[pt] Os estudos de modelos de previsão de falência tiveram seu início há quase 90 anos, sempre com o intuito de ser uma ferramenta de gestão útil para analistas e gestores das empresas. Embora as primeiras pesquisas sejam antigas, o assunto continua atual. Diversos setores da economia passaram, ou passam, por crises ao longo do tempo e não foi diferente para a indústria de aviação. Nesse contexto, o presente trabalho usou dados históricos de indicadores financeiros das empresas aéreas americanas de um período de três décadas para elaborar quatro modelos de previsão de falência e comparar suas performances preditivas com o Modelo Z-Score. Todas as elaborações foram calibragens do Modelo Z-Score, usando técnicas de simulação e estatística. Duas usaram Análise Discriminante Múltipla (MDA) e duas utilizaram Bootstrap junto com MDA. Um par de cada método utilizou as variáveis originais do Modelo Z-Score e o outro par apresentou sugestão de novo conjunto de variáveis. Os resultados mostraram que o modelo de previsão mais preciso, com 75,0 porcento de acerto na amostra In-Sample e 79,2 porcento na Out-of-Sample, utilizou o conjunto original de variáveis e as técnicas Bootstrap e MDA. / [en] Studies of bankruptcy prediction models started almost 90 years ago, with the intention of being a useful management tool for analysts and managers. Although the first researches are ancient, the subject remains current. Several sectors of the economy have experienced, or are experiencing, crises over time and the aviation industry is no exception. In this context, the present work used historical data of financial indicators of American airlines over a period of three decades to develop four models of bankruptcy forecast and compared their predictive performances with the Z-Score Model. All proposed models were calibrations of the Z-Score model, using simulation and statistical techniques. Two models were generated using Discriminant Analyzes Multiple (MDA) and two using Bootstrap along with MDA. A pair of each method used the original variables of the model s Z-Score and the other pair presented a novel set of variables. Results showed that the most accurate forecasting model, with 75.0 percent accuracy in-sample and 79.2 percent out-of-sample, used the original variables of the model s Z-Score and the Bootstrap e MDA techniques.

授信風險分析是決定授信品質的關鍵。不管是聯合貸款、企業授信或消費性貸款，所有申貸案件必定經過徵信程序(credit analysis)來評估授信風險，再決定是否准予貸放。尤其企業授信一般貸放金額甚高，必須藉著嚴謹的審查過程來分析授信戶的借款用途是否合理、還款來源是否無虞。而這又必須瞭解其財務狀況、產銷情形、產業前景、研發創新、營運模式、經營者專業素養、管理能力等構面來分析風險成分。 傳統授信風險分析方法、理論，如五P分析、產業分析、財務分析等已行之多年，亦是國內商業銀行最普遍採用。然而隨著統計學、計量工具的發展，各種衡量信用風險的模型model被架構推出，世界知名銀行亦投注人力物力發展計量分析為主的風險管理部門，建立授信風險量化指標。除消費金融業務已藉著評分(credit scoring)作為准駁依據外，企業授信則因basel II即將公佈實施，亦使銀行業近幾年亦積極投入發展計量模型以建立IRB。然而計量分析與專家分析目前在國內銀行並未結合。我們將在文中探討主要授信分析工具並以89-92年間發生下市及打入全額交割股事件之公司為選樣範圍作為倒帳率分析基礎。

