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Stulpinienė, Vaida. "Financial distress prediction model of family farms." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20140123_133545-56537.
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Designed financial distress prediction model is intended directly for the farmer (decision-maker) in order to diagnose the farm’s financial condition and predict the likelihood of financial distress, by using financial information of his farm. There are identified family farm characteristics in which family farms have higher risks to run in financial distress and are guidelines for the family farms that intend to more carefully monitor and control their financial condition. The aim of the research: after analysing the conception of financial distress and identifying the factors determining the financial condition as well as related indicators and prediction models, to methodologically justify and design financial distress prediction model of family farms.Parengtas finansinio išsekimo prognozavimo modelis tiesiogiai skirtas ūkininkui, kuris panaudodamas savo ūkio finansinę informaciją, galėtų diagnozuoti ūkio finansinę būklę ir iš anksto numatyti finansinio išsekimo grėsmę. Disertacijoje nustatytos ir įvardintos ūkininkų ūkių charakteristikos, kurioms esant ūkiai turi didesnes grėsmes finansiškai išsekti, yra gairės ūkininkų ūkiams, kurie ketina atidžiau stebėti savo veiklą ir kontroliuoti finansinę būklę. Tyrimo tikslas – ištyrus finansinio išsekimo sampratą, identifikavus finansinę būklę sąlygojančius veiksnius, indikatorius ir prognozavimo modelius, metodologiškai pagrįsti ir parengti ūkininkų ūkių finansinio išsekimo prognozavimo modelį.
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Mselmi, Nada. "Financial distress prediction and equity pricing models : Theory and empirical evidence in France." Thesis, Orléans, 2017. http://www.theses.fr/2017ORLE0502.
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Cette thèse porte sur la prédiction de la détresse financière et son impact sur le rendement des actions. L’objet principal de cette thèse est de : (i) prédire la détresse financière des petites et moyennes entreprises françaises en utilisant plusieurs spécifications économétriques tels que, le modèle Logit, les réseaux de neurones artificiels, la méthode SVM et la régression des moindres carrés partiels, et (ii) d’identifier les facteurs de risque de détresse financière à caractère systématique, explicatifs des rendements des actions, et additionnels au modèle de Fama et French (1993) tels que le momentum, la détresse relative, la liquidité et la Value-at-Risk, sur le marché boursier Français. Cette étude comporte deux parties. La première partie, composée de 2 chapitres, s’interroge sur les principaux indicateurs discriminants entre les petites et moyennes entreprises françaises saines et celles en détresse financière un an et deux ans avant la défaillance. Elle mobilise différentes approches de prédiction et aboutit à des résultats empiriques qui font l’objet d’analyse. La deuxième partie, composée aussi de 2 chapitres, étudie le pouvoir explicatif, du modèle de Fama et French (1993) augmenté de certains facteurs de risque, mais aussi des modèles alternatifs à cette approche dans le contexte français. Les tests portent aussi sur le caractère systématique des facteurs de risque additionnels ou alternatifs, explicatifs des rendements des actions. Les résultats empiriques obtenus font l’objet d’analyse et permettent de proposer des implications managériales aux décideursThis thesis focuses on financial distress and its impact on stock returns. The main goal of this dissertation is: (i) to predict the financial distress of French small and medium-sized firms using a number of techniques namely Logit model, Artificial Neural Networks, Support Vector Machine techniques, and Partial Least Squares, and (ii) to identify the systematic risk factors of financial distress that can explain stock returns, in addition to those of Fama and French (1993) such as the momentum, the relative distress, the liquidity, and the Value-at-Risk in the French stock market. This study has been concretized in two parts. The first part, composed of 2 chapters, wonders about the main indicators that can discriminate between distressed and non-distressed French small and medium-sized firms one and two years before default. It mobilizes different prediction techniques and leads to the empirical results that are the subject of the analysis. The second part, composed also of 2 chapters, investigates the explanatory power of Fama and French (1993) model augmented by a number of risk factors, as well as alternative models in the French context. The tests also focus on the systematic nature of the additional or alternative risk factors, explaining the stock returns. The obtained empirical results are analyzed and propose managerial implications to decision makers
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Omar, Mohd Azmi. "The sensitivity of distress prediction models to the nonnormality of bounded and unbounded financial ratios : an application in Malaysia." Thesis, Bangor University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239854.
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Malíková, Pavlína. "Finanční analýza společnosti Metrostav a.s." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-76786.
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The thesis aim is to examine and evaluate the Metrostav a.s financial health during the years 2005 and 2009 even in the context of economic crisis. The thesis is divided into two main parts. The first one, theoretical - methodological part, describes the various methods of financial analysis, which are gradually being applied in the practical part. The content of the practical part is a brief description of the company and the construction sector, followed by the very core of financial analysis. At the end there are summarized learned knowledge of applied methods and interpreted results of financial analysis.
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Sova, Lukáš. "Predpoveď finančnej tiesne podniku pomocou bankrotných a bonitných modelov." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-193213.
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In the diploma thesis we are examining possibilities of utilization of financial standing models and bankruptcy models for the purpose of prediction of financial distress of a company. We start the analysis with a broad description of methods provided by financial analyses used for prediction of the financial distress, followed by a more particular investigation into the problematics of financial standing and bankruptcy models. In the thesis we aim to define 9 of these models including their variations followed by application onto four companies in the time scale of five years up front the incoming financial distress. Whilst applying the models we will have a closer look at the discrepancies coming from the different nature of the predictive formulas, meanwhile observing how the key changes in financial statements transfer into the scores. Moreover, we will try to point out the key elements causing deformation in the relevance of the models. In the conclusion we will summarize the findings and confront the assumptions.
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Jan, Yitzung, and 詹益宗. "Comparison Between Financial Distress Prediction Models." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/86423538258426182202.
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碩士國立交通大學
財務金融研究所
94
Based on the data of Taiwan corporations trading in TSE and OTC, this study used financial accounting variables and market variables to construct financial distress prediction models, such as Logit model, MDA model and discrete-time hazard model. With such methodology, I examined whether the added-in market variables could enhance the model’s discrimination ability and predicting capability or not, furthermore, I compared the accuracy of three statistical models. This study classified the variables into four categories, which are financial accounting variable group, financial accounting variable plus market variable group, market variable group and Shumway’s variable group, separately. The methods used in analyzing the models’ prediction accuracy are the default probability table, misclassification table, ROC curve and AUC, and EMC. The empirical results showed that the best model to discriminate in-sample data is Logit model composed of financial accounting variable plus market variable group; however, the best model to predict out-sample data is composed by financial accounting variable plus market variable group and Shumway’s variable group, but there are no difference between three statistical models in predicting capabilities. In summary, adding market variables does really enhance discrimination ability of in-sample data, but it doesn’t obviously enhance the prediction ability of out-sample data. Moreover, it is better to use financial distress prediction models alternatively in judging the tendency of the out-sample default firms.
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Hung, Min-Yu, and 洪旻郁. "When Will Financial Distress Prediction Models Fail?" Thesis, 2009. http://ndltd.ncl.edu.tw/handle/60003156094196645699.
