of Nippon Card Clothing, its subsidiary corporation, Toyo Fasteners, lost a keen price competition to its major competitor, Yoshida Kogyo, and finally became unable to repay its loan from its parent, Nippon Card Clothing, in the aggregate principal amount of Y900 million,l’ for which Nippon Card Clothing had not set up enough doubtful receivable reserves. In the case of Taio Seishi, there were internal troubles among its top executives, who, being disinterested in actively participating in the management of the company, sold out their company-issued shares. This company also faced a lack of unity among its major banks in rescue financing programs. These factors led the company to sudden bankruptcy. Giken Kogyo went bankrupt after advancing to real estate business through its subsidiary, as a part of its diversification program, and losing heavily in property speculation, Nichiman Kogyo, a coal mining company, was seriously affected by the energy ‘2Amounts to $3,750 thousand if translated at a rate of $ l.OO=Y 240.
K. Takahashi et al., Bankruptcy prediction in Japan
243
revolution. Having lost its customers to petroleum, the company finally went bankrupt after an unsuccessful diversification attempt. With respect to this bankruptcy, we saw no particular factors which could not be properly reflected in its financial statements. (2) In our discriminant analysis, Yamato Woolen Textile Mfg., Taio Seishi, and Japan Special Steel, which were all failed firms, were misclassified as non-failed firms (Type I errors). As far as Taio Seishi is concerned, the result of the discriminant analysis coincided with that of the principal component analysis. In the case of Japan Special Steel, there were a lot of factors which were not properly reflected in financial statements, such as internal dissension among top executives, disorderly management resulting therefrom, a huge amount of guarantee obligations which were assumed with respect to its subsidiaries’ loan obligations without the main banks’ knowledge, and extremely poor performance of those subsidiaries. In the case of Yamato Woolen Textile Mfg., which went bankrupt after being seriously affected by a depression in the textile industry and a failure in its new bowling alley operation business, there were no particular factors hidden from its financial statements. A careful study of the individual misclassification cases indicated that there were at least two factors which, as more fully discussed below made it difficult to correctly predict the bankruptcy of misclassified firms, or to prevent certain key factors predicting bankruptcy from emerging. 4.3. Factors causing misclassification of failed firms as non-failed firms 4.3.1. Incomplete disclosure system
0) In Japan
the preparation or disclosure of consolidated financial statements was not a mandatory obligation with respect to any fiscal year starting before April 1, 1977. If it had been made mandatory any earlier, Nippon Card Clothing and Giken Kogyo would have undoubtedly shown certain characteristics predicting their possible bankruptcy. (ii) No corporations were either permitted or required under the Japanese laws to set up reserves for their guarantee obligations until the April 20, 1982 amendment to the Accounting Principles came into effect. Had its contingent guarantee liabilities been properly disclosed in its financial statements, our prediction models could have properly classified Japan Special Steel as a failed firm. (iii) It is said that on and after October 1, 1974 when new legislation amending the Commercial Code came into effect requiring all listed corporations to make themselves subject to audit by outside auditors (certified public accountants), in addition to the audit required under the Securities and Exchange Act, as amended, cases of window dressing JBF
-II
244
K. Takahashi
et al., Bankruptcy
prediction in Japan
financial statements decreased substantially. Before that day, cases of window dressing of the magnitude equal to or exceeding that of the Tokyo Tokei Seizo case were seen periodically. Fortunately, these three points have been improved significantly, but the disclosure of sources and applications of funds statements has not been mandated in this country yet. The result of our study clearly indicates that an index reflecting short-term flow of funds should be somehow added to our list of variables to make our prediction models more accurate and efficient. The cash base financial statement data indices we used in our study were calculated from the cash base financial statement data of the sample firms which we prepared specifically for this study from and based on their accrual base profit and loss statements and balance sheets. We do hope that the applicable laws will be amended as soon as possible so that all public corporations will be required to prepare and disclose their audited statements of sources and applications of funds. Though there are several different concepts of fund,13 we calculated the value of the ratio of fund provided from operations of each failed and nonfailed sample firm for each of the three years before failure, using two representative concepts, working capital and cash fund. When the former concept was used, the average value of the ratio of the non-failed mate group showed a steady level of 1.10 throughout the three years, while that of the failed group varied and showed aggravation from a level of about 1.05 for the third year before failure to a level of 1.00 for the first year before failure. When the latter concept was used, the average value of the ratio of the nonfailed mate group showed a steady level of about 1.08 throughout the three years, while that of the failed group for the third year before failure showed a level of about 1.00, which, after falling to a 0.95 level for the second year, rose to a 1.02 level for the first year before failure. Thus, the value of the ratio of the failed group showed different behavior when different concepts were used. We believe this fact indicates that more accurate prediction can be made by using both concepts rather than using the latter concept alone. 4.3.2. Bank’s great influence Financial institutions, particularly banks, have great influence over the fate of corporations. Their influence is greater in Japan, where the average equity ratio of manufacturing corporations is nearly 20%. This figure indicates a heavy dependence on bank borrowings for raising funds, rather than on equity capital. In case of Taio Seishi and Japan Special Steel, the fact that they were deserted by their major banks was the immediate factor causing bankruptcy. This clearly indicates that in order to correctly predict the fate 13See APB Opinion
nos. 3 and 19.
K. Takahashi et al., Bankruptcy prediction in Japan
of any given corporation, it is essential to know major bank or banks decide whether they should support. In short, the key factors which bankers take deciding whether they should continue to support abandon them, letting them go bankrupt, are14
245
the criteria by which its continue or cut off their into consideration when their client companies or
(4 whether or not they can expect to have positive financial support or cooperation from their affiliated persons, (ii) whether or not their products, services and/or the markets which they, their products or services now serve or will serve in the future have growth potential, or whether or not their production or technical bases have enough potential for converting their operations profitable in the foreseeable future, (iii) whether or not they have assets which they can offer as collateral for additional financing, and (iv) whether or not their bankruptcy would cause any substantial social and/or economic problems. Unfortunately none of these factors can be properly reflected in financial statement data, except in the case of public corporations who are required to tile annual securities reports under the Securities and Exchange Act, as amended. One can probably learn factor (iii) mentioned above from annual securities reports. This means that in order to make an accurate prediction of the fate of a corporation, we must look carefully into the circumstances, whether known or hidden, which the corporation is in. In the case of the misclassification of Nichiman Kogyo and Yamato Woolen Textile Mfg., however, no insufficient disclosure or fatal uncooperativeness, on the part of their main banks, was involved. But one factor which was common to them was that they belonged to structurally depressed industries. 5. Concluding remarks Our principal component and discriminant analyses of Japanese firms endorsed the popular argument that it is necessary and desirable to use both financial statement data for two or more years before failure and indices relating to short-term flow of funds to make accurate corporate bankruptcy predictions. Although the corporate financial data disclosure system in Japan has improved incrementally in recent years, there are still a few points which ‘%ee Kitsukawa
(1980).
