Capital Structure and Corporate Failure Prediction

Electronic copy available at: http://ssrn.com/abstract=1978628Electronic copy available at: http://ssrn.com/abstract=1978628

2012

ABFI Institute Perbanas

Jakarta

Rowland Bismark

Fernando Pasaribu

CAPITAL STRUCTURE AND

CORPORATE FAILURE PREDICTION

Journal of Economics and Business Vol. 5, No. 3, November 2011 (209-220), ISSN: 1978 3116


Journal of Economics and Business

Vol. 5, No. 3, November 2011 (209-220) ISSN: 1978 3116

1

Capital Structure and Corporate Failure Prediction: Theory and Applications

Abstract

This paper addresses the theoretical foundations of corporate failure prediction, using the neo-classical theory of capital structure as a starting point. The paper intends to demonstrate the feasibility of such an approach in a simple setting, i.e. by using a simple theoretical model and a limited empirical analysis. A model of optimal capital structure is constructed and rewritten as a model of default probability. Its empirical implications are derived and tested on a sample of Indonesian data. It is concluded that this approach clearly has its limitations, but also that may it be a valuable contribution compared to the multitude of theory-less empirical studies and a useful alternative to the default theory.

Keywords: Default probabilities, Capital structures, Corporate Failure, Logistic regression

INTRODUCTION

Bankruptcy experienced by many global companies increasingly emphasized the importance of well-paid failure prediction in the context of academic and real applications. We seem to be increasingly important urgency to establish an early warning system that can help companies avoid paying the state fails on one side and facilitate elections issuers to be nominated where we will invest over these companies on the other side.

Research on the prediction failed to pay has been a long journey since the beginning of articulated through the work of Beaver (1966, 1968) and Altman (1968). Existing approaches for predicting the failure of a party is a large enterprise applications of statistical classification techniques (usually discriminant analysis) on samples consisting of non-firms fail, and fail, examples of such research has been done Deakin (1972) and Altman et al.(1977). After that there was a shift using the analytical techniques with probit or logit analysis. Martin (1977) and Ohlson (1980) is the first in applying this technique followed by Wiginton (1980), Zmijewski (1984), Zavgren (1985), Aziz and Lawson (1989), Lennox (1999) and Westgaard and van der Wijst (2001). other statistical technique that has also been introduced are: recursive partitioning (Frydman et.al, 1985); katastrophi theory (Gregory et.al, 1991); multidimensional scaling (Mar Molinero and Ezzamel, 1991); neural networks (Tam and Kiang, 1992); multinominal logit model (Johnsen and Melicher, 1994); methodology multi-criteria decision aid (Zopounidis and Doumpos, 1999) and; how direct determination (Dimitras et al., 1999).

General conclusions from this extensive research effort looks to make every study conducted to produce a reasonable discrimination between the companies that failed and non-failed, but also and perhaps more significant that a variety of research trying hard to show an agreement on what factors are important for the prediction of failure. In fact, it can be said that approximately 40 years of research on this topic has not been successfully produced where and why the variables, which is a good predictor. Disagreement conclusion is of course, can be addressed partially in the fact that these studies refer to the period, country, and different industries. Other factors may in practice not all such research has theoretical framework to




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direct empirical research effort. In the absence of a theory that provides a testable hypothesis, then the empirical results should be evaluated for quality and are just hoping that emerged a pattern of a large number of empirical results. Such conditions lead to the perception of a less elegant in the positioning of the failure prediction research topic because it has no reference to the basic theory when compared to other topics in financial management.

This study tried to establish a buffer pole theoretical prediction of the failure to capitalize on the neo-classical theory of capital structure as a starting point. Thus this theory then follow an alternative approach that compared the model that has been well-known Merton option pricing theory is based and then elaborated into a model of VMR. Origin of capital structure that underlies the theory fails to pay on one hand there is the model that connects to the fall risk assessment company claims. Elaboration of the last 2 can be found in Scott (1981). On the other hand, this theory is also included in the model of optimal capital structure, developed in his resurrection irrelevancy theorem Modigliani-Miller (Modigliani and Miller, 1958, 1963), Baxter (1967), Kraus and Litzenberger (1973), Scott (1976) , and Kim (1978).