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碩士臺灣大學
財務金融學研究所
98
he thesis investigates reasons behind failed distressed prediction models. Since variables of models reflect the current status of a company’s operating and financing situation. The quality of inputted information is quite influential for an effective distressed model. We compare our defined best accounting and market distressed forecasting models under five hypotheses to see the relative performance of both models. Our defined best accounting and market distressed prediction models have reached statistical significant in forecasting default. Particularly, the market model gets extra information after adjusting industry effect. On the other hand, accounting distressed forecasting model failed when the management has higher incentive and capability to manipulate information. Moreover, for small firms, the accounting model fails because of high information asymmetry. Nevertheless, market model fails when liquidity is too high and investors are too optimistic toward growth stocks. The thesis provides some empirical reasons for failed distressed prediction models. It also provides some references for people who will use these forecasting results in the future.
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Sera, Roxana. "Financial distress prediction for portuguese SMEs." Master's thesis, 2020. http://hdl.handle.net/1822/69982.
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Dissertação de mestrado em FinanceIn Portugal, small and medium-sized enterprises (SMEs) represent 99.9% of the total number of companies and are key generators of employment and contributors to the country`s economy. Given their key role and the fact that their main source of funding comes from financial institutions, it is vital that they have easy access to diversified financing instruments as well as the capacity of presenting their activity and results in an efficient way in order to gain access to them. In this context, a way of interpreting the information available about a company in a clear, concise and efficient manner is through the application of an accounting - based financial distress model. The analysis provided by such an instrument is beneficial to both financial institutions, that can use the results in order to understand the general situation of the company, and to the company`s management, who can foresee and prevent eventual financial problems. The objective of this study is to identify the main financial ratios that are relevant in order to discriminate between financially distressed and healthy companies and estimate financial distress prediction models based on them then use the estimated parameters to predict the probability of financial distress in Portuguese SMEs. In order to obtain a more balanced data set of companies the propensity score method, with matching of one-to-one as well as one-to-many, was applied. The model estimation was made with insolvent companies` data from one year prior to insolvency. Validation tests were performed on data samples for one, two and three years before insolvency, as well as for years one to three in a joint data set and also for the entire set of insolvent companies available, up to six years prior to insolvency. The five variables found to be the best predictors of insolvency are Current Assets to Total Assets, Operating Cash Flow to Total Assets, Operating Cash Flow to Debt, Retained Earnings to Total Assets and Equity to Debt. The overall forecasting accuracy of the final model was of over 85%, by which we conclude that the model could be successfully applied to the Portuguese market, in the context of the SMEs.
Em Portugal, as Pequenas e Médias Empresas (PMEs) representam 99.9% do número total de empresas e são um fator chave para a geração de emprego, com uma contribuição elevada para a economia geral do país. Considerando o papel estratégico desempenhado e o fato de que a maior fonte de recursos para as PMEs são as instituições financeiras, é fundamental que essas tenham tanto facilidade de acesso à instrumentos financeiros diversificados, quanto a possibilidade de apresentar a sua atividade e resultados obtidos de uma maneira adequada que lhes garante acesso a esses instrumentos. Nesse contexto, a aplicação de um modelo de previsão de insolvência baseado na análise de rácios financeiros é uma maneira de interpretar a informação disponível sobre uma empresa de uma forma clara, concisa e eficiente. A análise facilitada por tal instrumento beneficia tanto as instituições financeiras, que podem interpretar os resultados obtidos para melhor entender a situação geral da empresa, quanto os gestores da empresa, para quais facilita a detecção e prevenção de eventuais problemas financeiros. O objetivo deste estudo é identificar os principais rácios financeiros relevantes para distinguir entre empresas em dificuldades financeiras e empresas saudáveis, estimar com base neles um modelo de previsão de insolvência e utilizar os parámetros estimados para previsão de dificuldades financeiras nas PMEs portuguesas. Para obter uma amostra mais equilibrada de empresas foi aplicado o método Propensity Score Matching, com pareamentos de um-para-um e um-para-muitos. O modelo foi estimado com base nos dados financeiros de empresas insolventes de um ano antes da insolvência. Testes de validação foram feitos em amostras de um, dois e três anos antes da insolvência, amostra de um a três anos antes da falência, bem como no inteiro conjunto de empresas com dados disponíveis, até seis anos antes da insolvência. As cinco variáveis que mostraram melhor capacidade de previsão da insolvência são: Ativo Corrente/ Total do Ativo, Fluxo de Caixa Operacional/ Total do Ativo, Fluxo de Caixa Operacional/ Total do Ativo, Resultados Transitados/ Total do Ativo e Patrimônio Líquido/ Total do Passivo. A capacidade total preditiva do modelo é acima de 85%, o que leva à conclusão de que o modelo pode ser aplicado ao mercado Português, no contexto das PMEs.
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Wen, Tsou Hui, and 鄒惠雯. "Financial Distress Prediction Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/13836405838104904375.
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碩士健行科技大學
國際企業管理研究所
101
In this study, logical construct financial distress logistic regression model for the study period from 2007 to 2011, the Hong Kong enterprises as the research object, assess Hong Kong''s corporate financial variables on the early warning model predictive ability; empirical results show that the financial ratio variables debt and total asset turnover ratio greater impact on the enterprise; insufficient if the company''s profitability, debt ratio is higher, but will cause cash flow problems of the situation, the enterprise is the higher the likelihood that the financial crisis. In this study, Logica logistic regression model prediction accuracy, the closer point in time of financial distress, the higher the predictive ability of the model overall accuracy rate of 76.6%.
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XIE, MEI-SHUANG, and 謝美霜. "The study of sample designing in financial distress prediction models." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/85829856569285848926.
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hua, wei cheng, and 韋正華. "An Application of Data Mining on Financial Distress Prediction Models." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/34427509098128536229.
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碩士輔仁大學
應用統計學研究所
97
Whenever a financial distress occurs to some well -known enterprises, the influence will be catastrophic to the institution , banks , investors and the government. Therefore, if we can create a dependable financial distress model , it will be helpful to avoid or reduce the possible loss. This research’s variables include financial , ownership structure, and board structure information are collected from publicly traded companies in Taiwan. We used data mining technology to analyze those data and built three different financial distress models , ranging from one year to three years prior to the financial distress. We used Leave-One-Out Cross Validation method to verify that these models are stable.Then we exchanged variables to compare their correct rates. The result of this research is as following： 1. Variables from one year to three years prior to the financial distress are almost identical. 2. Models created in this research are stable. 3. The correct rates from one year to three years prior to the fi nancial distress are 84.69％、77.55％、66.33％。
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Lin, Hong-Yi, and 林鴻益. "A study related to the corporate financial distress prediction models." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/93352682821907119534.
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碩士國立臺灣科技大學
管理研究所企業管理學程
88
The study is about how to predict the possibility of business financial crisis. The research period is between 1992 to 1999. On October 16, 1992, mutual holdings was allowed in the stock market in Taiwan. When the economic disaster burst out in Taiwan in 1998, it was suspected that mutual holdings was a possible effect. It is hard to find out the best variable set to predict when the business financial crisis happened. The study tried to build up a model with small size of variable set and the model has excellent predict ability. To achieve this objective, two variable sets selected from references were adopted. One of them had nine variables. After adjusting multicollinearity between independent variables, the other variable set had six variables. Besides, the study still tried to connect the relationship between mutual holdings and the business financial crisis. One of the objectives in the study is to confirm the significance between them. To evaluate the degree of mutual holdings, a substitute variable was used. There were three conclusions in the study. First, the predict ability of the model was not lowered down by the multicollinearity between independent variables. Second, debt ratio was the most significant factor between all independent variables. Third, the substitute variable of mutual holdings wasn’t a significant factor related to business financial crisis.