246
K. Takahashi et al., Bankruptcy prediction in Japan
need further improvement. One of these points is, as mentioned earlier, that it is not yet mandatory for corporations to disclose their statements of sources and application of funds. Moreover, when one studies financial statement data of failed tirms, we believe one should pay special attention to their auditors’ reports. What is also important for those who study corporate bankruptcy in Japan is not to overlook the base on which the financial statement data of each individual sample firm is prepared, and their study should cover the behavior of financial institutions, particularly commercial banks, because Japanese corporations are expected to continue to rely heavily on borrowings from commercial banks for their funds. We believe that the accuracy of corporate bankruptcy could be significantly improved if (i) more sample failed and non-failed firms become available, stratifying those sample firms into several groups according to industry and asset size; and (ii) a best suitable discriminant function is developed which uses as variables the deviations of each individual sample firm’s indices from the means of the industry to which it belongs. References Altman, E.I., 1968, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, Sept. Altman, E.I., 1980, Commercial bank lending: Process, credit scoring and lending error costs, Working paper no. 176, (Salomon Brothers Center for the Study of Financial Institutions) Sept. Altman, E.I., R. Haldeman and P. Narayanan, 1977, ZETA analysis: A new model to identify bankruptcy risk of corporations, Journal of Banking and Finance, June. Beaver, W.H., 1966, Financial ratios as predictors of failure, Empirical Research in Accounting: Selected Studies, Supplement to Journal of Accounting Research 4. Cooley, P.L., 1975, Bayesian and cost considerations for optimal classification with discriminant analysis, The Journal of Risk and Insurance, June. _ Deakin. E.B.. 1972. A discriminant analvsis of Dredictors of business failure. Journal of Accounting Research, Spring. Hatta, S., 1972, Chosa/joho shushu ni yoru abunai kaisha no hakken (Finding risky corporations through investigation and information and data collection) Special issue of Kaikei Journal featuring corporate bankruptcy. Igarashi, O., 1979, Kigyo hyoka ni okeru hanbetsukansu riyou no kokoromi - tosan yosoku eno approach (Discriminant analysis on corporate evaluation - An approach to bankruptcy prediction) Operations Research, Dec. Itoh, R., 1977, Computer ni yoru kigyo hyoka to keiei yosoku no zissai (The practice of corporate evaluation and business prediction by computer) (Daiichi-Hoki Co., Tokyo). Kitsukawa, A., 1980, Kigyo tosan no mondaiten o megutte (Problem of corporate bankruptcy) Kaikei Journal, Nov. Kurokawa, Y., 1976, Zaimushohyo ni arawareta wagakuni no seicho kigyo no tokusei (Characteristics of Japanese corporate growth in financial statements), unpublished School of Faculty of Engineering dissertation for a master’s degree [Graduate (Administration Engineering) Keio University, Yokohama]. Murakamt, M., 1979, Tosan no operations research (Operations research of corporate ^ bankruptcy) .Operations Research, Nov. Nomura Research Institute, 1973, Kigyo tosan no hanbetsu bunseki (Discriminant analysis and corporate bankruptcy) Sect. 2, ch. 2 in: Shasai no zaimuseigenjouko oyobi kakutsuke.
K. Takahashi
et al., Bankruptcy
prediction
in Japan
247
Ohlson, J.A., 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, Spring. Ohta, S., 1978, Kigyo tosan yosoku no zissyoteki kenkyu (A failure prediction model) Journal of Chiba Commerce University 16, no. 3, Dec. Okuno, C. and F. Yamada, 1978, Johoka jidai no keiei bunseki (Management analysis in information-oriented age) (Tokyo University Press, Tokyo). Ozeki, M. and T. Ohno, 1980, Chusho kigyo ni okeru tosan no zikei retsu ni yoru bunrui (Bankruptcy classification based on time series criterion with data from small and middle sized firms) Conference, May (Japan Academy of Managerial Science, Tokyo). Takahashi, K., 1976, Toshi seika keisan ni yoru keiei bunseki no jikken (Experiment on management analysis using investment performance accounting) Sangyo Keiri, June. Takahashi, K., Y. Kurokawa and K. Watase, 1979, Zaimushyhyo ni arawareta tosan kigyo no tokucho (Characteristics of failed firms observed from their financial statements) [Kelo Keiei Ronshu (Keio Gijuku Business Administration Society, Yokohama)] April. Takeda, R., 1978, Shokentorihikiho kaiji seido no gaiyo (Brief review of the disclosure system under the Securities and Exchange Act) Sangyo Keiri, Nov. Tanaka, T. and A. Wakagi, 1978, Kigyo no tosan to sono kaihisaku no kento 1 (Bankruptcy prediction of corporations and considerations on bankruptcy aversion) Conference, May (Japan Academy of Managerial Science, Tokyo). Toda, T., 1974, Nihon ni okeru tosan no yosoku ni kansuru ichi kousatsu (A comment on the prediction of corporate bankruptcy in Japan) in: Japan Academy of Business Management, ed., Internationalization of business management and its problems (Chikura). Wilcox, J.W., 1973, A prediction of business failure using accounting data in empirical research in accounting: Selected studies, supplement to Journal of Accounting Research 11.
of Banking
and Finance
CORPORATE Kichinosuke
8 (1948) 229-247.
BANKRUPTCY TAKAHASHI Keio University.
