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• W2167096960 abstract "Despite increasing researches on business failure prediction by employing statistical techniques and intelligent ones, how to generate reasoning knowledge that can helps enterprise managers, investors, employees and governmental officials intuitively distinguish companies in distress from healthy ones has been only cursorily studied. The objective of this research is to fill this gap by utilizing the data mining technique of two-step clustering to outline relationships between listed companies’ various financial states and their financial ratios in China. Reasoning knowledge implying these relationships can be used as an ‘early warning’ expert system latter on. When assessing a company’s financial state before three years, companies whose values of these financial ratios, (net profit to fixed assets, account payable turnover, total assets turnover, the ratio of cash to current liability, ratio of liability to market value of equity, the proportion of fixed assets and net assets per share), are around 0.2059, 11.9769, 0.5923, 0.1940, 174.4857, 0.3540 and 2.7490, respectively, yield to be healthy in at least three years. While those are around 0.1145, 8.3363, 0.4469, 0.0212, 258.6049, 0.2697 and 2.3027, respectively, are possible to fall into distress in three years. For listed companies in China, long-time liability, activity, short-time liability, per share items and yields and structure ratios are important in descending sequence to guarantee them healthy companies. While activity, short-time liability, profitability and structural ratios are important in descending sequence to avoid them falling into distress. Key words: Business failure predictive knowledge, data mining, two-step clustering, expert system." @default.
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• W2167096960 date "2011-06-30" @default.
• W2167096960 modified "2023-09-23" @default.
• W2167096960 title "Mining business failure predictive knowledge using two-step clustering" @default.
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• W2167096960 doi "https://doi.org/10.5897/ajbm10.158" @default.
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