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Qiong-Yi, Guo, and 郭瓊宜. "Application of Artificial Neural Network to Financial Distress Prediction Models." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/29104226757502690768.
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Huang, Shih-ting, and 黃詩婷. "Prediction Models for Financial Distress –from Corporation Life-cycle Viewpoint." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/28664605554627029312.
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碩士國立臺灣科技大學
財務金融研究所
98
The main purpose of this research is to add corporation life-cycle in prediction model of financial distress. And use the methods of logistic regression and DEA-DA by the financial ratios and non-financial information. The results indicate that the DEA-DA models can provide better prediction. Corporations in growth stage should pay more attention to the Taiwan corporate credit risk index (TCRI), retained earning on assets and the change frequency of financial chief to near financial distress time. Corporations in maturity stage are easy to borrow money, but facing the pressure of growth to slow down. With coming of financial distress time, Corporations should pay more attention to the Taiwan corporate credit risk index (TCRI), leverage ratio, earnings per share, operating return growth ratio and reediting frequency of financial statement. Furthermore, corporations in decline stage should pay more attention to the Taiwan corporate credit risk index (TCRI) and return on common stockholders’ equity.
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Leou, Jia-Jiun, and 柳佳君. "A Study on the Prediction Models of Corporate Financial Distress." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/89522616437910115193.
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Lin, Pei-Chien, and 林珮阡. "Corporate Governance and Financial Distress Prediction Model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/50528742452250301843.
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碩士國立高雄大學
金融管理學系碩士班
98
To financial institutions, measuring and managing credit risk has always been an important issue. Credit risk has been not only a major factor of making financial decision for financial institutions, but also for in the management of firms. Measuring the credit risk of a firm could detect the financial problems earlier and it is helpful to prevent from financial distress in advance. Previous literatures focused on the financial ratio analysis, but the effect of “window dressing” makes the analysis unreliable. Furthermore, Taiwanese government is promoting the governance mechanism. Therefore, this research investigates financial distress firms which had been listed in TSE, OTC, and ROTC and matches with healthy firms from the first quarter of 1996 to the third quarter of 2009. Based on the above reasons, in addition to the financial variables, corporate governance variables are also incorporated into logistic model. This research uses the data two years before financial distress and find corporate governance enhances the predictability of the model. Furthermore excluding the bailout-distress samples improves the predictability of financial distress and healthy firms apparently. Weighted financial prediction model also strengthens the predictability of the two groups.
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Po-HsunHsu and 許伯勳. "The Research of Financial Distress Prediction Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/a37sn7.
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碩士國立成功大學
財務金融研究所
107
The impact of financial distress is quite extensive. In order to foresee the precursor of financial distress firms, this study is going to construct a model with different types of variables and is applicable for all industries. Investors and banks can avoid the firms with potential risk of financial distress with this prediction model. This study will discuss whether employing different types variables will increase the prediction power of the model and decrease the type II error. The variables employed by this study are the accounting-based, corporate governance and market information variables. Finally, the result shows that when applying different types of variables into the financial distress prediction model, the prediction power will increase and type II error will decrease in the test data of two and three years before financial distress occurs. However, the prediction power dose not increase and type II error does not decrease when adding the market information variables in the data of one year before financial distress occurs.
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SHENG, HUANG YU, and 黃裕盛. "A Study on Building the Prediction Models of Corporate Financial Distress." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/08792149579244375870.
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碩士逢甲大學
金融碩士在職專班
104
This study builds a Financial Distress Prediction Model to investigate the causal relationship of financial ratio and corporate governance as variables to the financial distress of an enterprise. The Financial Model, Corporate Governance Model, and Integrated Model are built respectively in order to compare the prediction ability of the models. The studied object is listed and OTC domestic companies, and the studied period is 2005-2015; this study chose 74 companies with financial distress and 148 normal companies based on the paired sample method, and used Logit Regression Model as the Prediction Model. The modeled period is 2005-2012, and the verified period is 2013-2015. The empirical results showed that the variables having a significant positive correlation with the occurrence of financial distress include financial liabilities ratio due within one year, debt ratio, number of times CPA was switched in the last three years, and turnover of CFO within 3 years. Variables having a significant negative correlation with the occurrence of financial distress include earning before tax margin, operating margin, cash flow per share, earnings per share, equity ratio, total asset turnover, directors and supervisors holding rate, control rights, pyramid structure, cross-shareholding structure, and independent directors and supervisors seats. The classification in this study is created according to the probability threshold value of 0.5 and the probability threshold value of Martin(1977), when the probability threshold value of Martin(1977) is adopted, all models can be used to effectively reduce Type I error, and improve the classification accuracy of distress of the company, enabling banks to make less credit loans to high-risk customers and avoid bad debt and losses, but on the other hand, the classification error rate of Type II error will be raised, so the overall classification accuracy cannot be improved. The closer the data is to the year before the financial distress occurred, the overall classification accuracy of modeled samples in the integrated model is more likely to reach 98.85%, and the overall classification accuracy of verified samples in the integrated model is more likely to reach 91.67%, indicating that the serious financial deterioration and ineffective corporate governance play an important role on corporate financial distress, the integrated model can more effectively detect indicators of financial and corporate governance weakening, and achieve the early prediction. Analyzing the samples of the year before the financial distress occurred which are classified as having a Type I error in the integrated model, the study found that they were already classified as financial distress in the Prediction Model 2 and 3 years before the financial distress occurred, indicating that before a financial distress occurs, it is actually traceable; using the integrated model in any year can achieve the purpose of early prediction. When the sample is classified as having a Type II error in the integrated model the year before the financial distress occurred, it may be an indicator that the companys financial ratios and corporate governance are weakening. Predicted Default Rate of finance and corporate governance implies some risk information that can be provided to decision-makers and investors to determine the risks. Logit Regression Model calculates the predicted default rates, enabling a better understanding of the meaning of credit risk degree of each sample, as well as the acquirement of credit risk information of the counterparty, to achieve the purpose of risk management. Based on risk preference, banks can assess the most beneficial and appropriate model to manage and monitor enterprises credit risks.
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Wang, Chuan-Ying, and 王傳英. "Financial Distress Prediction Model─ Constructed by Industry Samples." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/e93dxd.