Institute
PREDICTION and Yukiharu
Yokohama
Kazunori Nagasaki
North-Holland
IN JAPAN
KUROKAWA
and Tokyo, Japan
WATASE
of Applied Science, Nagasaki,
Japan
The purpose of this study is to highlight the financial characteristics of failed firms in Japan, and to construct corporate bankruptcy prediction models with greater prediction accuracy. Our principal component analysis indicated that failed firms in Japan could be classified into two groups: a group having negative financial structures and a group having a declining flow of funds. Additionally, they can be classified into two other different categories of groups: one whose financial position during three years before shows a ‘V’ shape and another group that shows a ‘A’ shape. Our discriminant analysis indicated that improved prediction accuracy could be obtained by using, as predictor variables, both ratios and absolute amounts based on cash base financial statement data three years before failure. This data was adjusted to properly reflect the exceptrons, reservations, and qualifications appearing in the audit reports and those based on accrual base financial statement data.
1. Introduction Study of the development of corporate bankruptcy prediction models started in America in the mid-1960’s. Virtually all studies so far published use multivariate discriminant functions, except for that of Beaver (1966) who used a univariate prediction model and that of Wilcox (1973) who tried to predict corporations’ meantime to failure through the study of their states and the probability of their moving. Included in this related group are Altman (1968), Deakin (1972), Cooley’s (1975) examination of theoretical models, Altman et al. (1977), Altman (1980), and Ohlson (1980). In Japan, work started in the early 1970’s using the achievements of the studies in America as a base. The Japanese studies so far published include Nomura Research Institute (1973), Toda (1974), Itoh (1977), Ohta (197Q Tanaka and Wakagi (1978), Murakami (1979), Igarashi (1979), and Ozeki and Ohno (1980). They all use multivariate prediction models using discriminant functions, except for Tanaka and Wakagi (1978), who used a multiple regression model and Ozeki and Ohno (1980), who used a model combining principal component analysis and cluster analysis. They invariably deal with listed corporations as their sample firms, except for 03784266/84/$3.00
0
1984, Elsevier
Science Publishers
B.V. (North-Holland)
230
K. Takahashi
et al., Bankruptcy
prediction
in Japan
Igarashi (1979) and Ozeki and Ohno (1980) who deal only with non-listed corporations of relatively smaller sizes. Among those studies, Toda (1974) deals with the smallest number of failed sample firms 15 firms - while Murakami (1979) deals with the largest number of failed sample firms - 55 firms. Ohta (1978) uses the smallest number of indicator variables - four while Igarashi (1979) uses the largest number of indicator variables 14. They all use as indicator variables financial data ratios for the first year before failure alone, except for Murakami (1979) and Igarashi (1979), who use both financial data ratios and absolute amounts. Though Itoh (1977) and Igarashi (1979) use two or more cutoff points to have five or ten areas into which to classify sample firms, the other four models use only one cutoff point each. Here we can see great influence of the early American studies such as Altman (1968) and Deakin (1972). Since bankruptcy is very rare in the listed corporations in Japan, none of the studies mentioned above, with the exception of Igarashi (1979), uses any verification test samples (secondary samples) apart from samples for analysis purposes (initial samples). The purpose of this study is to clarify financial characteristics of failed firms in Japan and to construct a bankruptcy prediction model or models with greater accuracy than those previously developed.
2. Approach and methods of analysis 2.1. Data and indices used Among the listed Japanese firms which went bankrupt between 1961 and 1977, we have selected 40 corporations whose financial statements for at least three years before failure were available. The ‘first year before failure’ as used herein with respect to any failed firm means its fiscal year preceding the day of its bankruptcy. As Ohlson (1980) pointed out, there is an important relation between the date when a firm goes bankrupt and the date when it last publishes its financial statements. One of the features of this study is that light was cast on such a relation. Most prediction models developed in America, including Beaver (1966), use financial statement data from five years before failure. Most prediction models developed in Japan use data from three or four years before failure. This is obviously because of the fact that listed firms which have gone bankrupt are limited in number in this country and that many of those bankrupt firms had less than five years of business history between the date of their initial listing and the date of failure. There are numerous cases in this country where the auditors’ reports of failed firms contain exceptions, reservations and/or qualifications due primarily to window dressing (mostly overstatement of profits). In an attempt
K. Takahashi et al., Bankruptcy prediction in Japan
231
to properly reflect those exceptions in our models, we made such adjustments to the financial statement data of the sample firms as we considered appropriate under the circumstances. To our knowledge, there are few prior studies in this country in which such adjustments were made to the financial statement data.l 2.2. Principal component analysis We employed what is called ‘pair sampling method’. We selected an equal number of non-failed firms (non-failed mates) and failed tirms,2 and compared the failed and non-failed groups to find the characteristics, if any, peculiar to the failed group. For this purpose, we employed ‘principal component analysis’ to classify the failed sample firms into several pattern groups. Initially we selected 80 indices based on the following criteria:
(i)
whether or not the particular index is expected to show any meaningful change before failure, and (ii) whether or not the particular index performed well in any one of the previously developed prediction models. We calculated the value of each of the indices selected, using the financial statement data of the sample firms for each of the three years before failure. Then we indexed and determined the value of the ‘level’, ‘trend’, and ‘behavior’ (‘V’ or ‘/\’ shape) of every index based upon its values during the three-year period before failure. 3 A principal component analysis was then conducted in the following two different methods: ‘The average amount of operating losses of the failed sample group (40 failed firms) for the first year before failure calculated based on non-adjusted financial statement data was about Y 240 million per firm, which was approximately equal to $1.0 million, if translated at a rate of $l.OO=Y240.00. When the average amount of operating losses of those 21 failed sample firms, out of the 40 failed sample firms, which had exceptions, reservations and/or qualifications in their audit reports, was recalculated based on their financial statement data for the first year before failure, as adjusted to properly reflect those exceptions, reservations and/or qualifications, a downward adjustment of some Y 120 million ($500 thousand) per firm appeared to be necessary to correct overstatement of their operating profits and losses. ‘For the purpose of our study, pair sampling was conducted to select one solvent firm for each failed sample firm on the basis (i) that they both belonged to the one and same industry, (ii) that they had substantially the same fiscal year systems, and (iii) that they were as close to each other as possible in total assets size. 31f Xi, is the value of any given index for j year before failure, and wherein i= 1 to 80 and j= 1, 2 or 3, thee the following functions will stand: level of the index: x1, = 5 X,,/3, j=l
trend of the index: x2, = X, 1 -X, behavior
of the index: xa, =Xi,
3, +Xi,-2X,,,
K. Takahashi et al., Bankruptcy prediction in Japan
232
(i) (ii)
the method where the values of the indices for each of the three years before failure are used as they are, and the method where the values showing their ‘levels’, ‘trends’ and ‘behaviors’ are used instead.