In doing so, the whole model of optimal capital structure using fail-pay conditions in the derivation of optimal capital structure. These conditions fail to capture the essence of the decision-payment: occurs when the value of various cash flows available to companies is not sufficient to repay its debt obligations. Based on this, resulting ownership theory on optimal capital structure in its comparative balance is the basis for empirical analysis. Strangely, this model is rarely, if if, re-written and clearly states the possibility of corporate failure and its characteristics, namely: how the model is influenced by the determinants of optimal capital structures. Since the early eighties, this theoretical research line seems completely better with the default options based theory. Based on the short description above, this research aims to clarify the capacity of the concept of capital structure theory as a predictor of the probability of failure of the company.



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MODELLING

Failure Probability-Pay within framework of the Capital Structure

1. Simplified Model of Optimal Capital Structure

The model used here is simple, namely the optimal capital structure model of a single period. This model allows market imperfections: the taxes and the cost of failure, and inadequate to capture the essence of the trade-off theory, optimal capital structures are defined as a trade-off between tax advantages and the expected cost of failure. This model was adopted from Van der Wijst theorem (1989) are elaborated. The main assumption of the model are as follows:

Capital markets and assumed to be not cost competitive. Corporate income tax is constant and, according to the income tax system that allows deductions all creditor payments, including principal repayment of debt from the corporate income tax. Nevertheless, there are no tax deductions and tax items individually. All of the greedy and the market participants are assumed to act rationally.

Total revenues generated by the company assumed to be fixed, ie all investment decisions have been made but the decision has not been funding. Companies only use equity and debt. Debt is not only subject to the risk of default. Finally, investors are assumed to be positioned against the risk neutral and have limited liability. In determining these assumptions, cash flow is the source of funds that can be used to meet liabilities to creditors. Consequently, if the liabilities exceed the cash flow, then the failure-pay and the company declared bankruptcy. Hence the conditions of bankruptcy, b are:

b =



4

)3.2()1(

)()()1(

)1()(

r

dR

r

YeEVe b+

+=

Where r is the risk free rate. Value of creditors at the end of the period, YD obtained the same way. If bankruptcy occurs, the company transferred to creditors, which means they receive the cash flows minus the cost of failure. Limited value to avoid their obligation to accept a negative cash flow. So the value of debt at the end of the period are:

Yd = 0 if 0 Yd = ( ) If 0



5

to zero, giving creditors a maximum amount of debt that will be soluble or corporate debt capacity. Equation 2.7 is set equal to zero, providing a number of debt that maximizes firm value that is optimal capital structure. This can be shown that for normally distributed cash flows in the second order conditions for equations 2.7 and 2.8 are fulfilled. Because the corporate tax rate , has a value between 0 and 1, the amount of debt in the optimal capital structure is smaller than the amount of the debt will be liquidated creditors. This means that equation 2.8 does not limit the amount of debt that can be received by the company that is an optimal capital structure, debt capacity is reached before . Formulation of return equation 2.7 gives the following equation:

)9.2()1()()(

)1()1(

r

RfRBr

F+

=

+

The left side of the equation represents the current value of tax savings at the margin, while on the right side represents the current value at the margin the cost of failure. Thus capital structure is reached when a profit margin equal to the cost of debt financing margin. A more extensive discussion and further detail calculations can be seen in Van der Wijst (1989).

2. Failure Probability Model

In this chapter the sub-model of capital structure re-formulated as a failure probability model and then analyzed. Equation 2.9 represent optimal choice of capital structure as a function of tax rates, the cost of failure and the distribution of ownership of cash flows, including the probability of default

)10.2()()(1

RfRBF =

where all variables have been described previously.

