Financial Ratios, Deep Learning and the Prediction of Corporate Credit Events

碩士 === 靜宜大學 === 財務與計算數學系 === 105 === The traditional models assume linear relationship on the explanatory variables but, in the real economic world, economical factors have non-linear relationship. Thus linear assumption on variables limit the prediction accuracy. This study improves the accuracy of...

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Bibliographic Details
Main Authors:	Wang, Yi-Fang, 王宜方
Other Authors:	Chang, Chien-Hung
Format:	Others
Language:	zh-TW
Published:	2017
Online Access:	http://ndltd.ncl.edu.tw/handle/pgzmt9

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spelling	ndltd-TW-105PU0003050042019-05-15T23:17:36Z http://ndltd.ncl.edu.tw/handle/pgzmt9 Financial Ratios, Deep Learning and the Prediction of Corporate Credit Events 財務比率,深度學習與公司信用風險事件預測 Wang, Yi-Fang 王宜方碩士靜宜大學財務與計算數學系 105 The traditional models assume linear relationship on the explanatory variables but, in the real economic world, economical factors have non-linear relationship. Thus linear assumption on variables limit the prediction accuracy. This study improves the accuracy of the default model with linear assumption by using nonlinear properties in deep learning. Deep learning models introduce multi-linear transformation and add non-linear relationship on the factors to classify the default events. Using deep learning on financial ratios, default models is built for Taiwan’s exchange and OTC market that tries to improve the prediction accuracy of default events. Chang, Chien-Hung 張建鴻 2017 學位論文 ; thesis 53 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 靜宜大學 === 財務與計算數學系 === 105 === The traditional models assume linear relationship on the explanatory variables but, in the real economic world, economical factors have non-linear relationship. Thus linear assumption on variables limit the prediction accuracy. This study improves the accuracy of the default model with linear assumption by using nonlinear properties in deep learning. Deep learning models introduce multi-linear transformation and add non-linear relationship on the factors to classify the default events. Using deep learning on financial ratios, default models is built for Taiwan’s exchange and OTC market that tries to improve the prediction accuracy of default events.
author2	Chang, Chien-Hung
author_facet	Chang, Chien-Hung Wang, Yi-Fang 王宜方
author	Wang, Yi-Fang 王宜方
spellingShingle	Wang, Yi-Fang 王宜方 Financial Ratios, Deep Learning and the Prediction of Corporate Credit Events
author_sort	Wang, Yi-Fang
title	Financial Ratios, Deep Learning and the Prediction of Corporate Credit Events
title_short	Financial Ratios, Deep Learning and the Prediction of Corporate Credit Events
title_full	Financial Ratios, Deep Learning and the Prediction of Corporate Credit Events
title_fullStr	Financial Ratios, Deep Learning and the Prediction of Corporate Credit Events
title_full_unstemmed	Financial Ratios, Deep Learning and the Prediction of Corporate Credit Events
title_sort	financial ratios, deep learning and the prediction of corporate credit events
publishDate	2017
url	http://ndltd.ncl.edu.tw/handle/pgzmt9
work_keys_str_mv	AT wangyifang financialratiosdeeplearningandthepredictionofcorporatecreditevents AT wángyífāng financialratiosdeeplearningandthepredictionofcorporatecreditevents AT wangyifang cáiwùbǐlǜshēndùxuéxíyǔgōngsīxìnyòngfēngxiǎnshìjiànyùcè AT wángyífāng cáiwùbǐlǜshēndùxuéxíyǔgōngsīxìnyòngfēngxiǎnshìjiànyùcè
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Financial Ratios, Deep Learning and the Prediction of Corporate Credit Events

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