The recent dramatic increase in the corporate bankruptcy rate, coupled with a similar rate of increase in the bank failure rate, has re-awakened investor, lender and government interest in the area of bankruptcy prediction. Bankruptcy prediction models are of particular value to a firm's current and future creditors who often do not have the benefit of an actively traded market in the firm's securities from which to make inferences about the debtor's viability. The models commonly used by many experts in an endeavour to predict the possibility of disaster are outlined in this paper. The proportional hazard model, pioneered by Cox [1972], assumes that the hazard function, the risk of failure, given failure has not already occurred, is a function of various explanatory variables and estimated coefficients multiplied by an arbitrary and unknown function of time. The Cox Proportional Hazard model is usually applied in medical studies; but, has recently been applied to the bank failure question [Lane, Looney & Wansley, 1986]. The model performed well in the narrowly defined, highly regulated, banking industry. The principal advantage of this approach is that the model incorporates both the survival times observed and any censoring of data thereby using more of the available information in the analysis. Unlike many bankruptcy prediction models, such as logit and probit based regression models, the Cox model estimates the probability distribution of survival times. The proportional hazard model would, therefore, appear to offer a useful addition to the more traditional bankruptcy prediction models mentioned above. This paper evaluates the applicability of the Cox proportional hazard model in the more diverse industrial environment. In order to test this model, a sample of 109 firms was selected from the Compustat Industrial and Research Industrial data tapes. Forty one of these firms filed petitions under the various bankruptcy acts applicable between 1972 and 1985 and were matched to 67 firms which had not filed petitions for bankruptcy during the same period. In view of the dramatic changes in the bankruptcy regulatory environment caused by the Bankruptcy reform act of 1978, the legal framework of the bankruptcy process was also examined. The performance of the estimated Cox model was then evaluated by comparing its classification and descriptive capabilities to those of an estimated discriminant analysis based model. The results of this study indicate that while the classification capability of the Cox model was less than that of discriminant analysis, the model provides additional information beyond that available from the discriminant analysis. / Business, Sauder School of / Graduate

Bankruptcy is a costly event. Holders of publicly traded securities can rely on security prices to reflect their risk. Other stakeholders have no such mechanism. Hence, methods for accurately forecasting bankruptcy would be valuable to them. A large body of literature has arisen on bankruptcy forecasting with statistical classification since Beaver (1967) and Altman (1968). Reported total error rates typically are 10%-20%, suggesting that these models reveal information which otherwise is unavailable and has value after financial data is released. This conflicts with evidence on market efficiency which indicates that securities markets adjust rapidly and actually anticipate announcements of financial data. Efforts to resolve this conflict with event study methodology have run afoul of market model specification difficulties. A different approach is taken here. Most extant criticism of research design in this literature concerns inferential techniques but not sampling design. This paper attempts to resolve major sampling design issues. The most important conclusion concerns the usual choice of the individual firm as the sampling unit. While this choice is logically inconsistent with how a forecaster observes financial data over time, no evidence of bias could be found. In this paper, prediction performance is evaluated in terms of expected loss. Most authors calculate total error rates, which fail to reflect documented asymmetries in misclassification costs and prior probabilities. Expected loss overcomes this weakness and also offers a formal means to evaluate forecasts from the perspective of stakeholders other than investors. This study shows that cost of misclassifying bankruptcy must be at least an order of magnitude greater than cost of misclassifying nonbankruptcy before discriminant analysis methods have value. This conclusion follows from both sampling experiments on historical financial data and Monte Carlo experiments on simulated data. However, the Monte Carlo experiments reveal that as the cost ratio increases, robustness of linear discriminant rules improves; performance appears to depend more on the cost ratio than form of the distributions.

An explicit solution is given for the value of a risk neutral firm with stochastic revenue facing the possibility of bankruptcy. The analysis is conducted in continuous time. Uncertainty is modeled using an Ito process and bankruptcy is modeled as an absorbing boundary. The analysis yields an ordinary differential equation with a closed form solution. The value function is used to calculate the firm's demand for high interest rate loans, showing a positive demand at interest rates which appear intuitively to be excessive. A value function is also derived for a risk neutral lender advancing funds to the firm. The borrowing and lending value functions are then used to examine various aspects of lender-borrower transactions under different bargaining structures. In a competitive lending market, the model shows that credit rationing occurs inevitably. In a monopoly lending market, the lender sets interest rates and maximum loan levels which reduce the borrower to zero profit. When a second borrower is introduced, the lender must allocate limited funds between two borrowers. A lender is shown to squeeze the smaller "riskier" borrower out of the market when the lender's overall credit constraint is tight. Under each bargaining structure, the model is also used to examine changes in the respective "salvage" recoveries of the lender and borrower on bankruptcy. Accepted:

Baigiamajame magistro darbe nagrinėjami įmonių bankroto analizės ir prognozavimo priimant investicinius sprendimus ypatumai. Pirmoje darbo dalyje yra analizuojami investicijų ir investavimo ypatumai, išskiriant investicijų formas ir pateikiant jų klasifikaciją bei įtaką šalies įmonėms. Antroje darbo dalyje atskleidžiama bankroto esmė, analizuojamos jo atsiradimo priežastys ir požymiai, pateikiama Lietuvos įmonių bankrotų ir tyčinių bankrotų apžvalga bei analizuojama bankroto prognozavimo svarba ir prevenciniai prognozavimo metodai. Trečiojoje dalyje atliekamas praktinis šalies statybų sektoriaus įmonių vertinimas UAB „Telvalda“ ir UAB „Mažeiva“ pavyzdžiu. Šioje dalyje išskirti ir įvertinti įmonių išorės – vidaus aplinkos veiksniai, įvertinta veiklos struktūra ir turto bei nuosavo kapitalo įtaka pelnui (nuostoliui), pateikta santykinių finansinių rodiklių analizė ir įvertinta statybos sektoriaus įmonių bankroto tikimybė. Darbą sudaro 5 dalys: įvadas, teorinė dalis, UAB „Telvalda“ ir UAB „Mažeiva“ bankroto tikimybės analizė, išvados ir siūlymai, literatūros sąrašas. Darbo apimtis – 86 p. teksto be priedų, 35 pav., 19 lent., 75 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / This final Master thesis analyses the corporate bankruptcy and forecasting in making investment decisions. The first part of the work presents the analysis of investments and investment characteristics, highlighting the investment forms and presenting their classification and their influence on the country's businesses. The second part of the work reveals the essence of bankruptcy, and analyzes its causes and symptoms, presents the overview of the Lithuanian corporate bankruptcies and fraudulent bankruptcy and analysis of the importance of bankruptcy forecasting, and preventive methods of forecasting. The third part contains the practical assessment of the national companies in the construction sector based on the cases of UAB Telvalda and UAB Mažeiva. This section identifies and assesses the company's external – internal environmental factors, evaluates the operational structure and performance, impact of assets and equity on profit (loss), presents the analysis of relative financial indicators and evaluates the bankruptcy likelihood of companies in the construction industry. The work consists of 5 parts: introduction, theoretical part, bankruptcy probability analysis of UAB Telvalda and UAB Mažeiva, conclusions and recommendations, references. Volume of the work – 86 pages of text without annexes, 35 figures, 19 tables, 75 bibliographical sources. Supplements to the work are attached separately.

An explicit solution is given for the value of a risk neutral firm with stochastic revenue facing the possibility of bankruptcy. The analysis is conducted in continuous time. Uncertainty is modeled using an Ito process and bankruptcy is modeled as an absorbing boundary. The analysis yields an ordinary differential equation with a closed form solution. The value function is used to calculate the firm's demand for high interest rate loans, showing a positive demand at interest rates which appear intuitively to be excessive. A value function is also derived for a risk neutral lender advancing funds to the firm. The borrowing and lending value functions are then used to examine various aspects of lender-borrower transactions under different bargaining structures. In a competitive lending market, the model shows that credit rationing occurs inevitably. In a monopoly lending market, the lender sets interest rates and maximum loan levels which reduce the borrower to zero profit. When a second borrower is introduced, the lender must allocate limited funds between two borrowers. A lender is shown to squeeze the smaller "riskier" borrower out of the market when the lender's overall credit constraint is tight. Under each bargaining structure, the model is also used to examine changes in the respective "salvage" recoveries of the lender and borrower on bankruptcy. Accepted: / Arts, Faculty of / Vancouver School of Economics / Graduate

This thesis investigates the accuracy of a model that the Department of the Navy uses to predict the financial health of major defense hardware contractors. The inputs to this model are six financial ratios derived from a firm’s income sheet and balance statement. The output of this model is a single Z-score that indicates the health of a firm. Depending on the score, a firm’s financial standing is classified as healthy, distressed, or uncertain. The model is tested using a database compiled for this thesis that includes financial information for a total of 72 defense and non-defense firms. The test database is unique relative to the underlying model database; it reflects a more recent timeframe and a greater number of firms, none of which were used to develop the model. Model accuracy was computed by measuring how often the model correctly classified a bankrupt firm as distressed and a nonbankrupt firm as healthy. Then, model accuracy was evaluated by comparing these test results (i.e., percent correctly classified) to results published by the model’s developers. This comparison produced mixed results. In light of this fact, the thesis concludes that the model should be improved and recommends a course of action. / Master of Arts