Equations of 2:10 reflect the consequences of default probabilities on the decision to maximization of corporate value by using the capital structure as an instrument. Probability of fail-variable pay is not itself a goal (to be minimized or optimized) or directs instruments. Probability of fail-paid of course indirectly manipulated by selecting the level of R. In the equation the probability of fail-paid 2:10 depending on the level of taxes, the cost of failure and the ownership distribution of cash flows.

To analyze the model, further comparative static calculations. This shows the influence on the probability of fail-paid, F to changes in variables in the model. Comparative static model calculations described below where some more detail added:

a. Default probability, F, depending on the size of debt in the following ways:

)11.2(0)('))(()( 2 RkalauRBRRBRf

RF

xx

x



6

Because f (R), corporate tax rates, the cost of failure, the variance of cash flows and the first derivation of the costs of failure are all positive, (2.11) will be negative if x R. And another sign depends on the relative sizes of other variables and cannot be determined definitively. This means that the effect of leverage on the probability of fail-paid cannot be definitively determined, and in a range that can be definitively determined because of the two contradictory effects on the prediction of conventional wisdom

b. Changes in the F associated with changes in tax rates are:

)12.2(0)()( 2 >=

RfRBF

Both the cost of failure, i.e., f (R) and , the corporate tax rate is positive. This means an increase in tax rates would increase the probability of fail-pay. This makes the debt more attractive funding margin, will lead to greater amount of debt in the optimal capital structure and the probability of fail-pay is higher.

c. Derivation of F associated with the costs of failure are:

)13.2(0)()( 0 if R- x



7

e. Changes in expectations of future cash flows, x, on F is:

0)()()()(

2 >=

=

RRBRf

fRBF

xx

x

x

if x> R (2:16) = 0 if x = R



8

METHODOLOGY

Data Collection Method

The population in this study are all companies registered at the Indonesian Stock Exchange. The sampling method judgment sampling, ie sample selection based on certain criteria. These criteria are the issuers of the following industry types: a) basic and chemical industries; b) miscellaneous; c) the consumer goods industry; d) the trade industry. companies whose shares are always listed and actively traded on the Indonesian Stock Exchange (BEI) at least since 2002 and always presenting financial information during the observation period (Siagian, 2000). Data used in this research is secondary data for the period 2002-2006, obtained fromwww.jsx.co.id ,

Empirical model and variable proxies

In the initial classification in the category of non-failing and failing, this study uses assumptions that have been widely used in corporate failure prediction of the previous literature. The assumption is a binary variable 9, each worth 1 (non-fail) if the requirements of conditions are met, and 0 (failed) if reverse:

i. Profit before minority interest in net income of subsidiaries, positive; ii. Operating cash flow, positive;

iii. Changes in ROA, positive; iv. Operating cash flow exceeding earnings before minority interest in net income of

subsidiaries; v. Changes in leverage (long-term debt / total assets) is negative;

vi. Changes in liquidity, positive; vii. Changes in gross margin ratio (1 - COGS / sales) is positive;

viii. Changes in turnover (sales / total assets) is positive; ix. The Company had operating cash flow positive from the sale of shares.

Many variables in the theoretical model based on expectations of future values can not be measured directly, therefore the empirical proxies used variables derived from accounting data that are available. Proxy variables used in the analysis are:

Debt: DTA Tax: TAX / EBIT Expected Cash Flow (x), CF = (net profit + Beban) / total assets Standard deviation of cash flows (x): Failure Cost B (x): approximated by firm size (ln (sales))

Because this transformation of variables is directly from the accounting figures do not require a lot of discussion about it. Cash flow and leverage variables are included in the analysis without explicit or explicit hypotheses about their influence. Tax rates are hypothesized to relate positively to the probability of fail-pay. The cost of failure is usually assumed to be related to the size of the company upside down, ie the cost of failure as part of the value of a



9

company that reduces the size of the company. Inside this research model, the cost of failure to negatively affect the probability of fail-pay, so this leads to the hypothesis bahawa company size is positively related to the probability of fail-pay.

