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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...
To eliminate limitations of traditional early-warning of financial distress methods, an artificial immune algorithm based early-warning of financial distress is presented. To begin with, both antigens and memory antibodies with class information added to artificial immune network are trained to learn the feature of training samples. In this way, memory antibody cells pool can represent these samples...
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...
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...
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,...
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