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On account of great significance of financial distress prediction for corporations, it is essential to construct an effective prediction model for managers and investors. Traditional financial distress prediction methods design static models using samples within a period of time, but the static models are insensitive to changes, such as concept drift in financial distress. This paper proposed a dynamic...
This paper investigates the modeling of risk due to market and funding liquidity by capturing the joint dynamics of three time series: the treasury-Eurodollar spread, the VIX, and a metric derived from the S&P 500 spread. We propose a two-regime mean-reverting model for explaining the behaviour of three time series, which mirror liquidity levels for financial markets. An expectation-maximisation...
The purpose of this paper is to propose and validate the combined model for bankruptcy prediction for the Malaysian firms. This combined model is adopted from previous studies by combining Ohlson logit model, Springate-Canadian model and macroeconomic factors. The proposed combined model is developed by using the financial and macroeconomic constructs. The result indicates that logistic regression...
This paper uses the global optimization of genetic algorithm to construct a genetic neural network model (GANN) forecasting listed company financial crisis. The model optimizes input variables of neural network model forecasting financial crisis. Forecasting of financial distress of listed companies in Shanghai and Shenzhen A share markets indicates that this model bears a better ability to predict...
Based on financial information of listed manufacturing companies, the research aims at predicting financial distress using the integrated model of factor analysis and discriminant analysis to establish the model, and tests the prediction accuracy of the model. The results show that the model in this paper has higher discriminant precision, and it makes further explanation that the choice of financial...
This paper does the empirical research on the relevance of ownership structure and financial distress in listed companies based on the data of ST corporations because of abnormal financial positions in year 2005–2009 and the paired corporations. The ownership structure is described by the nature of controlling shareholder, ownership centralization, ownership balancing and managements' holding percentage...
This paper samples 30 listed companies which were the first time to ST (special treatment) because of abnormal financial situation after the announcement of 2008 Annual Report. The paper sets out from the perspective of financial indicators, introduces a new indicator of "total stock market value / total liabilities", and establishes the financial distress alert model with the use of Binary...
Based on the forefathers' research, this paper made an empirical study on reason of financial distress. We chose the panel data of new ST companies between 2004 and 2006 and used the method of Logistic regression to find the result. The empirical results indicated that not all the countermeasures we chosen are effective. At last, we got our conclusion that the indicators of current ratio, cash flow...
Most scholars applied dichotomy to the company financial distress research, which classifies listed companies into two categories. We apply trisection method, which classifies listed companies into three categories: financial distress companies, financial unstable companies and financial healthy companies, and apply principal component analysis method and ternary Logistic model to construct a financial...
Based on a lot of related literatures, the authors suggest a Financial Distress Prediction System incorporated the Expected Default Frequency (EDF) into Logit regression model. The empirical findings suggest that the EDF calculated by KMV model is significantly associated with the probability of default in both 3rd and 4th quarters prior to the financial crisis of sample firms. Thus, an incorporation...
Lately, many notorious financial distress and bankruptcy events occurred in the world economic. As we known, bankruptcy of Lehman Brothers Holdings Inc. (LEH) is the largest bankruptcy filing in U.S. history in 2008. These events have serious impacted on the socio-economic and investment in public wealth. Due to solve this dilemma, this research collected 68 listed companies as the raw data from Taiwan...
In the predicting financial distress, we know that irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper use rough sets as a preprocessor of SVR to select a subset of input variables and employ the particle swarm optimization algorithm (PSOA) to optimize...
This paper studies how to establish models for predicting financial distress in China's listed companies. We firstly select 26 companies with financial distress and 54 matching companies' panel data as samples, then use panel data model to conduct an empirical study. The research indicates that: (1) The predictability precision is 91.25%, 92.5%, 91.25% and 87.5% for T-1, T-2, T-3 and T-4, respectively,...
In the analysis of predicting financial distress based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the immune clone...
Financial distress and bankruptcy of companies may cause the resources to be wasted and the investment opportunities to be faded. Bankruptcy prediction by providing necessary warnings can make the companies aware of this problem so they can take appropriate measures with these warnings. The aim of this study is model development for financial distress prediction of listed companies in Tehran stocks...
Neural networks (NNs) have been widely used to predict financial distress because of their excellent performances of treating non-linear data with self-learning capability. However, the shortcoming of NNs is also significant due to a ldquoblack boxrdquo syndrome. Moreover, in many situations NNs more or less suffer from the slow convergence and occasionally involve in a local optimal solution, which...
In this paper, we applied culture particle swarm optimization algorithm (CPSO) to optimize the parameters of SVM. Utilizing the colony aptitude of particle swarm and the ability of conserving the evolving knowledge of the culture algorithm, this CPSO algorithm constructed the population space based on particle swarm and the knowledge space. The two spaces evolved independently, at the same time, the...
Neural networks (NNs) have been widely used to predict financial distress because of their excellent performances of treating non-linear data with self-learning capability. However, common neural networks often suffer from long convergent processes and occasionally involve in a local optimal solution that more or less limited their applications in practice. To overcome the drawbacks of neural networks,...
In the analysis of predicting financial distress based on support vector machine (SVM), the two parameters of SVM, c and sigma, which its value have important effect on the predicting accuracy, must be predetermined carefully. In order to solve this problem, this paper proposed a new culture particle swarm optimization algorithm (CPSO) to optimize the parameters of SVM. Utilizing the colony aptitude...
Recent outbreak of corporate financial crises worldwide has brought attention to the need for a new international financial architecture which rests on crisis prediction and crisis management. Financial data have been widely used by researchers to predict financial crisis, but few studies exploit the use of non-financial indicators in corporate governance to construct financial crisis prediction model...
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