Engineering Analysis and Model Analysis

Dependent variables used in this study is the failure of the company which is a condition of categorical variables: 0 for firms syang failure and 1 for non-failed firms. Independent variables used in this study is the ratio of debt finance, tax, cash flow expectations, the standard deviation of cash flows, and the cost of failure

Hypothesis Testing

In a research to see whether the independent variable X affect variable Y in the form of categories, logistic model used was:

)(11)(),,,1( 21 iXi

eXPXXXYP k ++

=== K

or

logit P (X) = + i X i

where Y = 1 if the event is observed as the dependent variable and the variable X i as independent variables.

Simultaneous Test Logistic Regression Model

To test significance / suitability of statistical models used Hosmer and Lemeshow test the hypothesis:

Ho = There is no real difference between the classification prediction and classification of observation. Ha = There are real differences between the prediction and classification classification observations.

Ho refused to criteria established if

with the test criteria:

Partial Test

Ho = no significant regression coefficient Ha = Coefficient significant regression. Ho refused to criteria established if



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DISCUSSION

Tabel 2. Simultaneous Results

Sampel Whole Misc.Ind Basic &

Chemical Ind.

Consumer

Goods Trade

Model 1 0.000 0.005 0.638 0.337 0.000

Model 2 0.363 0.274 0.708 0.745 0.856

Model 3 0.866 0.978 0.582 0.496 0.634

Model 4 0.296 0.118 0.038 0.201 0.142

Model 5 0.500 0.021 0.083 0.885 0.240

Model 6 0.283 0.554 0.703 0.726 0.170

Model 7 0.347 0.154 0.550 0.389 0.414

Model 8 0.197 0.291 0.217 0.261 0.650

Model 9 0.558 0.389 0.466 0.089 0.286

Detailed discussion will be based on the results of simultaneous significance test of the model

in mendikriminasi sample. Based on aggregate data sample, only two models that proved to

be significant regarding the use of the concept of capital structure to corporate failure

prediction, model 1 (sig.H & L = 0000) and model 8 (sig.H & L = 0044). While for samples of different industries; model 1 (sig.H & L = 0005) and model 5 (sig.H & L = 0021). In the basic industries and the chemical only 4 models (sig.H & L = 0038) is significant, as well as the trade industry, only model 1 (sig.H & L = 0000), which proved significant. While for the consumer goods industry that there is no single model has significant influence, this may mean that the concept of capital structure has no significant in identifying the failure of the

company.



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Table 3.. Nagelkerke Coefficient

Sampel Whole Misc.Ind Basic &

Chemical Ind.

Consumer Goods Trade

Model 1 32.85% 66.07% 73.53% 41.00% 64.15%

Model 2 8.80% 27.88% 4.84% 34.48% 13.58%

Model 3 1.34% 10.67% 3.07% 10.05% 1.59%

Model 4 3.73% 9.70% 17.53% 1.36% 11.78%

Model 5 4.91% 11.15% 10.53% 7.26% 4.50%

Model 6 8.78% 21.08% 9.34% 15.85% 8.72%

Model 7 4.69% 11.94% 7.44% 12.43% 3.14%

Model 8 2.28% 26.23% 6.73% 7.68% 2.31%

Model 9 6.59% 23.00% 1.21% 13.33% 6.38%

Based on the value of Nagelkerke coefficient can be known ability to explain variations in capital structure concept model is formed. According to the table can be seen that model 1 has the highest value of both industrial classification or among existing models. In aggregate data, the range of values ranging from 1.34% nagelkerke -32.85%. In this value range of different industries 9.7% -66.07%. On the basis of industrial and chemical coefficients ranged from 1.21%, -73.53%, for the consumer goods industries 1.36% -41%. Last on the trade industry, Nagelkerke value range between 1.59% -64.15%.