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碩士國立臺灣大學
國際企業學研究所
103
Company’s financial distress has been an important issue for decades because it is a huge cost for no matter corporations or nation’s economy. For solving this problem, previous scholars tried to find out a pattern to predict financial distress’s coming, for investors, to avoid the losses, for business owners, to avoid bankruptcy. In terms of prediction variables in financial distress prediction model, studies from accounting based variables, market based variables to macro and non-financial factors. As for methodology in modeling, researches form descriptive statistics, multiple discriminant analysis to Logistic model and more complicated algorithm by using computing. In this study, we will use the data from three different industries in the period of 2008-2014, textile, construction and electronics industry, to construct financial distress prediction model by Logistic regression. We hope that the models constructed by industry data set, textile, construction and electronics industry, would have higher power in predicting than which constructed by total samples. Our research result shows that, models constructed by the samples of textile, construction and electronics industry have higher predictive power in identifying distress companies than constructed by total samples. In textile industry, debt-paying ability, operation ability and profitability is the key factors in affecting the probability of being in distress in the short run. In the long run, interest expenses to debt ratio is the only significant variable. In construction industry, company’s profitability ability is the key in the short run. In the long run, operation ability is crucial in predicting financial distress as well. In electronics industry, financial structure, operation ability and profitability are the important factors over previous three years. However, due to the capital-intensive and high-growth features, it is necessary for companies in electronics industry to be well-performed on profitability to sustain it.
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Yang, Wei-Chu, and 楊偉鉅. "The Financial Distress Prediction Model of Construction Industry." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/40564076612807738247.
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碩士國立東華大學
會計與財務金融碩士學位學程
98
There are many studies have been discussed about how to form a financial prediction model, and most of the models are only to analyze the data on the whole market. However, not many papers have been looked into the characteristics of just only one single industry, and use the findings to set up a model that is suitable for the single industry. In this research, we attempts to study on Taiwan's construction industry. Using the accounting variables, general economic variables, and the modified market variable (The modified distance to default variable) as the independent variables of the Logit model, then we can form a most effective financial prediction model on Taiwan's construction industry. The empirical results show that, after modified the market variable, we had the highest prediction ability overall the models when the data is on a quarter after the default point. Also, we find that earnings per share, and inventory turnover rates are all important variables whom can explain default rate the most.
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Ruan, Yu-Chun, and 阮鈺純. "The Effects of Financial Distress Prediction Models ─ Comparative Analysis before and after the Financial Crisis." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/832z6u.
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碩士大葉大學
會計資訊學系碩士班
102
We adopted the effects of financial ratio, corporate governance variables and earnings management index on financial distress. This paper uses the Taiwan Economic Journal Database (TEJ), All the electronics industry ,we construct descriptive statistics, correlation coefficients , the logistic regression to examine the effects for financial distress, the analysis financial ratio of the financial crisis, Join the corporate governance of the financial crisis early warning model will be more accurate, as well as adding to the financial crisis early warning of earnings management index model is more accurate. The results reveals that: In the event of a financial crisis before the financial ratio predictive accuracy of 98.2%, Join corporate governance variables predictive accuracy of 98.3%, Join earnings management index predictive accuracy of 98.4%.financial crisis after the financial ratio predictive accuracy of 97.9%, join the corporate governance variables predictive accuracy of 97.8%, Join earnings management index predictive accuracy of 97.8%, Representatives of the financial tsunami occurred will affect the predictive power of financial distress.
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Chen, Jen Laing, and 陳俊良. "Financial Distress Prediction Stability Model ：Industry- Relative Ratio Application." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/14530270772616842984.
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Yu-Chia, Tsai, and 蔡育嘉. "A Study on Prediction Model of Corporate Financial Distress." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/98497289243157292400.
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碩士國立臺北大學
統計學系
93
Recently, because of the economic recession, unstable political environment, and unclear cross-strait relationship, many enterprises faced financial distress and bankruptcy. The failure of an enterprise would impact not only individual but also the whole society. Therefore, the purpose of this study was to establish an effective prediction model to detect corporate financial distress in advance and lower the impact. A matched pair of 35 distressed firms and 35 normal firms was random sampling from listed companies in the period between Year 2001 and 2004. 18 financial ratio variables one year before the crisis occurred and 6 non-financial variables three years before the crisis occurred were collected and divided into 5 aspects. The method of this project was first use descriptive statistics to analyze basic characteristics and the relationship among variables. Two-pair population test was also used to select critical variables to distinguish distressed firms from normal firms. Next, the factor analysis was employed to reduce variables, lower the multicollinearity among variables, and extract representative factors for the original data character. Taking these representative factors into Logistic Regression, an effective model to distinguish distressed firms was created. The research outcome showed that the explained variance of the factor analysis was 69.09% when only considering financial variables and the distinction accuracy for modeling sample and model-examining sample were 93.3% and 90%, respective under Logistic Regression model. After adding non-financial variables, the explained variance of the factor analysis increased to 72% and the distinction accuracy for modeling sample and model-examining sample also increased to 98.3% and 100%, respective under Logistic Regression model.
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Wu, Mei-Ling, and 吳美玲. "Research on the Prediction Model of Corporate Financial Distress." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/55841140396922236051.
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碩士國立中央大學
財務金融學系碩士在職專班
93
Financial Crisis Warning Models have been developed for a long time and it main purpose is to find out the financial problems within companies in advance for bankers or investors. Though some complicated warning models, such as Artificial Neural Net Work Model or KMV Model have been investigated recently, in order to assist investors aware the finial crisis in advance by using the brief financial report, this study focuses on whether some traditional financial crisis warning models, such as The Discriminate Model and Logistic Regression Model are still available presently. Companies with the financial crisis which listed on TSE and OTC between 1990 and 2004 in Taiwan were targets of this study. This study investigates not only the identification ratio of original Altman Z-Score model, but re-regress the parameters of the five financial variables within the original Altman Z-Score models. In addition, this study focuses on the market factors and financial ratios in five main divisions that have been used frequently. Furthermore, the statistics methods were used to find out the most dependable indication within the six main divisions which predicts the financial systems of the enterprise and then to build up the two popular financial crisis warning models that has been used theoretically and practically (i.e., the Discriminate Model and the Logistic Regression Model). This study compares the identification ration of these two warning models and the findings reveal that: 1.The results of T-test: It can be understood that within the six main divisions the net worth/total assets and total liability/ total assets ratio are the most important variables of the indication of company paying ability followed by the variables of EPS and cash flow ratio which means the distinguishable differences between companies with and without financial crisis is the degree of paying and earning ability. The findings coincide with people’s intuition to the companies with financial crisis. Moreover, the cash flow ratio is not only a distinguishable variable which is used to analyze the asset of the companies, but it also coincides with the present scholars’ concept of cast flow. 2.Starting from one to three years prior to the financial crisis, the justified predication rate of the Discriminate Model in order is 88.75%, 80.00%, and 73.75%. On the other hand, the justified predication rate of the Logistic Regression Model in order is 90.00%, 72.50% and 67.50%. Based on these findings, it suggests that one year before the financial crisis, the Logistic Regression Model provides a more correct predication while the Discriminate Model provides a better predication two and three years before the financial crisis.
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Sun, Pai-Ching, and 孫百慶. "Applying Merton Jump Diffusion Model in Financial Distress Prediction." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/88997394532241821719.
