【正文】 統(tǒng)計檢驗拒絕財務(wù)困境和破產(chǎn)是相同的過程的這一假設(shè)。由于企業(yè)的這種分歧，一個 行業(yè)的財務(wù)困境（而不是破產(chǎn)）相對預(yù)警模型，通過使用 14 個行業(yè)的數(shù)據(jù)被建成了。它們還提供了一個概念框架，其中每個行業(yè)并不需要一套獨特的參數(shù)估計。轉(zhuǎn)換始于公司比率然后除以 公司同行業(yè)平均比率為值的商。 表 3 列出了從標(biāo)準(zhǔn)普爾數(shù)據(jù)庫獲取的具體財務(wù)項目和為衡量盈利能力，流動性，運營效率，利用和增長而產(chǎn)生的財務(wù)比率。因此，財務(wù)困境/健康配對比破產(chǎn) /健康配對更相近，這表明相比預(yù)測破產(chǎn)而言，預(yù)測財務(wù)困境更難。 1127個非財務(wù)困境企業(yè)的對照組包括了 14個行業(yè)標(biāo)準(zhǔn)普爾數(shù)據(jù)庫的所有企業(yè) ，這些企業(yè)未被識別為財務(wù)困境，并且具有 1999 年和 2020 年的完整數(shù)據(jù)。多個屏幕方法通過 %到 %的十四個行業(yè)之間來減少財務(wù)困境企業(yè)的數(shù)目，并與一種當(dāng)任何一個屏被違反時就稱為財務(wù)困境的方法對比。被三屏測試指出的在重工業(yè)和高科技行業(yè)部分的弱點被廣泛報道于 2020 年的商業(yè)媒體中。 這三屏系統(tǒng)產(chǎn)生了 14個行業(yè)的 總數(shù)為 276 的財務(wù)困境樣本，如表 1所示。這種方法將 1403 家公司為分析樣本，其中包括 276 家財務(wù)困境企業(yè)和 1127 家非財務(wù)困境企業(yè)。 ? 特殊項目之前的前凈收入為負(fù)（類似于 Hofer (1980 年 )）。 標(biāo)準(zhǔn)普爾數(shù)據(jù)庫中記錄的公司被三分體系以兩年為期間（ 1999 到 2020 年）分為財務(wù)困境樣本和有償債能力的樣本兩部分。 這項研究包括的公司被列示于表一中，這些公司是屬于 14 個制造行業(yè)并來自于 2020 年行業(yè)標(biāo)準(zhǔn)普爾數(shù)據(jù)庫的年度數(shù)據(jù)。該行業(yè)的相對框架是解決彈性系數(shù)問題的一種方法，并為用一個共同平臺預(yù)測很多行業(yè)間事件提供實際優(yōu)勢。如果這兩個過程是相關(guān)聯(lián)的，那么破產(chǎn)預(yù)測模型的變型可能會產(chǎn)生財務(wù)困境的預(yù)測；或者，如果預(yù)測破產(chǎn)變量沒有關(guān)于財務(wù)困境的預(yù)測能力，那么一個全新的解釋模式是必需的。盡管存在這種不確定性，但很明顯的是，企業(yè)處于財務(wù)困境的狀態(tài)也與被描述為相似破產(chǎn)的企業(yè)不同。健康企業(yè)的識別 可能成為財務(wù)困境可以采取的補救措施，該措施有可能糾正在破產(chǎn)發(fā)生之前導(dǎo)致企業(yè)衰退的原因。也就是說，這些在企業(yè)衰退過 程中的預(yù)測來得晚，而在企業(yè)臨近最后階段不能給予充分的預(yù)警。兩個模型之間解釋因子的部分重疊表明了財務(wù)困境和破產(chǎn)之間具有較強聯(lián)系，因為某些因素導(dǎo)致公司陷入財務(wù)困境而后來沒有導(dǎo)致公司破產(chǎn)。一個多層次的篩選降低了將一個非財務(wù)困境公司錯誤識別為財務(wù)危機困境公司的概率。 F i rm ?????????????（ 1） ?where firm i is a member of industry j and 100 adjusts percentage ratios to scalar values greater than . The transformation starts with a pany’s ratio and then divides that quotient by the value of that same ratio for the average firm in the industry. Industryrelative ratios bine changes occurring at individual panies and across their aggregate industry. They reveal when a pany’s ratio deviates from its industry norm. Industry relative advocates such as Lev (1969) and Platt and Platt (1991) argue that these ratios are more stable and result in less disparity between ex ante and ex post forecasts. They also provide a conceptual framework in which each industry does not require a unique set of parameter estimates. Throughout the paper, industry relative notation is suppressed to simplify notation. Conclusion Alternate means of identifying panies in financial distress have been proposed by a variety of researchers. We show that a threescreen criteria bining several previously proposed definitions yields equivalent or lower model standard errors than any one or two criteria screening models. With this bifurcation of panies, an industry relative early warning model of financial distress, not bankruptcy, was built using data for 14 industries. The classification results suggest that it may be possible to take corrective actions to ameliorate financial distress before it disrupts production. Distinguishing between financial distressed and healthy panies is more difficult than the traditional parison between bankrupt and healthy panies and hence building early warning system models to detect financial distress is more difficult. A second inquiry pared the financial distress model to a previously estimated bankruptcy prediction model. Statistical tests reject the hypothesis that financial distress and bankruptcy are same process. This partially explains why many financially distressed firms do not ultimately file for bankruptcy protection. 文獻(xiàn)翻譯： 財務(wù)困境與破產(chǎn)的比較 摘要 從大多數(shù)解決財務(wù)困境的問題的研究來看，實際上是以破產(chǎn)企業(yè)作為研究樣本的。 in contrast, financial distress happens in stages and to different degrees. If the two processes are related then variants of bankruptcy prediction models could yield financial distress predictions。 alternatively, i