Tabel 4. Classification Model (%)

Sampel Whole Misc.Ind

Basic &

Chemical

Ind.

Consumer

Goods Trade

Model 1 82.37 87.50 88.13 84.82 92.22

Model 2 72.12 74.04 72.50 83.93 70.56

Model 3 67.81 75.96 64.38 69.64 66.67

Model 4 64.75 70.19 68.13 58.93 66.67

Model 5 72.30 82.69 71.25 81.25 62.22

Model 6 58.09 69.23 60.63 61.61 58.89

Model 7 59.71 71.15 65.00 62.50 56.11

Model 8 62.59 75.96 65.63 63.39 57.78

Model 9 65.29 69.23 66.88 58.93 64.44

Table 4 contains data that formed the model of classification power. This classification power is the ability of the model in correctly classifying the samples used in research. Viewed either industry or the number of approaches existing models, it is known that the model 1 superior to both. This means the use of criteria of profit before minority interests in subsidiaries' net income as an initial classification of failures with the concept of corporate capital structure has adequate reliability statistics . But when viewed under the industry approach, the maximum value of the classification power of all models which have formed in various industries (although the significance of simultaneous only model 1 and model 5).



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Partial Test Results

Discussion partial test results are based on the model simultaneously significant, namely: the whole industry (model 1); miscellaneous industry (model 1 and model 5); basic industry and chemicals (model 4) and; trade industry (model 1).

a. Industry as a whole (see table 5)

Model 1

Nagelkerke values for model 1 this amounted to 0.3285, which means the variability of the dependent variable can be explained by the variability of the independent variables of 32.85%. Overall, this model has a power classification of 82.37%. Based on these Nagelkarke value can be said that the use of income before minority interest in net income of subsidiaries that are used not sufficient to explain variations in corporate failures will occur, although the model is formed has a high classification power when associated with the capital structure of listed companies. Partially, except for deviations of cash flows and taxes other variables significantly influence the probability of failure of the company. But in this case the empirical results indicate that the cost of failure (BX) apparently has a significant positive impact on the probability of failure of the company. In other words, the higher the resulting predictions of failure costs, ceteris paribus indicates the probability of failure is too high. While leverage has a significant negative effect on the probability of failure. In other words, the higher theleverage held issuer, ceteris paribus actually lower the probability of failure. Based on the approach of income before minority interest in net income of subsidiaries which formed the power classification model correctly classifies 54.2% of issuers failed and non-failed company at 95.76%.

b. Miscellaneous Industry (see Table 6)

Model 1.

Nagelkerke values for model 1 this amounted to 0.6606, which means the variability of the dependent variable can be explained by the variability of the independent variables of 66.06%. Overall, this model has a classification power of 87.5%. Based on these Nagelkarke value can be said that the use of income before minority interest in net income of subsidiaries that are used as the initial discriminator is not sufficient to produce the ability to explain variations in corporate failures that will occur in a variety of industrial issuers, although the model prediction that is formed has a high classification power . Partially, only the operating cash flow which significantly influence the probability of failure of the company. Based on the approach of income before minority interest in net income of subsidiaries which formed the power classification model correctly classifies issuers failed at 87.23% and non-failed company at 87.22%.



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Model 5.

Nagelkerke values for model 5 at 0.1115, which means the variability of the dependent variable can be explained by the variability of the independent variables of 11.15%. Overall, this model has a power classification of 82.69%. Based on these Nagelkarke value can be said that the use of income before minority interest in net income of subsidiaries that are used as an initial discriminator used is not sufficient to produce the ability to explain variations in corporate failures that will occur in a variety of industrial issuers, although the model which has formed the classification power high. Partially, only the cost of failure (BX), which has a significant negative effect on the probability of failure of the company. By using the approach as an indication of the failure of changes in corporate leverage, the higher the resulting predictions of failure costs, ceteris paribus firms actually reduce the probability of failure. Based approach to leverage changes, the power that formed the classification model correctly classifies issuers failed at 5.26% and non-failed company at 100% or in the aggregate have amounted to 82.69% classification power.

c. Basic Industry (see Table 7)

Model 4.