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碩士國立高雄第一科技大學
金融所
98
The empirical evidence shows that the existence of fat-tail or jump in many financial assets return or assets value distribution is a really common phenomenon. In this paper, we try to add a jump component in order to describe the sudden drop or increase in firm’s asset value, so we consider the well-know jump diffusion process in setting the asset dynamic process for capturing the discontinuousness of asset value. As for the parameters estimation, we rely on the method called EM algorithm instead of maximum likelihood estimation. Finally, we calculate the risk neutral default probability under Merton jump model and constructed a default risk predictive model. In this study, we also compare the prediction performance to the commonly adopted model, Merton model, Z-score model and even the new version of Z-score model. We find evidence that the Z-score models outperform our default predictive model. And between the prediction performance of our model and of the traditional Merton model, we cannot tell which one is better off.
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Hsiu-Mei, Chang, and 張秀美. "A Study of Microfinance and Financial Distress Prediction Model." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/05674259694087342322.
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碩士國立臺北大學
合作經濟學系
91
From the experience of the Microfinance in Aisa, Europe, and North America, we could observe that the Microfinance could help the poor people increasing income, and their independence, and solving the unemployment problems in the local. Microfinance institutions depend on charitable funding are more fragile. Moreover, they would lose their focus more quickly than that obtained funds from depositors. Microfinance institutions want to maximize the effect and outreach of their lending activities to the target population of poor borrowers while remaining financially sustainable. The study analyzes what the Logit model and the artificial neural network─Back Propagation Network for building Credit Union’s financial distress prediction the problem Credit Union in early stage. This study applies the financial index to two models. The analyzing period is from 1997 to 2000. The scale of the sample is 72, including 18 fail Credit Unions, 54 continuing operation Credit Unions. The results have two aspects: 1.In the prediction model aspect: the before one year of financial distress, BPN’s prediction correct rate were higher than Logit model. But in the before two year of financial distress, BPN’s prediction correct rate were the same as Logit model. 2.In the financial index aspect: BPN could have higher prediction rate than Logit model, BPN’s drawback were what factors could influence Credit Union’s performance. And in the Logit model, Credit Union’s cash factor, lending factor, and manage factor would be important factors and could have be better prediction rate.
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Tai, Feng-Ling, and 戴鳳鈴. "Financial Distress Prediction Model:Compare Neural Network with Logit Model." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/66417431695882448400.
Full textAbstract:
碩士東吳大學
企業管理學系
85
Business operations is closely related to the status quo of the society.Once a business is in danger, it will cause the whole society a serious lossespecially the listed stock companies. By way of absoring numerous financial materials, this paper aims to construct a financial distress prediction modelso as to discover potential distress earlier and seek for solution. According to the past research of business failure prediction and fianacialdistress prediction,multivariate statistical techniques are the major approaches to construct prediction models. But since 1990, there have been moreand more neural network applications to this field. After evaluating the feasibility, this paper decides to apply neural networks to construct the prediction models of financial distress. This paper apply nerual network to construct the prediction models of fi nancial distress. And the neural network''s ability to discriminate between distressed and healthy firms is compared to the logit model.In the selection of variables , this paper use two groups of input variables to construct prediction model : one is whole financial ratios (there are 17 ratios in this paper),the other is representative ratios obtained from factor analysis. A back-propagation neural network methodolgy is applied to a sample of 25 distressed firms and 25 healthy firms. Results indicate that theneural network in representative financial ratios more accurately predicts distressed firms than the whole financial ratios and logitmodels.
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OU, TA-WEI, and 歐大維. "China Bond Market and Corporate Financial Distress Prediction Model." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/08139816284303137103.
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碩士東吳大學
財務工程與精算數學系
103
With the rise of China, the worldwide demand for RMB (Renminbi) is significantly growing. At September 31, 2012, the PBC (People’s Bank of China) and CBC (Central Bank of the Republic of China) had signed the "MOU". After PBC had designated Bank of China branch in Taipei as RMB clearing bank in Taiwan, Taiwan had officially launched RMB business in 2013. According to CBC statistics, by the end of January 2015, the RMB deposits of DBU (Domestic Banking Unit) and OBU (Offshore Banking Unit) had amounted to 310.2 billion. Taiwan had accumulated quite a large pool of RMB funds within a short time. At the same time, we also face the problem of how to use the funds efficiently and profitably. In recent years, the development of Chinese bond market is very fast, and it has been focused by many domestic and foreign investment institutions. In the future it may provide a good pipeline for the RMB funds in Taiwan. However, in the face of unfamiliar Chinese bond market, the risk is high. We attempt to collect China’s bond market data in this study. We also construct a corporate financial distress prediction model, in hoping to help investors to understand Chinese bond market before entering it and reduce their investment risk. Keywords: China bond market, Corporate financial distress prediction model, Logistic regression
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Chyn, Shiue-Fu, and 秦學甫. "A Study of the Effectiveness on the Applying of KMV Model for Financial Distress Prediction Models." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/14648711386374574105.
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TSAI, SHU-YUAN, and 蔡淑媛. "Financial Distress Prediction Models Combing Z-score Model and KMV Model: Evidence from Publicly Firms in Taiwan." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3ytv43.
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碩士國立高雄應用科技大學
金融系金融資訊碩士班
105
This study provides a financial distress prediction model that combines the Altman’s Z-score Model and the KMV Model’s DD and EDF. We use the data of Taiwanese listed companies or OTC from January first, 2001 to September 30th, 2016 to build revised Z-score Model for different time periods and different industries and then compare the accuracies of the models. The empirical result shows that, if focusing on the Type I Correct, the original Altman’s Z-score model is outperforming than other models no matter in the different time periods or industries. If focusing on the Type II Correct, the backward model is the best in the different time periods or in the electronics industry and the other industry, the stepwise model is recommended in building material and construction, and for the optoelectronic and the manufacturing the best models are the forward and the stepwise model. Moreover, in the industries those easily affected by the market; for instance, the optoelectronic and the building material and construction, the accuracies are obviously improved after adding the DD or EDF.
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Chen, Lung-Chieh, and 陳隆傑. "Application of Neural Networks on the Financial Distress Prediction Models for Taiwan Listed Companies." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/10393100232581228454.
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碩士國立高雄第一科技大學
風險管理與保險所
92
Abstract This study empirically examines the application of neutral networks on the financial distress prediction models for Taiwan listed companies over the January 2002-May 2004 period. Applying BPN with 36 listed companies and 15 financial ratios, the empirical results are as follows: 1.Financial ratios exist significant differences between financial distress and normal companies when distress year is approaching. 2.Applying BPN is able to build financial distress prediction model effectively especially in the preceding year of the distress year. 3.The CPA opinion shows neutral explanatory power for financial distress and normal companies.
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Chen, Sen-Ho, and 陳森河. "Exploring corporate financial distress prediction models – A case study of Taiwan’s publicly listed companies." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/2k62f8.
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碩士國立虎尾科技大學
經營管理研究所在職專班
99
Being a main source of profit for banks, the lending business is vital to the financial system. However, the lending business carries credit risk. When debtors default or violate the terms of their loans it can have a great impact on individual banks in particular and the financial markets in general. It is for this reason that effective risk management is such an important issue. This study uses companies in Taiwan who experienced financial distress from 1982-2009 as samples to investigate variables in companies’ crisis management. This study refers to the companies as sample that come through financial crisis during 1982-2009 in Taiwan in order to investigate variables of crisis management. The study analyzes two type of models, one as original , another as variational (The financial ratio of variation subtract previous two years from preceding year.) To avoid the Collinearity problem of indepent variable, the study sifts through the material to gather variables with higher discriminatory power, using logistic regression to establish a prediction model based on financial ratios alone, as well as a prediction model based on multiple indicators, including financial ratios, bank loan variables and non-financial ratios. Then the study compares the discriminatory power of the two models. The study finds the prediction model based on multiple financial distress indicators has a higher overall accuracy rate than the prediction model based on a single indicator.
