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Bankruptcy prediction is the art of predicting bankruptcy and various measures of financial distress of public firms. It is a vast area of finance and accounting research. The importance of the area is due in part to the relevance for creditors and investors in evaluating the likelihood that a firm may go bankrupt. The quantity of research is also a function of the availability of data: for public firms which went bankrupt or did not, numerous accounting ratios that might indicate danger can be calculated, and numerous other potential explanatory variables are also available. Consequently, the area is well-suited for testing of increasingly sophisticated, data-intensive forecasting approaches. ==History== The history of bankruptcy prediction includes application of numerous statistical tools which gradually became available, and involves deepening appreciation of various pitfalls in early analyses. Interestingly, research is still published that suffers pitfalls that have been understood for many years. Bankruptcy prediction has been a subject of formal analysis since at least 1932, when FitzPatrick published a study of 20 pairs of firms, one failed and one surviving, matched by date, size and industry, in ''The Certified Public Accountant''. He did not perform statistical analysis as is now common, but he thoughtfully interpreted the ratios and trends in the ratios. His interpretation was effectively a complex, multiple variable analysis. In 1967, William Beaver applied t-tests to evaluate the importance of individual accounting ratios within a similar pair-matched sample. In 1968, in the first formal multiple variable analysis, Edward I. Altman applied multiple discriminant analysis within a pair-matched sample. One of the most prominent early models of bankruptcy prediction is the Z-Score Financial Analysis Tool, which is still applied today. In 1980, James Ohlson applied logit regression in a much larger sample that did not involve pair-matching. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「bankruptcy prediction」の詳細全文を読む スポンサード リンク
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