In both methods, we used six indices (or 18 variables) selected original list of indices in accordance with the following procedures: (i)
(ii)
out of the
to determine the correlation coefficient of each index in the original list of indices, to pick up indices having higher correlation coefficients among them to form a group, and to select from among indices in each of the groups one representative index; and to select, from among the indices selected in accordance with the procedures set fourth in (i) above, six indices which show the most significant differences between the failed and non-failed groups.4
2.3. Discriminant
analysis
The initial sample we selected for our discriminant analysis consisted of 72 firms; 36 failed firms selected from the 40 failed firms mentioned above, which went bankrupt before 1977, and 36 non-failed firms selected by pair sampling. The secondary sample which we selected for verification test purposes consisted of 48 firms, namely, the other four failed firms which went bankrupt in 1977, four non-failed firms which were selected by pair sampling, and 40 other firms then existing in 1977. We selected for our discriminant analysis 75 accrual base financial data indices (61 ratios and 14 absolute amounts) and 54 cash base financial data indices (45 ratios and nine absolute amounts).
2.3.1. Construction
of prediction
models
One can develop several different types of prediction models depending upon what financial statement data and indices one will use - (a) nonadjusted data or data adjusted to reflect the exceptions, reservations and/or qualifications appearing in the audit reports; (b) accrual or cash base financial data indices; (c) index values for three years before failure or only for the first year before failure; or (d) ratios alone, or a combination of ratios and absolute amounts. Theoretically, combination of (a), (b), (c), and (d) above could produce 16 different model types. The prediction models developed by Altman (1968) and Altman et al. (1977) are different in that the former uses financial statement data ratios for the first year before failure alone, while the latter uses both financial 4The “T’-test’ and Mann-Whitney’s
‘rank sum test’.
K. Takahashi et al., Bankruptcy prediction in Japan
233
statement data ratios and absolute amounts for more than one year before failure. In the case of Japanese firms, as mentioned before, it would be necessary and appropriate for discriminant analysis purposes to adjust financial statement data to reflect the exceptions, reservations and/or qualifications appearing in audit reports, and cash base financial data is more reliable because it is less susceptible to window dressing by management. Therefore, in addition to the 16 models, we also developed another discriminant function, as the 17th model, which uses both ratios and absolute amounts derived from adjusted accrual base and cash base financial statement data for three years before failure. Those 17 models are shown in fig. 1. For each of the 17 models constructed, we used eight indices (or 24 variables) or 24 indices which we selected on the same base as the one we used for our principal component analysis purpose.5 2.3.2. Verljkation of prediction models There is a big difference between the Type I and Type II errors in costs of errors.6 Type I errors cause investors and creditors to suffer actual economic loss resulting from their inability to collect loans, or the charging off of their equity investments, resulting from the depreciation of their market value. However, no actual damage is recognized from type II errors, other than opportunity costs resulting from the loss of investment opportunities due to too conservative investment decisions. Generally the probability of failure is far smaller than the probability of non-failure. Cooley (1975) has demonstrated by using a simulation model that the cutoff point varies depending on whether or not misclassification costs and/or the probability of misclassification are to be taken into consideration. To select the best cutoff point or points, Altman et al. (1977) and Altman (1980) determined misclassification costs based on the results of rather extensive inquiries conducted by them with respect to commercial bank lendings alone. It is, however, practically very difficult to determine misclassification costs of this kind. 5During the course of our study we encountered cases where we could not reduce the number of variables to 2.5 [the maximum number permitted by the computer program we used (BMD 04M)] when we attempted to select variables merely by those standards. (The variables selected merely by those standards are hereinafter referred to as ‘potentially useful variables’.) Therefore we prepared several combinations of potentially useful variables, each having variables not exceeding the said limit. A discriminant function was then constructed for each of the combinations. The discriminant functions were then tested and compared for discriminant efficiency as determined by Mahalanobis’ square distance between the respective barycenters of the two groups to choose a discriminant function having the highest discriminant efficiency. This procedure was followed for each of the 17 prediction model types we constructed. 6‘Type I error’ means misclassification of a failed firm as non-failed, and ‘Type II error’ means misclassification of a non-failed firm as failed.
K. Takahashi
234
Materials studied
Data
et al., Bankruptcy
prediction
Period before failure covered
used
in Japan
Indices
Model code name
Ratios
(1)
Ratios/absolute amounts
(2)
3 years I Non-adjusted
data
(31 1st year <~~~;bsolute (4)
Accrual base financial statements
Ratios
(5)
3 years < t
Adjusted
Ratios/absolute amounts
data ( lst year <~~~~~/absolute amounts Ratios
(81 (91
3 years < Non-adjusted
data
Rattos/absolute amounts
(101 (11)
( 1st year Cash base financial statements
amounts
(12)
Ratios
(13)
3 years Adjusted
data
Ratios/absolute amounts
(14)
Ratios
(151
< Ratios/absolute amounts Accrual and cash bases financial statements
Adjusted
data-
3 years before failure
Fig. 1. 17 prediction
models
Ratios/absolute amounts
used for the study.
(I71
K. Takahashi
et al., Bankruptcy
prediction in Japan
235
When the Japanese actually try to predict bankruptcy of a firm, we do not believe they will make such predictions using a single cutoff point. Instead, they would prefer to have more than one cutoff point, such as the most conservative point, the most optimistic point, or the point where bankruptcy may occur with a certain statistical probability percentage. We therefore adopted six cutoff points which have their own justification for existence in our actual prediction models. This will enable the users of our models to have plural prediction results depending on which one of the six cutoff points they will use, leaving the final judgment up to them. Those six cutoff points are: the point at which the overall probability of (A) cutoff point C, misclassification becomes minimal, assuming that both populations have normal distribution, (B) cutoff point C, - the point at which the probability of the Type I error (misclassification of failed firms as non-failed firms) becomes lx, assuming that both populations have normal distribution, (C) cutoff point C, - the point at which the probability of the Type I error becomes 5%, assuming that both populations have normal distribution, (D) cutoff point C, - the point at which the number of firms misclassified becomes minimal, (E) cutoff point C, - the point at which the number of failed firms misclassified as non-failed becomes zero, and (F) cutoff point Cf - the point at which the number of non-failed firms misclassified as failed (Type II error) becomes zero. We applied each of the 17 discriminant functions to the verification (secondary) sample to verify their prediction accuracy, using the six different cutoff points. To determine the predictive ability ranking of the 17 discriminant functions by cutoff points, the following two criteria were employed: (i)
that the smaller its ratio of the Type I error was, the higher it ranked, and (ii) that the smaller its ratio of the Type II error was, the higher it ranked.