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Chung, Meng-Chieh, and 鍾孟杰. "Using Data Mining and Multi-classification Technologies to Construct Corporate Financial Distress Prediction Models." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/63333682208160421050.
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碩士中原大學
資訊管理研究所
102
In recent years, the business environment, with the advent of the information age of globalization while there have been major changes in the overall economic situation more difficult, the likelihood of financial crises have been followed by increases year by year. Corporate investors, companies will be able to continue to operate if they are willing to put money into the capital markets the main reason. The enterprise financial crisis is at stake with the company's most important key point to survive or not, so if the financial crisis early to predict the business will be able to reduce the loss of business and even the general public, so enterprise financial crisis mode gradually developed. Therefore, the establishment of an effective early-warning model of financial crisis, is a current academia and practitioners important issue. Research from the past can be found, data mining models constructed superior to traditional statistical models, among which the decision tree and neural network models of the most popular, in addition, there are scholars of many recent classification model integration, to construct a multi-classifier warning model, also made many achievements in the improvement. However, we believe that this area is still room for further improvement; Based on the above issues, this study will propose a single classifier, multiple classifiers, hybrid classifier other three categories warning model, and use a variety of classification techniques, such as: decision trees, class neural networks, nearest neighbor method, random forests and other methods, combined with data sampling Bagging technology to construct multiple sets of financial crisis early warning model and comprehensive analysis of the predicted effect. In the experimental test environment, we use most of the scholars identified the University of California, Irvine (University of California at Irvine, UCI) database of corporate information, hope that through a more complete model of diversified financial crisis early warning, providing business and academic community based follow-up study.
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Liu, Huang-You, and 劉皇佑. "Financial distress prediction models during business cycle- evidence for industry factor and group factor." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/03543305159706959175.
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碩士國立臺灣科技大學
財務金融研究所
97
Financial distress prediction model is generally using financial statements to observe the whole operation condition of a company, and discriminate whether a company is financial distress corporation. Preview research has indicated the bankruptcy cost is very huge and important, it could damage firm operation and reputation and hurt investor with investment loss. Therefore, how to construct a financial distress model which is suit the business condition of Taiwan is worth to discuss. There are many factors might influence financial distress prediction model, including macroeconomic environment factor, industry factor and business group factor. The difference between economic boom and recession might influence financial performance and the information on corporate financial statements; industry factor and business group factor might effect the corporate management style, it will also effect financial performance. Nevertheless, to date, there only few researchs consider to include the factors mentioned before into financial distress prediction models. Therefore, this project would investigate three financial distress prediction model, including discriminant analysis model, logit regression model and DEA-DA model and discuss the influence of the macroeconomic environment factor, industry factor and business group on prediction model by data classification. The goal of this research is to construct a practical financial distress prediction model for Taiwan business groups, firms in electronic and construction industry during economic up- trend and down- trend. We hope this research can provide corporate managers and investors some valuable information on their decision making and also lead to some important direction for future research.
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Jheng, Shu-Jyun, and 鄭淑君. "The Application of Logit Models and Extreme Value Theory in Company’s Financial Distress Prediction." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/14721134517718059740.
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碩士國立高雄應用科技大學
金融資訊研究所
95
This study is mainly aimed at using statistics method - Stepwise Regression and factor analysis, we attempt to choose financial variable which have explainable ability to explain why listed companies have finance crisis, and we can predict the probability of having financial crisis according to Logistic model. Differing from past literature, this study considers the companies may have enough time to Window Dressing for financial information before the companies have financial crisis. This paper uses stock data to proceed enterprise crisis predictive model, and apply Extreme Value Theory for estimating risk value. Furthermore, we expect to raise the crisis predictive ability of companies. In this context, the financial crisis predictive model uses financial ratio which assorts five type: capital structure、cash flow、liquidity ability、operate ability、profit ability; total is eighteen financial variable; financial distress companies adopt the data which is announced from TSEC, it mainly for suspend listed companies. The sample is chosen since 2001/Jan/1 until 2006/ Dec/31, includes 6 years and 59 crisis companies, then we aim at crisis companies to choose another similar and normal company be a pair. As the result of study, using factor analysis obtains the financial variables to build predictive model is better than using Stepwise Regression at the same year; when the time is closing to crisis year, the predictive ability of model is better. Applying Hill estimator and Historical Simulation of Extreme Value Theory to calculate Value at Risk to put Value at Risk into the logistic model can raise the predictive ability effectively.
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Hong, Yu-Chiun, and 洪于珺. "A Study of Constructing Corporation Financial Distress Prediction Models Using LOGIT and Discriminant Analyses." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/23086995135046745269.
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碩士國立臺北大學
企業管理學系
102
The purpose of this study is to construct corporation financial distress prediction models based upon financial ratios and corporate governance. The samples are from Taiwan listed companies, which had been announced distressed during the period of 2002 to 2012. The matching healthy samples are collected using similar year, industry and total assets. This study uses five year financial ratios and corporate governance variables prior to financial distress of both distress companies and healthy companies. A total of 96 companies with 36 financial ratio variables and 8 corporate governance variables are collected. This study compares both LOGIT and Discriminant models to check which model is suitable for predicting potential financial distressed firms. The results are summarized as follows: 1. Based on LOGIT and Discriminant models, the most important variables for prediction among financial ratios are debt ratio, EPS, inventory turnover ratio, sales growth rate, fixed assets turnover, the fund requirement, current ratio, payment days, days of working capital supply. Manager’s holding ratio is the only corporate governance variable that is significant for prediction. 2. In terms of each year prediction, both LOGIT and Discriminant models have the same 100% prediction accuracy. However, for the 5-year total sample, the LOGIT model demonstrated an 84.58% predictive accuracy and the Discriminant model only resulted in 68.54% accuracy. Thus, it might be inferred that the LOGIT model could provide better prediction.
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Wei, Ｈsiao-Chin, and 魏曉琴. "A Study of Financial Distress Prediction Models-The Case of Companies Listed on TSE." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/437ky4.