Nagelkerke values for model 4 at 0.1753 means that the variability of the dependent variable can be explained by the variability of the independent variables of 17.53%. Overall, this model has a power classification of 68.13%. Based on these Nagelkarke value can be said that the use of comparative operating cash flow to earnings before minority interest in net income of subsidiaries that are used not sufficient to produce the ability to explain variations in corporate failures that will occur at the issuer base and chemical industry. Partially, only the operating cash flow which significantly influence the probability of failure of the company. Based on a comparative approach to operating cash flow to earnings before minority interest in net income of subsidiaries which formed the power classification model correctly classified 25% of issuers failed and non-failed companies amounted to 88.89%.

d. Trade industry (see Table 8)

Model 1.

Nagelkerke values for model 1 at 0.6415 means that the variability of the dependent variable can be explained by the variability of the independent variables of 64.15%. Overall classification of this model has a power of 64.15%. Based on these Nagelkarke value can be said that the use of indicators of income before minority interest in net income of subsidiaries as an initial discriminator adequate ability to explain variations in yield corporate failures that will occur on issuers trade industry, this is also indicated by the high power model of calcification. Partially, only the operating cash flow which significantly influence the probability of failure of the company. Based on the approach of income before minority interest in net income of subsidiaries that form the power classification model correctly classifying issuers failed at 76.47% and non-failed company at 98.45%.



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CONCLUSIONS AND RECOMMENDATIONS

Based on the nine approaches that are used as initial classification model, the criterion of profit before minority interest in net income of subsidiaries proved superior in identifying the probability of failure of the issuer associated with its capital structure. From the empirical calculation, partial cash flow variables are proved to have a flexible effect (negative and positive). Similarly, other variables of capital structure also has no effect on the probability of a certain absolute failure. Implementation of the concept of objective industry data showed some variation in the capital structure of listed companies, which indirectly characterize the characteristics of the industry itself. This is the potential positive and negative signs on each coefficient of the variable capital structure.

Although empirically impressed contradiction with the spirit of the theory of capital structure, it does not mean that theory does not apply in Indonesia because the results of statistical calculations using the simplification of the fact that much there, on the other hand theoretical concepts are still not sufficient research in the use of assumptions. So for further research can be developed theoretical concepts on the company's capital structure and other relevant proxy as a predictor of corporate failure, for example: the distribution of information which is assumed to be proxies of all market participants the same as what the internal issuers, how big is happening asymmetric information, corporate actions undertaken in communicating the quality and value of the company, and others.



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Table 5. Partial Test of Aggregate Industries - Model 1

Independent Variables B Sig. DTA -2.00403 0000 TxEBIT 0.010984 0508 CF 6.309301 0000 STDEV_CF 1.6E-06 0311 Bx 0.378749 0000 Constant -3.59989 0001 Sig. Hosmer & Lemeshow Test 0000 Nagelkerke 0.328491 Power Classification Observations % Failed 97 54.19 Non-Failed 361 95.76 Total 458 82.37

Table 6. Partial Test Models Miscellaneous Industry

Model 1 Model 5 Independent

Variables B Sig. Independent

Variables B Sig.

DTA -2.31 0168 DTA -1.61 0208 TxEBIT 0:44 0428 TxEBIT -0.45 0044 CF 32.08 0000 CF 0.78 0814 STDEV_CF 0:00 0115 STDEV_CF 0:00 0476 BX 0:07 0835 Bx -0.22 0420 Constant -1.13 0802 Constant 5:17 0143 Sig. Hosmer & Lemeshow Test 0005 Sig. Hosmer & Lemeshow Test 0021 Nagelkerke 0.6606 Nagelkerke 0.1115302

Power Classification

Observations % Power

Classification

Observations %

Failed 41 87.23 Failed 1 5:26 Non-Failed 50 87.72 Non-Failed 85 100.00 Total 91 87.50 Total 86 82.69



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Table 7. Partial Test of Basic Industry

Model 4 Independent

Variables B Sig.