When applying the criteria, priority was given to criterion (i) over criterion (ii). Their predictive abilities were compared with each other by applying criterion (i) first. If, as a result of such comparison, any two or more discriminant functions ranked pari passu, we then applied criterion (ii) to determine the ranking among them. The reason why we gave priority to criterion (i) was that we thought that the cost of misclassifying failed firms as non-failed would be far greater than that of misclassifying non-failed firms as failed.
236
K. Takahashi
et al., Bankruptcy
prediction
in Japan
3. Results 3.1. Results of principal component analysis The following six indices component analysis:
(or
18 variables)
were
used
in our
net worth to total assets, working capital to total assets, borrowed expenses to sales, value added to total assets at the beginning of the year, cash provided from operations7 to total assets at the beginning year, and working capital provided from operations’ to total assets beginning of the year. As shown in table 1, the components Z- 1 and 2-2 cover as much as 54%.of all information, when method when we used method (ii).
principal
of the at
the
together alone could (i) was used and 48%
Table 1 Elgenvalue
and ratio accumulated
Component
contribution.
Z-l
z-2
z-3
z-4
z-5
1.32
2.47
190
1.54
1.32
0.41
0.54
0.65
0.74
0.81
4.97
3.75
1.92
1.38
1.08
0.28
0.48
0.59
0.67
0.73
Method (i) Eigenvalue Ratio of accumulated
contribution
Method (ii) Eigenvalue Ratio of accumulated
contribution
We then determined the meaning, if any, of the components based on the respective factor loadings of their relevant original variables. The results of such determination are shown in table 2. The Z-1 component under method (i) seemed to indicate the ‘financial structure (or static financial condition)’ of the sample firms, and the 2-2 component ‘flow of funds (or dynamic “Cash provided from operations’ as used herein for any given year means the balance between cash revenues and cash expenses for that year accruing from transactions of the recurring nature in the ordinary course of business. “Working capital provided from operations’ as used herein for any given year means net increase (decrease) in working capital for that year accruing from transactions of the recurring nature in the ordinary course of business.
K. Takahashi et al., Bankruptcy prediction in Japan
237
Table 2 Component
Variables
_
loadings.
Year before failure
Components Z-1
z-2
z-3
3 2 1 3 2 1 3 2 1 3 2 1
0.711 0.836 0.846 0.446 0.604 0.706 -0.624 - 0.706 -0.695 0.583 0.698 0.430
0.308 0.199 0.155 - 0.409 - 0.458 - 0.026 0.117 0.243 -0.001 - 0.076 -0.050 0.781
0.278 0.158 0.200 0.694 0.517 0.415 0.492 0.475 0.396 -0.126 -0.108 -0.021
0.441 0.432 0.461
-0.119 - 0.208 0.588
- 0.340 - 0.025 0.145
0.725 0.712 0.572
- 0.428 -0.392 0.713
-0.187 -0.101 -0.054
Level Trend Behavior
0.499 0.161 0.026
- 0.596 -0.644 0.250
- 0.069 0.132 0.688
Level Trend Behavior
0.168 0.601 0.566
-0.541 - 0.226 0.211
0.087 -0.329 0.395
0.585 0.154 -0.392
0.202 - 0.665 0.105
Method (i) Net worth
Working
Borrowed
to total assets
capital
to total assets
expenses
to sales
Value added to total assets
Cash provided to total assets
from operations
Working capital provided operations to total assets
from
Method (ii) Net worth
to total assets
Working capital total assets
Borrowed
to
Value added
Cash provided to total assets
-0.311 -0.393 -0.462
to sales
Level Trend Behavior
to total assets
Level Trend Behavior
0.647 0.831 0.682
-0.332 0.245 0.564
-0.131 0.114 - 0.064
Level Trend Behavior
0.365 0.429 0.248
-0.604 0.387 0.142
0.174 0.046 -0.755
Level Trend Behavior
0.535 0.868 0.725
-0.785 0.266 0.550
-0.011 0.059 -0.148
expenses
from operations
Working capital provided operations to total assets
from
238
K. Takahashi et al., Bankruptcy prediction in Japan
financial condition)‘. And Z- 1 and 2-2 components under method (ii) seemed to indicate the ‘levels of the indices’ and ‘changes of the indices in time series’. Finally we tried to find if the failed sample firms could be classified into several pattern groups, using a map showing the distribution of the sample scores. Under method (i), it seemed that the failed sample firms9 could be classified into the following three groups: (A) Type A, (B) Type B, (C) Type C, -
Firms having bad financial structure. 27 failed sample firms were classified into this type. Firms having declining flow of funds. Nine failed sample firms were classified into this type. Firms having neither bad financial structure nor declining flow of funds. Three failed sample firms were classified into this type.
Under method (ii), it seemed that the failed sample firms could classified also into the following three groups: (A) Type A, -
(B) Type B, -
(C) Type Cz -
be
Firms in bad financial position with the time series change of their financial statements data indices behavior approximately showing ‘V’ shape.l’ 28 failed sample firms were classified into this type. Firms in bad financial position with the time series change of their financial statements data indices behavior approximately showing ‘/\, shape.” Seven failed sample firms were classified into this type. Firms whose financial position was not bad. Four failed sample firms were classified into this type.
3.2. Results of discriminant analysis All 17 prediction models constructed by us were made subject to a prediction accuracy test. The results of the test are set forth in table 3. The test showed that models (6) and (17) had relatively higher prediction accuracy regardless of the cutoff points used. Each of the 17 models was then subjected to a comparative study to see 9For this classification purpose, one failed sample firm whose financial statement data indices showed extraordinary values was excluded from the failed sample group. ioIf the behavior of the time series change of any index shows ‘V’ shape, it means that the value of the index for the first year and that for the third year before failure were both greater than that for the second year before failure. “If the behavior of the time series change of any index shows ‘A’ shape, it means that the value of the index for the first year and that for the third year before failure were both less than for the second year before failure.
K. Takahashi et al., Bankruptcy prediction in Japan
239
Table 3 Ranking
in terms of prediction
ability.”