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碩士國立交通大學
財務金融研究所
92
For a long time many economists and accountants have been forecasting bankruptcy by single-period classification models, one set of independent variables for each firm, which Shumway (2001) refers to as static models, with multiple-period bankruptcy data. Shumway develops a discrete-time survival model that uses all available information to produce bankruptcy probability estimates for all firms at each point in time. By using all the available data, it avoids the selection biases inherent in static models. While static models produce biased and inconsistent bankruptcy probability estimates, the discrete-time survival model proposed here is consistent in general and unbiased in some cases. Shumway interprets it outperforms static models in out-of-sample forecasts. Shumway estimates a multi-period logit models that can be interpreted as discrete-time survival model. A logit estimation program can be used to calculate maximum likelihood estimates. I modify the discrete-time survival model’s likelihood function because it ignores the probability of surviving at time t. This idea completely considers the probability of failure at time t, surviving up to and at time t for all firms. I estimate discrete-time survival model, logit model, probit model, and multivariate discriminant analysis with two different sets of independent variables that incorporate Altman’s (1968) 5 variables and Zmijewski’s (1984) 3 variables, as well as Shumway’s (2001) variable of the log of firm age. I find that the log of firm age is not statistically significant in the all models. There appears to be little duration dependence in bankruptcy probability. According to the set of Altman’s variables, the only statistically significant variable is RE/TA. While according to the set of Zmijewski’s variables only NI/TA is excellent bankruptcy predictor. Both of them represent the higher the (cumulative) profitability the lower the financial distress. Because all models use the set of Altman’s variables can get larger power given the type II error rate out-of-sample, so the out-of-sample accuracy of the set of Altman’s variables is higher than the set of Zmijewski’s variables. Although discrete-time survival model is preferable to static models theoretically, empirical result produces contradictory. If I exclude MDA, combining the discrete-time survival model with the set of Altman’s variables, then I estimate it is quite accurate in out-of-sample test.
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Hlahla, Bothwell Farai. "Assessing corporate financial distress in South Africa." Thesis, 2011. http://hdl.handle.net/10539/10761.
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This study develops a bankruptcy prediction model for South African companies listed on the Johannesburg Stock Exchange. The model is of considerable efficiency and the findings reported extend bankruptcy literature to developing countries. 64 financial ratios for 28 companies, grouped into failed and non-failed companies, were tested using multiple discriminant analysis after conducting normality tests. Three variables were found to be significant which are: Times Interest Earned, Cash to Debt and Working Capital to Turnover. The model correctly classified about 75% of failed and non-failed in the original and cross validation procedures. This study went on to conduct an external validation of the model superiority by introducing a sample of failed companies, which showed that the model predictive accuracy is more than chance.
Despite the popularity of the topic among researchers this study highlighted the importance and relevance of the topic to corporate managers, policy makers and to investors especially in a developing market perspective, thereby contributing significantly towards understanding the factors that lead to corporate bankruptcy.
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GORN-HWEH, CHENG, and 鄭功煇. "A study of the financial distress prediction model for corporations." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/61839486427069365890.
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碩士中國文化大學
會計研究所
91
Since the Asian financial crisis beginning in 1998, many corporations in Taiwan have been found to be in financial failures We attempt to make investors or managers find timely signs of the aggravation of the financial constitution by the model, so as to address some solutions in advance to avoid the happening of financial Distress. We collected data from Taiwan listed companies that encountered financial distress between January 1997 and December 2002. The logistic regression model employing conventional accrual-based ratios, cash flow ratios and corporate governance factors in prediction model. According to empirical results of logistic regression model, we found EPS, Debt Ratio, Cash Flow Ratio and the percentage of directors'' shareholding were powerful in explaining the causes of financial distress. The prediction ratio of logistic model is 91.67%, so we can tell that the logistic model has its applicability.
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Tsai, I.-Ling, and 蔡依玲. "Visualized Prediction Model of Financial Distress Using Multidimensional Scaling Map." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/45024721785247682296.
Full textAbstract:
碩士國立交通大學
財務金融研究所
97
The purpose of this study is to construct a visualized early warning model of financial distress using the multidimensional scaling method. As a result, we can provide investors and managers a visualized instrument to find out the problem companies in their early stages. We use ten financial ratios as the inputs of our model and illustrate how the multidimensional scaling technique can help practitioners when assessing the financial distress of a company. Finally, we compare the performance of the logit model with the MDS model. Empirical result shows that the overall performance of the MDS model is better than that of the logit model. We find that the MDS model has better prediction of the financial distress.
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Cheng, Kuo-Rei, and 鄭國瑞. "The Study of the Multinomial Prediction Model of Financial Distress." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/19076766224979065530.
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碩士國立高雄第一科技大學
金融營運所
90
The Study of the Multinomial Prediction Model of Financial Distress Student：Kuo-Jui ChengAdvisors：Dr. Andy Chien Department of Financial Operations National Kaohsiung First University of Science and Technology ABSTRACT By many measures, the prediction of financial distress, historically and currently, always counted the two main factors-the damage corporates and normal corporates, They emphasized the significant differences between these two types of factors, However, owing to the internal managerial problems or macro-economic impact, a normal corporate could still cause a financial distress. The study is designed to strengthen the ability of binary-choice models predictions of financial distress.and to construct a scoring model for predicting the financial distress between the damaged corporates. which we further separate into two types－“excellent” and “fair”. The major findings of the result of the empirical examination： 1.“The damaged corporates and excellent corporates” have more significant explanatory in the first 2 accrual years. 2.“The damaged corporates and the fair corporates”, and “the fair corporates and the excellent corporates” can successfully predict a result of total ratio of abnormality. 3.The addition of the credit scoring model into the construction and empirical model of this study as a part of factors has a significant explanatory with the prediction of financial distress with multiple models.
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TSENG, WEN-JUI, and 曾文瑞. "Applying Data Mining Techniques on Enterprise Financial Distress Prediction Model." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/p9em2a.
Full textAbstract:
碩士銘傳大學
資訊管理學系碩士在職專班
105
Listing companies often broke out financial distress, making the investors and the creditor banks suffered heavy losses. Since the directors of the board hold inside information,and the three corporate bodies (foreign investors, Investment Trust and securities dealers) own considerable pool of research staffs. Owing to that, this researchnot only used the finiancial data but also considering the stock-holder'sinformation. We used the decision tree and neural network to set up enterprise financial distress prediction model. We used the listed company finiancial data before 2015 as training samples.It consisted of 173 distressedcompanies in and 865 normal companies, about 1:5. We used the listed company finiancial data after 2015 as testing datawhich included 33 distressedcompanies, and 165 normal companies. The training samples of the upturn prediction model consisted 13 upturn companies and 65 distressed companies. The resultsshowed thtthe criticalfinancial variables ofdistressprediction model are return on equity, debt ratio, and dividend payout ratio. The criticalstock-holder's variables of distress prediction model are equity pledge ratio of directors and supervisors, ratio of custody inventory,and ratio of directors and supervisors. The critical variables offinancial upturn prediction model are retained earnings to total assets (RE/TA) ratio, reinvestment rate and return on assets(ROA).The criticalstock-holder's variables of upturn prediction model are ratio of custody inventory, ratio of directors and supervisors, and blockholder shareholding ratio .
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Tseng, Cheng-Hsing, and 曾政興. "The financial distress prediction model of enterprise – logistic regression analysis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nsjzuv.
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碩士淡江大學
企業管理學系碩士在職專班
106
Due to the changes of economic environment, business failure events occurred endlessly, thus, predicting the risk of business failure has become the most important subject for banks. This research uses Logistic Regression Model to identify the main independent variables for the public offering companies, which were delisted from 2015 to 2017 owing to financial distress, in three years prior to the occurrence of financial distress. The independent variables in this research are debt ratio, fixed asset on equity, current ratio, quick ratio, accounts receivable turnover ratio, total asset turnover ratio, ROE (return on equity), and ROA (return on assets).The empirical results show that the debt ratio and the ROA are at significant level. The higher debt ratio or the less ROA, the higher possibility financial distress happened. Furthermore, the results cannot be explained when shareholders'' equity being the denominator is negative in some financial ratios, such as fixed asset on equity and ROE. The financial distress prediction model is effective for the public offering companies in three years before the occurrence of financial distress in Logistic Regression, and the predictive accuracy could be approximately up to 85.4%. As a result, the model is useful for banks to identify if the companies have the possibilities of the occurrence of financial distress when they assess financial conditions and recheck their credit system.
