987 resultados para Financial distress


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This study investigates the impact of liquidity crises on the relationship between stock (value and size) premiums and default risk in the US market. It first examines whether financial distress can explain value and size premiums, and then, subsequently, aims to determine whether liquidity crises increase the risk of value and size premium investment strategies. The study employs a time-varying approach and a sample of US stock returns for the period between January 1982 and March 2011, a period which includes the current liquidity crisis, so as to examine the relationship between default risk, liquidity crises and value and size premiums. The findings indicate that the default premium has explanatory power for value and size and premiums, which affect firms with different characteristics. We also find that liquidity crises may actually increase the risks related to size and value premium strategies.

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This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.

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Analyzes the use of linear and neural network models for financial distress classification, with emphasis on the issues of input variable selection and model pruning. A data-driven method for selecting input variables (financial ratios, in this case) is proposed. A case study involving 60 British firms in the period 1997-2000 is used for illustration. It is shown that the use of the Optimal Brain Damage pruning technique can considerably improve the generalization ability of a neural model. Moreover, the set of financial ratios obtained with the proposed selection procedure is shown to be an appropriate alternative to the ratios usually employed by practitioners.

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This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.

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Over 120 years ago Sir James Burns founded an organisation that is today, the international business group of Burns Philp and Company Ltd. The Group is widely known as a leading producer of yeast products and manufacturer of other bakery ingredients. Its ability to adapt to the ever-changing demands of business is widely recognised. During the late 1980’s however, after the group expanded into the herbs and spices industry its financial state deteriorated. Yet, arguably the Group had entered a market that complimented its then existing core-activities. This paper examines circumstances surrounding that venture into herbs and spices. It argues that the Group’s financial predicament, at that time, was exacerbated by the use of conventional accounting procedures. It illustrates that up-to-date market related financial details, in lieu of accounting book constructs, more aptly assist directors, managers, all stakeholders to conduct business and make informed economic decisions. This paper suggests that it is an entity’s current financial state of affairs, with regard to tangible market referents, that enables a firm’s strategic progress and facilitates proactive management; and in turn, assists in the sustainable development of business throughout the world.

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© 2015 World Scientific Publishing Co. and Center for Pacific Basin Business, Economics and Finance Research. This study examines whether negative book equity (BE) firms are in financial distress by analyzing their operating performance, financial characteristics, distress risk, and survivability when they first report negative BE. Firms with small magnitude of negative BE (SNBE firms) suffer from persistent negative earnings and financial distress, while firms with large magnitude of negative BE (LNBE firms) experience a temporary non-distress related earnings shock. LNBE firms report consecutive years of negative BE, but have lower distress risk and failure rate than both SNBE and control firms. However, all negative BE stocks have abysmal returns subsequent to their first report of negative BE.

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This paper explaina why workers lack motivation near bankruptcy, why they tend to leave companies in financiai distreas, and why thoae who remam require higher compensation. Theae indirect costa of financiai diatresa adie becauae the optimal combination of debt and incentive achem.ea, deaigned to minimize agency costa, ends up underpaying managers when there ia a bankruptcy threat. The paper a1so providea new empirica1 implications on the intera.ction between financiai reatructuring and changea in managerial compensation. Theae predictions are supported by the findings of Gilson and Vetsuypens (1992).

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Nesta dissertação foi analisada se há uma relação significante entre estruturas de governança (estrutura e composição de conselho) e financial distress. Este trabalho focou neste tema porque os estudos acadêmicos em governança corporativa e sua relação com financial distress ainda são pouco explorados. Além disso, o tema tem relevância no mundo corporativo, pois entender quais estruturas e composições de conselho seriam mais eficientes para evitar financial distress é interessante para diversos stakeholders, principalmente para os acionistas e os credores. Para verificar a existência dessa relação, foram utilizados dados de empresas brasileiras de capital aberto e foram desenvolvidos modelos logit de financial distress. Sendo a variável resposta financial distress, partiu-se de um modelo base com variáveis financeiras de controle e, por etapas, foram adicionadas novos determinantes e combinações dessas variáveis para montar modelos intermediários. Por fim, o modelo final contou com todas as variáveis explicativas mais relevantes. As variáveis de estudo podem ser classificadas em variáveis de estrutura de governança (DUA, GOV e COF), qualidade do conselho (QUA) e estrutura de propriedade (PRO1 e PRO2). Os modelos base utilizados foram: Daily e Dalton (1994a) e um próprio, desenvolvido para modelar melhor financial distress e sua relação com as variáveis de estrutura de governança. Nos diversos modelos testados foram encontradas relações significativas no percentual de conselheiros dependentes (GOV), percentual de conselheiros da elite educacional (QUA), percentual de ações discriminadas (PRO1) e percentual de ações de acionista estatal relevante (PRO2). Portanto, não se descartam as hipóteses de que mais conselheiros dependentes, menos conselheiros da elite educacional e estrutura de propriedade menos concentrada contribuem para uma situação de financial distress futura. Entretanto, as variáveis dummy de dualidade (DUA) e de conselho fiscal (COF) não apresentaram significância estatística.