Cutoff points Initial sample Model code name
sample
ABCDEFABCDEF
(l) 3 4 ii; (4) (5) (6) (7) (8) (9)
Secondary
2
3 1
2
;’
1 31 31241412
314314 2
4
4
4 1 4
2
1
1 4
3 4 2 4
2
2
2 3
(10) (11) I::; (14) (15) I:;;
2332321231
“Only the first through shown in this table.
1 fourth
places in the ranking
are
whether or not its prediction accuracy would show any significant change, if different components, different financial statement data, different number of years before failure, and/or financial indices of different types were used. The comparative study revealed the following facts:
0) that prediction
models using adjusted financial statement data had greater prediction accuracy than those using non-adjusted financial statement data, (ii) that prediction models using accrual base indices had greater prediction accuracy than those using cash base ones, (iii) that prediction models using financial statement data for three years before failure had greater prediction accuracy than those using financial statement data for only the first year before failure, and that there was no significant difference in prediction accuracy between (iv) prediction models using only ratios and those using both ratios and absolute amounts. Also, when we applied the 17 prediction models to the verification (secondary) samples, they showed virtually the same discriminant efficiencies.
K. Takahashi et al., Bankruptcy prediction in Japan
240
Althou~ it seemed at first glance that models (4) and (17) had almost equal prediction ability, we consider model (17) to be the best prediction model because of its relatively small variance in ranking, caused by the use of different cutoff points (table 3). Tables 4 and 5 show the prediction ability of the model (17) and its discriminant function, respectively. As shown in these tables, the discriminant efficiency was si~i~~ant at the level of 0.5%. Model (17) showed, when it was applied to the verification (secondary) sample, no Type I errors at all, regardless of which one of the six cutoff points was used, while its Type II error varied within a range of 20.5% to 43.2% depending on which one of those cutoff points was used. Table 4 Results of prediction ability test of best prediction model.”
Cutoff points
G
Secondary sample
Initial sample
Numbers Ratios
Numbers Ratios
Type I error Type II error
0 9
Type I error Type II error
0
0
0
0
19
43.2
19
52.8
Type I error Type II error
0 13
0 29.5
1 9
2.x 25.0
13.9
Type I error Type IT error
0 20.5 0 31.8
1 3
2.8 8.3
5.6
Type I error Type II error
0 9 0 14
16
44.4
22.2
Type I error Type II error
0 9
0 20.5
6 -
16.7 -
8.4
0 20.5
3 2
“Numbers renresent the number of misclassified represent percentage of mtsclassification ratios.
4.
8.3 5.6
6.9 26.4
firms, and ratios
Discussion
4.1.
Ch~ra~~eri~~i~~
p~c~liur to failed
~arnpl~bus
Our principal component analysis revealed two major characteristics peculiar to the failed sample firms. First, they could be classified into two major types - (i) those suffering persistent aggravation of the financial structure (Type-A, tirms”), and (ii) those suffering aggravation of flow of funds within a relatively short period of time (‘Type-B, firms’). These phenomena have often been pointed out in literature concerning corporate bankruptcy. Second, from the viewpoint of the time series change, they also
241
K. Takahashi et al., Bankruptcy prediction in Japan Table 5 Discriminant
function
of best prediction
model.
Year before failure Variables
l(xt 1)
x r, =Net worth to fixed assets xZj = Current liabilities ratio X3, = Voluntary reserves plus unappropriated surplus to total assets x 4, = Borrowed expenses to sales xsj = Earned surplus x 6, = Increase in residual value to cash sales” xTj =Cash provided from operations to total assetsb xsj = Cash sales’- cash purchase@ Mahalanobis’ square distance F-value (sigmticance level) Mean value Standard deviation
2(-k)
0.01039 -0.05687
0.07658 0.05147
-0.040231
0.22167
3(x,,) -0.01444 -0.03447
0.13213
1.00945 0.008 14
-0.72363 -0.01366
-0.34111 0.00685
0.14522
-0.03596
0.00545
-0.13034 0.0003 1
0.07848 0.00010
3.13721
6.22992 (0.005)
Non-failed Failed Non-failed Failed
0.02901 - 0.00007
0.06469 -0.02431 0.03353 0.03767
“Increase in residual value to cash sales ratio= [operating and other income (cash sales + interest and dividends received + cash proceeds realized from sale of fixed assets+funds generated by net decrease in short-term loans) -operating and other expenses (cash purchases +personnel expenses outlaid+cost and expenses outlaid+funds outlaid due to net mcrease in short-term loans) -cost of capital (dividends paid f interest paid + bond premiums paid + bond issue cost paid + stock issue cost paid) -original cost of long-term investment liquidated] -cash sales. bCash provided from operations to total assets = [(cash sales + interest and dividends received) -(cash purchases + personnel expenses outlaid + expenses outlaid+cost of capital)] L(cash on hand and at bank+long-term investment). “Cash sales = sales -net increase in account receivables-trade + net increase in advance received. %ash purchases = total purchases of raw materials and merchandise-net increase in account payables-trade + net increase in advance payments.
could be classified into two major types showed some improvement in their financial second year before failure, instead of straight and (ii) those which temporarily showed some position sometime during the first year before improvements seem to indicate that the firms rescue operation before failure.