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Tseng, Mei Ying, and 曾梅櫻. "An Application of Distress Prediction Model for Taiwan Financial Institution." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/5p75k3.
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碩士國立虎尾科技大學
經營管理研究所在職專班
100
This study examines 10 crisis banks in Taiwan and their matching 40 normal banks, utilizing one of the survival analysis methods—AFT (Accelerated failure time) model to set up three kinds of risk precaution models (Weibull model, Log-normal model and Log-logistic model), and uses residual analysis to compare the fitness of the three risk precaution models. Our empirical results show that the fitness of the lognormal model is the best. We therefore suggest that the model be used as the distress precaution model for financial institutions. This study also establishes a survival rate precaution model and compares with the popularly used logit model for Discriminant Analysis. We hope to find the practical applicability and superiority of the precaution model, and to provide advice for financial inspection and supervision.
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ZHUO, YI-TING, and 卓奕婷. "The Effect of Market Structure on Financial Distress Prediction Model." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/95802434571322529546.
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碩士嶺東科技大學
企業管理系碩士班
104
When financial distress occurs, not only does it affect the company itself, but it also makes its investors bear enormous risk and loss. Previous studies have used different statistical methods and financial variables to construct financial distress prediction models for improving their forecasting ability. However, besides financial variables, industry structures are also related to financial distress. This study examines whether three market structure variables—industry, market concentration rate, and industry failure rate affect the forecasting ability of distress prediction models. The results show that the prediction accuracy of neural network is the highest, then logistic model, and discriminant analysis is the lowest. Secondly, the accuracy of the previous year is the highest. The longer the distress happens, the lower the accuracy is. Next, three market structure variables will improve the previous two and three year’s prediction accuracy. Finally, after classifying by three market structure variables, the prediction accuracy of high market concentration, high industry failure, and non-electronic groups are apparently superior to other groups. Also, the longer the distress occurs, the larger the accuracy difference is. Thus, market structure variables truly improve the forecasting ability of financial distress prediction models.
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沈紋任. "Visualized Prediction Model of Financial Distress using Self Organization Map." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/43760229764304772522.
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碩士國立交通大學
財務金融研究所
93
This study tries to construct a visualed early warning model of financial distress for the listed companies in Taiwan using the self organization map method. The result can help the investor find out the problem companies in the future, and warn the managers to modify the operational strategy. The first part of the study is to train the model using the financial ratios in the financial statements. The model’s early warning ability and how it is affected by the input data can be analyzed. The second part is to check the tracks of financial ratios of the distressed companies over a three year period. In this way we can really observe the financial performance of these distressed companies going from bad to worse in three years. Although much to be done, the results of this study show that the SOM model can achieve the purpose for visualized warning of problem companies in advance.
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Chen, Fu-Hsiang, and 陳富祥. "Applying Corporate Governance and Financial Ratios to Develop a Financial Distress Prediction Model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/89137403253498168135.
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碩士台南科技大學
商學與管理研究所
98
With the layout of the era of corporate globalization, if corporate has improper management of financial crises, the level of attection will be more widely, not only company employees and their families within their own, but also affect the external investors and creditors and may even affect the normal operation of the stock market and the economic and social stability. Many scholars provided multi-use financial statement information to predict whether the company into financial crisis. However, the financial statements can be manipulated to achieve stocks effect and the financial information may not be directly obtained on the real glimpse of the picture. In recent years, some scholars have proposed the non-financial information into consideration rather than the financial information on the financial crisis which also provide a degree of interpretation. In this study, we selected 27 financial crises companies except the financial sector between 2006 and 2008 and the constituent stocks of the Taiwan 50 Index Fund and the constituent stocks of medium-sized 100 companies in 108 normal subjects in 2008. We develop the logistic regression models to corporate governance and financial information of two themes for predicting a financial crisis. With early warning and hope to help businesses, investors, creditors and other early insight into the financial crises of the symptoms as well as prior to the control and prevention to let the losses of minimum. Many empirical results reveal the most explanatory power configuration is financial structure of company. The correct prior to the crisis year of financial distress can reach to 89% over the previous three years and show the model in this study with significant differences between the risk of capacity.
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Guo, Zhi-An, and 郭志安. "Estimation of Financial Distress Prediction Model with Cox''s Regression Model." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/28993516090760527903.
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Chen, Shu-Ping, and 陳淑萍. "A Study of Using Data Mining in Financial Distress Prediction Model for Financial Crisis." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/65510956185977314621.
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碩士銘傳大學
資訊管理研究所
91
This paper using data mining to discuss the content of public financial statements in order to making the financial distress prediction model. This model can not only help the enterprise to modify its operational way but also help the government to take care the crisis enterprises. This paper use the bankruptcy from 1997 the third season to 1998 the forth season for the sample. In the same time, find the 52 normal pairs during this seasons as the normal sample and using their previous fifth financial statements, the total sample data are 520 records. Using factor analysis and decision tree classification of data mining technique to analyze these financial ratios and build the financial distress prediction model. The research result discover that 「Fixed Assets to Owner’s Equities Ratio」,「Leverage Ratio」,「Gross Profit Ratio」,「Current Ratio」,「Quick Ratio」,「Inventory Turnover Ratio」,「Net Income After Taxes」,「Net Income After Taxes to Long-term Capital Ratio」,「Cash Flow Ratio」,「Cash Flow to Long-term Ratio」are the obvious variables in the financial distress prediction model. The object of the research is to build a financial distress prediction model in order to find the best method for economic crisis. Using this model, we can help the investors to reduce the risk.
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Wang, Pey-Ling, and 王珮玲. "The prediction model of financial distress--combination of financial ratios and corporate governance factors." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/14036002526336686927.
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碩士國立高雄第一科技大學
金融營運所
94
ABSTRACT Financial distress is one of the most essential problems in the field of financial management. The high individual and social costs encountered in business failure make this decision problem very important to parties such as bank auditors, management, government policy makers, and investors. The prediction model of financial distress can help stakeholders more accurately to assess the probability of the proposed business distress. Based on prior empirical studies of financial distress that have concentrated exclusively on financial ratio data, this study increases the director and supervisor factors in corporate governance domain to develop a prediction model of financial distress. All of the data come from the on-line database of the Taiwan Economic Journal Co. Ltd and the Market Observation Open System. A total of 182 usable sample data were collected and analyzed: 91 from distressed and 91 from regular firms. First, we test whether the differences exist between the distressed and regular firms. Then, we employ stepwise logistic regressions to establish a parsimonious prediction model for business financial distress. The model has significantly high prediction accuracy and employs fewer variables. Three variables (total debt ratio, pledge ratio of the directors and iii supervisors, return on assets) are proposed as predictors of financial distress. Implications for both financial prediction model and future research are explored.