Balance-Sheet-Based or Equity-Based Credit Scoring: A Dynamic Perspective Using Binary Quantile Regression
碩士 === 國立成功大學 === 財務金融研究所 === 96 === Financial data for U.S. firms (bankrupt/healthy firms) listed during 1996-2006 are analyzed. A key feature of this study is analysis of changing distribution of corporate nature across firms and over time by binary quantile regression (hereafter BQR) model and co...
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ndltd-TW-096NCKU53040042016-05-16T04:10:41Z http://ndltd.ncl.edu.tw/handle/01220511317462716264 Balance-Sheet-Based or Equity-Based Credit Scoring: A Dynamic Perspective Using Binary Quantile Regression 以資產負債表基礎之信用評分法或以權益基礎之信用評分法? Hsin-Hsin Huang 黃心欣 碩士 國立成功大學 財務金融研究所 96 Financial data for U.S. firms (bankrupt/healthy firms) listed during 1996-2006 are analyzed. A key feature of this study is analysis of changing distribution of corporate nature across firms and over time by binary quantile regression (hereafter BQR) model and comparison of the results with Logistic Regression (hereafter LR). The nonlinearities derived from conditional BQR reveal considerable discrimination between different sources of credit scoring indicator vary by firms-nature (bankrupt/ healthy firms). On view of pre-quarter prediction, source from market indicator have interpretation on bankrupt firms, source form accounting indicator has less interpretation on bankrupt firms. By validation of credit model, we evaluating BQR and LR performance have separate goodness. BQR model at each quantile outperform on type I error. For type II error, LR model outperform than BQR. In view of investor risk preference, BQR model is suitable for risk-averse investor. Ming-Yuan Li 黎明淵 2008 學位論文 ; thesis 36 en_US |
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碩士 === 國立成功大學 === 財務金融研究所 === 96 === Financial data for U.S. firms (bankrupt/healthy firms) listed during 1996-2006 are analyzed. A key feature of this study is analysis of changing distribution of corporate nature across firms and over time by binary quantile regression (hereafter BQR) model and comparison of the results with Logistic Regression (hereafter LR). The nonlinearities derived from conditional BQR reveal considerable discrimination between different sources of credit scoring indicator vary by firms-nature (bankrupt/ healthy firms). On view of pre-quarter prediction, source from market indicator have interpretation on bankrupt firms, source form accounting indicator has less interpretation on bankrupt firms. By validation of credit model, we evaluating BQR and LR performance have separate goodness. BQR model at each quantile outperform on type I error. For type II error, LR model outperform than BQR. In view of investor risk preference, BQR model is suitable for risk-averse investor.
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author2 |
Ming-Yuan Li |
author_facet |
Ming-Yuan Li Hsin-Hsin Huang 黃心欣 |
author |
Hsin-Hsin Huang 黃心欣 |
spellingShingle |
Hsin-Hsin Huang 黃心欣 Balance-Sheet-Based or Equity-Based Credit Scoring: A Dynamic Perspective Using Binary Quantile Regression |
author_sort |
Hsin-Hsin Huang |
title |
Balance-Sheet-Based or Equity-Based Credit Scoring: A Dynamic Perspective Using Binary Quantile Regression |
title_short |
Balance-Sheet-Based or Equity-Based Credit Scoring: A Dynamic Perspective Using Binary Quantile Regression |
title_full |
Balance-Sheet-Based or Equity-Based Credit Scoring: A Dynamic Perspective Using Binary Quantile Regression |
title_fullStr |
Balance-Sheet-Based or Equity-Based Credit Scoring: A Dynamic Perspective Using Binary Quantile Regression |
title_full_unstemmed |
Balance-Sheet-Based or Equity-Based Credit Scoring: A Dynamic Perspective Using Binary Quantile Regression |
title_sort |
balance-sheet-based or equity-based credit scoring: a dynamic perspective using binary quantile regression |
publishDate |
2008 |
url |
http://ndltd.ncl.edu.tw/handle/01220511317462716264 |
work_keys_str_mv |
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