(i) those which temporarily position sometime during the aggravation (‘Type-B, firms’); improvement in their financial failure (Type-A, firms’). These underwent some unsuccessful
242
K. Takahashi
et al., Bankruptcy
prediction
in Japan
The foregoing seems to indicate that in order to make accurate corporate bankruptcy predictions, we should use both cash base financial statement data indices, which can better show short-term flow of funds, and accrual base financial indices. To reflect the changes in the financial position before failure, in our prediction models, we should use financial statement data for two or more years before failure. Model (17), which we consider the best among the 17 prediction models, satisfies these requirements. Among the failed sample firms, however, were certain firms which showed no significant difference from the non-failed sample firms, as far as the results of the principal component analysis of their financial statement data were concerned. (We named these failed sample firms ‘Type-C, firms’ failed firms which had neither bad financial structure nor bad flow of funds - and ‘Type-C, firms’ - failed firms whose financial position was not so bad.) We then carefully reviewed those failed firms to see if they had been subject to any special conditions or circumstances to which none of the other failed sample firms had been subject. 4.2. Failed sample firms which showed sample firms
no signijkant
difference from non-failed
(1) Included in those failed sample firms were Nichiman Kogyo, Tokyo Tokei Seizo, Taio Seishi, Nippon Card Clothing and Giken Kogyo. Among the failed sample firms named above, Nichiman Kogyo and Taio Seishi were Type C, firms as well as Type C, firms. In the case of Tokyo Tokei Seizo, a staggering window dressing of financial statement was revealed after its bankruptcy, and its outside auditors were punished for having overlooked this window dressing. This case illustrates that no one can be aware of the true financial status of any corporation from false or twisted financial statements. In the case of Nippon Card Clothing, its subsidiary corporation, Toyo Fasteners, lost a keen price competition to its major competitor, Yoshida Kogyo, and finally became unable to repay its loan from its parent, Nippon Card Clothing, in the aggregate principal amount of Y900 million,l’ for which Nippon Card Clothing had not set up enough doubtful receivable reserves. In the case of Taio Seishi, there were internal troubles among its top executives, who, being disinterested in actively participating in the management of the company, sold out their company-issued shares. This company also faced a lack of unity among its major banks in rescue financing programs. These factors led the company to sudden bankruptcy. Giken Kogyo went bankrupt after advancing to real estate business through its subsidiary, as a part of its diversification program, and losing heavily in property speculation, Nichiman Kogyo, a coal mining company, was seriously affected by the energy ‘2Amounts to $3,750 thousand if translated at a rate of $ l.OO=Y 240.
K. Takahashi et al., Bankruptcy prediction in Japan
243
revolution. Having lost its customers to petroleum, the company finally went bankrupt after an unsuccessful diversification attempt. With respect to this bankruptcy, we saw no particular factors which could not be properly reflected in its financial statements. (2) In our discriminant analysis, Yamato Woolen Textile Mfg., Taio Seishi, and Japan Special Steel, which were all failed firms, were misclassified as non-failed firms (Type I errors). As far as Taio Seishi is concerned, the result of the discriminant analysis coincided with that of the principal component analysis. In the case of Japan Special Steel, there were a lot of factors which were not properly reflected in financial statements, such as internal dissension among top executives, disorderly management resulting therefrom, a huge amount of guarantee obligations which were assumed with respect to its subsidiaries’ loan obligations without the main banks’ knowledge, and extremely poor performance of those subsidiaries. In the case of Yamato Woolen Textile Mfg., which went bankrupt after being seriously affected by a depression in the textile industry and a failure in its new bowling alley operation business, there were no particular factors hidden from its financial statements. A careful study of the individual misclassification cases indicated that there were at least two factors which, as more fully discussed below made it difficult to correctly predict the bankruptcy of misclassified firms, or to prevent certain key factors predicting bankruptcy from emerging. 4.3. Factors causing misclassification of failed firms as non-failed firms 4.3.1. Incomplete disclosure system
0) In Japan
the preparation or disclosure of consolidated financial statements was not a mandatory obligation with respect to any fiscal year starting before April 1, 1977. If it had been made mandatory any earlier, Nippon Card Clothing and Giken Kogyo would have undoubtedly shown certain characteristics predicting their possible bankruptcy. (ii) No corporations were either permitted or required under the Japanese laws to set up reserves for their guarantee obligations until the April 20, 1982 amendment to the Accounting Principles came into effect. Had its contingent guarantee liabilities been properly disclosed in its financial statements, our prediction models could have properly classified Japan Special Steel as a failed firm. (iii) It is said that on and after October 1, 1974 when new legislation amending the Commercial Code came into effect requiring all listed corporations to make themselves subject to audit by outside auditors (certified public accountants), in addition to the audit required under the Securities and Exchange Act, as amended, cases of window dressing JBF
-II
244
K. Takahashi
et al., Bankruptcy
prediction in Japan
financial statements decreased substantially. Before that day, cases of window dressing of the magnitude equal to or exceeding that of the Tokyo Tokei Seizo case were seen periodically. Fortunately, these three points have been improved significantly, but the disclosure of sources and applications of funds statements has not been mandated in this country yet. The result of our study clearly indicates that an index reflecting short-term flow of funds should be somehow added to our list of variables to make our prediction models more accurate and efficient. The cash base financial statement data indices we used in our study were calculated from the cash base financial statement data of the sample firms which we prepared specifically for this study from and based on their accrual base profit and loss statements and balance sheets. We do hope that the applicable laws will be amended as soon as possible so that all public corporations will be required to prepare and disclose their audited statements of sources and applications of funds. Though there are several different concepts of fund,13 we calculated the value of the ratio of fund provided from operations of each failed and nonfailed sample firm for each of the three years before failure, using two representative concepts, working capital and cash fund. When the former concept was used, the average value of the ratio of the non-failed mate group showed a steady level of 1.10 throughout the three years, while that of the failed group varied and showed aggravation from a level of about 1.05 for the third year before failure to a level of 1.00 for the first year before failure. When the latter concept was used, the average value of the ratio of the nonfailed mate group showed a steady level of about 1.08 throughout the three years, while that of the failed group for the third year before failure showed a level of about 1.00, which, after falling to a 0.95 level for the second year, rose to a 1.02 level for the first year before failure. Thus, the value of the ratio of the failed group showed different behavior when different concepts were used. We believe this fact indicates that more accurate prediction can be made by using both concepts rather than using the latter concept alone. 4.3.2. Bank’s great influence Financial institutions, particularly banks, have great influence over the fate of corporations. Their influence is greater in Japan, where the average equity ratio of manufacturing corporations is nearly 20%. This figure indicates a heavy dependence on bank borrowings for raising funds, rather than on equity capital. In case of Taio Seishi and Japan Special Steel, the fact that they were deserted by their major banks was the immediate factor causing bankruptcy. This clearly indicates that in order to correctly predict the fate 13See APB Opinion
nos. 3 and 19.