The prediction of a company's financial health is of critical importance to a variety of stakeholders ranging from auditors, creditors, customers, employees, financial institutions and investors through to management. There has been considerable research in this field, ranging from the univariate dichotomous approach of Beaver (1966) to the multivariate multi-state approaches of Lau (1987) and Ward (1994). All of the South African studies namely, Strebel and Andrews (1977), Daya (1977), De La Rey (1981), Clarke et al (1991) and Court et al (1999), and even, Lukhwareni's (2005) four separate models, were dichotomous in nature providing either a "Healthy" or a "Failed" state; or a "Winner" or "Loser" as in the latter case. Notwithstanding, all of these models would be classified as first stage, initial screening models. This study has focused on following a two stage approach to identifying (first stage) and analysing (second stage) the States of Health in a company. It has not adopted the rigid "Healthy" or "Failed" dichotomous methodology. For the first stage, three-state models were developed classifying a company as Healthy, Intermittent or Distressed. Both three year and five year Profit after Tax (PAT) averages for Real Earnings Growth (REG) calculations were used to determine the superior definition for the Intermittent state; with the latter coming out as superior. Models were developed for the current year (Yn), one (Yn-1), two (Yn-2) and three years (Yn-3) forward using a Test sample of twenty companies and their predictive accuracy determined by using a Holdout sample of twenty-two companies and all their data points or years of information. The statistical methods employed were a Naïve model using the simple Shareholder Value Added (SVA) ratio, CHAID and MDA, with the latter providing very disappointing results - for the Yn year (five year average), the Test sample results were 100%, 95% and 95%, respectively; with the Holdout sample results being 81.3%, 83.8% and 52.5%, respectively. The Yn-1 to Yn-3 models produced very good results for the Test sample but somewhat disappointing Holdout sample results. The best two Yn models namely, the Naïve and the CHAID models, were modified so as to enable a comparison with the notable, dichotomous De La Rey (1981) model. As such, three different approaches were adopted and in all cases, both the modified Naïve (100%, 81.3%, 100%) and the modified CHAID (100%, 85.9%, 98%) produced superior results to the De La Rey model (84.8%, 62.6%, 75.3%). For the second stage, a Financial Risk Analysis Model (FRAM) using ratios in the categories of Growth, Performance Analysis, Investment Analysis and Financial Status were used to provide underlying information or clues, independent of the first stage model, so as to enable the stakeholder to establish a more meaningful picture of the company. This would pave the way for the appropriate strategy and course of action to be followed, to take the company to the next level; whether it be taking the company out of a Distressed State (D) or further improving on its Healthy status (H). / Business Management / D. BL.

The prediction of a company's financial health is of critical importance to a variety of stakeholders ranging from auditors, creditors, customers, employees, financial institutions and investors through to management. There has been considerable research in this field, ranging from the univariate dichotomous approach of Beaver (1966) to the multivariate multi-state approaches of Lau (1987) and Ward (1994). All of the South African studies namely, Strebel and Andrews (1977), Daya (1977), De La Rey (1981), Clarke et al (1991) and Court et al (1999), and even, Lukhwareni's (2005) four separate models, were dichotomous in nature providing either a "Healthy" or a "Failed" state; or a "Winner" or "Loser" as in the latter case. Notwithstanding, all of these models would be classified as first stage, initial screening models. This study has focused on following a two stage approach to identifying (first stage) and analysing (second stage) the States of Health in a company. It has not adopted the rigid "Healthy" or "Failed" dichotomous methodology. For the first stage, three-state models were developed classifying a company as Healthy, Intermittent or Distressed. Both three year and five year Profit after Tax (PAT) averages for Real Earnings Growth (REG) calculations were used to determine the superior definition for the Intermittent state; with the latter coming out as superior. Models were developed for the current year (Yn), one (Yn-1), two (Yn-2) and three years (Yn-3) forward using a Test sample of twenty companies and their predictive accuracy determined by using a Holdout sample of twenty-two companies and all their data points or years of information. The statistical methods employed were a Naïve model using the simple Shareholder Value Added (SVA) ratio, CHAID and MDA, with the latter providing very disappointing results - for the Yn year (five year average), the Test sample results were 100%, 95% and 95%, respectively; with the Holdout sample results being 81.3%, 83.8% and 52.5%, respectively. The Yn-1 to Yn-3 models produced very good results for the Test sample but somewhat disappointing Holdout sample results. The best two Yn models namely, the Naïve and the CHAID models, were modified so as to enable a comparison with the notable, dichotomous De La Rey (1981) model. As such, three different approaches were adopted and in all cases, both the modified Naïve (100%, 81.3%, 100%) and the modified CHAID (100%, 85.9%, 98%) produced superior results to the De La Rey model (84.8%, 62.6%, 75.3%). For the second stage, a Financial Risk Analysis Model (FRAM) using ratios in the categories of Growth, Performance Analysis, Investment Analysis and Financial Status were used to provide underlying information or clues, independent of the first stage model, so as to enable the stakeholder to establish a more meaningful picture of the company. This would pave the way for the appropriate strategy and course of action to be followed, to take the company to the next level; whether it be taking the company out of a Distressed State (D) or further improving on its Healthy status (H). / Business Management / D. BL.