The Prediction of Financial Distress of Cross-Ownership Public Listed Companies in Taiwan

碩士 === 華梵大學 === 資訊管理學系碩士班 === 91 === Recent years, the majority of enterprises with a financial crisis are those with higher degree of cross-ownership and the property of their financial affairs is distinct from that of usual companies. In addition to using traditional accrual-based financial ratios...

Full description

Bibliographic Details
Main Authors: Bin-Chun Hsu, 許彬淳
Other Authors: Tsung-Yuan Tseng
Format: Others
Language:zh-TW
Published: 2003
Online Access:http://ndltd.ncl.edu.tw/handle/01911346935443805244
Description
Summary:碩士 === 華梵大學 === 資訊管理學系碩士班 === 91 === Recent years, the majority of enterprises with a financial crisis are those with higher degree of cross-ownership and the property of their financial affairs is distinct from that of usual companies. In addition to using traditional accrual-based financial ratios, the research further combines cash-flow, balance sheet and related ratio information of income statement to cull high representative ratio variables which can apply to three sorts of prediction models: (1). the logit regression, (2). the backpropagation network and (3). the decision tree to construct respective financial precaution models for analysis and comparison. We expect to establish a more accurate financial precaution model for the enterprise manager and investors to consult and make effective measures and decisions, immediately. The sample sources are ten companies with a financial crisis and nineteen ones without a financial crisis between 1998 to 1999, which are selected by industry. The research has found that: (1) The performance of the prediction ability of cash-flow ratio precaution model is not worse than that of accrual-based ratio precaution model. Nonetheless, it doesn’t obviously improve the prediction ability to add cash-flow ratios to accrual-based ratios. (2) The prediction ability of the decision tree is much better than that of the backpropagation network or that of the logit regression.