The Forecasting Model of Financial Crisis for the Credit Departments of Farmers’ Associations in Taiwan — the Application of Neural Fuzzy Network System

博士 === 國立中興大學 === 農業經濟學系 === 90 === The credit departments of Framers’ Associations have been playing a very important role in the agricultural finance system in Taiwan. Currently, some credit departments are in serious financial problems, meanwhile, without efficient and effective financial inspect...

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Main Authors: Lee-Yu Shih, 施麗玉
Other Authors: Ming-Che Lo
Format: Others
Language:zh-TW
Published: 2002
Online Access:http://ndltd.ncl.edu.tw/handle/77688447040827061754
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spelling ndltd-TW-090NCHU04120082016-06-27T16:08:43Z http://ndltd.ncl.edu.tw/handle/77688447040827061754 The Forecasting Model of Financial Crisis for the Credit Departments of Farmers’ Associations in Taiwan — the Application of Neural Fuzzy Network System 農會信用部財務危機預測模型之研究-模糊類神經網路系統之應用 Lee-Yu Shih 施麗玉 博士 國立中興大學 農業經濟學系 90 The credit departments of Framers’ Associations have been playing a very important role in the agricultural finance system in Taiwan. Currently, some credit departments are in serious financial problems, meanwhile, without efficient and effective financial inspections and operational assistance under financial investigation mechanism. As a result, a more effective financial forecasting model should be built for the reduction of operational crisis of credit departments. The above unsound situation was resulted from the imprecise fuzzy phenomenon on evaluating the operational performance of credit departments such as the scale of operational risks, the level of capital adequacy, the degree of financial crisis, etc. Mathematical parameters are not able to accurately explore these fuzzy phenomenon. At the same time, past researchers used Neural Network System which adopted only hard dichotomous classification method to create certain degree of forecast errors. This research adopted Self-Constructing Neural Fuzzy Inference Network System to forecast different A to E downward ranks of financial crisis for credit departments. The parameters of CAMELS are used as inputs and rank variables calculated from Factor Analysis are as outputs. Totally, 268 credit department samples during 1998 and 2000 are analyzed with excellent forecasted output results. The results showed that the liquidated 22 credit departments in the September of 2001 under government enforcement had 18 and 21 credit departments as D and E ranks in 1998 and 1999, respectively. If this research forecasted of financial crisis performed in 1998, then 18 and 4 out of the 22 liquidated credit departments would have been dealt with proper supervision mechanism three and two years ago, respectively. The solvency crisis and enforced consolidation would not happen. Ming-Che Lo 羅明哲 2002 學位論文 ; thesis 126 zh-TW
collection NDLTD
language zh-TW
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description 博士 === 國立中興大學 === 農業經濟學系 === 90 === The credit departments of Framers’ Associations have been playing a very important role in the agricultural finance system in Taiwan. Currently, some credit departments are in serious financial problems, meanwhile, without efficient and effective financial inspections and operational assistance under financial investigation mechanism. As a result, a more effective financial forecasting model should be built for the reduction of operational crisis of credit departments. The above unsound situation was resulted from the imprecise fuzzy phenomenon on evaluating the operational performance of credit departments such as the scale of operational risks, the level of capital adequacy, the degree of financial crisis, etc. Mathematical parameters are not able to accurately explore these fuzzy phenomenon. At the same time, past researchers used Neural Network System which adopted only hard dichotomous classification method to create certain degree of forecast errors. This research adopted Self-Constructing Neural Fuzzy Inference Network System to forecast different A to E downward ranks of financial crisis for credit departments. The parameters of CAMELS are used as inputs and rank variables calculated from Factor Analysis are as outputs. Totally, 268 credit department samples during 1998 and 2000 are analyzed with excellent forecasted output results. The results showed that the liquidated 22 credit departments in the September of 2001 under government enforcement had 18 and 21 credit departments as D and E ranks in 1998 and 1999, respectively. If this research forecasted of financial crisis performed in 1998, then 18 and 4 out of the 22 liquidated credit departments would have been dealt with proper supervision mechanism three and two years ago, respectively. The solvency crisis and enforced consolidation would not happen.
author2 Ming-Che Lo
author_facet Ming-Che Lo
Lee-Yu Shih
施麗玉
author Lee-Yu Shih
施麗玉
spellingShingle Lee-Yu Shih
施麗玉
The Forecasting Model of Financial Crisis for the Credit Departments of Farmers’ Associations in Taiwan — the Application of Neural Fuzzy Network System
author_sort Lee-Yu Shih
title The Forecasting Model of Financial Crisis for the Credit Departments of Farmers’ Associations in Taiwan — the Application of Neural Fuzzy Network System
title_short The Forecasting Model of Financial Crisis for the Credit Departments of Farmers’ Associations in Taiwan — the Application of Neural Fuzzy Network System
title_full The Forecasting Model of Financial Crisis for the Credit Departments of Farmers’ Associations in Taiwan — the Application of Neural Fuzzy Network System
title_fullStr The Forecasting Model of Financial Crisis for the Credit Departments of Farmers’ Associations in Taiwan — the Application of Neural Fuzzy Network System
title_full_unstemmed The Forecasting Model of Financial Crisis for the Credit Departments of Farmers’ Associations in Taiwan — the Application of Neural Fuzzy Network System
title_sort forecasting model of financial crisis for the credit departments of farmers’ associations in taiwan — the application of neural fuzzy network system
publishDate 2002
url http://ndltd.ncl.edu.tw/handle/77688447040827061754
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