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Nesta dissertação foi analisada se há uma relação significante entre estruturas de governança (estrutura e composição de conselho) e financial distress. Este trabalho focou neste tema porque os estudos acadêmicos em governança corporativa e sua relação com financial distress ainda são pouco explorados. Além disso, o tema tem relevância no mundo corporativo, pois entender quais estruturas e composições de conselho seriam mais eficientes para evitar financial distress é interessante para diversos stakeholders, principalmente para os acionistas e os credores. Para verificar a existência dessa relação, foram utilizados dados de empresas brasileiras de capital aberto e foram desenvolvidos modelos logit de financial distress. Sendo a variável resposta financial distress, partiu-se de um modelo base com variáveis financeiras de controle e, por etapas, foram adicionadas novos determinantes e combinações dessas variáveis para montar modelos intermediários. Por fim, o modelo final contou com todas as variáveis explicativas mais relevantes. As variáveis de estudo podem ser classificadas em variáveis de estrutura de governança (DUA, GOV e COF), qualidade do conselho (QUA) e estrutura de propriedade (PRO1 e PRO2). Os modelos base utilizados foram: Daily e Dalton (1994a) e um próprio, desenvolvido para modelar melhor financial distress e sua relação com as variáveis de estrutura de governança. Nos diversos modelos testados foram encontradas relações significativas no percentual de conselheiros dependentes (GOV), percentual de conselheiros da elite educacional (QUA), percentual de ações discriminadas (PRO1) e percentual de ações de acionista estatal relevante (PRO2). Portanto, não se descartam as hipóteses de que mais conselheiros dependentes, menos conselheiros da elite educacional e estrutura de propriedade menos concentrada contribuem para uma situação de financial distress futura. Entretanto, as variáveis dummy de dualidade (DUA) e de conselho fiscal (COF) não apresentaram significância estatística

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We propose and estimate a financial distress model that explicitly accounts for the interactions or spill-over effects between financial institutions, through the use of a spatial continuity matrix that is build from financial network data of inter bank transactions. Such setup of the financial distress model allows for the empirical validation of the importance of network externalities in determining financial distress, in addition to institution specific and macroeconomic covariates. The relevance of such specification is that it incorporates simultaneously micro-prudential factors (Basel 2) as well as macro-prudential and systemic factors (Basel 3) as determinants of financial distress. Results indicate network externalities are an important determinant of financial health of a financial institutions. The parameter that measures the effect of network externalities is both economically and statistical significant and its inclusion as a risk factor reduces the importance of the firm specific variables such as the size or degree of leverage of the financial institution. In addition we analyze the policy implications of the network factor model for capital requirements and deposit insurance pricing.

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The benefits of applying tree-based methods to the purpose of modelling financial assets as opposed to linear factor analysis are increasingly being understood by market practitioners. Tree-based models such as CART (classification and regression trees) are particularly well suited to analysing stock market data which is noisy and often contains non-linear relationships and high-order interactions. CART was originally developed in the 1980s by medical researchers disheartened by the stringent assumptions applied by traditional regression analysis (Brieman et al. [1984]). In the intervening years, CART has been successfully applied to many areas of finance such as the classification of financial distress of firms (see Frydman, Altman and Kao [1985]), asset allocation (see Sorensen, Mezrich and Miller [1996]), equity style timing (see Kao and Shumaker [1999]) and stock selection (see Sorensen, Miller and Ooi [2000])...

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Sempre foi do interesse das instituições financeiras de crédito determinar o risco de incumprimento associado a uma empresa por forma a avaliar o seu perfil. No entanto, esta informação é útil a todos os stakeholders de uma empresa, já que também estes comprometem uma parte de si ao interagirem com esta. O aumento do número de insolvências nos últimos anos tem reafirmado a necessidade de ampliar e aprofundar a pesquisa sobre o stress financeiro. A identificação dos fatores que influenciam a determinação do preço dos ativos sempre foi do interesse de todos os stakeholders, por forma a antecipar a variação dos retornos e agir em sua conformidade. Nesta dissertação será estudada a influência do risco de incumprimento sobre os retornos de capital, usando como indicador do risco de incumprimento a probabilidade de incumprimento obtida segundo o modelo de opções de Merton (1974). Efetuou-se esta análise durante o período de Fevereiro de 2002 a Dezembro de 2011, utilizando dados de empresas Portuguesas, Espanholas e Gregas. Os resultados evidenciam uma relação negativa do risco de incumprimento com os retornos de capital, que é devida a um efeito momentum e à volatilidade. A par disso, também se demonstra que o tamanho e o book-to-market não são representativos do risco de incumprimento na amostra aqui utilizada, ao contrário do que Fama & French (1992; 1996) afirmavam.

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Double Degree. A Work Project, presented as part of the requirements for the Award of a Master’s Degree in Finance from NOVA – School of Business and Economics and a Masters Degree in Management from Louvain School of Management