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Title of research paper: _____________________________ DETAILS: | - RESEARCH OBJECTIVES(s)- RESEARCH QUESTION(s) | - HYPOTHESES- VARIABLES: | METHODOLOGY: | FINDINGS and DISCUSSION: | - FUTURE STUDIES- RECOMMENDATIONS | -Jae Kwon Bae-Predicting financial distress of the South Korean manufacturing industries | - The financial distress forecasting is basically a dichotomous decision, either being financial distress or not.- To find out is it RSVM always outperforms other models in the performance of corporate financial distress predicting, and hence we can predict future financial distress more correctly than any other models. | This experiment use several variable to test and there are:-Interest expense after sale-Portfolio sales-operating profit to sales-ordinary profit to total capitalCurrent liabilities to total capital-growth rate of tangible asset-Turnover of managerial asset-net financing cost-net working capital to total capital-growth rate of current asset-ordinary income to net worth | -analyzed the yearly financial data of 1888 manufacturing corporations collected by the Korean Credit Guarantee Fund (KODIT)-then they developed a financial distress model based on radial basis function support vector mashines (RSVM)-then compare the classification accuracy performance between RSVM and artificial intelligence technique-created models using statistical methods (multiple discriminant analysis, logistic regression), multi-layer perceptron (MLP), classification tree algorithms (C5.0), Bayesian networks, and RSVM to predict financial distress | - RSVM is significantly better than the traditional statistical methods and machine learning techniques when they are applied to prediction of corporate financial distress. | - The first issue for future research relates to a structured method of selecting an optimal value of parameters in RSVM for the best prediction performance-Secondly, the results from the study should be generalized. Our study only uses one chosen

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