K. Takahashi et al., Bankruptcy prediction in Japan
of any given corporation, it is essential to know major bank or banks decide whether they should support. In short, the key factors which bankers take deciding whether they should continue to support abandon them, letting them go bankrupt, are14
245
the criteria by which its continue or cut off their into consideration when their client companies or
(4 whether or not they can expect to have positive financial support or cooperation from their affiliated persons, (ii) whether or not their products, services and/or the markets which they, their products or services now serve or will serve in the future have growth potential, or whether or not their production or technical bases have enough potential for converting their operations profitable in the foreseeable future, (iii) whether or not they have assets which they can offer as collateral for additional financing, and (iv) whether or not their bankruptcy would cause any substantial social and/or economic problems. Unfortunately none of these factors can be properly reflected in financial statement data, except in the case of public corporations who are required to tile annual securities reports under the Securities and Exchange Act, as amended. One can probably learn factor (iii) mentioned above from annual securities reports. This means that in order to make an accurate prediction of the fate of a corporation, we must look carefully into the circumstances, whether known or hidden, which the corporation is in. In the case of the misclassification of Nichiman Kogyo and Yamato Woolen Textile Mfg., however, no insufficient disclosure or fatal uncooperativeness, on the part of their main banks, was involved. But one factor which was common to them was that they belonged to structurally depressed industries. 5. Concluding remarks Our principal component and discriminant analyses of Japanese firms endorsed the popular argument that it is necessary and desirable to use both financial statement data for two or more years before failure and indices relating to short-term flow of funds to make accurate corporate bankruptcy predictions. Although the corporate financial data disclosure system in Japan has improved incrementally in recent years, there are still a few points which ‘%ee Kitsukawa
(1980).
246
K. Takahashi et al., Bankruptcy prediction in Japan
need further improvement. One of these points is, as mentioned earlier, that it is not yet mandatory for corporations to disclose their statements of sources and application of funds. Moreover, when one studies financial statement data of failed tirms, we believe one should pay special attention to their auditors’ reports. What is also important for those who study corporate bankruptcy in Japan is not to overlook the base on which the financial statement data of each individual sample firm is prepared, and their study should cover the behavior of financial institutions, particularly commercial banks, because Japanese corporations are expected to continue to rely heavily on borrowings from commercial banks for their funds. We believe that the accuracy of corporate bankruptcy could be significantly improved if (i) more sample failed and non-failed firms become available, stratifying those sample firms into several groups according to industry and asset size; and (ii) a best suitable discriminant function is developed which uses as variables the deviations of each individual sample firm’s indices from the means of the industry to which it belongs. References Altman, E.I., 1968, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, Sept. Altman, E.I., 1980, Commercial bank lending: Process, credit scoring and lending error costs, Working paper no. 176, (Salomon Brothers Center for the Study of Financial Institutions) Sept. Altman, E.I., R. Haldeman and P. Narayanan, 1977, ZETA analysis: A new model to identify bankruptcy risk of corporations, Journal of Banking and Finance, June. Beaver, W.H., 1966, Financial ratios as predictors of failure, Empirical Research in Accounting: Selected Studies, Supplement to Journal of Accounting Research 4. Cooley, P.L., 1975, Bayesian and cost considerations for optimal classification with discriminant analysis, The Journal of Risk and Insurance, June. _ Deakin. E.B.. 1972. A discriminant analvsis of Dredictors of business failure. Journal of Accounting Research, Spring. Hatta, S., 1972, Chosa/joho shushu ni yoru abunai kaisha no hakken (Finding risky corporations through investigation and information and data collection) Special issue of Kaikei Journal featuring corporate bankruptcy. Igarashi, O., 1979, Kigyo hyoka ni okeru hanbetsukansu riyou no kokoromi - tosan yosoku eno approach (Discriminant analysis on corporate evaluation - An approach to bankruptcy prediction) Operations Research, Dec. Itoh, R., 1977, Computer ni yoru kigyo hyoka to keiei yosoku no zissai (The practice of corporate evaluation and business prediction by computer) (Daiichi-Hoki Co., Tokyo). Kitsukawa, A., 1980, Kigyo tosan no mondaiten o megutte (Problem of corporate bankruptcy) Kaikei Journal, Nov. Kurokawa, Y., 1976, Zaimushohyo ni arawareta wagakuni no seicho kigyo no tokusei (Characteristics of Japanese corporate growth in financial statements), unpublished School of Faculty of Engineering dissertation for a master’s degree [Graduate (Administration Engineering) Keio University, Yokohama]. Murakamt, M., 1979, Tosan no operations research (Operations research of corporate ^ bankruptcy) .Operations Research, Nov. Nomura Research Institute, 1973, Kigyo tosan no hanbetsu bunseki (Discriminant analysis and corporate bankruptcy) Sect. 2, ch. 2 in: Shasai no zaimuseigenjouko oyobi kakutsuke.
K. Takahashi
et al., Bankruptcy
prediction
in Japan
247
Ohlson, J.A., 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, Spring. Ohta, S., 1978, Kigyo tosan yosoku no zissyoteki kenkyu (A failure prediction model) Journal of Chiba Commerce University 16, no. 3, Dec. Okuno, C. and F. Yamada, 1978, Johoka jidai no keiei bunseki (Management analysis in information-oriented age) (Tokyo University Press, Tokyo). Ozeki, M. and T. Ohno, 1980, Chusho kigyo ni okeru tosan no zikei retsu ni yoru bunrui (Bankruptcy classification based on time series criterion with data from small and middle sized firms) Conference, May (Japan Academy of Managerial Science, Tokyo). Takahashi, K., 1976, Toshi seika keisan ni yoru keiei bunseki no jikken (Experiment on management analysis using investment performance accounting) Sangyo Keiri, June. Takahashi, K., Y. Kurokawa and K. Watase, 1979, Zaimushyhyo ni arawareta tosan kigyo no tokucho (Characteristics of failed firms observed from their financial statements) [Kelo Keiei Ronshu (Keio Gijuku Business Administration Society, Yokohama)] April. Takeda, R., 1978, Shokentorihikiho kaiji seido no gaiyo (Brief review of the disclosure system under the Securities and Exchange Act) Sangyo Keiri, Nov. Tanaka, T. and A. Wakagi, 1978, Kigyo no tosan to sono kaihisaku no kento 1 (Bankruptcy prediction of corporations and considerations on bankruptcy aversion) Conference, May (Japan Academy of Managerial Science, Tokyo). Toda, T., 1974, Nihon ni okeru tosan no yosoku ni kansuru ichi kousatsu (A comment on the prediction of corporate bankruptcy in Japan) in: Japan Academy of Business Management, ed., Internationalization of business management and its problems (Chikura). Wilcox, J.W., 1973, A prediction of business failure using accounting data in empirical research in accounting: Selected studies, supplement to Journal of Accounting Research 11.