DTA 0:48 0445 TxEBIT 0:02 0863 CF -6.96 0005 STDEV_CF -0.00001 0110 Bx 0:16 0433 Constant -0.98 0712 Sig. Hosmer & Lemeshow Test 0038 Nagelkerke 0.17533 Power Classification

Observations %

Failed 13 25.00 Non-Failed 96 88.89 Total 109 68.13

Table 8. Partial Test of Trade Industry

Model 1. Independent Variables

B Sig.

DTA 0:27 0745 TxEBIT 0:01 0450 CF 35.07 0000 STDEV_CF 0:00 0639 Bx 0:27 0085 Constant -3.30 0092 Sig. Hosmer & Lemeshow Test 0000 Nagelkerke 0.6415 Power Classification

Observations %

Failed 39 76.47 Non-Failed 12 98.45 Total 51 92.22



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REFERENCES

Altman, Edward I. 1968. "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy." Journal of Finance 23 (4): 589-609.

Altman, Edward I., R. Haldeman dan P. Narayaman. 1977. "ZETA analysis: A New Model to Identify Bankruptcy Risk of Corporations." Journal of Banking and Finance June: 29-54.

Altman, Edward I. 1984b. "The Success of Business Failure Prediction Models." Journal of Banking and Finance 8: 171-198.

Aziz, A, dan GH Lawson, 1989, Cash Flow Reporting and Financial Distress Models: Testing of Hypotheses, Financial Management , Vol. 18. no. 1, 55-63

Baxter, ND 1967. "Leverage, the Risk of Ruin and the Cost of Capital." Journal of Finance 22 (3): 395-403.

Beaver, W. 1966. "Financial Ratios as Predictors of Failure." Journal of Accounting Research 5: 71-111.

Beaver, W. 1968. "Market Prices, Financial Ratios and Prediction of Failure." Journal of Accounting Research 6 (2), 179-192

Deakin, Edward B. 1972. "A Discriminant Analysis of Predictors of Business Failure." Journal of Accounting Research 10 (1): 167-179.

Dimitras, AI, SH Zanakis dan C. Zopounidis. 1996. "A Survey of business Failures with an Emphasis on Prediction Methods and Industrial Applications." European Journal of Operational Research 90: 487-513.

Dimitras, AI, Slowinski, R., Susmaga, R., Zopounidis, C., 1999. Business failure prediction using rough sets. European Journal of Operational Research , 114, pp.263-280

Eisenbeis, RA, 1977, Pitfalls in the application of discriminant analysis in business, finance and economics, Journal of Finance , Vol. 22 no. 3, 875-900

Frydman, Halina, Edward I. Altman and Duen-Li Kao. 1985. Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. Journal of Finance 40 (1): 269-291

Gordon, MJ, 1971, Towards a Theory of Financial Distress, Journal of Finance, Vol. 26 issue 2, 347-356

Greene, HW, 1993, Econometric Analysis, Prentice-Hall, Englewood Cliffs NY.



18

Gregory, A., B. Russell dan GV Henderson. 1991. A Brief Review of Catastrophe Theory and a Test in Corporate Failure Context. Financial Review 26 (2): 127-155.

Johnsen, Thomajean dan Ronald W. Melicher. 1994. Predicting Corporate Bankruptcy and Financial Distress: Information Value Added by Multinomial Logit Models. Journal of Economics & Business 46: 269-286.

Jones, Frederick L. 1987. Current Techniques in Bankruptcy Prediction. Journal of Accounting Literature 6: 131-164.

Karels, GV dan AJPrakash. 1987. Multivariate Normality and Forecasting of Corporate Bankruptcy. Journal of Business Finance and Accounting, Vol. 14 no. 4, 573-592.

Kim, EH, 1978, A mean-variance theory of optimal capital structure and corporate debt capacity, Journal of Finance , Vol. 23 no. 1, 45-63

Kinnear, Paul R. and Colin D. Gray. 2001. SPSS for Windows made simple, release 10 . Hove, East Sussex: Psychology Press Ltd.

Kraus, Alan danRobert H. Litzenberger. 1973. "State Preference Model of Optimal Financial Leverage." Journal of Finance 28 (4): 911-922.

Lennox, C., 1999, Identifying failing companies: A re-evaluation of the logit, probit and DA approaches, Journal of Economics and Business , Vol. 51 issue 4, 347 364.

Mar Molinero, M. and M. Ezzamel. 1991. Multidimensional Scaling Applied to Corporate Failure. Omega International Journal of Management Science 19 (4): 259-274.

Martin, D., 1977, Early warnings of bank failure: A logit regression approach, Journal of Banking and Finance , 1, 249-276.

Merton, R., 1974, On the Pricing of Corporate Debt: The Risk Structure of Interest Rates, The Journal of Finance, Vol. 29 issue 2, 449-470

Modigliani, Franco dan Merton H. Miller. 1958. "The Cost of Capital, Corporation Finance and the Theory of Investment." The American Economic Review 48 (3): 261-297.

Modigliani, Franco dan Merton H. Miller. 1963. "Corporate Income Taxes and the Cost of Capital: A Correction." The American Economic Review 53 (3): 433-443.

Ohlson, James A. 1980. "Financial ratios and Probabilistic Prediction of Bankruptcy." Journal of Accounting Research 18 (1): 109-131.

Scott, JH, 1976. "A Theory of Optimal Capital Structure" Bell Journal of Economics, Vol. 7 issue 1, 33-54



19

Scott, James H. Jr.. 1977. "Bankruptcy, Secured Debt, and Optimal Capital Structure." Journal of Finance 32 (1): 1-19.

Scott, J., 1981. "The Probability of Bankruptcy, A Comparison of Empirical Predictions and Theoretical Models." Journal of Banking and Finance, Vol. 5, 317-344

Silberberg, E., 1981, The structure of economics: a mathematical analysis, (McGraw-Hill, NewYork).

SPSS Inc./Marija J. Norusis. 2008. SPSS Regression models 14.0 . Chicago: SPSS Inc.

Tam, KY and MY Kiang. 1992. Managerial Applications of Neural Networks: the Case of Bank-failure Predictions. Management Science 38 (7): 926-947.

Vinso, JD, 1979, A Determination of the Risk of Ruin, Journal of Financial and Quantitative Analysis, Vol. 14 issue 1, 77-100

Westgaard, Sjur dan Nico van der Wijst. 2001. "Default Probabilities in a Corporate Bank Portfolio: A Logistic Model Approach." European Journal of Operational Research, Vol. 135 no. 2: 338-349.

Wiginton, JC, 1980, A note on the comparison of logit and discriminant models of consumer credit behavior, Journal of Financial and Quantitative Analysis , Vol. 15 no. 3, 757-770.

Wijst, D van der. 1989. Financial Structure in Small Business: Theory, Tests and Applications . Berlin Heidelberg: Springer-Verlag.

Zavgren, Christine V. 1983. The Prediction of Corporate Failure: The State of the Art. Journal of Accounting Literature 2: 1-38.

Zmijewski, ME 1984. Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 20 (0): 59-82.

Zopounidis, C., Doumpos, M., 1999. A Multicriteria Aid Methodology for Sorting Decision Problems: The Case of Financial Distress, Computational Economics , 14, pp. 197-218.

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Capital Structure and Corporate Failure Prediction